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177 Great Artificial Intelligence Research Paper Topics to Use

In this top-notch post, we will look at the definition of artificial intelligence, its applications, and writing tips on how to come up with AI topics. Finally, we shall lock at top artificial intelligence research topics for your inspiration.

What Is Artificial Intelligence?

It refers to intelligence as demonstrated by machines, unlike that which animals and humans display. The latter involves emotionality and consciousness. The field of AI has gained proliferation in recent days, with many scientists investing their time and effort in research.

How To Develop Topics in Artificial Intelligence

Developing AI topics is a critical thinking process that also incorporates a lot of creativity. Due to the ever-dynamic nature of the discipline, most students find it hard to develop impressive topics in artificial intelligence. However, here are some general rules to get you started:

Read widely on the subject of artificial intelligence Have an interest in news and other current updates about AI Consult your supervisor

Once you are ready with these steps, nothing is holding you from developing top-rated topics in artificial intelligence. Now let’s look at what the pros have in store for you.

Artificial Intelligence Research Paper Topics

The role of artificial intelligence in evolving the workforce
Are there tasks that require unique human abilities apart from machines?
The transformative economic impact of artificial intelligence
Managing a global autonomous arms race in the face of AI
The legal and ethical boundaries of artificial intelligence
Is the destructive role of AI more than its constructive role in society?
How to build AI algorithms to achieve the far-reaching goals of humans
How privacy gets compromised with the everyday collection of data
How businesses and governments can suffer at the hands of AI
Is it possible for AI to devolve into social oppression?
Augmentation of the work humans do through artificial intelligence
The role of AI in monitoring and diagnosing capabilities

Artificial Intelligence Topics For Presentation

How AI helps to uncover criminal activity and solve serial crimes
The place of facial recognition technologies in security systems
How to use AI without crossing an individual’s privacy
What are the disadvantages of using a computer-controlled robot in performing tasks?
How to develop systems endowed with intellectual processes
The challenge of programming computers to perform complex tasks
Discuss some of the mathematical theorems for artificial intelligence systems
The role of computer processing speed and memory capacity in AI
Can computer machines achieve the performance levels of human experts?
Discuss the application of artificial intelligence in handwriting recognition
A case study of the key people involved in developing AI systems
Computational aesthetics when developing artificial intelligence systems

Topics in AI For Tip-Top Grades

Describe the necessities for artificial programming language
The impact of American companies possessing about 2/3 of investments in AI
The relationship between human neural networks and A.I
The role of psychologists in developing human intelligence
How to apply past experiences to analogous new situations
How machine learning helps in achieving artificial intelligence
The role of discernment and human intelligence in developing AI systems
Discuss the various methods and goals in artificial intelligence
What is the relationship between applied AI, strong AI, and cognitive simulation
Discuss the implications of the first AI programs
Logical reasoning and problem-solving in artificial intelligence
Challenges involved in controlled learning environments

AI Research Topics For High School Students

How quantum computing is affecting artificial intelligence
The role of the Internet of Things in advancing artificial intelligence
Using Artificial intelligence to enable machines to perform programming tasks
Why do machines learn automatically without human hand holding
Implementing decisions based on data processing in the human mind
Describe the web-like structure of artificial neural networks
Machine learning algorithms for optimal functions through trial and error
A case study of Google’s AlphaGo computer program
How robots solve problems in an intelligent manner
Evaluate the significant role of M.I.T.’s artificial intelligence lab
A case study of Robonaut developed by NASA to work with astronauts in space
Discuss natural language processing where machines analyze language and speech

Argument Debate Topics on AI

How chatbots use ML and N.L.P. to interact with the users
How do computers use and understand images?
The impact of genetic engineering on the life of man
Why are micro-chips not recommended in human body systems?
Can humans work alongside robots in a workplace system?
Have computers contributed to the intrusion of privacy for many?
Why artificial intelligence systems should not be made accessible to children
How artificial intelligence systems are contributing to healthcare problems
Does artificial intelligence alleviate human problems or add to them?
Why governments should put more stringent measures for AI inventions
How artificial intelligence is affecting the character traits of children born
Is virtual reality taking people out of the real-world situation?

Quality AI Topics For Research Paper

The use of recommender systems in choosing movies and series
Collaborative filtering in designing systems
How do developers arrive at a content-based recommendation
Creation of systems that can emulate human tasks
How IoT devices generate a lot of data
Artificial intelligence algorithms convert data to useful, actionable results.
How AI is progressing rapidly with the 5G technology
How to develop robots with human-like characteristics
Developing Google search algorithms
The role of artificial intelligence in developing autonomous weapons
Discuss the long-term goal of artificial intelligence
Will artificial intelligence outperform humans at every cognitive task?

Computer Science AI Topics

Computational intelligence magazine in computer science
Swarm and evolutionary computation procedures for college students
Discuss computational transactions on intelligent transportation systems
The structure and function of knowledge-based systems
A review of the artificial intelligence systems in developing systems
Conduct a review of the expert systems with applications
Critique the various foundations and trends in information retrieval
The role of specialized systems in transactions on knowledge and data engineering
An analysis of a journal on ambient intelligence and humanized computing
Discuss the various computer transactions on cognitive communications and networking
What is the role of artificial intelligence in medicine?
Computer engineering applications of artificial intelligence

AI Ethics Topics

How the automation of jobs is going to make many jobless
Discuss inequality challenges in distributing wealth created by machines
The impact of machines on human behavior and interactions
How artificial intelligence is going to affect how we act accordingly
The process of eliminating bias in Artificial intelligence: A case of racist robots
Measures that can keep artificial intelligence safe from adversaries
Protecting artificial intelligence discoveries from unintended consequences
How a man can stay in control despite the complex, intelligent systems
Robot rights: A case of how man is mistreating and misusing robots
The balance between mitigating suffering and interfering with set ethics
The role of artificial intelligence in negative outcomes: Is it worth it?
How to ethically use artificial intelligence for bettering lives

Advanced AI Topics

Discuss how long it will take until machines greatly supersede human intelligence
Is it possible to achieve superhuman artificial intelligence in this century?
The impact of techno-skeptic prediction on the performance of A.I
The role of quarks and electrons in the human brain
The impact of artificial intelligence safety research institutes
Will robots be disastrous for humanity shortly?
Robots: A concern about consciousness and evil
Discuss whether a self-driving car has a subjective experience or not
Should humans worry about machines turning evil in the end?
Discuss how machines exhibit goal-oriented behavior in their functions
Should man continue to develop lethal autonomous weapons?
What is the implication of machine-produced wealth?

AI Essay Topics Technology

Discuss the implication of the fourth technological revelation in cloud computing
Big database technologies used in sensors
The combination of technologies typical of the technological revolution
Key determinants of the civilization process of industry 4.0
Discuss some of the concepts of technological management
Evaluate the creation of internet-based companies in the U.S.
The most dominant scientific research in the field of artificial intelligence
Discuss the application of artificial intelligence in the literature
How enterprises use artificial intelligence in blockchain business operations
Discuss the various immersive experiences as a result of digital AI
Elaborate on various enterprise architects and technology innovations
Mega-trends that are future impacts on business operations

Interesting Topics in AI

The role of the industrial revolution of the 18 th century in A.I
The electricity era of the late 19 th century and its contribution to the development of robots
How the widespread use of the internet contributes to the AI revolution
The short-term economic crisis as a result of artificial intelligence business technologies
Designing and creating artificial intelligence production processes
Analyzing large collections of information for technological solutions
How biotechnology is transforming the field of agriculture
Innovative business projects that work using artificial intelligence systems
Process and marketing innovations in the 21 st century
Medical intelligence in the era of smart cities
Advanced data processing technologies in developed nations
Discuss the development of stelliform technologies

Good Research Topics For AI

Development of new technological solutions in I.T
Innovative organizational solutions that develop machine learning
How to develop branches of a knowledge-based economy
Discuss the implications of advanced computerized neural network systems
How to solve complex problems with the help of algorithms
Why artificial intelligence systems are predominating over their creator
How to determine artificial emotional intelligence
Discuss the negative and positive aspects of technological advancement
How internet technology companies like Facebook are managing large social media portals
The application of analytical business intelligence systems
How artificial intelligence improves business management systems
Strategic and ongoing management of artificial intelligence systems

Graduate AI NLP Research Topics

Morphological segmentation in artificial intelligence
Sentiment analysis and breaking machine language
Discuss input utterance for language interpretation
Festival speech synthesis system for natural language processing
Discuss the role of the Google language translator
Evaluate the various analysis methodologies in N.L.P.
Native language identification procedure for deep analytics
Modular audio recognition framework
Deep linguistic processing techniques
Fact recognition and extraction techniques
Dialogue and text-based applications
Speaker verification and identification systems

Controversial Topics in AI

Ethical implication of AI in movies: A case study of The Terminator
Will machines take over the world and enslave humanity?
Does human intelligence paint a dark future for humanity?
Ethical and practical issues of artificial intelligence
The impact of mimicking human cognitive functions
Why the integration of AI technologies into society should be limited
Should robots get paid hourly?
What if AI is a mistake?
Why did Microsoft shut down chatbots immediately?
Should there be AI systems for killing?
Should machines be created to do what they want?
Is the computerized gun ethical?

Hot AI Topics

Why predator drones should not exist
Do the U.S. laws restrict meaningful innovations in AI
Why did the campaign to stop killer robots fail in the end?
Fully autonomous weapons and human safety
How to deal with rogues artificial intelligence systems in the United States
Is it okay to have a monopoly and control over artificial intelligence innovations?
Should robots have human rights or citizenship?
Biases when detecting people’s gender using Artificial intelligence
Considerations for the adoption of a particular artificial intelligence technology

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12 Best Artificial Intelligence Topics for Research in 2024

Explore the "12 Best Artificial Intelligence Topics for Research in 2024." Dive into the top AI research areas, including Natural Language Processing, Computer Vision, Reinforcement Learning, Explainable AI (XAI), AI in Healthcare, Autonomous Vehicles, and AI Ethics and Bias. Stay ahead of the curve and make informed choices for your AI research endeavours.

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Table of Contents

1) Top Artificial Intelligence Topics for Research

a) Natural Language Processing

b) Computer vision

c) Reinforcement Learning

d) Explainable AI (XAI)

e) Generative Adversarial Networks (GANs)

f) Robotics and AI

g) AI in healthcare

h) AI for social good

i) Autonomous vehicles

j) AI ethics and bias

2) Conclusion

Natural Language Processing

Natural Language Processing (NLP) is centred around empowering machines to comprehend, interpret, and even generate human language. Within this domain, three distinctive research avenues beckon:

1) Sentiment analysis: This entails the study of methodologies to decipher and discern emotions encapsulated within textual content. Understanding sentiments is pivotal in applications ranging from brand perception analysis to social media insights.

2) Language generation: Generating coherent and contextually apt text is an ongoing pursuit. Investigating mechanisms that allow machines to produce human-like narratives and responses holds immense potential across sectors.

3) Question answering systems: Constructing systems that can grasp the nuances of natural language questions and provide accurate, coherent responses is a cornerstone of NLP research. This facet has implications for knowledge dissemination, customer support, and more.

Computer Vision

Computer Vision, a discipline that bestows machines with the ability to interpret visual data, is replete with intriguing avenues for research:

1) Object detection and tracking: The development of algorithms capable of identifying and tracking objects within images and videos finds relevance in surveillance, automotive safety, and beyond.

2) Image captioning: Bridging the gap between visual and textual comprehension, this research area focuses on generating descriptive captions for images, catering to visually impaired individuals and enhancing multimedia indexing.

3) Facial recognition: Advancements in facial recognition technology hold implications for security, personalisation, and accessibility, necessitating ongoing research into accuracy and ethical considerations.

Reinforcement Learning

Reinforcement Learning revolves around training agents to make sequential decisions in order to maximise rewards. Within this realm, three prominent Artificial Intelligence Topics emerge:

1) Autonomous agents: Crafting AI agents that exhibit decision-making prowess in dynamic environments paves the way for applications like autonomous robotics and adaptive systems.

2) Deep Q-Networks (DQN): Deep Q-Networks, a class of reinforcement learning algorithms, remain under active research for refining value-based decision-making in complex scenarios.

3) Policy gradient methods: These methods, aiming to optimise policies directly, play a crucial role in fine-tuning decision-making processes across domains like gaming, finance, and robotics.

Introduction To Artificial Intelligence Training

Explainable AI (XAI)

The pursuit of Explainable AI seeks to demystify the decision-making processes of AI systems. This area comprises Artificial Intelligence Topics such as:

1) Model interpretability: Unravelling the inner workings of complex models to elucidate the factors influencing their outputs, thus fostering transparency and accountability.

2) Visualising neural networks: Transforming abstract neural network structures into visual representations aids in comprehending their functionality and behaviour.

3) Rule-based systems: Augmenting AI decision-making with interpretable, rule-based systems holds promise in domains requiring logical explanations for actions taken.

Generative Adversarial Networks (GANs)

The captivating world of Generative Adversarial Networks (GANs) unfolds through the interplay of generator and discriminator networks, birthing remarkable research avenues:

1) Image generation: Crafting realistic images from random noise showcases the creative potential of GANs, with applications spanning art, design, and data augmentation.

2) Style transfer: Enabling the transfer of artistic styles between images, merging creativity and technology to yield visually captivating results.

3) Anomaly detection: GANs find utility in identifying anomalies within datasets, bolstering fraud detection, quality control, and anomaly-sensitive industries.

Robotics and AI

The synergy between Robotics and AI is a fertile ground for exploration, with Artificial Intelligence Topics such as:

1) Human-robot collaboration: Research in this arena strives to establish harmonious collaboration between humans and robots, augmenting industry productivity and efficiency.

2) Robot learning: By enabling robots to learn and adapt from their experiences, Researchers foster robots' autonomy and the ability to handle diverse tasks.

3) Ethical considerations: Delving into the ethical implications surrounding AI-powered robots helps establish responsible guidelines for their deployment.

AI in healthcare

AI presents a transformative potential within healthcare, spurring research into:

1) Medical diagnosis: AI aids in accurately diagnosing medical conditions, revolutionising early detection and patient care.

2) Drug discovery: Leveraging AI for drug discovery expedites the identification of potential candidates, accelerating the development of new treatments.

3) Personalised treatment: Tailoring medical interventions to individual patient profiles enhances treatment outcomes and patient well-being.

AI for social good

Harnessing the prowess of AI for Social Good entails addressing pressing global challenges:

1) Environmental monitoring: AI-powered solutions facilitate real-time monitoring of ecological changes, supporting conservation and sustainable practices.

2) Disaster response: Research in this area bolsters disaster response efforts by employing AI to analyse data and optimise resource allocation.

3) Poverty alleviation: Researchers contribute to humanitarian efforts and socioeconomic equality by devising AI solutions to tackle poverty.

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Autonomous vehicles

Autonomous Vehicles represent a realm brimming with potential and complexities, necessitating research in Artificial Intelligence Topics such as:

1) Sensor fusion: Integrating data from diverse sensors enhances perception accuracy, which is essential for safe autonomous navigation.

2) Path planning: Developing advanced algorithms for path planning ensures optimal routes while adhering to safety protocols.

3) Safety and ethics: Ethical considerations, such as programming vehicles to make difficult decisions in potential accident scenarios, require meticulous research and deliberation.

AI ethics and bias

Ethical underpinnings in AI drive research efforts in these directions:

1) Fairness in AI: Ensuring AI systems remain impartial and unbiased across diverse demographic groups.

2) Bias detection and mitigation: Identifying and rectifying biases present within AI models guarantees equitable outcomes.

3) Ethical decision-making: Developing frameworks that imbue AI with ethical decision-making capabilities aligns technology with societal values.

Future of AI

The vanguard of AI beckons Researchers to explore these horizons:

1) Artificial General Intelligence (AGI): Speculating on the potential emergence of AI systems capable of emulating human-like intelligence opens dialogues on the implications and challenges.

2) AI and creativity: Probing the interface between AI and creative domains, such as art and music, unveils the coalescence of human ingenuity and technological prowess.

3) Ethical and regulatory challenges: Researching the ethical dilemmas and regulatory frameworks underpinning AI's evolution fortifies responsible innovation.

AI and education

The intersection of AI and Education opens doors to innovative learning paradigms:

1) Personalised learning: Developing AI systems that adapt educational content to individual learning styles and paces.

2) Intelligent tutoring systems: Creating AI-driven tutoring systems that provide targeted support to students.

3) Educational data mining: Applying AI to analyse educational data for insights into learning patterns and trends.

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Conclusion

The domain of AI is ever-expanding, rich with intriguing topics about Artificial Intelligence that beckon Researchers to explore, question, and innovate. Through the pursuit of these twelve diverse Artificial Intelligence Topics, we pave the way for not only technological advancement but also a deeper understanding of the societal impact of AI. By delving into these realms, Researchers stand poised to shape the trajectory of AI, ensuring it remains a force for progress, empowerment, and positive transformation in our world.

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216 Artificial Intelligence Essay Topics & Research Questions about AI

If you’re looking for interesting AI research questions or essay topics, you’ve come to the right place! In this list, we’ve compiled the latest trending essay topics on artificial intelligence, research questions, and project ideas. It doesn’t matter if you’re a high school student or a Ph.D. holder: here, you will find research questions about artificial intelligence for beginners as well as professionals.

