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Artificial intelligence
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Study: When allocating scarce resources with AI, randomization can improve fairness
Introducing structured randomization into decisions based on machine-learning model predictions can address inherent uncertainties while maintaining efficiency.
July 24, 2024
Read full story →
MIT researchers advance automated interpretability in AI models
MAIA is a multimodal agent that can iteratively design experiments to better understand various components of AI systems.
July 23, 2024
Proton-conducting materials could enable new green energy technologies
Analysis and materials identified by MIT engineers could lead to more energy-efficient fuel cells, electrolyzers, batteries, or computing devices.
Large language models don’t behave like people, even though we may expect them to
A new study shows someone’s beliefs about an LLM play a significant role in the model’s performance and are important for how it is deployed.
AI model identifies certain breast tumor stages likely to progress to invasive cancer
The model could help clinicians assess breast cancer stage and ultimately help in reducing overtreatment.
July 22, 2024
Machine learning unlocks secrets to advanced alloys
An MIT team uses computer models to measure atomic patterns in metals, essential for designing custom materials for use in aerospace, biomedicine, electronics, and more.
July 18, 2024
Creating and verifying stable AI-controlled systems in a rigorous and flexible way
Neural network controllers provide complex robots with stability guarantees, paving the way for the safer deployment of autonomous vehicles and industrial machines.
July 17, 2024
AI method radically speeds predictions of materials’ thermal properties
The approach could help engineers design more efficient energy-conversion systems and faster microelectronic devices, reducing waste heat.
July 16, 2024
How to assess a general-purpose AI model’s reliability before it’s deployed
A new technique enables users to compare several large models and choose the one that works best for their task.
Marking a milestone: Dedication ceremony celebrates the new MIT Schwarzman College of Computing building
Members of the MIT community, supporters, and guests commemorate the opening of the new college headquarters.
July 12, 2024
Reasoning skills of large language models are often overestimated
New CSAIL research highlights how LLMs excel in familiar scenarios but struggle in novel ones, questioning their true reasoning abilities versus reliance on memorization.
July 11, 2024
When to trust an AI model
More accurate uncertainty estimates could help users decide about how and when to use machine-learning models in the real world.
MIT ARCLab announces winners of inaugural Prize for AI Innovation in Space
The challenge asked teams to develop AI algorithms to track and predict satellites’ patterns of life in orbit using passively collected data
“They can see themselves shaping the world they live in”
Developed by MIT RAISE, the Day of AI curriculum empowers K-12 students to collaborate on local and global challenges using AI.
July 8, 2024
MIT researchers introduce generative AI for databases
This new tool offers an easier way for people to analyze complex tabular data.
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Science and the new age of AI
Updated 6 December 2023
Credit: Carlo Cadenas
Across disciplines as varied as biology, physics, mathematics and social science, artificial intelligence (AI) is transforming the scientific enterprise. From machine-learning techniques that hunt for patterns in data, to the latest general-purpose algorithms that can generate realistic synthetic outputs from vast corpuses of text and code, AI tools are accelerating the pace of research and providing fresh directions for scientific exploration.
This special website looks at how these changes are affecting different areas of science — and how it should respond to the challenges the tools present. It includes selected articles from journalists as well as editorials and comment from Nature , including subscriber-only content. The site will be updated with more content as it is published.
Editorial: AI will transform science — now researchers must tame it
Latest articles
Is AI leading to a reproducibility crisis in science?
Scientists worry that ill-informed use of artificial intelligence is driving a deluge of unreliable or useless research.
ChatGPT has entered the classroom: how LLMs could transform education
Researchers, educators and companies are experimenting with ways to turn flawed but famous large language models into trustworthy, accurate ‘thought partners’ for learning.
Garbage in, garbage out: mitigating risks and maximizing benefits of AI in research
Artificial-intelligence tools are transforming data-driven science — better ethical standards and more robust data curation are needed to fuel the boom and prevent a bust.
NEWS FEATURE
How ChatGPT and other AI tools could disrupt scientific publishing
Scientists who regularly use LLMs are still in the minority, but many expect that generative AI tools will become more prevalent. Here's how a world of AI-assisted writing and reviewing might transform the nature of the scientific paper.
AI and science: what 1,600 researchers think
A Nature survey finds that scientists are concerned, as well as excited, by the increasing use of artificial-intelligence tools in research.
