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Research analysis - latest articles

Skin in the game — locally made antibodies fight resident bacteria

How immune responses arise against microorganisms living on the skin is not fully understood. The discovery that skin maintains its own system to form antibody responses reveals an unexpected vaccination route.

Sea ice is shrinking during Antarctic winter: here’s what it means for Earth’s oceans and atmosphere

The extent of Antarctic sea ice dropped precipitously in 2023. Analysis shows that this decline has increased the transfer of ocean heat to the atmosphere — in turn affecting ocean circulation and the frequency of storms.

A bile acid could explain how calorie restriction slows ageing

Could lithocholic acid, a compound produced when gut bacteria process bile, be the missing link between a low-calorie diet and its age-defying effects? Experiments in mice, flies and nematode worms provide clues.

Algae use the underwater light spectrum to sense depth

Aquatic algae called diatoms have been found to use light-sensing proteins as depth indicators. Detecting depth-related changes in the intensity and spectrum of light enables algae to modulate their physiology accordingly.

Animals in sticky situations

Descriptions of different creatures’ attachment mechanisms, and muddy banks of calm in the Indian Ocean, in our weekly dip into Nature’s archive.

Green steel, thunderstorms and next-generation gene editing: a year of remarkable science

A lipid made by tumour cells reprograms immune cells

Lipid-delivery system could treat life-threatening pregnancy complication

Distant sparkles hint at how the Milky Way formed

A gut fungus protects mice against parasitic worms but increases allergies

Personalized ranges for blood-test results enable precision diagnostics

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Liquid metal pumps itself out of gels to make artificial vasculature

Is the telescope pointing the right way?

Pregnancy restructures the brain to prepare for childbirth and parenthood

Fragments of the brain’s myelin proteins train T cells to ward off autoimmune attacks

The liver converts fructose into lipids to fuel tumours

Ageing of stem cells reduces their capacity to form tumours

Abundant plant gas forms aerosol particles at high altitude — and could affect the climate

How to ensure only dying cells rupture

What is the best time of the month to treat breast cancer?

Bird-inspired leg enables robots to jump into flight

Where does poetry come from?

Design tips for reproducible studies linking the brain to behaviour

Quantum scars make their mark in graphene

Wastes of time — faeces and vomit track how dinosaurs rose to prominence

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Scientists around the world report millions of new discoveries every year − but this explosive research growth wasn’t what experts predicted

David P. Baker , Penn State and Justin J.W. Powell , University of Luxembourg

China-Africa relations: new priorities have driven major shifts over the last 24 years - 5 essential reads

Moina Spooner , The Conversation

Aristotle, Aelian and the giant octopus: the earliest ‘citizen science’ goes back more than 2,000 years

Konstantine Panegyres , The University of Melbourne

Multiple goals, multiple solutions, plenty of second-guessing and revising − here’s how science really works

Soazig Le Bihan , University of Montana

Giant waves, monster winds and Earth’s strongest current: here’s why the Southern Ocean is a global engine room

Luke Bennetts , University of Adelaide ; Callum Shakespeare , Australian National University , and Catherine Vreugdenhil , The University of Melbourne

Federal funding for major science agencies is at a 25-year  low

Chris Impey , University of Arizona

Is scientific discovery driven by great individuals or by great teams?

Denisa Mindruta , HEC Paris Business School ; Janet Bercovitz , University of Colorado Boulder ; Maryann Feldman , Arizona State University , and Vlad Mares , INSEAD

Early COVID-19 research is riddled with poor methods and low-quality results − a problem for science the pandemic worsened but didn’t create

Dennis M. Gorman , Texas A&M University

Netflix’s You Are What You Eat uses a twin study. Here’s why studying twins is so important for science

Nathan Kettlewell , University of Technology Sydney

Fact-bombing by experts doesn’t change hearts and minds. But good science communication can

Tom Carruthers , The University of Western Australia ; Heather Bray , The University of Western Australia , and Matthew Nurse , Australian National University

Talking about science and technology has positive impacts on research and society

Ashley Rose Mehlenbacher , University of Waterloo ; Donna Strickland , University of Waterloo , and Mary Wells , University of Waterloo

Tenacious curiosity in the lab can lead to a Nobel Prize – mRNA research exemplifies the unpredictable value of basic scientific research