🏆 Best Essay Topics on Artificial Intelligence

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Ethics of Artificial Intelligence
The Impact of Artificial Intelligence on Young People
Artificial Intelligence and Unemployment
Artificial Intelligence Versus Human Intelligence
Artificial Intelligence Pros and Cons: Essay Sample
Artificial Intelligence in Healthcare
Will Artificial Intelligence Replace Humans?
Artificial Intelligence and Its Impact on the Future AI will have a beneficial influence on the future, whereas it is essential not to underestimate the threats and ultimately solve all the problems encountered in that respect.
Artificial Intelligence in the Workplace The paper states that artificial intelligence has enabled many companies to achieve their dreams. It has also improved the human knowledge of AI.
E-Commerce: The Role of Artificial Intelligence Though the implementation of artificial intelligence within e-commerce continues to develop rapidly, there are several areas in which it affects the experiences of customers.
Artificial Intelligence as a Potential Threat to Humanity Artificial intelligence (AI) demonstrates immense potential in terms of improving society as long it is developed and implemented properly.
Artificial Intelligence: Effects on Business Artificial intelligence is a wide range of technological advancements that deal with current and future effects on the business sector to enhance profitability.
Artificial Intelligence and Global Societal Issues Examination of the latest trends in the sphere of development of manufacturing processes indicates significant growth in the interest in artificial intelligence.
AI In Accounting Essay Example Introducing artificial intelligence (AI) into accounting is viewed by many researchers as a promising practice, which has been the subject of numerous studies.
Helpmewrite.AI Software’s Business Feasibility The report offers research on Helpmewrite.ai software, which is a product that helps writers, lawyers, and paralegals to compose distinct legal pamphlets promptly.
Artificial Intelligence and Effects of Its Rise This work will address unemployment and humanity’s ethical dilemmas, conceived because of the rise of artificial intelligence as a globalization consequence.
Artificial Intelligence in the Labor Market The paper states that the continuous improvements in terms of developing artificial intelligence make render this technology close to reality.
Artificial Intelligence and Its Role in Business The paper looks into the peculiarities of replacing human work with AI to define its potential for development and issues associated with such implementation.
Artificial Intelligence in the Hotel Industry The results of the analysis have shown that, overall, Artificial Intelligence proves to have a positive impact on the quality of hotel services and risk management.
Artificial Intelligence and Music The paper discusses use of Artificial Intelligence is rapidly expanding, with several innovative companies adopting it to create music.
Artificial Intelligence in Hospitality Industry The purpose of this paper is to explore the use application of AI in the hospitality industries. The paper focuses on the utilization of booking engines and hotel software systems.
Artificial Intelligence: Use and Potential Risks Automation and intelligent algorithms can significantly benefit business owners by inducing substantial savings.
Artificial Intelligence in Self-Driving Cars The paper states that artificial intelligence in self-driving vehicles cannot conclude several favorable outcomes – or, the “least bad” effects.
Impact of Artificial Intelligence on the Education System The paper analyzes how the education system can maximize the advantages of Artificial Intelligence. It compares the traditional education system.
Artificial Intelligence Threat for Employees The article “U.S. Lost Over 60 Million Jobs” published focuses on the ongoing unrest around the future of the job market and AI presence within.
Artificial Intelligence in the Oil and Gas Industry Construction, mining, and oil and gas companies are the latecomers in this digitalization, increasingly depending on AI and machine learning (ML) based solutions frenzy.
Artificial Intelligence in Supply Chain Management This paper establishes the benefits, opportunities, and challenges of adopting machine learning technologies in logistics to help SMEs boost their performance.
The Impact of Artificial Intelligence on the Future of Work Artificial intelligence is requested in the modern business world because it can lead to many positive outcomes when applied to employee monitoring.
AI System in Smart Energy Consumption The primary aim of the paper is to expose the significant impacts of AI integration in intelligent energy consumption methods.
Usage of AI and Robotics in Project Management Technological progress has allowed humanity to use the technologies they could not implement in the past centuries.
Business Model Canvas and Artificial Intelligence The nine blocks in the business model canvas, which include vital partners, cost structure, and others in relation to AI, can be summarized to explain their role in business.
Artificial Intelligence: Ethical, Social, Legal Issues The field of artificial intelligence indeed brings numerous ethical, social, professional and legal issues; but are those so disturbing as some people claim?
Artificial Intelligence Projects Failed The research paper provides a detailed worldwide timeline of artificial intelligence projects that were attempted and failed and the threats they have caused.
Artificial Intelligence as an Enhancer of Human Abilities The paper states that using Artificial Intelligence to enhance human capabilities is a trending factor that is growing and receiving attention.
Artificial Intelligence in Accounting Artificial Intelligence is already replacing some accounting operations, and research indicates that automating these processes is a cost-effective option.
Artificial Intelligence in Economics Currently, the amount of data available to businesses continues to grow at an exceptional rate due to the developments in artificial intelligence and big data.
Algorithmic Bias of Artificial Intelligence Artificial intelligence is a rapidly developing technology which is already extensively utilized in different spheres, yet it has many considerable issues.
The Dawn of Artificial Intelligence: Robots Robots were created by people to satisfy their large insatiable appetites. Such a sacrilegious act against the miracle of creation may cost a lot.
Game Playing in Artificial Intelligence The 9th of March, 2016, was a watershed moment in the development of artificial intelligence when the Go champion Lee Sedol was beaten by AlphaGo.
The Promises and Perils of Artificial Intelligence Artificial intelligence is a powerful technology that can generate economic gains; therefore, it is critical to explore its prehistory and practical and ethical concerns.
Artificial Intelligence in Business Management The use of artificial intelligence in business management is a sound practice due to many benefits that this technology offers and an opportunity to secure operational controls.
Artificial Intelligence in Medical Field The medical field constantly innovates and develops new technologies to improve patient care. Societies, in general, are significantly impacted by technological innovations.
How AI and Machine Learning Influence Marketing in the Fashion Industry The study aims to determine if the perception of AI in fashion is a novel concept and whether it holds enough appeal to impact the purchasing decisions of fashion consumers.
Using Information Technology and Artificial Intelligence in Critical Care Medicine Artificial Intelligence in critical care is helping to care for patients faster, supervise more patients, calculate the exact dosage for patients, and collect more detailed data.
Artificial Intelligence Economy This annotated bibliography aims to discuss seven articles devoted to the topic of the artificial intelligence economy.
Artificial Intelligence in Scientific and Fiction Works I decided to research what possible benefits can come from cooperation between scientists and science fiction writers regarding the negative image of artificial intelligence.
Technologies & Artificial Intelligence Challenges For innovative organizations, new technologies introduce not only benefits but also new challenges as the use of artificial intelligence (AI) changes the way organizations work.
Artificial Intelligence in Healthcare: Pros & Cons Rapidly advancing artificial intelligence technologies are gradually changing health care practices and bring a paradigm shift to the medical system.
Artificial Intelligence and Ethical Implications If we create artificial intelligence based on human intelligence, some of the less needed qualities will be omitted during the process of abstraction.
Artificial Intelligence and Its Positive Outcomes The development and growth of AI have brought positive impacts to society, from improved healthcare and education to increased business efficiency.
The Role and Impact of Artificial Intelligence AI-based techniques take an interdisciplinary approach such that they are applicable in different fields, like health and medicine.
The Limits of Global Inclusion in AI (Artificial Intelligence) Development This article is devoted to the theme of the development and implementation of elements of artificial intelligence (AI) in the context of various countries.
Impact of Artificial Intelligence on the Labor Market The document presents annotated article in question considers the impact the spread of artificial intelligence technology may have on the labor market.
Artificial Intelligence (AI) and Universal Basic Income Articles included in the annotated bibliography describe problems of Automation and the spread of Artificial Intelligence (AI)-based technologies.
Artificial Intelligence Bias and Ethical Algorithms The paper argues in order to solve the problem of lack of diversity and assessing human needs correctly, there is a need to implement better guidelines for Artificial Intelligence.
Artificial Intelligence and Emerging Ethical Risks Technological progress went far beyond our imagination, and Artificial Intelligence became an indispensable companion in everyday life.
Fire Scene Investigation: Artificial Intelligence Each container should be labeled uniquely, including the investigator’s name, date and time, sample number, case number, and location of recovery.
Artificial Intelligence Through Human Inquiry Much about the possible uses of A.I. and its potential capacities and abilities remains uncertain, which raises many questions as to what the future of A.I. will hold for humans.
Artificial Intelligence-Related Images Analysis The paper states that the leading scientific and technological idea demonstrated in the photographs is the interaction of artificial intelligence with humans.
AI Prediction of Telecom Churners and Using AI Recommender System The paper aims to have a cost reduction in the OPEX of the company while targeting an improved customer experience throughout the lifecycle of the customer.
The Mayo Clinic’s Artificial Intelligence Program The Mayo Clinic is one of the companies that evaluated its Artificial Intelligence program, looking at both the positive and negative aspects of the change.
Position on AI’s Role in Education Today, educational establishments can benefit from numerous technologies, and artificial intelligence (AI) is among them.
The “Why AI Will Never Fully Capture…” Article by Wilson The article “Why AI Will Never Fully Capture Human Language” by Joseph Wilson, discusses the reasons why artificial intelligence will never fully capture human language.
Artificial Intelligence: Emergence of Employment Issues Artificial intelligence has become particularly widespread in the modern world, but there are significant controversies about the benefits of this technology in people’s lives.
Artificial Intelligence and Human Intelligence Comparison AI performs many tasks that are impossible for humans to perform and can be equal to human tasks in interpreting CT scans, recognizing faces and voices, and playing games.
Comparing Artificial Intelligence to Human Intelligence Intelligence is essential for humanity, as it can isolate important information from the environment and systematize it into knowledge used to solve specific problems.
Artificial Intelligence in Aviation and Human-Machine Interfaces The following study analyzes the research results to determine the impact of artificial intelligence (AI) on aviation.
Customer’s Brand Engagement: The Use of Artificial Intelligence Marketers are currently using artificial intelligence in marketing to automate procedures and provide clients with a distinctive brand experience.
Risks of Artificial Intelligence Data-Mining by Tech Corporations With the exponential advancement of Artificial Intelligence, the notion of data being valuable regarding marketability has permeated the cultural zeitgeist.
Integrated Apple Home-Based Artificial Intelligence System Integrated Apple Home-based Artificial Intelligence system is an artificial intelligence system that has been tailored to meet the end-users’ home needs.
The Turing Test and Development of Artificial Intelligence The Turing test is conducted with two people and a program, in which the program and one person communicate with a judge.
Artificial Intelligence: Pros and Cons Artificial intelligence is definitely a huge step in global technological progress, and like any other technology, it can be both a weapon and a lifesaver.
Artificial Intelligence and the Future of Business AI’s modern field came into being in the 1950s; still, decades were spent on making serious progress in the development of an AI system and turning it from a dream into a reality.
Artificial Intelligence, Insurtech, and Virtual Reality from a Market Perspective AI, Insurtech, and Virtual reality will be presented and discussed in relation to their impact on the market, as well as the disruptions and benefits they may cause.
Artificial Intelligence: Impact on Labor Workforce The development of artificial intelligence often affects drivers and retail workers, healthcare workers, lawyers, accountants, and financial professionals.
The Issue of Artificial Intelligence Integration in Private Health Sector It is possible to develop a particular insight into the perspectives of Artificial Intelligence integration in the private health sector.
What Will Happen When AI Picks Up Social Biases About Gender? Social biases on gender will not have room when Artificial Intelligence takes over and the systems are put into everyday life.
Artificial Intelligence as a Part of Imperialism: Challenges and Solutions Artificial intelligence is part of the process of imperialism, its offshoot, which is commonly called information imperialism.
Enabling Successful AI Implementation in the Department of Defense This paper seeks to provide a summary and discuss three main points of the article “Enabling Successful AI Implementation in the Department of Defense.”
Marketing Artificial Intelligence Problems The alignment problem when applying artificial intelligence in marketing occurs when managers ask a question that does not align with the set objectives.
Can the World Have a Fair Artificial Intelligence? It is important to consider issues to do with AI because the matter has adverse effects on the depreciation of human labor, information protection, and manipulation of people.
AI-Improved Management Information System This paper evaluates a current management information system and directs on ways to improve it using artificial intelligence and machine learning.
Artificial Intelligence and the Labor Market This essay will argue that although the use of AI is a controversial issue, AI could be implemented positively, allowing the effective cooperation of people and robots.
Artificial Intelligence: Human Trust in Healthcare In the modern epoch of digitalization, artificial intelligence (AI) is widely utilized in education, transportation, media, banking, navigation, and healthcare.
Artificial Intelligence: The Monstrous Entity The conversation around the artificial intelligence as a monstrous entity can provide new perspectives for all discourse communities revolving around this topic.
Implementing Artificial Intelligence and Managing Change in Nursing This paper is going to talk about a planned change, namely the implementation of Artificial Intelligence (AI) in perception, thinking, planning, learning, etc.
AI and Transitional Management The article presented the two sides of artificial intelligence from an objective perspective since the general implementation of AI is almost inevitable.
Artificial Intelligence in Machinery This essay explores an operation case, discussing the tools in AI, particularly TensorFlow and Theano, and their implementation issues.
The Portrayal of Artificial Intelligence Artificial intelligence seems to be Frankenstein’s monster of the new age. Different sources provide significant insight into the portrayal of AI as monstrosity.
Artificial Intelligence and Related Ethical Concerns Technological progress allows people to use AI capabilities increasingly, but this concept is also related to many ethical issues about human rights.
Thinking Processes of Artificial Intelligence This essay will discuss the topic of artificial intelligence in whether artificial intelligence can be capable of thinking processes.
The Finance Portfolio Management: Impact of Artificial Intelligence Despite the existing limitations, various artificial intelligence applications can make portfolio management much more accessible.
The Future of Artificial Intelligence in Fiction and Science Although there are numerous technological advancements, not many of them have caused such a tremendous controversy as artificial intelligence.
AI, Human Control and Safety The given evaluative analysis will primarily focus on the topic of artificial intelligence, human control, and safety.
Artificial Intelligence: The Articles Review This paper presents the annotated bibliography dedicated the artificial intelligence technologies, their safety or harm to society.
Artificial Intelligence Implementation in Accounting Processes Artificial intelligence seems to be a prospective technology, and its implementation in accounting processes is inevitable.
How to Create a Fair Artificial Intelligence The current research aims to find possible ways to create a fair AI: exploring power concentration, mass manipulation, depreciation of human labor, and information protection.
AI Development, Unemployment, and Universal Basic Income The theme of AI-human relationships takes an important place in science fiction literature, movies, and video games, but it is not limited by them.
Artificial Intelligence: Advantages and Applications The advantages mentioned above introduce multiple opportunities for applying AI to acquire improved outcomes. Discussion of such applications.
AI in Customer Service: Argument Flaws Analyzing AI’s comprehensive functionality can provide sufficient arguments for a variety of options to implement to attract and retain customers.
Artificial Intelligence: Integrated in Healthcare This paper aims to talk about AI as an innovative idea that can be integrated into healthcare. It will detail the strategies used in executing AI.
AI and Hardware Integration in Business Work Processes AI-driven hardware within businesses has little competition as it is the leading tool for time-saving, cost-reduced, and efficient method processes.
Artificial Intelligence and Singularity Technological development will inevitably shift humanity’s future in a highly radical way. It is especially true in the case of artificial intelligence (AI).
New Technology in the Air Cargo Industry: Artificial Intelligence The article “Transport logistic: Artificial Intelligence at Air cargo” discusses how artificial intelligence will revolutionize the air cargo industry.
Artificial Intelligence and Its Usage in Modern Warfare and Healthcare This paper discusses the question of AI usage in modern warfare, and the usage of Artificial Intelligence used in healthcare in the current situation with the ongoing pandemic.
Artificial Intelligence: Potential Problems and Threats Artificial intelligence can be used for unsuitable purposes, but this is not a scientific problem but rather a moral and ethical one.
Implementation of AI in Law Practice There are many benefits of AI application to large firms that have a lot of unprocessed data or smaller firms that do not have the staff to cover all the tasks.
Companies’ Reputation and Artificial Intelligence This paper discusses companies’ reputations and whether artificial intelligence (AI) has the capacity to predict customer and competitor behavior.
Artificial Intelligence in Business Administration Changes The current state of AI technology does not allow launching ambitious projects that will completely change the way businesses operate.
Artificial Intelligence in the Working Process The purpose of this paper is to describe the impact of artificial intelligence (AI) on the job and its results. AI can do the job that was done by the employee for decades.
Artificial Intelligence. Unmanned Mission Communications Communication networks are essential in facilitating the operations of autonomous systems as they are used in monitoring, collecting data, and exploring hard-to-reach areas.
Could Artificial Intelligence ‘End Mankind’ or Is It All Alarmist Nonsense? The idea of AI ending humankind and leading to a global catastrophe does not represent modern reality accurately.
Medical Innovations: 3D Bioprinting Artificial Intelligence This paper will discuss two medical technological innovations that are significant for the future of a medical organization and how different stakeholders could benefit from them.
Artificial Intelligence and How It Affects Hospitality The main challenge in regards to Artificial Intelligence is its current state, which still requires extensive development in order for it to become practical and useful.
Artificial Intelligence: Its Potential and Use Artificial intelligence has been presented as a technology that will not replace human beings, but help them perform tasks better.
Artificial Intelligence: Science Fiction Novels Many writers created stories and novels in the science fiction genre in an attempt to predict how the life where robots are not just machines but equal members of society would be.
Artificial Intelligence and the Future of Nursing The benefits of AI technologies include time and cost efficiency, as well as a high level of care consistency and comprehensiveness.
“Artificial Intelligence: A Competitive Advantage for the US Army” Review The document offers a substantial review of how the implementation of artificial intelligence (AI) may become a crucial competitive advantage for the US military.
The Use of Starcraft II Video Games for AI Research The article is devoted to the rules for writing effective thesises, for each rule there are examples of good and bad writing.
Artificial Intelligence: Article Review Review of an article by Vinyals, Gaffney & Ewalds (2017) discussing the use of the StarCraft II video game as a platform for AI development and testing.
AI and Machine Self-Learning Machine self-learning has become a perfect solution for complex business problems that cannot be solved by software engineering or human judgment.
Explainable Artificial Intelligence in Accounting The broad implementation of AI in such fields as accounting lays the ground for the drastic changes in management and methods that are utilized by specialists.
Blockchain and Other Artificial Intelligence Systems The project describes the basic features of blockchain and AI technologies, along with the possibilities for their future use in different spheres of human activity.
The Fundamental Role of Artificial Intelligence in the IT Industry Artificial intelligence is aimed at machine learning and providing software to address the problems in a way similar to human intelligence.
Artificial Intelligence (AI) in Health Care The use of AI has increased over the past decades, making it easier for researchers to investigate the most complicated issues.
Artificial Intelligence in Enterprise Processes AI affects ERP systems even though AI-driven solutions are not implemented by the majority of businesses. AI is integrated into ERP systems to increase customer satisfaction
Artificial Intelligence, Internet of Things, and the Impact on Facilities’ Environments The use of AI and IoT is unlikely to replace facilities’ teams because the decision-making process still requires human input.
Artificial Intelligence and Big Data Impacts on Citizens The concept of artificial intelligence is complex and broad. However, researchers, theorists, and writers contribute to the creation of a clear and factual definition of this term in different ways.
The Artificial Intelligence Machine AlphaGo Zero The selected technology is an artificial intelligence (AI) machine by the name of AlphaGo Zero. It is an evolution of previous well-known machines from the company Deep Mind.
Artificial Intelligence in Strategic Business Management Artificial intelligence basically refers to the intelligence that is created in the software or machines by mankind.
Regional Employment and Artificial Intelligence in Japan
Artificial Intelligence and the Human Race
Medicine and Artificial Intelligence
Artificial Intelligence and Machine Learning Applied at the Point of Care
Difference Between Artificial Intelligence and Human
The Four Debatable Viewpoints One May Have About Artificial Intelligence
Artificial Intelligence and Its Impact on Accounting
Rational Choice and Artificial Intelligence
The Ethics and Its Relation To Artificial Intelligence
Artificial Intelligence and Medicine
Privacy, Algorithms, and Artificial Intelligence
Artificial Intelligence: Can Computers Think
Cognitive Science and Its Link to Artificial Intelligence
Artificial Intelligence Replacing the Art of Traditional Selling
The Beauty and Danger of Artificial Intelligence
Digital Devices for Artificial Intelligence Applications
Artificial Intelligence and the Field of Robotics
Could Artificial Intelligence Replace Teachers
Artificial Intelligence and Neuromorphic Engineering
Artificial Intelligence Based Improvised Explosive Devices
Big Data Technologies and Artificial Intelligence
Artificial Intelligence and Its Effects on Business
Modern Technology and Artificial Intelligence
Multilayered Perceptron and Artificial Intelligence
Distributed, Decentralized, and Democratized Artificial Intelligence
Artificial Intelligence and Video Games
Some Considerations About Artificial Intelligence and Its Implications
Comparing Human Intelligence With Artificial Intelligence
Artificial Intelligence During the World Today
Artificial Intelligence and the Future of Human Rights
Economic Policy for Artificial Intelligence
Artificial Intelligence for Human Intelligence and Industrial
The Morality and Utility of Artificial Intelligence
Artificial Intelligence and Behavioral Economics
Blockchain and Artificial Intelligence Technologies
The Effects Artificial Intelligence Has Had on Society and Business
Marketing and Artificial Intelligence
Artificial Intelligence and Machines Automation
People Copy the Actions of Artificial Intelligence
Artificial Intelligence for Healthcare in Africa
Healthcare System Using Artificial Intelligence
Artificial Intelligence for the Future Radiology Diagnostic Service
Artificial Intelligence and Marketing
Copyright Protection for Artificial Intelligence
The Potential and Future of Artificial Intelligence
Artificial Intelligence and the Human Mind
Expert Systems and Its Relationship With Artificial Intelligence
Artificial Intelligence and Its Effect on Mankind
The Nexus Between Artificial Intelligence and Economics
Artificial Intelligence, Based Training and Placement Management
Artificial Intelligence and Its Implications for Income Distribution and Unemployment
Machine Learning and Artificial Intelligence in Finance
The Pros and Cons of Artificial Intelligence
Artificial Intelligence and the Legal Profession
Continual Learning: The Next Generation of Artificial Intelligence
Artificial Intelligence and Its Uses
Regulation Within the Development of Artificial Intelligence
Artificial Intelligence and Computer Science
Mysteries, Epistemological Modesty, and Artificial Intelligence in Surgery
Artificial Intelligence and Cognitive Reasoning
Can Artificial Intelligence Become Smarter Than Humans?
Should Humanity Fear Advances in Artificial Intelligence?
How Does Artificial Intelligence Affect the Retail Industry?
What Are Some of the Ethical Challenges Posed by the Use of Artificial Intelligence for Hiring?
Does Artificial Intelligence Impact the Creative Industries?
Can Artificial Intelligence Change the Way in Which Companies Recruit, Train, Develop, and Manage Human Resources in Workplace?
Will Artificial Intelligence Defeat Human Intelligence?
How Can Artificial Intelligence Help Modern Society?
Can Artificial Intelligence Lead to a More Sustainable Society?
What Role Will Artificial Intelligence Play in Human Affairs in the Next Few Decades?
How Can Artificial Intelligence Help Us Understand Human Creativity?
Will Artificial Intelligence Devices Become Human Best Friend?
Why Must Artificial Intelligence Be Regulated?
Should Artificial Intelligence Have Human Rights?
Why Artificial Intelligence Won’t Dominate the Future?
How Does Artificial Intelligence Impact Today’s Society?
Will Artificial Intelligence Overpower Human Beings?
Should Artificial Intelligence Take Over the Jobs of the Tertiary Sector?
How Will Artificial Intelligence Impact the World?
Should People Develop Artificial Intelligence?
How Does Mary Shelley’s Depiction Show the Threats of Artificial Intelligence?
What Can Artificial Intelligence Offer Coral Reef Managers?
How Will Artificial Intelligence Affect the Job Industry in the Future?
Should the Innovative Evolution of Artificial Intelligence be Regulated?
Will Artificial Intelligence Have a Progressive or Retrogressive Impact on Our Society?

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StudyCorgi. (2022, March 1). 216 Artificial Intelligence Essay Topics & Research Questions about AI. https://studycorgi.com/ideas/artificial-intelligence-essay-topics/

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StudyCorgi . "216 Artificial Intelligence Essay Topics & Research Questions about AI." March 1, 2022. https://studycorgi.com/ideas/artificial-intelligence-essay-topics/.

StudyCorgi . 2022. "216 Artificial Intelligence Essay Topics & Research Questions about AI." March 1, 2022. https://studycorgi.com/ideas/artificial-intelligence-essay-topics/.

These essay examples and topics on Artificial Intelligence were carefully selected by the StudyCorgi editorial team. They meet our highest standards in terms of grammar, punctuation, style, and fact accuracy. Please ensure you properly reference the materials if you’re using them to write your assignment.

This essay topic collection was updated on July 12, 2024 .

Research Topics & Ideas

Artifical Intelligence (AI) and Machine Learning (ML)

Research topics and ideas about AI and machine learning

If you’re just starting out exploring AI-related research topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research topic ideation process by providing a hearty list of research topics and ideas , including examples from past studies.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan to fill that gap.

