Transistors, the building blocks of modern electronics, are typically made of silicon. Because it’s a semiconductor, this material can control the flow of electricity in a circuit. But silicon has fundamental physical limits that restrict how compact and energy-efficient a transistor can be.

MIT researchers have now replaced silicon with a magnetic semiconductor, creating a magnetic transistor that could enable smaller, faster, and more energy-efficient circuits. The material’s magnetism strongly influences its electronic behavior, leading to more efficient control of the flow of electricity. 

The team used a novel magnetic material and an optimization process that reduces the material’s defects, which boosts the transistor’s performance.

The material’s unique magnetic properties also allow for transistors with built-in memory, which would simplify circuit design and unlock new applications for high-performance electronics.

“People have known about magnets for thousands of years, but there are very limited ways to incorporate magnetism into electronics. We have shown a new way to efficiently utilize magnetism that opens up a lot of possibilities for future applications and research,” says Chung-Tao Chou, an MIT graduate student in the departments of Electrical Engineering and Computer Science (EECS) and Physics, and co-lead author of a paper on this advance.

Chou is joined on the paper by co-lead author Eugene Park, a graduate student in the Department of Materials Science and Engineering (DMSE); Julian Klein, a DMSE research scientist; Josep Ingla-Aynes, a postdoc in the MIT Plasma Science and Fusion Center; Jagadeesh S. Moodera, a senior research scientist in the Department of Physics; and senior authors Frances Ross, TDK Professor in DMSE; and Luqiao Liu, an associate professor in EECS, and a member of the Research Laboratory of Electronics; as well as others at the University of Chemistry and Technology in Prague. The paper appears today in Physical Review Letters.

Overcoming the limits

In an electronic device, silicon semiconductor transistors act like tiny light switches that turn a circuit on and off, or amplify weak signals in a communication system. They do this using a small input voltage.

But a fundamental physical limit of silicon semiconductors prevents a transistor from operating below a certain voltage, which hinders its energy efficiency.

To make more efficient electronics, researchers have spent decades working toward magnetic transistors that utilize electron spin to control the flow of electricity. Electron spin is a fundamental property that enables electrons to behave like tiny magnets.

So far, scientists have mostly been limited to using certain magnetic materials. These lack the favorable electronic properties of semiconductors, constraining device performance.

“In this work, we combine magnetism and semiconductor physics to realize useful spintronic devices,” Liu says.

The researchers replace the silicon in the surface layer of a transistor with chromium sulfur bromide, a two-dimensional material that acts as a magnetic semiconductor.

Due to the material’s structure, researchers can switch between two magnetic states very cleanly. This makes it ideal for use in a transistor that smoothly switches between “on” and “off.”

“One of the biggest challenges we faced was finding the right material. We tried many other materials that didn’t work,” Chou says.

They discovered that changing these magnetic states modifies the material’s electronic properties, enabling low-energy operation. And unlike many other 2D materials, chromium sulfur bromide remains stable in air.

To make a transistor, the researchers pattern electrodes onto a silicon substrate, then carefully align and transfer the 2D material on top. They use tape to pick up a tiny piece of material, only a few tens of nanometers thick, and place it onto the substrate.

“A lot of researchers will use solvents or glue to do the transfer, but transistors require a very clean surface. We eliminate all those risks by simplifying this step,” Chou says.

Leveraging magnetism

This lack of contamination enables their device to outperform existing magnetic transistors. Most others can only create a weak magnetic effect, changing the flow of current by a few percent or less. Their new transistor can switch or amplify the electric current by a factor of 10.

They use an external magnetic field to change the magnetic state of the material, switching the transistor using significantly less energy than would usually be required.

The material also allows them to control the magnetic states with electric current. This is important because engineers cannot apply magnetic fields to individual transistors in an electronic device. They need to control each one electrically.

The material’s magnetic properties could also enable transistors with built-in memory, simplifying the design of logic or memory circuits.

A typical memory device has a magnetic cell to store information and a transistor to read it out. Their method can combine both into one magnetic transistor.