How to stop AI deepfakes from sinking society — and science
Deceptive videos and images created using generative AI could sway elections, crash stock markets and ruin reputations. Researchers are developing methods to limit their harm.
Background to the AI revolution
Whereas the 2010s saw the creation of machine-learning algorithms that can help to discern patterns in giant, complex sets of scientific data, the 2020s are bringing in a new age with the widespread adoption of generative AI tools. These algorithms are based on neural networks and produce convincing synthetic outputs, sampling from the statistical distribution of the data they have been trained on.
The sheer pace of innovation is breathtaking and, for many, bewildering — requiring a level-headed assessment of what the tools have already achieved, and of what they can reasonably be expected to do in the future.
Scientific discovery in the age of artificial intelligence
Breakthroughs over the past decade in self-supervised learning, geometric deep learning and generative AI methods can help scientists throughout the scientific process — but also require a deeper understanding across scientific disciplines of the techniques’ pitfalls and limitations.
What ChatGPT and generative AI mean for science
The advent of generative AI based on large language models (LLMs) that can generate realistic synthetic outputs from vast corpuses of text and code is accelerating discovery and providing fresh directions for scientific exploration. That’s a reason for excitement, but also apprehension.
ChatGPT broke the Turing test — the race is on for new ways to assess AI
Large language models mimic human chatter, but scientists disagree on their ability to reason. Finding out where their limitations lie, and how their intelligence differs from that of humans, is crucial to assessing how best to use them.
In AI, is bigger always better?
Recent advances in the capabilities of AI seem to be based on ever-larger models fed with increasing amounts of data. That suggests many tasks could be conquered by AIs simply by continuing those trends — but some experts beg to differ.
AI in scientific life
From designing proteins and formulating mathematical theories, to enabling quick literature syntheses or helping to write research papers, AI tools are revolutionizing how scientists conduct their research and what they are able to achieve.
But these developments are playing out differently across the scientific enterprise. Diving into the trends in different disciplines provides a guide to the potential of AI-fuelled research and its possible pitfalls.
CAREER COLUMN
What’s the best chatbot for me? Researchers put LLMs through their paces
Large language models are becoming indispensable aids for coding, writing, teaching and more. But different research tasks call for different chatbots — here’s how to find the most appropriate match.
AI can help to speed up drug discovery — but only if we give it the right data
Drug development is labour-intensive and time-consuming. Used in the right way, AI tools that enable companies to share data about drug candidates while protecting sensitive information could help to short-circuit the process for the common good.
TECHNOLOGY FEATURE
Artificial-intelligence search engines wrangle academic literature
A new generation of search engines, powered by machine learning and large language models, is moving beyond keyword searches to pull connections from the tangled web of scientific literature. But can the results be trusted?
- How will AI change mathematics? Rise of chatbots highlights discussion
- For chemists, the AI revolution has yet to happen
- Is the world ready for ChatGPT therapists?
Challenges of AI – and how to deal with them
Although there is little doubt about the potential of AI to supercharge certain aspects of scientific discovery, there is also widespread disquiet. Many of the concerns surrounding the use of AI tools in science mirror those in wider society — transparency, accountability, reproducibility, and the reliability and biases of the data used to train them.
Living guidelines for generative AI — why scientists must oversee its use
Establish an independent scientific body to test and certify generative artificial intelligence, before the technology damages science and public trust.
NATURE PODCAST
This isn’t the Nature Podcast — how deepfakes are distorting reality
It has long been possible to create deceptive images, videos and audio to entertain or mislead audiences. Now, with the rise of AI technologies, such manipulations have become easier than ever.
AI tools as science policy advisers? The potential and the pitfalls
Synthesizing scientific evidence for policymakers is a data-intensive task often undertaken under significant time pressure. Large language models and other AI systems could excel at it — but only with appropriate safeguards and humans in the loop.
Rules to keep AI in check: nations carve different paths for tech regulation
The clamour for legal guardrails surrounding the use of AI is growing — but in practice, people still dispute precisely what needs reining in, how risky AI is and what actually needs to be restricted. China, the European Union and the United States are each approaching the issues in different ways.
ChatGPT: five priorities for research
Regardless of wider regulatory issues, the rise of conversational AI requires researchers to develop sensible guidelines for its use in science. What such guidance might look like is still up for debate, but it is clear where the focus for further research should lie.