If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, if you’d like hands-on help, consider our 1-on-1 coaching service .

AI-Related Research Topics & Ideas

Below you’ll find a list of AI and machine learning-related research topics ideas. These are intentionally broad and generic , so keep in mind that you will need to refine them a little. Nevertheless, they should inspire some ideas for your project.

Developing AI algorithms for early detection of chronic diseases using patient data.
The use of deep learning in enhancing the accuracy of weather prediction models.
Machine learning techniques for real-time language translation in social media platforms.
AI-driven approaches to improve cybersecurity in financial transactions.
The role of AI in optimizing supply chain logistics for e-commerce.
Investigating the impact of machine learning in personalized education systems.
The use of AI in predictive maintenance for industrial machinery.
Developing ethical frameworks for AI decision-making in healthcare.
The application of ML algorithms in autonomous vehicle navigation systems.
AI in agricultural technology: Optimizing crop yield predictions.
Machine learning techniques for enhancing image recognition in security systems.
AI-powered chatbots: Improving customer service efficiency in retail.
The impact of AI on enhancing energy efficiency in smart buildings.
Deep learning in drug discovery and pharmaceutical research.
The use of AI in detecting and combating online misinformation.
Machine learning models for real-time traffic prediction and management.
AI applications in facial recognition: Privacy and ethical considerations.
The effectiveness of ML in financial market prediction and analysis.
Developing AI tools for real-time monitoring of environmental pollution.
Machine learning for automated content moderation on social platforms.
The role of AI in enhancing the accuracy of medical diagnostics.
AI in space exploration: Automated data analysis and interpretation.
Machine learning techniques in identifying genetic markers for diseases.
AI-driven personal finance management tools.
The use of AI in developing adaptive learning technologies for disabled students.

AI & ML Research Topic Ideas (Continued)

Machine learning in cybersecurity threat detection and response.
AI applications in virtual reality and augmented reality experiences.
Developing ethical AI systems for recruitment and hiring processes.
Machine learning for sentiment analysis in customer feedback.
AI in sports analytics for performance enhancement and injury prevention.
The role of AI in improving urban planning and smart city initiatives.
Machine learning models for predicting consumer behaviour trends.
AI and ML in artistic creation: Music, visual arts, and literature.
The use of AI in automated drone navigation for delivery services.
Developing AI algorithms for effective waste management and recycling.
Machine learning in seismology for earthquake prediction.
AI-powered tools for enhancing online privacy and data protection.
The application of ML in enhancing speech recognition technologies.
Investigating the role of AI in mental health assessment and therapy.
Machine learning for optimization of renewable energy systems.
AI in fashion: Predicting trends and personalizing customer experiences.
The impact of AI on legal research and case analysis.
Developing AI systems for real-time language interpretation for the deaf and hard of hearing.
Machine learning in genomic data analysis for personalized medicine.
AI-driven algorithms for credit scoring in microfinance.
The use of AI in enhancing public safety and emergency response systems.
Machine learning for improving water quality monitoring and management.
AI applications in wildlife conservation and habitat monitoring.
The role of AI in streamlining manufacturing processes.
Investigating the use of AI in enhancing the accessibility of digital content for visually impaired users.

Recent AI & ML-Related Studies

While the ideas we’ve presented above are a decent starting point for finding a research topic in AI, they are fairly generic and non-specific. So, it helps to look at actual studies in the AI and machine learning space to see how this all comes together in practice.

Below, we’ve included a selection of AI-related studies to help refine your thinking. These are actual studies, so they can provide some useful insight as to what a research topic looks like in practice.

An overview of artificial intelligence in diabetic retinopathy and other ocular diseases (Sheng et al., 2022)
HOW DOES ARTIFICIAL INTELLIGENCE HELP ASTRONOMY? A REVIEW (Patel, 2022)
Editorial: Artificial Intelligence in Bioinformatics and Drug Repurposing: Methods and Applications (Zheng et al., 2022)
Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities, and Challenges (Mukhamediev et al., 2022)
Will digitization, big data, and artificial intelligence – and deep learning–based algorithm govern the practice of medicine? (Goh, 2022)
Flower Classifier Web App Using Ml & Flask Web Framework (Singh et al., 2022)
Object-based Classification of Natural Scenes Using Machine Learning Methods (Jasim & Younis, 2023)
Automated Training Data Construction using Measurements for High-Level Learning-Based FPGA Power Modeling (Richa et al., 2022)
Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare (Manickam et al., 2022)
Critical Review of Air Quality Prediction using Machine Learning Techniques (Sharma et al., 2022)
Artificial Intelligence: New Frontiers in Real–Time Inverse Scattering and Electromagnetic Imaging (Salucci et al., 2022)
Machine learning alternative to systems biology should not solely depend on data (Yeo & Selvarajoo, 2022)
Measurement-While-Drilling Based Estimation of Dynamic Penetrometer Values Using Decision Trees and Random Forests (García et al., 2022).
Artificial Intelligence in the Diagnosis of Oral Diseases: Applications and Pitfalls (Patil et al., 2022).
Automated Machine Learning on High Dimensional Big Data for Prediction Tasks (Jayanthi & Devi, 2022)
Breakdown of Machine Learning Algorithms (Meena & Sehrawat, 2022)
Technology-Enabled, Evidence-Driven, and Patient-Centered: The Way Forward for Regulating Software as a Medical Device (Carolan et al., 2021)
Machine Learning in Tourism (Rugge, 2022)
Towards a training data model for artificial intelligence in earth observation (Yue et al., 2022)
Classification of Music Generality using ANN, CNN and RNN-LSTM (Tripathy & Patel, 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, in order for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest. In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

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If you’re still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

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8 Best Topics for Research and Thesis in Artificial Intelligence

Imagine a future in which intelligence is not restricted to humans!!! A future where machines can think as well as humans and work with them to create an even more exciting universe. While this future is still far away, Artificial Intelligence has still made a lot of advancement in these times. There is a lot of research being conducted in almost all fields of AI like Quantum Computing, Healthcare, Autonomous Vehicles, Internet of Things , Robotics , etc. So much so that there is an increase of 90% in the number of annually published research papers on Artificial Intelligence since 1996.

Keeping this in mind, if you want to research and write a thesis based on Artificial Intelligence, there are many sub-topics that you can focus on. Some of these topics along with a brief introduction are provided in this article. We have also mentioned some published research papers related to each of these topics so that you can better understand the research process.

Table of Content

1. Machine Learning

2. deep learning, 3. reinforcement learning, 4. robotics, 5. natural language processing (nlp), 6. computer vision, 7. recommender systems, 8. internet of things.

Best-Topics-for-Research-and-Thesis-in-Artificial-Intelligence

So without further ado, let’s see the different Topics for Research and Thesis in Artificial Intelligence!

Machine Learning involves the use of Artificial Intelligence to enable machines to learn a task from experience without programming them specifically about that task. (In short, Machines learn automatically without human hand holding!!!) This process starts with feeding them good quality data and then training the machines by building various machine learning models using the data and different algorithms. The choice of algorithms depends on what type of data do we have and what kind of task we are trying to automate.

However, generally speaking, Machine Learning Algorithms are generally divided into 3 types: Supervised Machine Learning Algorithms , Unsupervised Machine Learning Algorithms , and Reinforcement Machine Learning Algorithms . If you are interested in gaining practical experience and understanding these algorithms in-depth, check out the Data Science Live Course by us.

Deep Learning is a subset of Machine Learning that learns by imitating the inner working of the human brain in order to process data and implement decisions based on that data. Basically, Deep Learning uses artificial neural networks to implement machine learning. These neural networks are connected in a web-like structure like the networks in the human brain (Basically a simplified version of our brain!).

This web-like structure of artificial neural networks means that they are able to process data in a nonlinear approach which is a significant advantage over traditional algorithms that can only process data in a linear approach. An example of a deep neural network is RankBrain which is one of the factors in the Google Search algorithm.

Reinforcement Learning is a part of Artificial Intelligence in which the machine learns something in a way that is similar to how humans learn. As an example, assume that the machine is a student. Here the hypothetical student learns from its own mistakes over time (like we had to!!). So the Reinforcement Machine Learning Algorithms learn optimal actions through trial and error.

This means that the algorithm decides the next action by learning behaviors that are based on its current state and that will maximize the reward in the future. And like humans, this works for machines as well! For example, Google’s AlphaGo computer program was able to beat the world champion in the game of Go (that’s a human!) in 2017 using Reinforcement Learning.

Robotics is a field that deals with creating humanoid machines that can behave like humans and perform some actions like human beings. Now, robots can act like humans in certain situations but can they think like humans as well? This is where artificial intelligence comes in! AI allows robots to act intelligently in certain situations. These robots may be able to solve problems in a limited sphere or even learn in controlled environments.

An example of this is Kismet , which is a social interaction robot developed at M.I.T’s Artificial Intelligence Lab. It recognizes the human body language and also our voice and interacts with humans accordingly. Another example is Robonaut , which was developed by NASA to work alongside the astronauts in space.

It’s obvious that humans can converse with each other using speech but now machines can too! This is known as Natural Language Processing where machines analyze and understand language and speech as it is spoken (Now if you talk to a machine it may just talk back!). There are many subparts of NLP that deal with language such as speech recognition, natural language generation, natural language translation , etc. NLP is currently extremely popular for customer support applications, particularly the chatbot . These chatbots use ML and NLP to interact with the users in textual form and solve their queries. So you get the human touch in your customer support interactions without ever directly interacting with a human.

Some Research Papers published in the field of Natural Language Processing are provided here. You can study them to get more ideas about research and thesis on this topic.

The internet is full of images! This is the selfie age, where taking an image and sharing it has never been easier. In fact, millions of images are uploaded and viewed every day on the internet. To make the most use of this huge amount of images online, it’s important that computers can see and understand images. And while humans can do this easily without a thought, it’s not so easy for computers! This is where Computer Vision comes in.

Computer Vision uses Artificial Intelligence to extract information from images. This information can be object detection in the image, identification of image content to group various images together, etc. An application of computer vision is navigation for autonomous vehicles by analyzing images of surroundings such as AutoNav used in the Spirit and Opportunity rovers which landed on Mars.

When you are using Netflix, do you get a recommendation of movies and series based on your past choices or genres you like? This is done by Recommender Systems that provide you some guidance on what to choose next among the vast choices available online. A Recommender System can be based on Content-based Recommendation or even Collaborative Filtering.

Content-Based Recommendation is done by analyzing the content of all the items. For example, you can be recommended books you might like based on Natural Language Processing done on the books. On the other hand, Collaborative Filtering is done by analyzing your past reading behavior and then recommending books based on that.

Artificial Intelligence deals with the creation of systems that can learn to emulate human tasks using their prior experience and without any manual intervention. Internet of Things , on the other hand, is a network of various devices that are connected over the internet and they can collect and exchange data with each other.

Now, all these IoT devices generate a lot of data that needs to be collected and mined for actionable results. This is where Artificial Intelligence comes into the picture. Internet of Things is used to collect and handle the huge amount of data that is required by the Artificial Intelligence algorithms. In turn, these algorithms convert the data into useful actionable results that can be implemented by the IoT devices.

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175+ Great PhD Research Topics In Artificial Intelligence

Uncover best PhD research topics in Artificial Intelligence, spanning from machine learning to human-computer interaction. This comprehensive list is designed to spark your creativity and guide your next big research endeavor.

As AI rapidly transforms our world, there’s an increasing demand for innovative research. Explore these topics to discover where your research journey might lead and find the perfect project to pursue. Dive in and let these ideas inspire your next breakthrough!

Table of Contents

PhD Research Topics In Artificial Intelligence PDF

The growing importance of ai research.

Artificial Intelligence (AI) has quickly become a transformative force, reshaping industries and sparking global research.

Key reasons AI research is essential

Economic Growth: Drives new business models and boosts economies.
Societal Impact: Tackles global issues like climate change and healthcare.
Tech Advances: Fuels ongoing innovation.
Global Competition: Nations invest heavily to stay ahead.
Ethical Questions: Raises complex issues that need careful study.

As AI advances, skilled researchers are increasingly needed to develop and manage these technologies responsibly.

Importance of PhD research in advancing AI

PhD research is crucial for advancing AI:

Fundamental Research: Explores theoretical foundations.
Algorithm Development: Creates and improves algorithms.
Ethical Considerations: Develops ethical guidelines.
Real-World Applications: Applies AI to practical problems.
Talent Development: Trains future AI experts.
Knowledge Sharing: Publishes and shares findings.

PhD students drive the future of AI through in-depth research and innovation.

PhD Research Topics In Artificial Intelligence

Check out PHD research topics in Artificial Intelligence:-

Machine Learning

Disease Detection : Improve disease diagnosis using image analysis.
Game Strategies : Enhance game-playing algorithms.
Language Adaptation : Customize models for less common languages.
Fraud Detection : Spot unusual financial transactions.
Few-Shot Learning : Train models with minimal data.
Feature Learning : Extract useful features from raw data.
Network Security : Identify suspicious network activities.
Image Classification : Use small labelled datasets for better accuracy.
Neural Architecture : Automate neural network design.
Data Augmentation : Generate synthetic data to improve training.

Natural Language Processing (NLP)

Contextual Text Generation : Make text generation more relevant.
Emotion Analysis : Detect emotions in text.
Multilingual Translation : Improve translation for multiple languages.
Health Chatbots : Design bots for medical advice.
Legal Summarization : Automatically summarize legal documents.
Social Media Entities : Extract entities from casual text.
Accurate Transcription : Improve speech-to-text for different accents.
Interactive Stories : Create dynamic narrative systems.
Legal Document Classification : Sort legal documents automatically.
Market Sentiment : Analyze social media for market trends.

Computer Vision

Object Detection : Enhance real-time object tracking.
Autonomous Vehicles : Improve road scene understanding.
Low-Light Facial Recognition : Better recognition in dim light.
Action Recognition : Identify actions in videos.
Augmented Reality : Use AI for real-time AR guidance.
Medical Image Analysis : Detect anomalies in medical images.
Image Captioning : Generate descriptions for images.
3D Reconstruction : Create 3D models from 2D images.
Style Transfer : Apply artistic styles to images.
Visual Question Answering : Answer questions about images.

Robotics and Autonomous Systems

Object Manipulation : Improve robot handling of objects.
Collaborative Robots : Robots working safely with humans.
Navigation in Complex Environments : Better pathfinding for robots.
Disaster Response : Robots aiding in emergencies.
Swarm Robotics : Coordinated robotic teams.
Drone Delivery : Autonomous package delivery.
Elderly Assistance : Robots helping with daily tasks.
Learning from Demonstration : Robots learning from human actions.
Localization and Mapping : Robots creating accurate maps.
Motion Planning : Robots adapting to new conditions.

Ethics and Fairness

Bias Detection : Identify biases in AI models.
Explainable AI : Make AI decisions understandable.
Privacy Preservation : Secure data while training models.
Ethical Guidelines : Develop responsible AI frameworks.
Transparency : Explain AI decision-making processes.
Fair Hiring : Ensure unbiased hiring algorithms.
Social Impact : Assess AI’s effects on communities.
Military Ethics : Explore AI use in defense.
Data Ethics : Respect privacy in data use.
Regulation Analysis : Review AI policies and suggest improvements.

AI in Healthcare

Early Cancer Detection : Detect cancer early from scans.
Patient Outcome Prediction : Forecast health outcomes.
Personalized Treatment : Tailor treatments to individuals.
Drug Repurposing : Find new uses for existing drugs.
Virtual Health Assistants : Create supportive health bots.
Automated Radiology Reports : Generate reports from scans.
Genomic Data Analysis : Interpret genetic data.
Clinical Trial Improvement : Enhance clinical trial designs.
Telemedicine Tools : Improve remote healthcare services.
Chronic Disease Management : Assist in managing chronic conditions.

AI for Social Good

Disaster Management : Predict and manage natural disasters.
Pollution Monitoring : Track environmental pollutants.
Education Tools : Create adaptive learning resources.
Assistive Technologies : Aid people with disabilities.
Health Surveillance : Monitor public health issues.
Waste Management : Optimize recycling processes.
Social Justice Analysis : Address inequality with AI.
Emergency Response : Improve disaster response coordination.
Urban Green Planning : Manage urban green spaces.
Volunteer Coordination : Organize volunteers effectively.

AI in Finance and Economics

Trading Algorithms : Optimize financial trading strategies.
Fraud Prevention : Detect fraudulent activities.
Economic Forecasting : Predict economic trends.
Finance Management Apps : Help users manage finances.
Credit Risk Models : Assess creditworthiness.
Portfolio Management : Improve investment strategies.
Policy Impact Analysis : Analyze economic policy effects.
Loan Default Prediction : Predict and prevent loan defaults.
Automated Reporting : Generate financial reports automatically.
Financial Advisory : Enhance robo-advisors with AI.

AI in Cybersecurity

Threat Detection : Identify cyber threats.
Malware Classification : Detect and categorize malware.
Cyber Threat Prediction : Forecast emerging threats.
Adversarial Defense : Protect against AI attacks.
Secure Communications : Safeguard digital communications.
Phishing Detection : Block phishing attempts.
Insider Threats : Detect suspicious internal behavior.
Network Security : Monitor and protect networks.
Incident Response : Automate response to security breaches.
Vulnerability Assessment : Identify system weaknesses.

AI in Education

Personalized Learning : Tailor education to individual needs.
Automated Grading : Grade essays and assignments automatically.
Intelligent Tutors : Provide subject-specific tutoring.
Learning Analytics : Analyze and improve student performance.
Educational Games : Make learning interactive and fun.
Virtual Classrooms : Enhance online learning experiences.
Test Prep Tools : Customize test preparation resources.
Special Education Support : Assist students with special needs.
Student Engagement : Measure and improve engagement.
Curriculum Design : Help educators design effective curricula.

AI in Entertainment and Media

Music Composition : Generate original music with AI.
Content Recommendations : Suggest personalized media.
VR Enhancement : Create immersive VR experiences.
Deepfake Detection : Identify fake media content.
Interactive Stories : Generate engaging narratives.
Video Editing : Automate video editing tasks.
Scriptwriting Assistance : Aid in writing and refining scripts.
Ad Targeting : Personalize advertising.
Game AI : Develop intelligent game opponents.
Synthetic Media : Create realistic synthetic audio and video.

AI in Agriculture

Crop Monitoring : Track crop health with AI.
Irrigation Optimization : Improve water use for crops.
Harvesting Robots : Automate crop harvesting.
Yield Prediction : Forecast crop production.
Pest Detection : Identify pests early.
Soil Analysis : Analyze soil conditions.
Climate Adaptation : Adjust farming practices for climate change.
Supply Chain Efficiency : Optimize agricultural logistics.
Resource Management : Manage resources sustainably.
Environmental Monitoring : Track environmental impacts.

AI in Transportation

Traffic Optimization : Improve traffic flow management.
Autonomous Vehicles : Enhance self-driving car tech.
Public Transit Routing : Optimize transit routes and schedules.
Smart Traffic Lights : Adjust lights based on traffic.
Fleet Management : Manage vehicle fleets efficiently.
Predictive Maintenance : Prevent transport equipment failures.
Ridesharing Optimization : Improve ridesharing services.
Logistics Management : Optimize supply chain and logistics.
Delivery Drones : Automate package delivery.
Parking Solutions : Manage parking more effectively.

AI in Manufacturing

Predictive Maintenance : Forecast equipment needs.
Quality Control : Automate product inspections.
Smart Production : Optimize manufacturing processes.
Inventory Management : Improve stock control.
Product Design : Assist in designing new products.
Energy Efficiency : Reduce energy use in factories.
Robotic Automation : Automate repetitive tasks.
Manufacturing Analytics : Analyze production data.
Custom Manufacturing : Create personalized products.
Safety Monitoring : Enhance workplace safety.

AI in Robotics

Human-Robot Interaction : Improve robot collaboration with people.
Learning from Demonstration : Robots learn by observing.
Behavior Adaptation : Robots adjust to new conditions.
Ethics in Robotics : Address ethical issues in robotics.
Soft Robotics : Develop flexible robots.
Perception Systems : Enhance robots’ sensory capabilities.
Control Algorithms : Improve robot movement precision.
Navigation : Enable robots to navigate complex areas.
Swarm Robotics : Coordinate groups of robots.
Healthcare Robots : Assist with medical tasks.

AI in Space Exploration

Autonomous Spacecraft : Manage spacecraft autonomously.
Data Analysis : Analyze space mission data.
Astrobiology Research : Search for extraterrestrial life.
Satellite Imaging : Process satellite images.
Mission Planning : Optimize space mission strategies.
Space Robotics : Develop robots for space tasks.
Communication Systems : Improve deep space communication.
Space Weather Prediction : Forecast space weather events.
Exoplanet Detection : Find and study exoplanets.
Resource Management : Manage space resources.

AI in Environmental Science

Climate Prediction : Forecast climate changes.
Resource Management : Manage natural resources.
Ecosystem Protection : Study and protect ecosystems.
Sustainable Practices : Promote eco-friendly practices.
Wildlife Monitoring : Protect endangered species.
Impact Assessment : Evaluate environmental impacts.
Renewable Energy : Optimize renewable energy use.
Disaster Recovery : Improve responses to environmental disasters.
Urban Green Spaces : Plan and manage green areas.