“Now, not only are transistors turning on and off, they are also remembering information. And because we can switch the transistor with greater magnitude, the signal is much stronger so we can read out the information faster, and in a much more reliable way,” Liu says.

Building on this demonstration, the researchers plan to further study the use of electrical current to control the device. They are also working to make their method scalable so they can fabricate arrays of transistors.

This research was supported, in part, by the Semiconductor Research Corporation, the U.S. Defense Advanced Research Projects Agency (DARPA), the U.S. National Science Foundation (NSF), the U.S. Department of Energy, the U.S. Army Research Office, and the Czech Ministry of Education, Youth, and Sports. The work was partially carried out at the MIT.nano facilities.

Nature Climate Change, Published online: 15 October 2026; doi:10.1038/s41558-026-02757-z

A distributional analysis of European Union climate policy pathways traces the costs of reducing carbon dioxide emissions to the level of households based on their spending and income patterns. We find that different equity considerations, including income inequality and within-group impact divergence, can shape optimal climate policy design with carbon prices, transfers, standards and subsidies.

Nature Climate Change, Published online: 15 October 2026; doi:10.1038/s41558-026-02782-y

Marine radar signal propagation is highly sensitive to fine-scale vertical changes in water vapour. Now research shows that long-term increases in sea surface temperatures are strengthening the frequency and intensity of the features that allow radar signals to travel further than expected.

Nature Climate Change, Published online: 15 October 2026; doi:10.1038/s41558-026-02774-y

Atmospheric ducts are waveguides that affect electromagnetic propagation in the atmosphere, affecting radar and other technologies. Here the authors show that warming intensifies such ducting over mid- to low-latitude oceans.

Nature Climate Change, Published online: 15 October 2026; doi:10.1038/s41558-026-02781-z

The role of wind in ecosystem water use efficiency remains unclear. The authors show that water use efficiency declines with increasing wind speed, driven mainly by increases in evapotranspiration and secondarily by reductions in gross primary productivity.

Nature Climate Change, Published online: 15 October 2026; doi:10.1038/s41558-026-02763-1

Clean hydrogen is widely proposed for hard-to-abate sector decarbonization, but its limited supply requires prioritizing sectors with the highest value. This study evaluates ten sectors and identifies key no-regret uses, while highlighting the need for specific demand-side policies.

Today, EFF joined the ACLU of Wisconsin in filing a petition to the Wisconsin Supreme Court, asking it to rule that the state’s constitution requires a warrant for retrospective searches of ALPR databases used by law enforcement in four cities: Milwaukee, Racine, Kenosha, and Green Bay. We represent a religious organization, the Wisconsin Council of Churches, as well as an individual pastor in Green Bay.

As the petition explains:

Right now, Automated License Plate Readers (“ALPRs”) are recording the movements of vast numbers of Wisconsin drivers as they seek medical care, attend 12-step recovery meetings, attend religious services, participate in protests, attend political meetings, and engage in many other private, sensitive activities. Law enforcement entities throughout Wisconsin collect, store, share, and aggregate this ALPR data. Then they search it, retrospectively tracking the movements of individuals and entire communities in ways that would have been prohibitively costly, if not outright impossible, before the advent of ALPR technology.

We all deserve to be free of warrantless mass surveillance. We all deserve to live in a country where surveillance vendors cannot help police upend our system of individualized suspicion. Religious organizations and officials like our clients have a heightened interest in protecting their location privacy because of the range of expressive associations their work entails, including facilitating gatherings for religious worship, as well as offering pastoral care, confidential counseling, and community support. Likewise, the people who visit houses of worship have a strong right to privacy against police monitoring of when they pray, seek addiction counseling, or attend a domestic violence survivor meeting.

This is our second lawsuit challenging warrantless ALPR searches, following SIREN v. San Jose. Earlier this month, a federal court in Oklahoma became the first to rule that ALPRs are a “type of indiscriminate mass surveillance” and suppress evidence from a warrantless ALPR search. We hope the Wisconsin Supreme Court will agree.