CAREER FEATURE
Why AI’s diversity crisis matters, and how to tackle it
The real-world performance of AIs relies on how they are trained and which data are used. The field desperately needs more people from under-represented groups to ensure that the technologies deliver for all.
- Scientific sleuths spot dishonest ChatGPT use in papers
- Six tips for better coding with ChatGPT
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What’s next for AI
Get a head start with our four big bets for 2023.
- Melissa Heikkilä archive page
- Will Douglas Heaven archive page
In 2022, AI got creative . AI models can now produce remarkably convincing pieces of text, pictures, and even videos, with just a little prompting.
It’s only been nine months since OpenAI set off the generative AI explosion with the launch of DALL-E 2, a deep-learning model that can produce images from text instructions. That was followed by a breakthrough from Google and Meta: AIs that can produce videos from text. And it’s only been a few weeks since OpenAI released ChatGPT, the latest large language model to set the internet ablaze with its surprising eloquence and coherence.
The pace of innovation this year has been remarkable—and at times overwhelming. Who could have seen it coming? And how can we predict what’s next?
Luckily, here at MIT Technology Review we’re blessed with not just one but two journalists who spend all day, every day obsessively following all the latest developments in AI, so we’re going to give it a go.
Here, Will Douglas Heaven and Melissa Heikkilä tell us the four biggest trends they expect to shape the AI landscape in 2023.
Over to you, Will and Melissa.
Get ready for multipurpose chatbots
GPT-4 may be able to handle more than just language
The last several years have seen a steady drip of bigger and better language models. The current high-water mark is ChatGPT , released by OpenAI at the start of December. This chatbot is a slicker, tuned-up version of the company’s GPT-3 , the AI that started this wave of uncanny language mimics back in 2020.
But three years is a long time in AI, and though ChatGPT took the world by storm—and inspired breathless social media posts and newspaper headlines thanks to its fluid, if mindless , conversational skills—all eyes now are on the next big thing: GPT-4. Smart money says that 2023 will be the year the next generation of large language models kicks off.
What should we expect? For a start, future language models may be more than just language models. OpenAI is interested in combining different modalities—such as image or video recognition—with text. We’ve seen this with DALL-E . But take the conversational skills of ChatGPT and mix them up with image manipulation in a single model and you’d get something a lot more general-purpose and powerful. Imagine being able to ask a chatbot what’s in an image, or asking it to generate an image, and have these interactions be part of a conversation so that you can refine the results more naturally than is possible with DALL-E.
We saw a glimpse of this with DeepMind’s Flamingo, a “visual language model” revealed in April, which can answer queries about images using natural language. And then, in May, DeepMind announced Gato , a “generalist” model that was trained using the same techniques behind large language models to perform different types of tasks, from describing images to playing video games to controlling a robot arm.
If GPT-4 builds on such tech, expect the power of the best language and image-making AI (and more) in one package. Combining skills in language and images could in theory make next-gen AI better at understanding both. And it won’t just be OpenAI. Expect other big labs, especially DeepMind, to push ahead with multimodal models next year.
But of course, there’s a downside. Next-generation language models will inherit most of this generation’s problems, such as an inability to tell fact from fiction, and a penchant for prejudice. Better language models will make it harder than ever to trust different types of media. And because nobody has fully figured out how to train models on data scraped from the internet without absorbing the worst of what the internet contains, they will still be filled with filth .
—Will Douglas Heaven
AI’s first red lines
New laws and hawkish regulators around the world want to put companies on the hook
Until now, the AI industry has been a Wild West, with few rules governing the use and development of the technology. In 2023 that is going to change. Regulators and lawmakers spent 2022 sharpening their claws. Next year, they are going to pounce.
We are going to see what the final version of the EU’s sweeping AI law, the AI Act , will look like as lawmakers finish amending the bill, potentially by the summer. It will almost certainly include bans on AI practices deemed detrimental to human rights, such as systems that score and rank people for trustworthiness.
The use of facial recognition in public places will also be restricted for law enforcement in Europe, and there’s even momentum to forbid that altogether for both law enforcement and private companies, although a total ban will face stiff resistance from countries that want to use these technologies to fight crime. The EU is also working on a new law to hold AI companies accountable when their products cause harm, such as privacy infringements or unfair decisions made by algorithms.