Emerging Trends and Future Directions

AI is advancing quickly with exciting trends and future directions:

Emerging Trends

Generative AI: Creates text, images, and music.
RLHF: Improves models with human feedback.
AI for Science: Accelerates scientific discoveries.
Explainable AI (XAI): Makes AI models clearer.
AI for Social Good: Addresses global challenges.

Future Directions

Embodied AI: Interacts with the physical world.
Neuromorphic Computing: Mimics the human brain.
AI Safety and Ethics: Ensures AI is safe and fair.
AI for Human Augmentation: Boosts human capabilities.
General AI (AGI): Develops highly intelligent machines.

These areas offer exciting opportunities for research and innovation.

How to choose a suitable AI research topic

Check out the best tips to choose a suitable AI research topic:-

Identify Your Interests

Core Areas: Pick your main AI interest (e.g., machine learning, computer vision).
Sub-fields: Find specific areas you’re passionate about.

Review Literature

Research Gaps: Spot where more research is needed.
Emerging Trends: Keep up with the latest developments.
Successful Projects: Look at past research for ideas.

Assess Feasibility

Data: Ensure you can access necessary data.
Resources: Check if you have the needed computational power.
Timeline: Align the project with your PhD timeline.
Skills: Evaluate your current skills and what you need.

Consider Impact and Originality

Impact: Think about how your research will contribute.
Novelty: Aim for a fresh perspective.
Applications: Consider real-world uses.

Seek Guidance

Advisors: Discuss with your PhD advisor.
Collaborators: Look for research partners.
Experts: Get input from industry professionals.

Challenges and Opportunities in AI Research

Check out challenges and opportunities in AI research:-

Challenges in AI Research

Data Quality: Accessing high-quality, labeled data.
Computational Power: Meeting high computing demands.
Interpretability: Understanding model decisions.
Ethics: Addressing bias, privacy, and safety.
Talent Shortage: Finding skilled researchers.

Opportunities in AI Research

Collaboration: Combining AI with other fields.
Impact: Solving global issues with AI.
Entrepreneurship: Starting AI-driven ventures.
Academic Growth: Advancing AI theory.
Lifelong Learning: Keeping up with rapid changes.

Finding and Selecting a PhD Research Topic

Check out the best tips for finding and selecting a PhD research topic:-

Identify Interests and Strengths

Passion: Find what excites you in AI.
Skills: Assess your current abilities.
Career Goals: Match with your long-term plans.

Conduct a Literature Review

Research Gaps: Find areas with limited study.
Trends: Stay updated on new developments.
Inspiration: Study successful research.

Evaluate Feasibility

Data: Ensure access to needed data.
Computing: Check required resources.
Timeline: Fit the project within your PhD period.
Skills: Identify what you need to learn.
Advisors: Talk with potential advisors.
Collaborators: Explore research partnerships.
Experts: Get advice from industry professionals.

Develop a Research Question

Focused: Define the specific problem.
Original: Aim for new contributions.
Feasible: Ensure it’s practical.
Relevant: Align with current trends.

These steps will help you choose a rewarding and impactful PhD topic.

Case studies of successful AI PhD projects

Studying successful AI PhD projects offers key insights:

Drug Discovery: Speeding up drug development.
Medical Imaging: Better disease diagnosis.
Personalized Medicine: Tailored treatments.
Algorithmic Trading: Automated stock trading.
Fraud Detection: Spotting fraud.
Risk Assessment: Evaluating risks.
Language Models: Advanced models.
Machine Translation: Improved translation.
Sentiment Analysis: Public opinion.
Object Detection: Accurate detection.
Image Generation: Realistic images.
Video Analysis: Content analysis.
Autonomous Vehicles: Self-driving tech.
Human-Robot Interaction: Better interactions.
Healthcare Robotics: Medical robots.

These examples highlight effective AI research areas.

Funding opportunities for AI research

Securing AI research funding involves:

Government Grants

NSF: AI research grants.
DARPA: High-risk projects.
NIH: AI in healthcare.
DOE: AI for energy and climate.
EU: Horizon Europe grants.

Industry Grants

Tech Giants: Google, Microsoft, Amazon, Meta.
Associations: AI Index funding .
Corporate Foundations: Company-funded research.

Philanthropic Organizations

Foundations: Gates Foundation, Sloan Foundation.

Other Sources

Universities: Internal grants.
Competitions: Funding through contests.
Crowdfunding: Online fundraising.

Keep updated on evolving funding opportunities.

Collaboration and networking in AI research

Connecting with others boosts your AI research. Here’s how:

Importance of Collaboration

Shared Expertise: Combine skills.
Resource Sharing: Access data and tools.
Faster Progress: Achieve breakthroughs quicker.
Knowledge Exchange: Learn and expand your network.

Building a Strong Network

Attend Events: Go to conferences and workshops.
Join Online Groups: Participate in AI forums and social media.
Industry Partnerships: Collaborate with companies.
Interdisciplinary Work: Engage with researchers from other fields.

Effective Networking Strategies

Build Relationships: Make genuine connections.
Share Info: Exchange research findings.
Seek Mentorship: Get advice from experienced researchers.
Give Back: Mentor others and volunteer.

Active networking and collaboration enhance your research and success.

What can you do with a PhD in AI?

A PhD in AI offers diverse career paths:

Professor: Teach and research at universities.
Researcher: Advance AI knowledge.
AI Research Scientist: Develop AI models.
Machine Learning Engineer: Build AI systems.
Data Scientist: Analyze data.
AI Product Manager: Manage AI products.
AI Consultant: Advise businesses on AI.

Other Roles

Entrepreneur: Start an AI company.
Policy Maker: Shape AI regulations.
Ethical AI Specialist: Ensure responsible AI use.

Choose a role that fits your interests and goals.

What is the hottest topic in AI?

Generative AI is trending with tools like ChatGPT and Midjourney.

Text-to-Image: Realistic images from text.
Text Generation: High-quality text, like articles.
Video Generation: Videos from text or images.
Audio Generation: Music, speech, or sound effects.

Other important areas include AI for Science, Explainable AI, and AI Safety.

Artificial Intelligence is a fast-moving field full of exciting research opportunities. This blog showcases some of the many ways AI is making a difference, from fundamental theories to real-world applications.

By picking interesting topics, diving deep into research, and working with others, AI researchers can push boundaries and tackle big global challenges. The future of AI is bright, and with dedicated researchers, we’re in for some amazing advancements ahead.

Which Topic Is Best For Artificial Intelligence?

The best topic for Artificial Intelligence depends on your specific interests, expertise, and the potential for meaningful contributions in that particular area.

How Is AI Used In PhD Research?

AI is utilized in PhD research to enhance data analysis, model complex systems, automate tasks, and develop innovative solutions across various fields.

149+ Most Interesting Civil Engineering Research Topics For Undergraduates

189+ Top-Rated Quantitative Research Topics For Accounting Students [Updated 2024]

13 Commonly Asked Questions on Artificial Intelligence (2023)

There’s no question that artificial intelligence is one of the most critical technologies being developed . As powerful as it is, people still have plenty of questions about it, like how can it be used and what impact will it have. I’ll answer 13 Commonly Asked Questions on Artificial Intelligence (AI) in this article.

Because this field is constantly changing, I’ve also updated my latest book about artificial intelligence , so check it out if you are keen to learn more about AI!

Learning as much as possible about AI is a great idea because it will soon begin to impact your work, the surrounding society, and the world you live in . At the same time, always make sure to verify the info that you encounter about AI, because there is a lot of misinformation about it out there.

1. What is the definition of artificial intelligence? 2. Who coined the term artificial intelligence? 3. Does artificial intelligence exist? 4. AI terminology – What are the most common AI terms you should know? 5. Can artificial intelligence be dangerous? 6. How will artificial intelligence change the future? 7. Why do we need artificial intelligence? 8. Why should you study artificial intelligence? 9. How can we apply artificial intelligence? 10. What are the typical jobs related to artificial intelligence? 11. Will China be the AI super power? 12. Which are the most powerful artificial intelligence companies? 13. What are some common benefits of artificial intelligence technology?

Here are the 13 commonly asked questions on artificial Intelligence (AI) for 2019 .

Table of Contents

1. What is the definition of artificial intelligence?

Over the years, many different definitions of artificial intelligence have been suggested. Because AI comes from a complex set of technologies, there are multiple ways to define it. Perhaps the most common and accurate way to describe it is as a way of computer systems analyzing data to be able to make decisions like a human would.

Here is a definition of artificial intelligence from European Union:

“ Artificial intelligence (AI) refers to systems that show intelligent behaviour: by analysing their environment they can perform various tasks with some degree of autonomy to achieve specific goals .” – European Union

One of the primary benefits of artificial intelligence is that it can analyze much more data than a human could. It can also dive deeper into the data with much more accuracy than a person, which makes AI a powerful tool for us to use as we make decisions about our daily lives.

AI is driven by data—in fact, it could not exist without a huge amount of data. If you’re considering using AI within your own business, you should think about the kinds of internal and external data that you have access to, as well as how to find and collect additional high quality data that could be used to create AI systems to perform different tasks. You might also want to see the definition of AI by Wikipedia .

2. Who coined the term artificial intelligence?

The term “ artificial intelligence ” actually goes back many decades, dating back to the year 1955 when John McCarthy first coined the term . The concept was so fascinating that just one year later, in 1956, he joined with others to create the very first artificial intelligence conference.

As you can imagine, back then there was much less data available, which is crucial for AI to be able to operate correctly. For this reason, it has only been recently that AI technologies have been able to evolve into the powerful tools that we see today.

Today, most of the commercial benefits of artificial intelligence come from a subfield known as “ deep learning ,” which uses huge amounts of data to analyze it, discover patterns, and help people and companies to make better decisions. You might like to see this deep learning course by Fast.AI

3. Does artificial intelligence exist?

Most people were first exposed to the idea of artificial intelligence from Hollywood movies, long before they ever started seeing it in their day-to-day lives. This means that many people misunderstand the technology. When they think about common examples that they’ve seen in movies or television shows, they may not realize that the killer robots they’ve seen were created to sell emotional storylines and drive the entertainment industry, rather than to reflect the actual state of AI technology.

The thought of AI can also make people nervous, especially if they are worried that they might lose their jobs to AI tools. Because of these fears, there is a lot of fake news and misinformation that gets spread about artificial intelligence.

AI does exist and is already being used in many industries. Because it is developing so quickly, it can be hard for us to imagine the eventual impact it will have on our world.

Right now, AI is excellent at dealing with very narrow tasks, analyzing data, and making accurate decisions based on that data. However, it is not necessarily excellent at doing several kinds of tasks all at once. This is known as narrow AI.

For example, artificial intelligence is perfect at successfully driving a car, as self-driving car technologies have shown. In fact, it may be better at doing so than the average human! But using that same AI tool for fraud protection, as many banks and financial institutions are starting to do, would not work. While you can develop an AI tool to go very deeply into one task, it cannot usually do two very different things at the same time.

General AI, which refers to artificial intelligence being able to do multiple things simultaneously, is something that experts have been predicting for many years. However, based on my own research, I believe it could take many years, even decades, to fully achieve this, so for now we should focus on bringing narrow AI into as many industries as possible.

4. AI terminology – What are the most common AI terms you should know?

As I mentioned before, AI is made up of a complex set of different technologies. This means that as the field grows and evolves, we’ll begin to see more terms being used to describe what the technology does.

For now, here are some of the most common AI terms that you should know:

Algorithm The step-by-step method that a computer uses to complete each task. Since a computer understands numbers best, the steps are put together as mathematical equations, for example: “If x=1, then….”.

Artificial Neural Networks The term used to refer to AI systems that simulate connected neural units, modeling the way neurons interact in the brain.

Cognitive Science A discipline that examines the various processes of the human brain such as linguistics, information processing and decision making. The goal is to discover more about cognition.

Deep Learning The use of neural networks consisting of many layers of large numbers (millions) of artificial neurons. Deep learning is perfect for projects involving huge, complex datasets.

Expert System A computer system that models the decision-making ability of a human expert. Expert systems are rule-based and normally use “if-then” statements.

Another term that you should become familiar with is computer vision . This refers to a computer’s ability to see. It’s important because until now, computers relied on human reporting for things that required vision. Now, computers can mimic this ability. Computer vision can be used for many purposes, especially in the security industry and for quality control.

The applications for computer vision will be wide ranging and are likely to grow in the next few years. There are already cloud-based computer vision services that allow companies to buy the technology from outside vendors so that they can take advantage of it right away without having to create their own tools from scratch.

The other term that you should know is natural language translation . This is the ability of a computer to hear something that is said to it and then answer back to the user. You might be familiar with this technology if you’ve ever used a chatbot or a smart assistant like Amazon’s Alexa or Google Home.

Thanks to these tools, AI can be used at home and on the go. A smart assistant can search information for us, reading through answers available online and communicating the best results back to us. We can use these tools to ask something like, “What is the weather like today?” and even more complicated questions, and get short and accurate answers back.

Over time, these features will continue to grow and develop, allowing AI tools to perform even harder tasks, like doing market research for businesses. For now, companies should start becoming familiar with these tools so that they can leverage them quickly and effectively as they become more advanced.

5. Can artificial intelligence be dangerous?

Artificial intelligence is a tool, and like many tools, its danger is fully dependent on humans and the ways that they use it.

Think about a hammer. It can be used for wonderful things, like building a home, but it can also be used to hurt someone else.

Unlike a hammer, however, which can only be used by one person at a time with relatively little impact, AI can be created by a single person and spread around, which can multiply its power for good or evil.

One way that artificial intelligence can be dangerous is when it is used to create autonomous weapons. Currently, almost every large nation is spending a lot of resources on the creation of autonomous weapons that can be used in upcoming conflicts.

This is a dangerous precedent for the application of AI. Currently, there is a petition that was created by the Future of Life Institute to help prevent the creation and spread of these kinds of weapons. I’ve signed it myself and I highly recommend that you do the same. You can find it in this link: https://futureoflife.org/lethal-autonomous-weapons-pledge/

Another way that AI can be dangerous is when it is applied in societies without due consideration and analysis of the long-term ethical and moral implications that it might create.

For example, there is a danger to building a society where certain decisions are made purely based on an AI algorithm.

For example, who will receive jail time for a crime? In the US, “criminal risk assessment algorithms” are already being used to analyze whether a person is likely to reoffend in the future. Many civil rights groups are against the use of these kinds of tools, claiming that they can make wrong suggestions and send innocent people to prison. You can read more about it in this in-depth article by Karen Hao: https://www.technologyreview.com/s/612775/algorithms-criminal-justice-ai/

AI will be used in a similar fashion to influence decision making in different areas of society such as finance and education, and it’s highly likely that the first machine learning models to be used will be biased. To learn more about this I recommend watching this presentation by my friend Tonima Afroze, which covers several examples. https://www.youtube.com/watch?v=5uc6jFFKgiI

When AI tools are used within a society, the algorithms that they are based upon should be transparent, allowing us to verify decisions made through the tools after the fact. They should also be designed to be secure against the efforts of hackers to change the algorithms behind the artificial intelligence.

Instead of relying fully on AI to make important decisions within a society, systems should always be created in such a way that the AI analysis is used along with human input.

6. How will artificial intelligence change the future?

The impact of artificial intelligence will be greater and will happen sooner than we can prepare for. It will likely change or disrupt just about everything we experience in our lives and in society as a whole.

This will have a positive impact in many ways, creating opportunities for those who are early to adopt new tools and follow trends as changes happen, but it will also be stressful, disappointing and confusing for people who ignore or aren’t fully prepared for the changes that AI will bring.

To stay on top of the changes and challenges that will come about from the introduction of AI tools, I recommend watching new trends on three different levels:

Individually : It can be helpful to create a “map” of the ways that the world around us is likely to change over the next 5 to 7 years. To do this, think outside the box, analyze new trends, and think critically about the information you encounter. Consider the ways that you can leverage new tools to make a positive impact for yourself and for society at large.
As a society : Politicians and decision makers need to use big data and AI correctly when making decisions. These decisions should be evidence-based and able to be independently verified after the fact, rather than made on the basis of political philosophy. This will be a huge challenge for most countries, as many policymakers don’t have a common understanding of the ways that AI can be used to make decisions. To leverage AI tools in the best possible way, there will need to be a consensus among leaders about the ways that AI will be used. When applied correctly, artificial intelligence systems can be used to positively impact public services like healthcare, education and transportation. The countries that are the quickest to apply AI in these areas will gain the greatest benefits.
For business : Many companies are already working in a digital economy and should be prepared to harness the power of AI, running pilot projects to test their ideas. For example, a business can begin to create AI-based chatbots for customer service. Leadership teams within the company should begin thinking about how they can introduce AI into their strategic plan for the next 2 to 3 years, while also considering how their business models could change over the next 5 to 7 years as a result of employing AI technologies. Companies that aren’t prepared to use AI may be left behind.

7. Why do we need artificial intelligence?

Life would be much simpler and easier to understand if we did not have all the technologies that are growing at an exponential speed (artificial intelligence, blockchain, 3D printing, Internet of Things) and affecting every area of our lives.

The basic premise of AI is that it allows computer programs to learn, rather than needing to be specifically programmed to perform certain tasks.

Because of AI, computers can now learn to do huge amounts of tasks and activities that used to require human intelligence. The more data the AI has, the better results it can produce.

One positive result of this is that AI will make our lives easier in certain areas in which we need to analyze data. Here are four simple examples:

Health care : analyzing patient data and conducting predictive analysis.
Agriculture : precision agriculture that helps save natural resources.
Business processes : AI has the ability to make almost everything better, faster and cheaper.
Education : Analyzing study data and providing teachers and students with tips on how to study better.

Another enormous benefit of AI is that it is already helping to improve the lives of children around the world. For example, in the developing world, many children currently spend their childhoods working in factories.

However, thanks to the developments of AI, these factories are increasingly being operated by robots and automation, allowing more children to spend their time studying, playing and enjoying their childhoods.

Additionally, AI will be able to help carry out many tasks and jobs which are too dangerous for humans.

8. Why should you study artificial intelligence?

Excited to study the Python and C++ programming languages?

Even though doing so would be useful, many of us, including myself, don’t have the time or patience to learn these languages, which are typically used for developing artificial intelligence applications.

If you are currently attending college or are interested in learning them, I highly encourage you to do so.

However, it can be much more beneficial to learn how to apply artificial intelligence than to actually code it. This is because finding skillful coders is now easier than ever because of freelance sites such as Upwork.

I strongly believe that everyone should study and learn about these areas related to artificial intelligence:

What is artificial intelligence?
How could I apply it in my area of interest?
How will it change our short term and long-term future?
What are the challenges and opportunities presented by AI?

These are some of the topics I cover in my book on artificial intelligence.

Everyone can benefit from learning about AI. Even if you have no interest in learning how to code, I urge you to develop an interest in artificial intelligence and seek to understand the impact it will have on our society.

In addition to my book on AI, there are several excellent sources that can help you to learn the basic concepts of AI. These are some of the ones I recommend:

Machine Learning Crash Course by Google
Introduction to AI technology
Data Science and Cognitive Computing Courses by IBM
Elements of AI by University of Helsinki

Artificial intelligence questions - Elements of AI course

9. How can we apply artificial intelligence?

How we can apply AI in our day-to-day lives is one of the most common question on artificial intelligence.

As with the other technologies of the third industrial revolution, you don’t need to be an AI developer, or work in the industry, to want to learn about AI. Every one of us should be thinking about how we can apply AI in the different areas of our personal and professional lives.

Smart Virtual Assistants

By now, most of us have used smart virtual assistants such as Siri, Google Assistant, or Cortana.

Although most of these still work in a quite rudimentary manner, they will undoubtedly improve significantly in the coming years.

Some experts predict that by 2025, most business communications will be handled directly between the participants’ virtual assistants.

One of the best ways to apply AI is to learn how to create chatbots. Although most of today’s chatbots are rule-based and work without AI, learning how to create them will give you a good head start for developing AI-based chatbots in the future.

Probably the easiest way to create a basic chatbot is by using chatfuel.com, a chatbot building tool which works on Facebook Messenger.

You can also create a chatbot that includes some AI features with the help of IBM’s Watson or Dialogflow by Google.

In my book about AI, I have devoted an entire chapter to this topic, which you might want to check out.

Market Research

Another useful area where AI can be applied is market research. There is a growing number of tools that promise to use AI for analyzing public opinion on certain topics. Meltwater is one example, but soon there will probably be several other similar services.

In addition to chatbots and market research, there are a multitude of other areas to which artificial intelligence could be applied.

One interesting exercise is to list all the business activities your company does on a daily basis, and then analyze what would happen if AI took over part or all of each of these tasks.

I think it’s important to highlight that however you apply AI in your life, it is crucial to always maintain high ethical guidelines and standards.

10. What are the typical jobs related to artificial intelligence?

AI is quickly creating large numbers of new jobs, the biggest challenge of which is having enough qualified workers to fill these positions. These are some of the AI-related job titles that are currently in the highest demand on job search portals: data scientists, software engineers, research scientists, machine learning experts, and deep learning experts.