Read the Wisconsin Supreme Court petition

Read the press release

Related Cases: Wisconsin Council of Churches v. Milwaukee, Racine, Kenosha & Green Bay

EFF legal intern Gabrielle Ladun was the principal author of this post. 

The Department of Homeland Security Office of Inspector General (DHS OIG) has published a summary of findings from its audit of the Transportation Security Administration’s (TSA) facial recognition technology. While the detailed findings are classified, the summary states that the OIG’s audit uncovered a “significant weakness” in the technology’s ability to protect the privacy of passenger data. This has massive privacy implications for the millions of passengers who travel and don’t opt out of the TSA’s biometric surveillance technology verification process. 

TSA uses facial comparison technology for identity verification of airline passengers. A camera takes a photo of passengers to verify that their provided identification matches their live photo. Although TSA states that it is “committed to protecting traveler privacy,” the audit highlights that this has not been the case.  

The OIG found that TSA’s Credential Authentication Technology 2’s (CAT-2) vendor “was able to access and extract sensitive passenger information, including driver’s license and passport images, during system troubleshooting and enhancements, without TSA’s knowledge.”  

The OIG further admonished the agency:  

TSA lacked clear policies, did not track when this data was extracted, could not verify that extracted information was properly deleted, and did not notify passengers when their biometric and biographic information may have been stored. As a result, although passenger information was generally protected during routine operations, weaknesses in vendor oversight created a risk that sensitive personal information could be retained or exposed. 

We hope the TSA will fix these issues. The OIG summary states: 

We issued one finding and three recommendations to strengthen controls, improve oversight, and ensure transparency and the proper handling of passenger data. TSA concurred with all three recommendations. Two recommendations are open and resolved, and one remains open and unresolved. 

Why EFF Opposes the Government’s Use of Facial Recognition Technology 

EFF has a long history of opposing governmental use of facial recognition technology. As the OIG’s audit underscores, this technology presents privacy risks associated with unsecured data, unauthorized access, and data breaches. The government’s use of this technology also creates a mass surveillance system and facilitates the creation of warrantless biometric records for unconsenting parties.  

The biometric data captured by this technology is not without errors. There have been multiple instances of law enforcement agencies using facial recognition technology to misidentify and arrest innocent individuals.  

Reminder: Opting Out of TSA Photos Is Possible 

Given that facial recognition technology is prone to mistakes, combined with the OIG’s audit revealing significant privacy weaknesses, it is imperative that travelers remember that opting out is possible. TSA’s own website emphasizes that travelers who wish to decline to have their photo taken for facial comparison can do so without any delay, disruption, or recourse.  

Considering the data protection weaknesses exposed by the OIG’s audit, EFF will continue to oppose this governmental system of mass surveillance and spread awareness to ensure that no one is forced to relinquish their biometric data without consent. 

More than 100 people filled the MIT Chapel on Sept. 24 to pay tribute to Assistant Professor Thomas Rose, the MIT archaeometallurgist who died last month following a bicycle crash in Cambridge, Massachusetts.

Faculty, students, and staff affiliated with the Department of Materials Science and Engineering (DMSE) and the Center for Materials Research in Archaeology and Ethnology (CMRAE), along with family and friends, gathered for a memorial service where colleagues spoke about the 37-year-old, who joined the DMSE faculty in January and had just started building a research program at MIT. They remembered him as a brilliant researcher and generous colleague with an extraordinary breadth of expertise and eclectic passions ranging from rowing to archery. 

A portrait of Rose sat on an easel next to the chapel’s flower-covered altar. A cellist played as guests entered and took their seats.

Chaplain to the Institute Thea Keith-Lucas opened the service by reflecting on the loss felt across MIT. 

“We honor the unique gifts that he brought to this world. We honor the ways that he was growing, and the possibilities that were cut short. We honor the small, unseen moments of courage and connection that create a life,” she said.