André O. Hudson , Rochester Institute of Technology

Pigs with human brain cells and biological chips: how lab-grown hybrid lifeforms bamboozle scientific ethics

Julian Koplin , Monash University

When Greenland was green: Ancient soil from beneath a mile of ice offers warnings for the future

Paul Bierman , University of Vermont and Tammy Rittenour , Utah State University

10 reasons humans kill animals – and why we can’t avoid it

Benjamin Allen , University of Southern Queensland

Hurricanes push heat deeper into the ocean than scientists realized, boosting long-term ocean warming, new research shows

Noel Gutiérrez Brizuela , University of California, San Diego and Sally Warner , Brandeis University

Colonialism has shaped scientific plant collections around the world – here’s why that matters

Daniel Park , Purdue University

You shed DNA everywhere you go – trace samples in the water, sand and air are enough to identify who you are, raising ethical questions about privacy

Jenny Whilde , University of Florida and Jessica Alice Farrell , University of Florida

Nigeria needs to take science more seriously - an agenda for the new president

Oyewale Tomori , Nigerian Academy of Science

Two decades of stagnant funding have rendered Canada uncompetitive in biomedical research. Here’s why it matters, and how to fix it.

Stephen L Archer , Queen's University, Ontario

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An audio version of “ Winning the Peace Means Winning the Information War ,” a new commentary by CSIS’s Emily Harding and Julia Dickson. This audio was generated with text-to-speech by Eleven Labs. 

In this episode, hosts James Lewis and Chris Painter are joined by Secretary General of the ITU Doreen Bogdan Martin. They discuss the breadth of issues the ITU tackles, diplomatic challenges and how to co-ordinate digital governance.

A short, spoken-word summary from CSIS’s Julia Dickson and Lauren Burke Preputnik on their recent commentaries, “Cyber Scamming Goes Global: Unveiling Southeast Asia’s High-Tech Fraud Factories,” and “Cyber Scamming Goes Global: Sourcing Forced Labor for Fraud Factories.”

Hosts James Lewis and Chris Painter discuss the year that was (so far) in cyber diplomacy.

Negotiations for peace in Ukraine will face a storm of zeros and ones. Critics and bots will stir anger on social media, amplifying extremes of the debate. Zelenskyy should see this as a distraction and remain focused.

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Analysis of approximately 75 million publications finds those employing AI are more likely to be a 'hit paper'

by Northwestern University

From designing new drug candidates in medicine to drafting new taxation policies in social sciences, the benefits of artificial intelligence (AI) in scientific research are all around.

Just this week, two scientists known for their pioneering AI research earned the Nobel Prize in Physics, and a trio of scientists earned the Nobel Prize in Chemistry, which recognized the use of advanced technology, including AI, to predict the shape of proteins. Despite its rapid progress and broad applications, however, many researchers lack a systematic understanding of how AI may benefit their research, and skepticism remains about whether AI is capable of advancing science in every field.

A new Northwestern University study analyzing 74.6 million publications, 7.1 million patents and 4.2 million university course syllabi finds papers that employ AI exhibit a "citation impact premium." However, the benefits of AI do not extend equitably to women and minority researchers, and, as AI plays more important roles in accelerating science, it may exacerbate existing disparities in science, with implications for building a diverse, equitable and inclusive research workforce.

The research team, led by the Kellogg School of Management's Dashun Wang and Jian Gao, developed a measurement framework to estimate the direct use and potential benefits of AI in scientific research by applying natural language processing (NLP) techniques to these vast datasets.

Wang is a professor of management and organizations at Kellogg and of industrial engineering and management sciences at McCormick, director of Kellogg's Center for Science of Science and Innovation (CSSI) and co-director of Kellogg's Ryan Institute on Complexity. Gao is a research assistant professor at Kellogg CSSI.

The study, "Quantifying the Use and Potential Benefits of Artificial Intelligence in Scientific Research," was published October 11 in the journal Nature Human Behaviour .

"These advances raise the possibility that, as AI continues to improve in accuracy, robustness and reach, it may bring even more meaningful benefits to science, propelling scientific progress across a wide range of research areas while significantly augmenting researchers' innovation capabilities," Gao said.