However, there will be an even greater demand for professionals who understand how AI works in general, as well as how to help companies and individuals apply these technologies for the benefit of businesses and society

Here are some of the jobs related to AI that I cover in my book:

AI Chatbot Designer : A professional who knows how to design AI-based chatbots that can attend to basic customer service needs and provide a positive user experience.
AI Digital Marketing Expert : Someone who understands how to leverage various digital marketing and social media tools that employ AI to create more effective marketing strategies.
AI Business Strategy Consultant : An expert who analyzes a company and recommends ways that company can build AI services and products with tools like IBM’s Watson, Microsoft Azure, or Amazon Web Services. While it can be helpful to develop internal AI tools, it is also possible to purchase existing solutions from well-known providers like the ones listed above.
AI Strategy Consultant for the Public Sector : An expert who can identify potential challenges that will arise due to the introduction of AI into society and can solve problems through AI training. This is an important role for helping society to become familiar and comfortable with the use of new AI technologies. This type of professional could also serve those who have lost their jobs to AI and automation by matching individuals with suitable retraining programs to help them obtain new types of employment.
Tech-Addiction Counselor or Coach : A skilled counselor or coach who understands, and knows how to treat, the emotional and physical impacts of the rapid growth of AI and the problems that may arise from overuse. With the increasing presence of AI technologies in our everyday lives comes the potential for users to become addicted to some of these products. Also, some people may suffer from negative emotional consequences due to an overreliance on AI at the expense of normal social interactions and relationships with humans.
Creativity Coach : A trained professional with experience in helping others to develop human-based skills including social and emotional intelligence, and creativity. This is an important role that, because it cannot be filled by robots, will hold a great deal of value for people in the future.

11. Will China be the AI super power?

The simple answer to this question is YES.

How China is achieving this is quite intriguing. China has created a national AI team comprised of its top artificial intelligence technology companies such as Baidu, Alibaba and Tencent.

Questions on artificial intelligence - Chinese companies

The objective is to be the world leader in AI by 2030, and China is currently one of the only countries that has set this type of goal.

In general, the Chinese have the reputation of being much more hardworking than the Americans or Europeans, and to me it is quite obvious that they will reach their goal.

In the first stage of China’s AI plan, the country wants to focus and work on these seven key areas of artificial intelligence:

Intelligent Connected Vehicles (ICV)
Intelligent Service Robots
Intelligent Unmanned Arial Vehicles
Computer Aided Medical Imaging Diagnosis Systems
Video Image Recognition
Artificial Audio Intelligence (AAI)
Computer Translation

(source: ChinaLawBlog.com )

This basically means that people in China’s largest cities will soon see many AI applications such as intelligent connected vehicles (self-driving cars) and intelligent service robots.

However, being an AI super power does not necessarily equal having the citizens with the greatest sense of wellbeing, or ensuring that the wealth generated by AI is distributed fairly among the country’s population.

For this, I predict that European countries will take the lead, demonstrating to the rest of the world the importance of applying AI ethically and fairly , and sharing the benefits equally throughout society. Here you can read how Finland adapts to the future of artificial intelligence .

12. Which are the most powerful artificial intelligence companies?

This is one of the most typical question on artificial intelligence.

Virtually every big technology company has the number one goal of being an important player in the AI marketplace, providing artificial intelligence products and services to consumers.

At this time, it’s safe to say that Google has a head start, including the most interesting AI products, as well as the most profound and complete AI research activities.

In fact, Google has changed the name of its research center to “Google AI,” demonstrating just how important AI research is to the company.

Google recently announced some new features that its AI assistant can handle, including making phone calls and booking appointments and reservations in an incredibly life-like voice.

You can hear a demo of Google Assistant calling to book an appointment at a hair salon here:

This product is not yet available to the public, and it might take a long time before Google releases it.

I cover Google’s AI activities in more depth in my book, but you can start researching their AI undertakings here: https://ai.google/

Amazon, Microsoft, Apple, Facebook, IBM and Nvidia

All of these companies are working in several key areas in order to offer AI products and solutions. They are also all competing to employ the best AI talent and trying to improve their AI research efforts.

Out of these six companies, I would say that Apple is currently the one with the weakest AI activities. Meanwhile, Amazon is probably growing the fastest in the consumer AI product field, by offering Amazon Alexa powered software, which can be embedded in almost every device.

Chinese AI companies

Baidu, Alibaba and Tencent are leading the Chinese AI efforts, as mentioned earlier. These companies are growing quickly in every area and should be carefully followed by everyone interested in the future of the AI field.

13. What are some common benefits of artificial intelligence technology?

As AI will impact so many areas of our lives and businesses, there are actually enormous amounts of direct and indirect benefits that can be brought about thanks to artificial intelligence.

Here is a list of some of the key benefits.

AI and Poverty : AI will be used to fight extreme poverty and improve quality of life for people in remote areas.
AI and Everyday Life : AI and robotics can take on tasks that are dangerous, boring or difficult for humans.
AI and Travel : AI will power autonomous vehicles, which will help to generate improved traffic efficiency, cheaper mobility options and greater safety on the streets.
AI and World Peace : AI research and development can be used to help in the quest for world peace.
AI and Businesses Opportunities : AI will create amazing opportunities for entrepreneurs and businesses worldwide and also increase productivity.
AI and Business Processes : AI will generate improvements to almost every business process.
AI and Industries : AI will drastically transform almost every commercial industry.

When talking about the benefits, we should also highlight the disadvantages and challenges generated by the growth of AI. This is especially important since there is not enough public discussion about the topic. This is in large part because the tech leaders who appear in the media rarely mention the possible disadvantages, naturally preferring to focus on the benefits instead, to generate more profits for their companies. I also recommend reviewing the infographic called “ 9 Reasons Why Artificial Intelligence is Important Now “

There are clearly many exceptions such as Elon Musk and Richard Branson, who advocate for a universal basic income, which would be a way to essentially provide “money for nothing” to those people whose jobs have been displaced by automation and AI.

What are your questions about AI?

Feel free to post them below.

ChatGPT for Writing a Business Plan
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12 ChatGPT alternatives

Looking to integrate ChatGPT into your business operations ? You are welcome to reach out to me directly through this link.

For more information on the developments on artificial intelligence technology check my new book:

Arficial Intelligence: 101 Things You Must Know Today About Our Future

Kindle edition – Printed edition – Audible edition

Artificial Intelligence:

101 things you must know today about our future.

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thanks a lot for enlighten me more on this aspect of AI. I also wish to be great professional in this Area of AI in future.

Aritficial Intelligence. Artificial in seeming to; Be, simple, natural, black or white, positive or negative, Attractive or Repelling, comparisons. What combinations of code would rethinkitself acording to an expression of some creatively singular expression or ways of expression, might awaken clear awareness in which there is only being. Observing, hum, would that be a threat, or a blessing? Depends on how free one truly is!

Copyright 2024 LasseRouhiainen.com. All rights reserved.

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65+ Topics In Artificial Intelligence: A Comprehensive Guide To The Field

Jane Ng • 24 July, 2023 • 8 min read

Welcome to the world of AI. Are you ready to dive into the 65+ best topics in artificial intelligenc e and make an impact with your research, presentations, essay, or thought-provoking debates?

In this blog post, we present a curated list of cutting-edge topics in AI that are perfect for exploration. From the ethical implications of AI algorithms to the future of AI in healthcare and the societal impact of autonomous vehicles, this "topics in artificial intelligence" collection will equip you with exciting ideas to captivate your audience and navigate the forefront of AI research.

Artificial intelligence research topics, artificial intelligence topics for presentation, ai projects for the final year, artificial intelligence seminar topics, artificial intelligence debate topics, artificial intelligence essay topics, interesting topics in artificial intelligence.

Key Takeaways

FAQs About Topics In Artificial Intelligence

Here are topics in artificial intelligence that cover various subfields and emerging areas:

AI in Healthcare: Applications of AI in medical diagnosis, treatment recommendation, and healthcare management.
AI in Drug Discovery : Applying AI methods to accelerate the process of drug discovery, including target identification and drug candidate screening.
Transfer Learning: Research methods to transfer knowledge learned from one task or domain to improve performance on another.
Ethical Considerations in AI: Examining the ethical implications and challenges associated with the deployment of AI systems.
Natural Language Processing: Developing AI models for language understanding, sentiment analysis, and language generation.
Fairness and Bias in AI: Examining approaches to mitigate biases and ensure fairness in AI decision-making processes.
AI applications to address societal challenges.
Multimodal Learning: Exploring techniques for integrating and learning from multiple modalities, such as text, images, and audio.
Deep Learning Architectures: Advancements in neural network architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs).

Here are topics in artificial intelligence suitable for presentations:

Deepfake Technology: Discussing the ethical and societal consequences of AI-generated synthetic media and its potential for misinformation and manipulation.
Cybersecurity: Presenting the applications of AI in detecting and mitigating cybersecurity threats and attacks.
AI in Game Development: Discuss how AI algorithms are used to create intelligent and lifelike behaviors in video games.
AI for Personalized Learning: Presenting how AI can personalize educational experiences, adapt content, and provide intelligent tutoring.
Smart Cities: Discuss how AI can optimize urban planning, transportation systems, energy consumption, and waste management in cities.
Social Media Analysis: Utilizing AI techniques for sentiment analysis, content recommendation, and user behavior modeling in social media platforms.
Personalized Marketing: Presenting how AI-driven approaches improve targeted advertising, customer segmentation, and campaign optimization.
AI and Data Ownership: Highlighting the debates around the ownership, control, and access to data used by AI systems and the implications for privacy and data rights.

AI-Powered Chatbot for Customer Support: Building a chatbot that uses natural language processing and machine learning to provide customer support in a specific domain or industry.
AI-Powered Virtual Personal Assistant: A virtual assistant that uses natural language processing and machine learning to perform tasks, answer questions, and provide recommendations.
Emotion Recognition : An AI system that can accurately recognize and interpret human emotions from facial expressions or speech.
AI-Based Financial Market Prediction: Creating an AI system that analyzes financial data and market trends to predict stock prices or market movements.
Traffic Flow Optimization: Developing an AI system that analyzes real-time traffic data to optimize traffic signal timings and improve traffic flow in urban areas.
Virtual Fashion Stylist: An AI-powered virtual stylist that provides personalized fashion recommendations and assists users in selecting outfits.

Here are the topics in artificial intelligence for the seminar:

How Can Artificial Intelligence Assist in Natural Disaster Prediction and Management?
AI in Healthcare: Applications of artificial intelligence in medical diagnosis, treatment recommendation, and patient care.
Ethical Implications of AI: Examining the ethical considerations and responsible development of AI Systems.
AI in Autonomous Vehicles: The role of AI in self-driving cars, including perception, decision-making, and safety.
AI in Agriculture: Discussing AI applications in precision farming, crop monitoring, and yield prediction.
How Can Artificial Intelligence Help Detect and Prevent Cybersecurity Attacks?
Can Artificial Intelligence Assist in Addressing Climate Change Challenges?
How Does Artificial Intelligence Impact Employment and the Future of Work?
What Ethical Concerns Arise with the Use of Artificial Intelligence in Autonomous Weapons?

Here are topics in artificial intelligence that can generate thought-provoking discussions and allow participants to critically analyze different perspectives on the subject.

Can AI ever truly understand and possess consciousness?
Can Artificial Intelligence Algorithms be Unbiased and Fair in Decision-Making?
Is it ethical to use AI for facial recognition and surveillance?
Can AI effectively replicate human creativity and artistic expression?
Does AI pose a threat to job security and the future of employment?
Should there be legal liability for AI errors or accidents caused by autonomous systems?
Is it ethical to use AI for social media manipulation and personalized advertising?
Should there be a universal code of ethics for AI developers and researchers?
Should there be strict regulations on the development and deployment of AI technologies?
Is artificial general intelligence (AGI) a realistic possibility in the near future?
Should AI algorithms be transparent and explainable in their decision-making processes?
Does AI have the potential to solve global challenges, such as climate change and poverty?
Does AI have the potential to surpass human intelligence, and if so, what are the implications?
Should AI be used for predictive policing and law enforcement decision-making?

Here are 30 essay topics in artificial intelligence:

AI and the Future of Work: Reshaping Industries and Skills
AI and Human Creativity: Companions or Competitors?
AI in Agriculture: Transforming Farming Practices for Sustainable Food Production
Artificial Intelligence in Financial Markets: Opportunities and Risks
The Impact of Artificial Intelligence on Employment and the Workforce
AI in Mental Health: Opportunities, Challenges, and Ethical Considerations
The Rise of Explainable AI: Necessity, Challenges, and Impacts
The Ethical Implications of AI-Based Humanoid Robots in Elderly Care
The Intersection of Artificial Intelligence and Cybersecurity: Challenges and Solutions
Artificial Intelligence and the Privacy Paradox: Balancing Innovation with Data Protection
The Future of Autonomous Vehicles and the Role of AI in Transportation

Here topics in artificial intelligence cover a broad spectrum of AI applications and research areas, providing ample opportunities for exploration, innovation, and further study.

What are the ethical considerations for using AI in educational assessments?
What are the potential biases and fairness concerns in AI algorithms for criminal sentencing?
Should AI algorithms be used to influence voting decisions or electoral processes?
Should AI models be used for predictive analysis in determining creditworthiness?
What are the challenges of integrating AI with augmented reality (AR) and virtual reality (VR)?
What are the challenges of deploying AI in developing countries?
What are the risks and benefits of AI in healthcare?
Is AI a solution or a hindrance to addressing social challenges?
How can we address the issue of algorithmic bias in AI systems?
What are the limitations of current deep learning models?
Can AI algorithms be completely unbiased and free from human bias?
How can AI contribute to wildlife conservation efforts?

Key Takeaways

The field of artificial intelligence encompasses a vast range of topics that continue to shape and redefine our world. In addition, AhaSlides offers a dynamic and engaging way to explore these topics. With AhaSlides, presenters can captivate their audience through interactive slide templates , live polls , quizzes , and other features allowing for real-time participation and feedback. By leveraging the power of AhaSlides, presenters can enhance their discussions on artificial intelligence and create memorable and impactful presentations.

As AI continues to evolve, the exploration of these topics becomes even more critical, and AhaSlides provides a platform for meaningful and interactive conversations in this exciting field.

What are the 8 types of artificial intelligence?

Here are some commonly recognized types of artificial intelligence:

Reactive Machines
Limited Memory AI
Theory of Mind AI
Self-Aware AI
Superintelligent AI
Artificial Superintelligence

What are the five big ideas in artificial intelligence?

The five big ideas in artificial intelligence, as outlined in the book " Artificial Intelligence: A Modern Approach " by Stuart Russell and Peter Norvig, are as follows:

Agents are AI systems that interact with and impact the world.
Uncertainty deals with incomplete information using probabilistic models.
Learning enables AI systems to improve performance through data and experience.
Reasoning involves logical inference to derive knowledge.
Perception involves interpreting sensory inputs like vision and language.

Are there 4 basic AI concepts?

The four fundamental concepts in artificial intelligence are problem-solving, knowledge representation, learning, and perception.

These concepts form the foundation for developing AI systems that can solve problems, store and reason with information, improve performance through learning, and interpret sensory inputs. They are essential in building intelligent systems and advancing the field of artificial intelligence.

Ref: Towards Data Science | Forbes | Thesis RUSH

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Tips to Engage with Polls & Trivia

163 Unique Artificial Intelligence Topics For Your Dissertation

The artificial intelligence industry is an industry of the future, but it’s also a course many students find difficult to write about. According to some students, the main reason is that there are many research topics on artificial intelligence. Several topics are already covered, and they claim not to know what to write about.

However, one of the interesting things about writing a dissertation or thesis is that you don’t need to be the number one author of an idea. It would be best if you write about the idea from a unique perspective instead. Writing from a unique perspective also means coupling your ideas with original research, giving your long essay quality and value to your professors and other students who may want to cover the same topic in the future.

This blog post will cover basic advanced AI topics and interesting ones for your next research paper or debate. This will help prepare you for your next long essay or presentation.

What is Artificial Intelligence?

Artificial Intelligence (AI) is the concept that enables humans to perform their tasks more smartly and faster through automated systems. AI is human intelligence packed in machines.

AI facilitates several computer systems such as voice recognition, machine vision, natural language processing, robotics engineering, and many others. All these systems revolutionize how work is done in today’s world.

Now that you know what artificial intelligence is, here are some advanced AI topics for your college research.

Writing Tips to Create a Good Thesis or Dissertation

Every student wants to create the best thesis and dissertation in their class. The first step to creating or researching the perfect dissertation is to write a great thesis. What are the things to be on the lookout for?

Create a Strong Thesis Statement You need this to have a concise approach to your research. Your thesis statement should, therefore, be specific, precise, factual, debatable, and logical enough to be an assertive point. Afterwards, the only way to create a competitive dissertation is to draw from existing research in journals and other sources.
Strong Arguments You can create a good dissertation if you have strong arguments. Your arguments must be backed by reputed sources such as academics, government, reputed media organizations, or statistic-oriented websites. All these make your arguments recognizable and accepted.
Well Organized and Logically Structured Your dissertation has different subsections, including an abstract, thesis statement, background to the study, chapters (where your body is), and concluding arguments. If you’ve embarked on quantitative data analysis, you must report the data you got and what it means for your discourse. You can even add recommendations for future research. The information you want to convey must be well structured to improve its reception by your university professors.
Concise and Free of Errors Your essay must also be straightforward. Your ideas must not be complex to understand, and you must always explain ambiguous industry terms. Revising your draft to check for grammatical errors several times is also important. Editing can be difficult, but it’s integral to determining whether your professors will love your dissertation or otherwise.

Artificial Intelligence Research Topics

Artificial intelligence is here to stay in several industries and sectors worldwide. It is the technology of the present and the future, and here are some AI topics to write about:

How will artificial intelligence contribute to the flight to Mars?
Machine learning and the challenges it poses to scientists
How can retail stores maximize machine learning?
Expatiate on what is meant by deep learning
General AI and Narrow AI: what does it mean?
AI changes the world: a case study of the gambling industry
AI improved business: a case study of SaaS industries
AI in homes: how smart homes change how humans live
The critical challenges scientists have not yet solved with AI
How students can contribute to both research and development of AI systems
Is automation the way forward for the interconnected world: an overview of the ethical issues in AI
How does cybernetics connect with AI?
How do artificial intelligence systems manifest in healthcare?
A case for artificial intelligence in how it facilitates the use of data in the criminal department
What are the innovations in the vision system applications
The inductive logic program: meaning and origin
Brain simulation and AI: right or wrong
How to maximize AI in Big data
How AI can increase cybersecurity threat
AI in companies: a case study of Telegram

Hot Topics in Artificial Intelligence

If you’d love to be one of the few who will cover hot topics in AI, researching some sub-sectors could be a way to go. There are several subsections of AI, some of which are hot AI topics causing several arguments among scholars and moralists today. Some of these are:

How natural language is generated and how AI maximizes it
Speech recognition: a case study of Alexa and how it works
How AI makes its decisions
What are known as virtual agents?
Key deep learning platforms for governments
Text analytics and the future of text-to-speech systems
How marketing automation works
Do robots operate based on rules?
AI and emotion recognition
AI and the future of biometrics
AI in content creation
AI and how data is used to create social media addiction
What can be considered core problems with AI?
What do five pieces of literature say about AI taking over the world?
How does AI help with predictive sales?
Motion planning and how AI is used in video editing
Distinguish between data science vs. artificial intelligence
Account for five failed AI experiments in the past decade
The world from the machine’s view
Project management systems from the machine’s view

Artificial Intelligence Topics for Presentation

Students are sometimes fond of presentations to show knowledge or win debates. If you’re in a debate club and would love to add a presentation to your AI topics, here are topics in artificial intelligence for you.

You can even expand these for your artificial intelligence research paper topics:

How AI has penetrated all industries
The future of cloud technologies
The future of AI in military equipment
The evolution of AI in a security application
Industrial robots: an account of Tesla’s factory
Industrial robots: an account of Amazon’s factories
An overview of deep generative models and what they mean
What are the space travel ideas fueling the innovation of AI?
What is amortized inference?
Examine the Monte Carlo methods in AI
How technology has improved maps
Comment on how AI is used to find fresh craters on the moon
Comment on two previous papers from your professor about AI

AI Research Topics

If you’d like to take a general perspective on AI, here are some topics in AI to discuss amongst your friends or for your next essay:

Are robots a threat to human jobs?
How automation has changed the world since 2020
Would you say Tesla produces robot cars?
What are the basic violations of artificial intelligence?
Account for the evolution of AI models
Weapon systems and the future of weaponry
Account for the interaction between machines and humans
Basic principles of AI risk management
How AI protects people against spam
Can AI predict election results?
What are the limits of AI?
Detailed reports on image recognition algorithms in two companies of your choice
How is AI used in customer service?
Telehealth and its significance
Can AI help predict the future?
How to measure water quality and cleanness through AI
Analyze the technology used for the Breathometer products
Key trends in AI and robotics research and development
How AI helps with fraud detection in a bank of your choice
How AI helps the academic industry.

Argument Debate Topics in AI

You’d expect controversial topics in AI, and here are some of them. These are topics for friendly debates in class or topics to start a conversation with industry leaders:

Will humans end all work when AI replaces them?
Who is liable for AI’s misdoing?
AI is smarter than humans: can it be controlled?
Machines will affect human interactions: discuss
AI bias exists and is here to stay
Artificial Intelligence cannot be humanized even if it understands emotions
New wealth and AI: how will it be distributed?
Can humans prevent AI bias?
Can AI be protected from hackers?
What will happen with the unintended consequences of using AI?