In the US, the Federal Trade Commission is also closely watching how companies collect data and use AI algorithms. Earlier this year, the FTC forced weight loss company Weight Watchers to destroy data and algorithms because it had collected data on children illegally. In late December, Epic, which makes games like Fortnite, dodged the same fate by agreeing to a $520 million settlement. The regulator has spent this year gathering feedback on potential rules around how companies handle data and build algorithms, and chair Lina Khan has said the agency intends to protect Americans from unlawful commercial surveillance and data security practices with “urgency and rigor.”
In China, authorities have recently banned creating deepfakes without the consent of the subject. Through the AI Act, the Europeans want to add warning signs to indicate that people are interacting with deepfakes or AI-generated images, audio, or video.
All these regulations could shape how technology companies build, use and sell AI technologies. However, regulators have to strike a tricky balance between protecting consumers and not hindering innovation — something tech lobbyists are not afraid of reminding them of.
AI is a field that is developing lightning fast, and the challenge will be to keep the rules precise enough to be effective, but not so specific that they become quickly outdated. As with EU efforts to regulate data protection, if new laws are implemented correctly, the next year could usher in a long-overdue era of AI development with more respect for privacy and fairness.
—Melissa Heikkilä
Big tech could lose its grip on fundamental AI research
AI startups flex their muscles
Big Tech companies are not the only players at the cutting edge of AI; an open-source revolution has begun to match, and sometimes surpass, what the richest labs are doing.
In 2022 we saw the first community-built, multilingual large language model, BLOOM , released by Hugging Face. We also saw an explosion of innovation around the open-source text-to-image AI model Stable Diffusion, which rivaled OpenAI's DALL-E 2 .
The big companies that have historically dominated AI research are implementing massive layoffs and hiring freezes as the global economic outlook darkens. AI research is expensive, and as purse strings are tightened, companies will have to be very careful about picking which projects they invest in—and are likely to choose whichever have the potential to make them the most money, rather than the most innovative, interesting, or experimental ones, says Oren Etzioni, the CEO of the Allen Institute for AI, a research organization.
That bottom-line focus is already taking effect at Meta, which has reorganized its AI research teams and moved many of them to work within teams that build products .
But while Big Tech is tightening its belt, flashy new upstarts working on generative AI are seeing a surge in interest from venture capital funds .
Next year could be a boon for AI startups, Etzioni says. There is a lot of talent floating around, and often in recessions people tend to rethink their lives—going back into academia or leaving a big corporation for a startup, for example.
Startups and academia could become the centers of gravity for fundamental research, says Mark Surman, the executive director of the Mozilla Foundation.
“We’re entering an era where [the AI research agenda] will be less defined by big companies,” he says. “That’s an opportunity.”
Big Pharma is never going to be the same again
From AI-produced protein banks to AI-designed drugs, biotech enters a new era
In the last few years, the potential for AI to shake up the pharmaceutical industry has become clear. DeepMind's AlphaFold , an AI that can predict the structures of proteins (the key to their functions), has cleared a path for new kinds of research in molecular biology , helping researchers understand how diseases work and how to create new drugs to treat them. In November, Meta revealed ESMFold , a much faster model for predicting protein structure—a kind of autocomplete for proteins, which uses a technique based on large language models.
Between them, DeepMind and Meta have produced structures for hundreds of millions of proteins , including all that are known to science, and shared them in vast public databases. Biologists and drug makers are already benefiting from these resources , which make looking up new protein structures almost as easy as searching the web. But 2023 could be the year that this groundwork really bears fruit. DeepMind has spun off its biotech work into a separate company, Isomorphic Labs, which has been tight-lipped for more than a year now. There’s a good chance it will come out with something big this year.
Further along the drug development pipeline, there are now hundreds of startups exploring ways to use AI to speed up drug discovery and even design previously unknown kinds of drugs. There are currently 19 drugs developed by AI drug companies in clinical trials (up from zero in 2020), with more to be submitted in the coming months. It’s possible that initial results from some of these may come out next year, allowing the first drug developed with the help of AI to hit the market.
But clinical trials can take years, so don’t hold your breath. Even so, the age of pharmatech is here and there’s no going back. “If done right, I think that we will see some unbelievable and quite amazing things happening in this space,” says Lovisa Afzelius at Flagship Pioneering, a venture capital firm that invests in biotech.
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