Most impactful research

The study found that the recent successes of AI, across fields, has been remarkable for research. There has been a growing use of AI in disciplinary research since 2015, proxied by the mention of AI-related terms (such as "artificial intelligence," "deep learning" and "convolutional neural network") in the title or abstract of publications.

From 2015 to 2019, disciplines including computer science (37%), engineering (24%), physics (24%), biology (22%), psychology (24%), economics (14%), sociology (30%) and political science (27%) have all shown notably sharp increases in direct AI use scores due to the development of new AI capabilities.

Researchers examine the number of times a paper is cited, and they define a "hit paper" as being in the top 5% by citations for papers published in the same field and year. Regardless of discipline, disciplinary papers that mention AI-related terms in their title or abstract receive more citations, being more likely to be a hit, and receive a higher fraction of citations from other disciplines.

"In addition to its expansion, the use and benefits of AI in research is pervasive across disciplines, but we found a systemic misalignment in AI education," Gao said. "The investment in AI in higher education is not at the same pace of the AI benefit in science."

These results suggest that the supply of AI talent and knowledge in most disciplines appears inadequate with the benefits these disciplines may extract from AI capabilities, highlighting a substantial AI use–AI training gap.

"The use of AI in scientific disciplines has raced ahead across science, while the educational focus on AI to upskill future scientists within each discipline has lagged," Gao said.

Discover the latest in science, tech, and space with over 100,000 subscribers who rely on Phys.org for daily insights. Sign up for our free newsletter and get updates on breakthroughs, innovations, and research that matter— daily or weekly .

Underrepresented groups in STEM

The study also highlights the unequal effects on women and minority researchers that the steadfast rise of AI use in scientific research may bring.

"Historically, we know that women and minorities are less represented in some fields, especially in STEM," Gao said. "We found that as the AI use in science continues to grow, those same groups are less likely to benefit from the new technologies."

Researchers suggest that an investment in making sure the training behind AI is equitable may have a positive impact on closing the demographic gap.

What's next?

As AI rapidly evolves, the researchers said we need to continuously monitor and update its benefit to science.

"Women and minorities are benefiting the least, so how do we mitigate these disparities along demographic lines?" Gao said.

The research team's analysis supports the hypothesis that collaboration between domain experts and AI researchers may represent a meaningful way to facilitate AI use across science and fill the AI use–AI training gap.

"There's a benefit to increasing AI training across disciplines, which would likely help the disciplines to develop domain-specific AI expertise, allowing them to enjoy greater and timelier benefits from AI advances," Gao said.

Journal information: Nature Human Behaviour

Provided by Northwestern University

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Is the Tech Industry Already on the Cusp of an A.I. Slowdown?

Companies like OpenAI and Google are running out of the data used to train artificial intelligence systems. Can new methods continue years of rapid progress?

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By Cade Metz and Tripp Mickle

Reporting from San Francisco

Demis Hassabis, one of the most influential artificial intelligence experts in the world, has a warning for the rest of the tech industry: Don’t expect chatbots to continue to improve as quickly as they have over the last few years.

A.I. researchers have for some time been relying on a fairly simple concept to improve their systems: the more data culled from the internet that they pumped into large language models — the technology behind chatbots — the better those systems performed.

But Dr. Hassabis, who oversees Google DeepMind, the company’s primary A.I. lab, now says that method is running out of steam simply because tech companies are running out of data.

“Everyone in the industry is seeing diminishing returns,” Dr. Hassabis said this month in an interview with The New York Times as he prepared to accept a Nobel Prize for his work on artificial intelligence .

Dr. Hassabis is not the only A.I. expert warning of a slowdown. Interviews with 20 executives and researchers showed a widespread belief that the tech industry is running into a problem many would have thought was unthinkable just a few years ago: They have used up most of the digital text available on the internet.

That problem is starting to surface even as billions of dollars continue to be poured into A.I. development. On Tuesday, Databricks, an A.I. data company, said it was closing in on $10 billion in funding — the largest-ever private funding round for a start-up. And the biggest companies in tech are signaling that they have no plans to slow down their spending on the giant data centers that run A.I. systems.

Not everyone in the A.I. world is concerned. Some, like OpenAI’s chief executive, Sam Altman, say that progress will continue at the same pace, albeit with some twists on old techniques. Dario Amodei, the chief executive of the A.I. start-up Anthropic, and Jensen Huang, Nvidia’s chief executive, are also bullish.