Computer Science AI Topics

Every computer science student also needs AI topics for research papers, presentations or scientific thesis . Whatever it is, here are some helpful ideas:

AI and machine learning: how does it help healthcare systems?
What does hierarchical deep learning neural network mean
AI in architecture and engineering: explain
Can robots safely perform surgery?
Can robots help with teaching?
Recent trends in machine learning
Recent trends in big data that will affect the future of the internet of things
How does AI contribute to the excavation management Industry?
Can AI help spot drug distribution?
AI and imaging system: Trends since 1990
Explain five pieces of literature on how AI can be contained
Discuss how AI reduced the escalation of COVID-19
How can natural language processing help interpret sign languages?
Review a recent book about AI and cybersecurity
Discuss the key discoveries from a recent popular seminar on AI and cybercrime
What does Stephen Hawking think about AI?
How did AI make Tesla a possibility?
How recommender systems work in the retail industry
What is the artificial Internet of Things (A-IoT)?
Explain the intricacies of enhanced AI in the pharmaceutical industry

AI Ethics Topics

There are always argumentative debate topics on AI, especially on the ethical and moral components. Here are a few ethical topics in artificial intelligence to discuss:

Is AI the end of all jobs?
Is artificial intelligence in concert with patent law?
Do humans understand machines?
What happens when robots gain self-control?
Can machines make catastrophic mistakes?
What happens when AI reads minds and executes actions even if they’re violent?
What can be done about racist robots?
Comments on how science can mediate human-machine interactions
What does Google CEO mean when he said AI would be the world’s saviour?
What are robots’ rights?
How does power balance shift with a rise in AI development?
How can human privacy be assured when robots are used as police?
What is morality for AI?
Can AI affect the environment?
Discuss ways to keep robots safe from enemies.

AI Essay Topics Technology

Technology is already intertwined with AI, but you may need hot AI topics that focus on the tech side of the innovation. Here are 20 custom topics for you:

How can we understand autonomous driving?
Pros and cons of artificial intelligence to the world?
How does modern science interact with AI?
Account for the scandalous innovations in AI in the 21st century
Account for the most destructive robots ever built
Review a documentary on AI
Review three books or journals that express AI as a threat to humans and draw conclusions based on your thoughts
What do non-experts think about AI?
Discuss the most ingenious robots developed in the past decade
Can the robotic population replace human significance?
Is it possible to be ruled by robots?
What would world domination look like: from the machine perspective
He who controls AI controls the world: discuss
Key areas in AI engineering that man must control
How Apple is using AI for its products
Would you say AI is a positive or negative invention?
AI and video gaming: how it changed the arcade Industry
Would you say eSports is toxic?
How AI helps in the hospitality industry
AI and its use in sustainable energy.

Interesting Topics in AI

There are interesting ways to look at the subject of AI in today’s world. Here are some good research topics for AI to answer some questions:

AI can be toxic: Should a high school student pursue a career in artificial intelligence?
Prediction vs. judgment: experimenting with AI
What makes AI know what’s right or wrong?
Human judgment in AI: explain
Effects of AI on businesses
Will AI play critical roles in human future affairs?
Tech devices and AI
Search application and AI: account for how AI maximizes programming languages
The history of artificial intelligence
How AI impacts market design
Data management and AI: discuss
How can AI influence the future of computing
How AI has changed the video viewing industry
How can AI contribute to the global economy?
How smart would you say artificial intelligence is?

Graduate AI NLP Research Topics

NLP (Natural Language Processing) is the aspect of artificial intelligence or computer science that deals with the ability of machines to understand spoken words and simplify them as humans can. It’s as simple as saying NLP is how computers understand human language.

If you’d like to focus your research topics on artificial intelligence on NLP, here are some topics for you:

How did natural language processing help with Twitter Space discussions?
How language is essential for regulatory and legal texts
NLP in the eCommerce industry: top trends
How NLP is used in language modelling and occlusion
How does AI manoeuvre semantic analysis in natural language processing?
History and top trends in NLP conference video call apps
Text mining techniques and the role of NLP
How physicians detected stroke since 2020 through NLP of radiology results
How does big data contribute to understanding medical acronyms in the NLP section of AI?
What does applied natural language processing mean in the mental health world?

Get Thesis Help Today

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The present and future of AI

Finale doshi-velez on how ai is shaping our lives and how we can shape ai.

image of Finale Doshi-Velez, the John L. Loeb Professor of Engineering and Applied Sciences

Finale Doshi-Velez, the John L. Loeb Professor of Engineering and Applied Sciences. (Photo courtesy of Eliza Grinnell/Harvard SEAS)

How has artificial intelligence changed and shaped our world over the last five years? How will AI continue to impact our lives in the coming years? Those were the questions addressed in the most recent report from the One Hundred Year Study on Artificial Intelligence (AI100), an ongoing project hosted at Stanford University, that will study the status of AI technology and its impacts on the world over the next 100 years.

The 2021 report is the second in a series that will be released every five years until 2116. Titled “Gathering Strength, Gathering Storms,” the report explores the various ways AI is increasingly touching people’s lives in settings that range from movie recommendations and voice assistants to autonomous driving and automated medical diagnoses .

Barbara Grosz , the Higgins Research Professor of Natural Sciences at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) is a member of the standing committee overseeing the AI100 project and Finale Doshi-Velez , Gordon McKay Professor of Computer Science, is part of the panel of interdisciplinary researchers who wrote this year’s report.

We spoke with Doshi-Velez about the report, what it says about the role AI is currently playing in our lives, and how it will change in the future.

Q: Let's start with a snapshot: What is the current state of AI and its potential?

Doshi-Velez: Some of the biggest changes in the last five years have been how well AIs now perform in large data regimes on specific types of tasks. We've seen [DeepMind’s] AlphaZero become the best Go player entirely through self-play, and everyday uses of AI such as grammar checks and autocomplete, automatic personal photo organization and search, and speech recognition become commonplace for large numbers of people.

In terms of potential, I'm most excited about AIs that might augment and assist people. They can be used to drive insights in drug discovery, help with decision making such as identifying a menu of likely treatment options for patients, and provide basic assistance, such as lane keeping while driving or text-to-speech based on images from a phone for the visually impaired. In many situations, people and AIs have complementary strengths. I think we're getting closer to unlocking the potential of people and AI teams.

There's a much greater recognition that we should not be waiting for AI tools to become mainstream before making sure they are ethical.

Q: Over the course of 100 years, these reports will tell the story of AI and its evolving role in society. Even though there have only been two reports, what's the story so far?

There's actually a lot of change even in five years. The first report is fairly rosy. For example, it mentions how algorithmic risk assessments may mitigate the human biases of judges. The second has a much more mixed view. I think this comes from the fact that as AI tools have come into the mainstream — both in higher stakes and everyday settings — we are appropriately much less willing to tolerate flaws, especially discriminatory ones. There's also been questions of information and disinformation control as people get their news, social media, and entertainment via searches and rankings personalized to them. So, there's a much greater recognition that we should not be waiting for AI tools to become mainstream before making sure they are ethical.

Q: What is the responsibility of institutes of higher education in preparing students and the next generation of computer scientists for the future of AI and its impact on society?

First, I'll say that the need to understand the basics of AI and data science starts much earlier than higher education! Children are being exposed to AIs as soon as they click on videos on YouTube or browse photo albums. They need to understand aspects of AI such as how their actions affect future recommendations.

But for computer science students in college, I think a key thing that future engineers need to realize is when to demand input and how to talk across disciplinary boundaries to get at often difficult-to-quantify notions of safety, equity, fairness, etc. I'm really excited that Harvard has the Embedded EthiCS program to provide some of this education. Of course, this is an addition to standard good engineering practices like building robust models, validating them, and so forth, which is all a bit harder with AI.

I think a key thing that future engineers need to realize is when to demand input and how to talk across disciplinary boundaries to get at often difficult-to-quantify notions of safety, equity, fairness, etc.

Q: Your work focuses on machine learning with applications to healthcare, which is also an area of focus of this report. What is the state of AI in healthcare?

A lot of AI in healthcare has been on the business end, used for optimizing billing, scheduling surgeries, that sort of thing. When it comes to AI for better patient care, which is what we usually think about, there are few legal, regulatory, and financial incentives to do so, and many disincentives. Still, there's been slow but steady integration of AI-based tools, often in the form of risk scoring and alert systems.

In the near future, two applications that I'm really excited about are triage in low-resource settings — having AIs do initial reads of pathology slides, for example, if there are not enough pathologists, or get an initial check of whether a mole looks suspicious — and ways in which AIs can help identify promising treatment options for discussion with a clinician team and patient.

Q: Any predictions for the next report?

I'll be keen to see where currently nascent AI regulation initiatives have gotten to. Accountability is such a difficult question in AI, it's tricky to nurture both innovation and basic protections. Perhaps the most important innovation will be in approaches for AI accountability.

Topics: AI / Machine Learning , Computer Science

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Finale Doshi-Velez

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SQ1. What are some examples of pictures that reflect important progress in AI and its influences?

One picture appears in each of the sections that follow.

View the full Study Panel response to SQ1

SQ2. What are the most important advances in AI?

Summary: People are using AI more today to dictate to their phone, get recommendations, enhance their backgrounds on conference calls, and much more. Machine-learning technologies have moved from the academic realm into the real world in a multitude of ways. Neural network language models learn about how words are used by identifying patterns in naturally occurring text, supporting applications such as machine translation, text classification, speech recognition, writing aids, and chatbots. Image-processing technology is now widespread, but applications such as creating photo-realistic pictures of people and recognizing faces are seeing a backlash worldwide. During 2020, robotics development was driven in part by the need to support social distancing during the COVID-19 pandemic. Predicted rapid progress in fully autonomous driving failed to materialize, but autonomous vehicles have begun operating in selected locales. AI tools now exist for identifying a variety of eye and skin disorders, detecting cancers, and supporting measurements needed for clinical diagnosis. For financial institutions, uses of AI are going beyond detecting fraud and enhancing cybersecurity to automating legal and compliance documentation and detecting money laundering. Recommender systems now have a dramatic influence on people’s consumption of products, services, and content, but they raise significant ethical concerns.

Read the full Study Panel response to SQ2

SQ3. What are the most inspiring open grand challenge problems?

Summary: Recent years have seen remarkable progress on some of the challenge problems that help drive AI research, such as answering questions based on reading a textbook, helping people drive so as to avoid accidents, and translating speech in real time. Others, like making independent mathematical discoveries, have remained open. A lesson learned from social science- and humanities-inspired research over the past five years is that AI research that is overly tuned to concrete benchmarks can take us further away from the goal of cooperative and well-aligned AI that serves humans’ needs, goals, and values. A number of broader challenges should be kept in mind: exhibiting greater generalizability, detecting and using causality, and noticing and exhibiting normativity are three particularly important ones. An overarching and inspiring challenge that brings many of these ideas together is to build machines that can cooperate and collaborate seamlessly with humans and can make decisions that are aligned with fluid and complex human values and preferences.

Read the full Study Panel response to SQ3

SQ4. How much have we progressed in understanding the key mysteries of human intelligence?

Summary: A view of human intelligence that has gained prominence over the last five years holds that it is collective—that individuals are just one cog in a larger intellectual machine. AI is developing in ways that improve its ability to collaborate with and support people, rather than in ways that mimic human intelligence. The study of intelligence has become the study of how people are able to adapt and succeed, not just how an impressive information-processing system works. Over the past half decade, major shifts in the understanding of human intelligence have favored three topics: collective intelligence, the view that intelligence is a property not only of individuals, but also of collectives; cognitive neuroscience, studying how the brain’s hardware is involved in implementing psychological and social processes; and computational modeling, which is now full of machine-learning-inspired models of visual recognition, language processing, and other cognitive activities. The nature of consciousness and how people integrate information from multiple modalities, multiple senses, and multiple sources remain largely mysterious. Insights in these areas seem essential in our quest for building machines that we would truly judge as “intelligent".

Read the full Study Panel response to SQ4

SQ5. What are the prospects for more general artificial intelligence?

Summary: The field is still far from producing fully general AI systems. However, in the last few years, important progress has been made in the form of three key capabilities. First is the ability for a system to learn in a self-supervised or self-motivated way. A self-supervised model called transformers has become the go-to approach for natural language processing, and has been used in diverse applications, including machine translation and Google web search. Second is the ability for a single AI system to learn in a continual way to solve problems from many different domains without requiring extensive retraining for each. One influential approach is to train a deep neural network on a variety of tasks, where the objective is for the network to learn general-purpose, transferable representations, as opposed to representations tailored specifically to any particular task. Third is the ability for an AI system to generalize between tasks—that is, to adapt the knowledge and skills the system has acquired for one task to new situations. A promising direction is the use of intrinsic motivation, in which an agent is rewarded for exploring new areas of the problem space. AI systems will likely remain very far from human abilities, however, without being more tightly coupled to the physical world.

Read the full Study Panel response to SQ5

SQ6. How has public sentiment towards AI evolved, and how should we inform/educate the public?

Summary: Over the last few years, AI and related topics have gained traction in the zeitgeist. In the 2017–18 session of the US Congress, for instance, mentions of AI-related words were more than ten times higher than in previous sessions. Media coverage of AI often distorts and exaggerates AI’s potential at both the positive and negative extremes, but it has helped to raise public awareness of legitimate concerns about AI bias, lack of transparency and accountability, and the potential of AI-driven automation to contribute to rising inequality. Governments, universities, and nonprofits are attempting to broaden the reach of AI education, including investing in new AI-related curricula. Nuanced views of AI as a human responsibility are growing, including an increasing effort to engage with ethical considerations. Broad international movements in Europe, the US, China, and the UK have been pushing back against the indiscriminate use of facial-recognition systems on the general public. More public outreach from AI scientists would be beneficial as society grapples with the impacts of these technologies. It is important that the AI research community move beyond the goal of educating or talking to the public and toward more participatory engagement and conversation with the public.

Read the full Study Panel response to SQ6

SQ7. How should governments act to ensure AI is developed and used responsibly?

Summary: Since the publication of the last AI100 report just five years ago, over 60 countries have engaged in national AI initiatives, and several significant new multilateral efforts are aimed at spurring effective international cooperation on related topics. To date, few countries have moved definitively to regulate AI specifically, outside of rules directly related to the use of data. As of 2020, 24 countries had opted for permissive laws to allow autonomous vehicles to operate in limited settings. As yet, only Belgium has enacted laws on the use of autonomous lethal weapons. The oversight of social media platforms has become a hotly debated issue worldwide. Cooperative efforts among countries have also emerged in the last several years. Appropriately addressing the risks of AI applications will inevitably involve adapting regulatory and policy systems to be more responsive to the rapidly advancing pace of technology development. Researchers, professional organizations, and governments have begun development of AI or algorithm impact assessments (akin to the use of environmental impact assessments before beginning new engineering projects).

Read the full Study Panel response to SQ7

SQ8. What should the roles of academia and industry be, respectively, in the development and deployment of AI technologies and the study of the impacts of AI?

Summary: As AI takes on added importance across most of society, there is potential for conflict between the private and public sectors regarding the development, deployment, and oversight of AI technologies. The commercial sector continues to lead in AI investment, and many researchers are opting out of academia for full-time roles in industry. The presence and influence of industry-led research at AI conferences has increased dramatically, raising concerns that published research is becoming more applied and that topics that might run counter to commercial interests will be underexplored. As student interest in computer science and AI continues to grow, more universities are developing standalone AI/machine-learning educational programs. Company-led courses are becoming increasingly common and can fill curricular gaps. Studying and assessing the societal impacts of AI, such as concerns about the potential for AI and machine-learning algorithms to shape polarization by influencing content consumption and user interactions, is easiest when academic-industry collaborations facilitate access to data and platforms.

Read the full Study Panel response to SQ8

SQ9. What are the most promising opportunities for AI?

Summary: AI approaches that augment human capabilities can be very valuable in situations where humans and AI have complementary strengths. An AI system might be better at synthesizing available data and making decisions in well-characterized parts of a problem, while a human may be better at understanding the implications of the data. It is becoming increasingly clear that all stakeholders need to be involved in the design of AI assistants to produce a human-AI team that outperforms either alone. AI software can also function autonomously, which is helpful when large amounts of data needs to be examined and acted upon. Summarization and interactive chat technologies have great potential. As AI becomes more applicable in lower-data regimes, predictions can increase the economic efficiency of everyday users by helping people and businesses find relevant opportunities, goods, and services, matching producers and consumers. We expect many mundane and potentially dangerous tasks to be taken over by AI systems in the near future. In most cases, the main factors holding back these applications are not in the algorithms themselves, but in the collection and organization of appropriate data and the effective integration of these algorithms into their broader sociotechnical systems.

Read the full Study Panel response to SQ9

SQ10. What are the most pressing dangers of AI?

Summary: As AI systems prove to be increasingly beneficial in real-world applications, they have broadened their reach, causing risks of misuse, overuse, and explicit abuse to proliferate. One of the most pressing dangers of AI is techno-solutionism, the view that AI can be seen as a panacea when it is merely a tool. There is an aura of neutrality and impartiality associated with AI decision-making in some corners of the public consciousness, resulting in systems being accepted as objective even though they may be the result of biased historical decisions or even blatant discrimination. Without transparency concerning either the data or the AI algorithms that interpret it, the public may be left in the dark as to how decisions that materially impact their lives are being made. AI systems are being used in service of disinformation on the internet, giving them the potential to become a threat to democracy and a tool for fascism. Insufficient thought given to the human factors of AI integration has led to oscillation between mistrust of the system and over-reliance on the system. AI algorithms are playing a role in decisions concerning distributing organs, vaccines, and other elements of healthcare, meaning these approaches have literal life-and-death stakes.

Read the full Study Panel response to SQ10

SQ11. How has AI impacted socioeconomic relationships?

Summary: Though characterized by some as a key to increasing material prosperity for human society, AI’s potential to replicate human labor at a lower cost has also raised concerns about its impact on the welfare of workers. To date, AI has not been responsible for large aggregate economic effects. But that may be because its impact is still relatively localized to narrow parts of the economy. In the grand scheme of rising inequality, AI has thus far played a very small role. The first reason, most importantly, is that the bulk of the increase in economic inequality across many countries predates significant commercial use of AI. Since these technologies might be adopted by firms simply to redistribute surplus/gains to their owners, AI could have a big impact on inequality in the labor market and economy without registering any impact on productivity growth. No evidence of such a trend is yet apparent, but it may become so in the future and is worth watching closely. To date, the economic significance of AI has been comparatively small—particularly relative to expectations, among both optimists and pessimists. Other forces—globalization, the business cycle, and a pandemic—have had a much, much bigger and more intense impact in recent decades. But if policymakers underreact to coming changes, innovations may simply result in a pie that is sliced ever more unequally.

Read the full Study Panel response to SQ11

SQ12. Does it appear “building in how we think” works as an engineering strategy in the long run?

Summary: AI has its own fundamental nature-versus-nurture-like question. Should we attack new challenges by applying general-purpose problem-solving methods, or is it better to write specialized algorithms, designed by experts, for each particular problem? Roughly, are specific AI solutions better engineered in advance by people (nature) or learned by the machine from data (nurture)? The pendulum has swung back and forth multiple times in the history of the field. In the 2010s, the addition of big data and faster processors allowed general-purpose methods like deep learning to outperform specialized hand-tuned methods. But now, in the 2020s, these general methods are running into limits—available computation, model size, sustainability, availability of data, brittleness, and a lack of semantics—that are starting to drive researchers back into designing specialized components of their systems to try to work around them. Indeed, even machine-learning systems benefit from designers using the right architecture for the right job. The recent dominance of deep learning may be coming to an end. To continue making progress, AI researchers will likely need to embrace both general- and special-purpose hand-coded methods, as well as ever faster processors and bigger data.

Read the full Study Panel response to SQ12

Cite This Report

Michael L. Littman, Ifeoma Ajunwa, Guy Berger, Craig Boutilier, Morgan Currie, Finale Doshi-Velez, Gillian Hadfield, Michael C. Horowitz, Charles Isbell, Hiroaki Kitano, Karen Levy, Terah Lyons, Melanie Mitchell, Julie Shah, Steven Sloman, Shannon Vallor, and Toby Walsh. "Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report." Stanford University, Stanford, CA, September 2021. Doc: http://ai100.stanford.edu/2021-report. Accessed: September 16, 2021.

Report Authors

AI100 Standing Committee and Study Panel

© 2021 by Stanford University. Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report is made available under a Creative Commons Attribution-NoDerivatives 4.0 License (International): https://creativecommons.org/licenses/by-nd/4.0/ .

Ask a Caltech Expert: Yaser Abu-Mostafa on Artificial Intelligence

ChatGPT has rocked the general public's, awareness, perception, and expectations of artificial intelligence (AI). In this Q&A, adapted from his Watson Lecture delivered on May 24, 2023, computer scientist Yaser Abu-Mostafa explains the history of AI and explores its risks and benefits.

Amid warnings that "AI will kill us all," or boasts that "AI will solve all our problems," a closer look at the science behind the technology can help us identify what is realistic and what is speculative, and help guide planning, legislation, and investment.

Highlights from the lecture are below.

The questions and answers below have been edited for clarity and length.

How did AI grow into the technology we know today?

The artificial intelligence (AI) we see today is the product of the field's journey from simple, brute force methodologies to complex, learning-based models that closely mimic the human brain's functionality. Early AI was effective for specific tasks like playing chess or Jeopardy! , but it was limited by the necessity of pre-programming every possible scenario. These systems, though groundbreaking, highlighted AI's limitations in flexibility and adaptability.

The transformative shift occurred in the 1980s with the move from brute force to learning approaches. This pivot was inspired by a deeper understanding of the learning process in the human brain. This era ushered in the development of neural networks: systems capable of learning from unstructured data without explicit programming for every scenario.

The historical development of AI reflects a continual effort to mirror the essence of human intelligence and learning. This evolution underscores the field's original goal: to create machines that can learn, adapt, and potentially think with a level of autonomy that was once the realm of science fiction.

What is the difference between discriminative and generative models in AI, and how is each type used?

The distinction lies in their approach to understanding and generating data. Discriminative models aim to categorize or differentiate between different types of data inputs. A common application of discriminative models is in facial recognition systems, where the model identifies who a particular face belongs to by learning from a dataset of labeled faces. This capability is applied in security systems, personalized user experiences, and verification processes.

On the other hand, generative models are designed to generate new data that resembles the training data. These models learn the underlying distribution of a dataset and can produce novel data points with similar characteristics. A notable application of generative models is in content creation, where they can generate realistic images, text, or even data for training other AI models. Generative models can contribute to fields such as pharmaceuticals, where they can help in discovering new molecular structures.

Do you worry about AI systems going rogue?

The perceived threat of rogue AI systems is a topic of considerable debate, fueled by speculative fiction and theoretical scenarios rather than grounded in the current capabilities and design of AI technologies. The concern revolves around the potential for AI systems to act autonomously in ways not intended or predicted by their creators, potentially causing harm to individuals, societies, or humanity at large. However, understanding the nature of this threat requires a nuanced consideration of what AI currently is and what it might become.

AI, as it exists today, operates within the confines of specific tasks it is designed for, lacking consciousness, desires, or intentions. AI has no intentions—no good intentions, no bad intentions. It learns what you teach it, period.

AI systems, including the most advanced neural networks, are tools created, controlled, and maintained by humans. The notion of AI going "rogue" and acting against human interests overlooks the practical and logistical constraints involved in developing and training AI systems. These activities require substantial human oversight, resources, and infrastructure, from gathering and preprocessing data to designing and adjusting algorithms. AI systems do not have the capability to access, manipulate, or control these resources independently.

In my opinion, the potential misuse of AI by humans poses a more immediate and practical concern. The development and deployment of AI in ways that are unethical, unregulated, or intended to deceive or harm, such as in autonomous weaponry, surveillance, or spreading misinformation, represent real challenges.

These issues underscore the importance of ethical AI development, robust regulatory frameworks, and international cooperation to ensure AI technologies are used for the benefit of humanity.

Why is regulating the deployment and development of AI challenging? What suggestions do you have for effective regulation to prevent misuse?

One significant hurdle is the pace at which AI technologies progress, outpacing regulatory frameworks and the understanding of policymakers.

The diverse applications of AI, from health care to autonomous vehicles, each bring their own set of ethical, safety, and privacy concerns, complicating the creation of a one-size-fits-all regulatory approach.

Additionally, the global nature of AI development, with contributions from academia, industry, and open-source communities worldwide, necessitates international cooperation in regulatory efforts, further complicating the process.

An effective regulatory framework for AI must navigate the delicate balance between preventing misuse and supporting innovation. It should address the ethical and societal implications of AI, such as bias, accountability, and the impact on employment while also fostering an environment that encourages technological advancement and economic growth.

I have one suggestion in terms of legislation that may at least put the brakes on the explosion of AI-related crimes in the coming years until we figure out what tailored legislation toward the crimes may be possible. What I suggest is to make the use of AI in a crime an  aggravating circumstance . Carrying a gun in and of itself may not be a crime. However, if you commit a robbery, it makes a lot of difference whether you are carrying a gun or not. It's an aggravating circumstance that makes the penalty go up significantly, and it stands to logic because now there is a greater existential threat. By classifying the utilization of AI in criminal activities as an aggravating factor, the legal system can impose harsher penalties on those who exploit AI for malicious purposes.

Why is it crucial for the global community to actively pursue AI research and innovation?

The future of AI should not be dictated by a handful of entities but developed through a global collaborative effort. Just as scientific endeavors like the LIGO project brought minds together to achieve what was once thought impossible [detecting gravitational waves], AI research demands a similar collective effort. We stand on the brink of discoveries that could redefine our understanding of intelligence, biology, and more. It's essential that we pursue these horizons together, ensuring the benefits of AI are shared widely and ethically.

Pausing or halting development efforts could inadvertently advantage those with malicious intent. If responsible researchers and developers were to cease their work in AI, it does not equate to a universal halt in AI advancement. If you put a moratorium on the development of AI, the good guys will abide by it and the bad guys will not. So, all we are achieving is giving the bad guys a "head start" to further their own agendas, potentially leading to the development and deployment of AI systems that are unethical, biased, or designed to harm. The development of AI technologies by those committed to ethical standards, transparency, and the public good acts as a counterbalance to potential misuse.

What potential does AI hold for the future, especially in terms of enhancing human capabilities rather than replacing them?

AI's role in automating routine and repetitive tasks frees humans to focus on more creative and strategic activities, thus elevating the nature of work and enabling new avenues for innovation. By removing mundane tasks, AI allows individuals to engage more deeply with the aspects of their work that require human insight, empathy, and creativity.

This shift not only has the potential to increase job satisfaction but also to drive forward industries and sectors with fresh ideas and approaches. The promise of AI lies not in replacing human capabilities but in significantly augmenting them, opening up a future where humans and machines collaborate to address some of the most pressing challenges facing the world today.

You can submit your own questions to the Caltech Science Exchange.

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AI and Human Enhancement: Americans’ Openness Is Tempered by a Range of Concerns
1. How Americans think about artificial intelligence

2. Public more likely to see facial recognition use by police as good, rather than bad for society
3. Mixed views about social media companies using algorithms to find false information
4. Americans cautious about the deployment of driverless cars
5. What Americans think about possibilities ahead for human enhancement
6. Public cautious about enhancing cognitive function using computer chip implants in the brain
7. Americans are closely divided over editing a baby’s genes to reduce serious health risk
8. Mixed views about a future with widespread use of robotic exoskeletons to increase strength for manual labor jobs
Acknowledgments
Methodology

Artificial intelligence (AI) is spreading through society into some of the most important sectors of people’s lives – from health care and legal services to agriculture and transportation. 1 As Americans watch this proliferation, they are worried in some ways and excited in others.

Chart shows Americans lean toward concern over excitement when it comes to the increased use of AI in daily life, and public views are varied when it comes to three specific AI applications

In broad strokes, a larger share of Americans say they are “more concerned than excited” by the increased use of AI in daily life than say the opposite. Nearly half of U.S. adults (45%) say they are equally concerned and excited. Asked to explain in their own words what concerns them most about AI, some of those who are more concerned than excited cite their worries about potential loss of jobs, privacy considerations and the prospect that AI’s ascent might surpass human skills – and others say it will lead to a loss of human connection, be misused or be relied on too much.

But others are “more excited than concerned,” and they mention such things as the societal improvements they hope will emerge, the time savings and efficiencies AI can bring to daily life and the ways in which AI systems might be helpful and safer at work. And people have mixed views on whether three specific AI applications are good or bad for society at large.

This chapter covers the general findings of the survey related to AI programs. It also runs through highlights from in-depth explorations of public attitudes about three AI-related applications that are fully explored in the three chapters after this. Some key findings:

How Pew Research Center approached this topic

The Center survey asked respondents a series of questions about three applications of artificial intelligence (AI):

Facial recognition technology that could be used by police to look for people who may have committed a crime or to monitor crowds in public spaces.
Computer programs, called algorithms, used by social media companies to find false information about important topics that appears on their sites.
Driverless passenger vehicles that are equipped with software allowing them to operate with computer assistance and are expected to be able to operate entirely on their own without a human driver in the future.

Other questions asked respondents their feelings about AI’s increased use, the way AI programs are designed and a range of other possible AI applications.

This study builds on prior Center research including surveys on Americans’ views about automation in everyday life , the role of algorithms in parts of society and the use of facial recognition technology . It also draws on insights from several canvassings of experts about the future of AI and humans .

Use of facial recognition by police: We chose to explore the use of facial recognition by police because police reform has been a major topic of debate , especially in the wake of the killing of George Floyd in May 2020 and the ensuing protests . The survey shows that a plurality (46%) thinks use of this technology by police is a good idea for society, while 27% believe it is a bad idea and 27% say they are not sure. At the same time, 57% think crime would stay about the same if the use of facial recognition by the police becomes widespread, while 33% think crime would decrease and 8% think it would rise.

Moreover, there are divided views about how the widespread use of facial recognition technology would impact the fairness of policing. Majorities believe it is definitely or probably likely that widespread police use of this technology would result in more missing persons being found by police and crimes being solved more quickly and efficiently. Still, about two-thirds also think police would be able to track everyone’s location at all times and that police would monitor Black and Hispanic neighborhoods much more often than other neighborhoods.

Use of computer programs by social media companies to find false information on their sites: We chose to study attitudes about the use of computer programs (algorithms) by social media companies because social media is used by a majority of U.S. adults . There are also concerns about the impact of made-up information and how efforts to target misinformation might affect freedom of information . The survey finds that 38% of U.S. adults think that the widespread use of computer programs by social media companies to find false information on their sites has been a good idea for society, compared with 31% who say it is a bad idea and 30% who say they are not sure.

When asked about specific possible impacts, public views are largely negative. Majorities believe widespread use of algorithms by social media companies to find false information is definitely or probably causing political views to be censored and news and information to be wrongly removed from the sites. And majorities do not think these algorithms are causing beneficial things to happen like making it easier to find trustworthy information or allowing people to have more meaningful conversations. There are substantial partisan differences on these questions, with Republicans and those who lean toward the GOP holding more negative views than Democrats and Democratic leaners.

Driverless passenger vehicles: We chose to study public views about driverless passenger vehicles because they are being tested on roads now and their rollout on a larger scale is being debated . The survey finds that a plurality of Americans (44%) believe that the widespread use of driverless passenger vehicles would be a bad idea for society. That compares with the 26% who think this would be a good idea. Some 29% say they are not sure. A majority say they definitely or probably would not want to ride in a driverless car if they had the opportunity. Some 39% believe widespread use of driverless cars would decrease the number of people killed or injured in traffic accidents, while 31% think there would not be much difference and 27% think there would be an increase in these types of deaths or injuries.

People envision a mix of positive and negative outcomes from widespread use of driverless cars. Majorities believe older adults and those with disabilities would be able to live more independently and that getting from place to place would be less stressful. At the same time, majorities also think many people who make their living by driving others or delivering things with passenger vehicles would lose their jobs and that the computer systems in driverless passenger vehicles would be easily hacked in ways that put safety at risk.

In their responses to survey questions about other possible developments in artificial intelligence, majorities express concern about the prospect that AI could know people’s thoughts and behaviors and make important life decisions for people. And when it comes to the use of AI for decision-making in a variety of fields, the public is more opposed than not to the use of computer programs (algorithms) to make final decisions about which patients should get a medical treatment, which people should be good candidates for parole, which job applicants should move on to a next round of interviews or which people should be approved for mortgages.

Still, there are some possible AI applications that draw public appeal. For example, more Americans are excited than concerned about AI applications that can do household chores. That is also the pattern when people are asked about AI apps that can perform repetitive workplace tasks.

There are patterns in views of three AI applications, but other opinions are unique to particular AI systems

The chapters following this one cover extensive findings about people’s views about three major applications of AI, including demographic differences and patterns that emerge.

Americans are split in their views about the use of facial recognition by police. Among these differences: While majorities across racial and ethnic groups say police would use facial recognition to monitor Black and Hispanic neighborhoods much more often than other neighborhoods if the technology became widespread, Black and Hispanic adults are more likely than White adults to say this. As for the way algorithms are being used by social media companies to identify false information, there are clear partisan differences in the public’s assessment of the use of those computer programs. And people believe that a mix of both positive and negative outcomes would occur if driverless cars became widely used.

When it comes to public awareness of these AI applications, majorities have heard at least a little about each of them, but some Americans have not heard about them at all and awareness can relate to views of these applications. For instance, those who have heard a lot about driverless passenger vehicles are more likely than those who have not heard anything about such cars to believe they are a good idea for society. But when it comes to the use of facial recognition by the police, those who have heard a lot are more likely to say it is a bad idea for society than those who have not heard anything about it. Views about whether the use of algorithms by social media companies to detect false information on their sites is good or bad for society lean negative among those who have heard a lot, while among those who have heard nothing, over half are not sure how they feel about this practice.

In addition to awareness being a factor associated with Americans’ views about these AI applications, there are patterns related to education. Those with higher levels of education often hold different views than those who have less formal education. For example, those with a postgraduate education are more likely than those with a high school education or less to think the widespread use of algorithms by social media companies to root out false information on the platforms and the use of driverless vehicles are good ideas for society. The reverse is true for facial recognition – those with a postgraduate degree are more likely to think its widespread use by police is a bad idea for society than those with a high school diploma or less education.

Additionally, the views of young adults and older adults diverge at times when these three AI applications are assessed. For instance, adults ages 18 to 29 are more likely than those 65 and older to say the widespread use of facial recognition by police is a bad idea for society. At the same time, this same group of young adults is more likely than those 65 and older to think the widespread use of self-driving cars is a good idea for society.

The next sections of this chapter cover the findings from the survey’s general questions about AI.

Americans more likely to be ‘more concerned than excited’ about increased use of AI in daily life than vice versa

In this survey, artificial intelligence computer programs were described as those designed to learn tasks that humans typically do, such as recognizing speech or pictures. Of course, an array of AI applications are being implemented in everything from game-playing to food growing to disease outbreak detection . Synthesis efforts now regularly chart the spread of AI .

As these developments unfold, a larger share of Americans say they are “more concerned than excited” about the increased use of AI in everyday life than say they are “more excited than concerned” about these prospects (37% vs. 18%). And nearly half (45%) say they are equally excited and concerned.

There are some differences by educational attainment and political affiliation. For instance, a larger share of those who have some college experience or a high school education or less say they are more concerned than excited, compared with their counterparts who have a bachelor’s or advanced degree (40% vs. 32%). Republicans are more likely than Democrats to say they are more concerned than excited (45% vs. 31%). Full details about the views of different groups on this question can be found in the Appendix .

When those who say they are more excited than concerned about the increased use of AI in daily life are asked to explain in their own words the main reason they feel that way, 31% said they believe AI has the ability to make key aspects of our lives and society better.

Chart showing Americans explain in their own words what makes them either more concerned or more excited about the increased presence of AI in daily life

As one man explained in his written comments:

“AI, if used to its fullest ‘best’ potential, could help to solve an unbelievable number of major problems in the world and help solve massive crises like world hunger, pollution, climate change, joblessness and others.” – Man, 30s

A woman made a similar point:

“[AI has] the ability to learn and create things that humans are incapable of doing. [AI programs] will have massive impacts to our daily life and will solve issues related to climate change and healthcare.” – Woman, 30s

Smaller shares of those who express more excitement than concern over AI mention its ability to save time and make tasks more efficient (13%), see it as a reflection of inevitable progress (10%), or cite the fact that it could handle mundane or tedious tasks (7%) as the main reasons why they lean enthusiastic about the prospect of AI’s increased presence in daily life.

Those who are excited about the increased use of AI in daily life also cite AI’s ability to improve work, their sense that AI is interesting and exciting and the ability of AI programs to perform difficult or dangerous tasks as a reason: 6% of those more excited than concerned mentioned each.

In addition, 4% of those who are more excited say AI is more accurate than humans, while an identical share say they are excited because AI can make things more accessible for those who have a disability or who are older. Some 2% offer personal anecdotes of how AI has already been beneficial to their lives, and another 2% wrote that many of the fears about AI are misplaced due to what they believe to be unrealistic depictions of AI in science fiction and popular culture.

The 37% of Americans who are more concerned than excited about AI’s increasing use in daily life also mention a number of reasons behind their reticence. About one-in-five among this group (19%) express concerns that increased use of AI will result in job loss for humans. As a woman in her 70s put it:

“[AI programs] will eventually eliminate jobs. Then what will those people do to survive in life?” – Woman, 70s

Meanwhile, 16% of those who are more concerned about the increased use of AI say it could lead to privacy problems, surveillance or hacking. A woman in her 30s wrote of this concern:

“I am concerned that the increased use of artificial intelligence programs will infringe on the privacy of individuals. I feel these programs are not regulated enough and can be used to obtain information without the person knowing.” – Woman, 30s

Another 12% of these respondents are concerned about dehumanization, or the belief that human connections and qualities will be lost, while 8% each mention the potential for AI becoming too powerful or for people to misuse the technology for nefarious reasons.

Some 7% who express more concern than excitement about AI offer that it would make people overly reliant on this technology, and 6% worry about the failures and flaws of the technology.

Small shares of those who are worried about the integration of AI also mention other concerns ranging from what technology companies or the government would do with this type of technology to human biases being embedded into these computer programs to what they see as a lack of regulation or oversight of the technology and the industries that develop them.

Mixed views about some ways AI applications could develop: People are more excited about some, more concerned about others

In addition to the broad question about where people stand in terms of their general excitement or concern about AI, this survey also asked about a number of more specific possible developments in AI programs.

There are widely varying public views about six different kinds of AI applications that were included in the survey. Some prompt relatively more excitement than concern, and some generate substantial concern. For instance, 57% say they would be very or somewhat excited for AI applications that could perform household chores, but just 9% express the same level of enthusiasm for AI making important life decisions for people or knowing their thoughts and behaviors.

Chart shows Americans are concerned about AI systems that could know people’s thoughts and make important life decisions for them

Nearly half (46%) would be very or somewhat excited about AI that could perform repetitive workplace tasks, compared with 26% who would be very or somewhat concerned about that. When it comes to AI that could diagnose medical problems, people are more evenly split: 40% would be at least somewhat excited and 35% would be at least somewhat concerned, while 24% say they are equally excited and concerned. More cautionary views are also evident when people are asked about AI that could handle customer service calls: 47% are very or somewhat concerned about this issue, compared with 27% who are at least somewhat excited.

It is important to note that on these issues, portions of Americans say they are equally excited and concerned about various possible AI developments. That share ranges from 16% to 27% depending on the possible development.

Some differences among groups stand out as Americans assess these various AI apps. Those with a high school education or less are more likely than those with postgraduate degrees to say they are at least somewhat concerned at the prospect that AI programs could perform repetitive workplace tasks (36% vs. 12%). Women are more likely than men to say they would be at least somewhat concerned if AI programs could diagnose medical problems (43% vs. 27%). A larger share of those ages 65 and older (82%) than of those 18 to 29 (63%) say they would be very or somewhat concerned if AI programs could make important life decisions for people.

Chart shows older adults and women are more likely than others to express at least some concern about some possible AI developments

Views of men, White adults seen as better represented than those of other groups when designing AI programs

In recent years, there have been significant revelations about and investigations into potential shortcomings of artificial intelligence programs. One of the central concerns is that AI computer systems may not factor in a diversity of perspectives , especially when it comes to gender , race and ethnicity .

In this survey, people were asked how well they thought that those who design AI programs take into account the experiences and views of some groups. Overall, about half of Americans (51%) believe the experiences and views of men are very or somewhat well taken into account by those who design AI programs. By contrast, smaller shares feel the views of women are taken into account very or somewhat well. And while just 12% of U.S. adults say the experiences of men are not well taken into account in the design of AI programs, about twice that share say the same about the experiences and views of women.

Chart shows whose experiences and views are taken into account when AI programs are designed? Views vary depending on the demographic group in question

Additionally, 48% think the views of White adults are at least somewhat well taken into account in the creation of AI programs, versus smaller shares who think the views of Asian, Black or Hispanic adults are well-represented. Just 13% feel the views and experiences of White adults are not well taken into account; 23% say the same about Asian adults and a third say this about Black or Hispanic adults.

Still, there are about four-in-ten in each case who, when asked these questions, say they are not sure how the experiences and views of different groups are taken into account as AI programs are designed.

Views on this topic vary across racial and ethnic groups:

Among White adults: They are more likely than other racial and ethnic groups to say they are “not sure” how well the designers of AI programs take into account each of the six sets of experiences and views queried in this survey. For instance, 45% of White adults say they are not sure if the experiences and views of White adults are well accounted for in the design of AI programs. That compares with 30% of Black adults, 28% of Hispanic adults and 21% of Asian adults who say they are not sure about this. Similar uncertainty among White adults appears when they are asked about other groups’ perspectives.

Among Black adults: About half of Black adults (47%) believe that the experiences and views of Black adults are not well taken into account by the people who design artificial intelligence programs, while a smaller share (24%) say Black adults’ experiences are well taken into account. Compared with Black adults, a similar share of Asian adults (39%) feel the experiences and views of Black adults are not well taken into account when AI programs are designed, while Hispanic adults (35%) and White adults (29%) are less likely than Black adults to hold this view.