(The New York Times has sued OpenAI, claiming copyright infringement of news content related to A.I. systems. OpenAI has denied the claims.)

The roots of the debate trace back to 2020 when Jared Kaplan, a theoretical physicist at Johns Hopkins University, published a research paper showing that large language models steadily grew more powerful and lifelike as they analyzed more data.

Researchers called Dr. Kaplan’s findings “the Scaling Laws.” Just as students learn more by reading more books, A.I. systems improved as they ingested increasingly large amounts of digital text culled from the internet, including news articles, chat logs and computer programs. Seeing the raw power of this phenomenon, companies like OpenAI, Google and Meta raced to get their hands on as much internet data as possible, cutting corners, ignoring corporate policies and even debating whether they should skirt the law, according to an examination this year by The New York Times .

It was the modern equivalent of Moore’s Law, the oft-quoted maxim coined in the 1960s by the Intel co-founder Gordon Moore. He showed that the number of transistors on a silicon chip doubled every two years or so, steadily increasing the power of the world’s computers. Moore’s Law held up for 40 years. But eventually, it started to slow.

The problem is, neither the Scaling Laws nor Moore’s Law are immutable laws of nature. They’re simply smart observations. One held up for decades. The others may have a much shorter shelf life. Google and Dr. Kaplan’s new employer, Anthropic , cannot just throw more text at their A.I. systems because there is little text left to throw.

“There were extraordinary returns over the last three or four years as the Scaling Laws were getting going,” Dr. Hassabis said. “But we are no longer getting the same progress.”

Dr. Hassabis said that existing techniques would continue to improve A.I. in some ways. But he said he believed that entirely new ideas were needed to reach the goal that Google and many others were chasing: a machine that could match the power of the human brain.

Ilya Sutskever, who was instrumental in pushing the industry to think big as a researcher at both Google and OpenAI before leaving OpenAI to create a new start-up this spring , made the same point during a speech last week . “We’ve achieved peak data, and there’ll be no more,” he said. “We have to deal with the data that we have. There’s only one internet.”

Dr. Hassabis and others are exploring a different approach. They are developing ways for large language models to learn from their own trial and error. By working through various math problems, for instance, language models can learn which methods lead to the right answer and which do not. In essence, the models train on data that they themselves generate. Researchers call this “ synthetic data .”

OpenAI recently released a new system called OpenAI o1 that was built this way . But the method only works in areas like math and computing programming, where there is a firm distinction between right and wrong .

Even in these areas, A.I. systems have a way of making mistakes and making things up. That can hamper efforts to build A.I. “agents” that can write their own computer programs and take actions on behalf of internet users , which experts see as one of A.I.’s most important skills.

Sorting through the wider expanses of human knowledge is even more difficult.

“These methods only work in areas where things are empirically true, like math and science,” said Dylan Patel, chief analyst for the research firm SemiAnalysis, who closely follows the rise of A.I. technologies. “The humanities and the arts, moral and philosophical problems are much more difficult.”

People like Mr. Altman of OpenAI say that these new techniques will continue to push the technology ahead. But if progress reaches a plateau, the implications could be far-reaching, even for Nvidia, which has become one of the most valuable companies in the world thanks to the A.I. boom.

During a call with analysts last month, Mr. Huang, Nvidia’s chief executive, was asked how the company was helping customers work through a potential slowdown and what the repercussions might be for its business. He said that evidence showed there were still gains being made, but that businesses were also testing new processes and techniques on A.I. chips.

“As a result of that, the demand for our infrastructure is really great,” Mr. Huang said.

Though he is confident about Nvidia’s prospects, some of the company’s biggest customers acknowledge that they must prepare for the possibility that A.I. will not advance as quickly as expected.

“We have had to grapple with this. Is this thing real or not?” said Rachel Peterson, vice president of data centers at Meta. “It is a great question because of all the dollars that are being thrown into this across the board.”

Cade Metz writes about artificial intelligence, driverless cars, robotics, virtual reality and other emerging areas of technology. More about Cade Metz

Tripp Mickle reports on Apple and Silicon Valley for The Times and is based in San Francisco. His focus on Apple includes product launches, manufacturing issues and political challenges. He also writes about trends across the tech industry, including layoffs, generative A.I. and robot taxis. More about Tripp Mickle

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