Among Hispanic adults: About one-third of Hispanic Americans (34%) believe the experiences and views of Hispanic adults are well taken into account as the programs are designed. This is the highest share among the groups in the survey: 24% of Asian adults, 22% of Black adults and 21% of White adults feel this way. Meanwhile, 36% of Hispanic adults say the experiences and views of Hispanic adults are not well taken into account as AI programs are designed. About three-in-ten Hispanic adults (29%) say they are not sure on this question.

Among Asian adults: Some 41% of Asian adults think that the experiences of Asian adults are well taken into account. Similar shares of Hispanic adults (42%) and Black adults (36%) say this about Asians’ views, versus a smaller share of White adults (29%) who think that is the case.

A plurality of Americans are not sure whether AI can be fairly designed

Chart shows public is divided on whether AI programs can be designed to make fair decisions consistently

In addition to gathering opinion on how well various perspectives are taken into account, the survey explored how people judge AI programs when it comes to fair decisions. Asked if it is possible for the people who design AI to create computer programs that can consistently make fair decisions in complex situations, Americans are divided: 30% say AI design for fair decisions is possible, 28% say it is not possible, while the largest share – 41% – say they are not sure.

Some noteworthy differences among different groups on this question are tied to gender. Men are more likely than women to believe it is possible to design AI programs that can consistently make fair decisions (38% vs. 22%), and women are more likely to say they are not sure (46% vs. 35%).

Pew Research Center has explored the spread of artificial intelligence in several reports about the future of the internet, including “ Experts Doubt Ethical AI Design Will Be Broadly Adopted as the Norm Within the Next Decade ,” “ Artificial Intelligence and the Future of Humans ,” “ Visions of the Internet in 2035 ” and “ AI, Robotics and the Future of Jobs .” ↩

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14 August 2024

Has your paper been used to train an AI model? Almost certainly

Elizabeth Gibney

You can also search for this author in PubMed Google Scholar

Academic publisher Wiley has sold access to its research papers to firms developing large language models. Credit: Timon Schneider/Alamy

Academic publishers are selling access to research papers to technology firms to train artificial-intelligence (AI) models. Some researchers have reacted with dismay at such deals happening without the consultation of authors. The trend is raising questions about the use of published and sometimes copyrighted work to train the exploding number of AI chatbots in development.

Experts say that, if a research paper hasn’t yet been used to train a large language model (LLM), it probably will be soon. Researchers are exploring technical ways for authors to spot if their content being used.

AI models fed AI-generated data quickly spew nonsense

Last month, it emerged that the UK academic publisher Taylor & Francis, had signed a US$10-million deal with Microsoft, allowing the US technology company to access the publisher’s data to improve its AI systems. And in June, an investor update showed that US publisher Wiley had earned $23 million from allowing an unnamed company to train generative-AI models on its content.

Anything that is available to read online — whether in an open-access repository or not — is “pretty likely” to have been fed into an LLM already, says Lucy Lu Wang, an AI researcher at the University of Washington in Seattle. “And if a paper has already been used as training data in a model, there’s no way to remove that paper after the model has been trained,” she adds.

Massive data sets

LLMs train on huge volumes of data, frequently scraped from the Internet. They derive patterns between the often billions of snippets of language in the training data, known as tokens, that allow them to generate text with uncanny fluency.

Generative-AI models rely on absorbing patterns from these swathes of data to output text, images or computer code. Academic papers are valuable for LLM builders owing to their length and “high information density”, says Stefan Baack, who analyses AI training data sets at the Mozilla Foundation, a global non-profit organization in San Francisco, California that aims to keep the Internet open for all to access.

How does ChatGPT ‘think’? Psychology and neuroscience crack open AI large language models

Training models on a large body of scientific information also give them a much better ability to reason about scientific topics, says Wang, who co-created S2ORC, a data set based on 81.1 million academic papers. The data set was originally developed for text mining — applying analytical techniques to find patterns in data — but has since been used to train LLMs.

The trend of buying high-quality data sets is growing. This year, the Financial Times has offered its content to ChatGPT developer OpenAI in a lucrative deal, as has the online forum Reddit, to Google. And given that scientific publishers probably view the alternative as their work being scraped without an agreement, “I think there will be more of these deals to come,” says Wang.

Information secrets

Some AI developers, such as the Large-scale Artificial Intelligence Network, intentionally keep their data sets open, but many firms developing generative-AI models have kept much of their training data secret, says Baack. “We have no idea what is in there,” he says. Open-source repositories such as arXiv and the scholarly database PubMed of abstracts are thought to be “very popular” sources, he says, although paywalled journal articles probably have their free-to-read abstracts scraped by big technology firms. “They are always on the hunt for that kind of stuff,” he adds.

Proving that an LLM has used any individual paper is difficult, says Yves-Alexandre de Montjoye, a computer scientist at Imperial College London. One way is to prompt the model with an unusual sentence from a text and see whether the output matches the next words in the original. If it does, that is good evidence that the paper is in the training set. But if it doesn’t, that doesn’t mean that the paper wasn’t used — not least because developers can code the LLM to filter responses to ensure they don’t match training data too closely. “It takes a lot for this to work,” he says.

Robo-writers: the rise and risks of language-generating AI

Another method to check whether data are in a training set is known as membership inference attack. This relies on the idea that a model will be more confident about its output when it is seeing something that it has seen before. De Montjoye’s team has developed a version of this, called a copyright trap, for LLMs.

To set the trap, the team generates sentences that look plausible but are nonsense, and hides them in a body of work, for example as white text on a white background or in a field that’s displayed as zero width on a webpage. If an LLM is more ‘surprised’ — a measure known as its perplexity — by an unused control sentence than it is by the one hidden in the text, “that is statistical evidence that the traps were seen before”, he says.

Copyright questions

Even if it were possible to prove that an LLM has been trained on a certain text, it is not clear what happens next. Publishers maintain that, if developers use copyrighted text in training and have not sought a licence, that counts as infringement. But a counter legal argument says that LLMs do not copy anything — they harvest information content from training data, which gets broken up, and use their learning to generate new text.

AI is complicating plagiarism. How should scientists respond?

Litigation might help to resolve this. In an ongoing US copyright case that could be precedent-setting, The New York Times is suing Microsoft and ChatGPT’s developer OpenAI in San Francisco, California. The newspaper accuses the firms of using its journalistic content to train their models without permission.

Many academics are happy to have their work included in LLM training data — especially if the models make them more accurate. “I personally don’t mind if I have a chatbot who writes in the style of me,” says Baack. But he acknowledges that his job is not threatened by LLM outputs in the way that those of other professions, such as artists and writers, are.

Individual scientific authors currently have little power if the publisher of their paper decides to sell access to their copyrighted works. For publicly available articles, there is no established means to apportion credit or know whether a text has been used.

Some researchers, including de Montjoye, are frustrated. “We want LLMs, but we still want something that is fair, and I think we’ve not invented what this looks like yet,” he says.

doi: https://doi.org/10.1038/d41586-024-02599-9

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4 questions leaders should ask themselves when considering ai tools.

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In 2017, a true crime reality series called Shattered launched in the United Kingdom. The opening title sequence shows the name of the series in white on a dark background, which then shatters like glass.

In order to create this title sequence, the show’s visual effects team went to work planning how to design computer graphics of shattering glass that wouldn’t look fake. The physics of how glass breaks, how it moves, and especially how it reflects lighting, were extremely complex.

“It was going to cost a fortune,” said Christopher Webb, owner of FX WRX , who was brought in to help with the project.

So instead of paying that fortune, Webb got a real piece of glass, screen printed the word “Shattered” on it, and broke it in real life.

Real glass shattering in a TV title sequence

Shattered’s title sequence looks fantastic. The FX WRX team added in various clever tricks to make the glass reflect images of characters in the show, and more. And it all cost far less money and effort to create than if they’d done it all in CG.

Sometimes, the best way to solve a problem is to not use the latest fancy technology.

Today’s NYT Mini Crossword Clues And Answers For Friday, August 16

Harris will propose $25,000 in down payment aid for first-time homebuyers as part of economic agenda, kamala harris releases economic agenda—here’s what to know.

I’ve had this story on my mind ever since Webb told it to me. His company specializes in using analog tools in order to make special effects for film and TV—which goes against the grain in an industry that’s desperate for innovation, and therefore willing to glom onto shiny new tools without thinking through whether they’re the right tools for the job.

But we shouldn’t beat up Hollywood for doing this (I run a film and TV technology company myself; folks who work in this ‘biz just want to do a good job!). When I look at how businesses at large are adopting AI tools into their workflows, I see the same desperation, and often, the same mistakes.

Reality is, new tools that purport to make work easier can sometimes make work take longer. Often, a new tool promises us more optionality… but in practice it presents us with more time-consuming choices. Often, a new tool helps speed up one facet of a job, but creates more work in total. (Anyone who’s ever spent hours trying to get MidJourney to output the perfect image and then realized they could have gotten a stock photo, or drawn something in Photoshop in much less time, knows what I’m talking about.)

We’re living in an era where so much new technology is being developed at such a speed, that leaders can’t afford not to pay attention—or try out—tools that could help us level up. Artificial Intelligence is the tools category of the moment in this regard. But whether it’s AI or whatever comes next, it pays to remember that just because a technology is new and exciting doesn’t mean that it’s going to be better for every use case. And just because a tool is cool doesn’t mean it’s the right tool for the job.

So how do we deal with today’s barrage of new AI tools without falling victim to the unintended pitfalls of technology that’s not quite right for us right now? It starts with understanding the ways that implementing new technologies can go wrong, so we can experiment with new tools while keeping our eyes open for signs that we’re going down unhelpful rabbit holes.

At the most basic level, leaders can ask questions that get at those common pitfalls that come with implementing new tools:

Will using this tool trade deliberate, directed work for guess-and-check work? (And if so, how will we make sure the new way of working doesn’t take longer?)
Will using this tool trade a perfect result for a quick result? (And if so, are we going to be okay with trading lower quality for higher speed?)
Will this tool cause people to think more inside the box? (And if so, is that what we want?)
What ripple effects could using this tool create for us or the system we operate in? (And are the potential second-order consequences we are okay with?)

Of course, any new tool ought to be weighed for its potential usefulness in accelerating our organizations toward our goals. But without balancing that potential gain with the potential second-order effects , we risk making our work harder, not better.

Shane Snow is author of Smartcuts : The Breakthrough Power of Lateral Thinking, and the CEO of the virtual production company SHOWRUNNER .

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How to ask like a human: Transform search efficiency

Head of Product, Atlassian Intelligence

If you’ve ever searched for information on a search engine or database and struggled to find what you’re looking for, you’re not alone. But imagine if searching for information felt more like chatting with a helpful friend or colleague. In this article, we’ll discuss everything you need to know about how to ask AI questions like a human to transform your search efficiency.

By learning to ask questions more naturally, as you would in a conversation with a teammate, you can make your search process more intuitive and effective using everyday language. For instance, in Jira, you can use Atlassian Intelligence to search for answers using natural language, which is then translated into JQL, simplifying your ability to find information.

Keep reading to learn how to ask AI questions like a human and get the answers you’re looking for quickly and efficiently.

Keyword search vs. querying like a teammate

When searching for information, you can use one of two methods: keyword search or natural language search. Each approach has its strengths and weaknesses, which can impact how effectively you navigate and retrieve information.

Keyword search

With keyword search, you input specific words or phrases into a search engine or database to locate relevant information. For instance, you might search “budget report 2023” to find documents or data directly associated with that exact phrase.

While straightforward, keyword searches can be limited. They rely heavily on exact matches and may struggle with synonyms, context, or variations in how information is expressed. This can lead to missed results or the need for multiple searches to find comprehensive information.

Querying like a teammate

Querying like a teammate means using natural language to ask questions or make requests, mimicking human conversation. For instance, you could ask, “What are the expenses for the current fiscal year?” instead of a specific keyword search.

This method helps search engines and databases understand the intent behind queries and accommodate variations in language and context. Querying like a teammate interprets meaning rather than focusing solely on keywords, enhancing efficiency by quickly retrieving more accurate and relevant results.

Using natural language queries offers several advantages over traditional keyword searches. It allows team members to ask questions in a way that feels intuitive and familiar, reducing the need to guess specific terms or phrases. Natural language queries also adapt to different ways information might be phrased, making it easier to find relevant data efficiently.

This approach saves time and improves the overall user experience by making finding information more intuitive and less reliant on precise keyword matching.

Practical tips and examples

When searching for information, asking questions in a natural, human-like way can improve the relevance and efficiency of your results. Here are some practical tips to help you master this approach:

Search tips

When trying to find information, be specific and ask your query in a way that mimics natural conversation. For example:

What is the permissions service in [platform name]?

In this example, you specify the specific platform you’re asking about.

When is [insert project name] due?

This is a more general query that can be used in your project management dashboard to help you find specific timelines and dates.

Who has worked on [insert project name] – show user card
Who is [insert person’s name]? – show user card

By specifying “show user card,” you’re instructing the AI tool to answer your question and then show you the user card so you know exactly who it’s talking about.

When is [insert project name] due? – show date

Specifying “show date” gives the AI clear instructions to understand exactly what you’re asking, which, in this case, is the project’s due date.

Using natural language in JQL and SQL

Jira query language (JQL) and structured query language (SQL) are used to find answers and information in databases and software systems. JQL is used in Atlassian products like Jira to retrieve data from issues and projects, while SQL is widely used in databases to manage and manipulate data.

JQL in Jira looks like this: assignee = (currentUser) AND status = “In Progress”

Unfortunately, this can be challenging for non-technical workers to understand, making it difficult for them to use search effectively. Instead, they might want to use natural language to ask, “What tasks am I assigned to that are currently in progress?”

Now, with Atlassian Intelligence, anyone can use JQL by translating natural language into a query, ensuring efficient searching of Jira issues. Whether you’re searching for issue status, assignee, priority, or any other relevant criteria, Atlassian Intelligence interprets your prompts into JQL to provide precise results.

To use natural language, phrase your queries to match how you would ask a colleague. Use specific terms or instructions to refine your query, such as mentioning the platform or specifying what information you need to see.

The benefits of asking like a human

How we interact with technology can impact productivity and collaboration . Asking questions in a natural way gives teams, project managers, and individual workers several advantages:

Increased efficiency

Asking questions in everyday language facilitates faster retrieval of information. It reduces the time spent formulating complex queries and improves search accuracy, enhancing overall efficiency.

Additionally, asking questions in a human-like manner lowers the barrier of technical knowledge required to understand and extract useful data from databases or search tools.

More user-friendly

Using natural language makes it easier for non-experts to access accurate information. This approach simplifies the interaction with search tools and databases, allowing users to articulate their queries intuitively without needing specialized training or deep technical understanding. It enhances the usability of systems and promotes a more accessible experience for all users.

Enhanced collaboration

Allowing users to ask simple questions promotes collaboration by making information more accessible across teams. It reduces communication barriers by offering clearer and more direct exchanges of information.

This accessibility makes the environment more collaborative, allowing team members to easily share insights from analytics , track project and task progress, and make better decisions based on readily available data.

Real-world applications

Natural language queries offer practical benefits across various industries and domains, from business operations to personal productivity.

Businesses can leverage natural language queries to streamline operations and enhance decision-making. For instance:

Customer support: Companies can use natural language processing to analyze customer queries and provide personalized responses, improving customer satisfaction.
Business intelligence: Executives and analysts can ask questions like “What were our sales figures last quarter?” to quickly retrieve insights without diving into complex reports or databases.
Marketing campaigns: Marketers can use natural language queries to analyze customer sentiment from social media mentions, helping them refine their campaigns in real time.

Individuals within organizations can also use natural language queries to boost personal productivity. Using tools, individuals can ask, “What are my tasks for today?” to prioritize and manage daily activities more efficiently. They can also ask project management tools, such as “What are the upcoming deadlines for [insert project name]?”

The power of natural language queries

Asking questions like a human can change the way you interact with technology. By using natural language queries, individuals and teams can effortlessly transform their search efficiency and decision-making processes.

Atlassian Intelligence allows you to ask like a human within Jira, seamlessly translating everyday language into Jira query language. This capability allows anyone to ask complex questions, bypassing the need for extensive technical expertise. Whether managing projects, analyzing data, or looking for specific information, Atlassian Intelligence streamlines the search process by understanding intent and context.

With Atlassian Intelligence, tasks that once required meticulous query crafting become intuitive interactions. Users can navigate between Basic and JQL modes to help them find information quickly for more efficient workflow management.

Understanding how Atlassian Intelligence works in Jira and Confluence can help teams work better while enhancing individual and team productivity. Learn more about how Atlassian Intelligence helps teams work smarter .

Advice, stories, and expertise about work life today.

How to cite ChatGPT

Use discount code STYLEBLOG15 for 15% off APA Style print products with free shipping in the United States.

We, the APA Style team, are not robots. We can all pass a CAPTCHA test , and we know our roles in a Turing test . And, like so many nonrobot human beings this year, we’ve spent a fair amount of time reading, learning, and thinking about issues related to large language models, artificial intelligence (AI), AI-generated text, and specifically ChatGPT . We’ve also been gathering opinions and feedback about the use and citation of ChatGPT. Thank you to everyone who has contributed and shared ideas, opinions, research, and feedback.

In this post, I discuss situations where students and researchers use ChatGPT to create text and to facilitate their research, not to write the full text of their paper or manuscript. We know instructors have differing opinions about how or even whether students should use ChatGPT, and we’ll be continuing to collect feedback about instructor and student questions. As always, defer to instructor guidelines when writing student papers. For more about guidelines and policies about student and author use of ChatGPT, see the last section of this post.

Quoting or reproducing the text created by ChatGPT in your paper

If you’ve used ChatGPT or other AI tools in your research, describe how you used the tool in your Method section or in a comparable section of your paper. For literature reviews or other types of essays or response or reaction papers, you might describe how you used the tool in your introduction. In your text, provide the prompt you used and then any portion of the relevant text that was generated in response.

Unfortunately, the results of a ChatGPT “chat” are not retrievable by other readers, and although nonretrievable data or quotations in APA Style papers are usually cited as personal communications , with ChatGPT-generated text there is no person communicating. Quoting ChatGPT’s text from a chat session is therefore more like sharing an algorithm’s output; thus, credit the author of the algorithm with a reference list entry and the corresponding in-text citation.

When prompted with “Is the left brain right brain divide real or a metaphor?” the ChatGPT-generated text indicated that although the two brain hemispheres are somewhat specialized, “the notation that people can be characterized as ‘left-brained’ or ‘right-brained’ is considered to be an oversimplification and a popular myth” (OpenAI, 2023).

OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat

You may also put the full text of long responses from ChatGPT in an appendix of your paper or in online supplemental materials, so readers have access to the exact text that was generated. It is particularly important to document the exact text created because ChatGPT will generate a unique response in each chat session, even if given the same prompt. If you create appendices or supplemental materials, remember that each should be called out at least once in the body of your APA Style paper.

When given a follow-up prompt of “What is a more accurate representation?” the ChatGPT-generated text indicated that “different brain regions work together to support various cognitive processes” and “the functional specialization of different regions can change in response to experience and environmental factors” (OpenAI, 2023; see Appendix A for the full transcript).

Creating a reference to ChatGPT or other AI models and software

The in-text citations and references above are adapted from the reference template for software in Section 10.10 of the Publication Manual (American Psychological Association, 2020, Chapter 10). Although here we focus on ChatGPT, because these guidelines are based on the software template, they can be adapted to note the use of other large language models (e.g., Bard), algorithms, and similar software.

The reference and in-text citations for ChatGPT are formatted as follows:

Parenthetical citation: (OpenAI, 2023)
Narrative citation: OpenAI (2023)

Let’s break that reference down and look at the four elements (author, date, title, and source):

Author: The author of the model is OpenAI.

Date: The date is the year of the version you used. Following the template in Section 10.10, you need to include only the year, not the exact date. The version number provides the specific date information a reader might need.

Title: The name of the model is “ChatGPT,” so that serves as the title and is italicized in your reference, as shown in the template. Although OpenAI labels unique iterations (i.e., ChatGPT-3, ChatGPT-4), they are using “ChatGPT” as the general name of the model, with updates identified with version numbers.

The version number is included after the title in parentheses. The format for the version number in ChatGPT references includes the date because that is how OpenAI is labeling the versions. Different large language models or software might use different version numbering; use the version number in the format the author or publisher provides, which may be a numbering system (e.g., Version 2.0) or other methods.

Bracketed text is used in references for additional descriptions when they are needed to help a reader understand what’s being cited. References for a number of common sources, such as journal articles and books, do not include bracketed descriptions, but things outside of the typical peer-reviewed system often do. In the case of a reference for ChatGPT, provide the descriptor “Large language model” in square brackets. OpenAI describes ChatGPT-4 as a “large multimodal model,” so that description may be provided instead if you are using ChatGPT-4. Later versions and software or models from other companies may need different descriptions, based on how the publishers describe the model. The goal of the bracketed text is to briefly describe the kind of model to your reader.

Source: When the publisher name and the author name are the same, do not repeat the publisher name in the source element of the reference, and move directly to the URL. This is the case for ChatGPT. The URL for ChatGPT is https://chat.openai.com/chat . For other models or products for which you may create a reference, use the URL that links as directly as possible to the source (i.e., the page where you can access the model, not the publisher’s homepage).

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