Finding a unicorn

To give a sense of what Rose meant to DMSE, department head Polina Anikeeva briefly explained archaeometallurgy — the study of how ancient societies produced and used metals — and its development at MIT, beginning in the 1960s. The field flourished after the establishment of CMRAE in the 1970s, but over the following decades it began to contract in the United States, and MIT had difficulty finding people with an increasingly rare set of skills. 

“We were looking for a unicorn. We were looking for two decades,” Anikeeva said. When Anikeeva and other DMSE faculty reviewed Rose’s resume in 2025, they were impressed by his unique training in geoscience, metallurgy, and archaeology and met him soon after. “We saw a brilliant, creative colleague — unassuming, intellectually fearless, adaptable, resourceful, and eagerly looking into the future.”

Mike Tarkanian, senior lecturer in DMSE and associate director for CMRAE, described the Zoom interview with Rose that kicked off the faculty search. Rose spoke fluently about his work, interests, and research vision — without the slides or notes that candidates typically rely on.

“I hung on every word — but it was so unusual and so risky that I wasn’t sure how it would be received,” Tarkanian said. “As soon as the call ended, my phone rang and it was a colleague who was also on the Zoom and he said, ‘That’s our guy. That has to be our guy.’”

Anikeeva also spoke of the enthusiasm he brought with him every day at MIT, noting his “almost childlike curiosity and his enormous positivity.”

“There were no obstacles, only opportunities and possibilities.”

To honor Rose and carry forward the work he had begun at MIT, Anikeeva said DMSE would launch the Thomas Rose Fellowship for Excellence in Archaeometallurgy, dedicated to supporting the most promising young scholars at the PhD and postdoctoral levels, “so that we can continue this field for the world and for the U.S. and for the memory of Thomas.”

A generous spirit

Speakers also remembered Rose’s kindness, generosity, and empathy toward everyone around him. Tarkanian stressed Rose’s commitment to community and helping others, noting his contributions to research tools used by archaeologists worldwide. He gave an example from Rose’s undergraduate years in his native Germany, when he gave tutorials for first-year archaeological science students who were struggling with chemistry and math. A former student told Tarkanian that an email Rose sent inviting students to the tutorial ended with, “I guess I’ll be more nervous than you folks tomorrow.”

“This was the Thomas we only started to get to know,” Tarkanian said, “someone who offered his expertise to others in need without question, while being thoughtful enough to worry out loud that he might be the nervous one and, by doing so, putting others at ease.”

DMSE Assistant Professor Tania Lopez-Silva, who started at MIT six months before Rose, recalled his patience and understanding. When she arranged a lunch with him, he said he would eat anything but mushrooms and eggplant.

“Somehow I misunderstood him, and I chose the place that probably has the most mushroom and eggplant options possible,” Lopez-Silva said. “He said that he still wanted to try the place, and he would find something to eat. That was Thomas — kind, patient, open-minded, and always willing to make the best of things.”

That quality came through in a story by another DMSE faculty member, Assistant Professor Thomas J. Wallin, who recalled a photo Rose had sent him of the apartment he had found overlooking the Charles River. Wallin was impressed, but Rose said with characteristic directness that it was quite small, but “very long and narrow” — which turned out to be a good thing. 

“He explained that because it was narrow, he could stand at the edge of his balcony and shoot at his archery target through his hallway, past his kitchen, at his door, and it would be the proper distance,” Wallin said, drawing chuckles in the chapel. “I don’t think that’s a request many Boston realtors get, but that was Thomas.”

Rose’s connections also extended beyond DMSE. Katya Tiukhtikova, joined by fellow MIT Archery Club member Kiarra Ning, remembered how Rose treated others. 

“He only focused on the good in people, and gave everyone a chance to show the best parts of themselves,” Tiukhtikova said.

She also recalled the contagious excitement Rose brought to archery practice, saying he “always brightened the days of everyone who was lucky enough to share the archery range with him.”

Tarkanian ended his remarks with a reflection on academic life and the importance of generosity and humility alongside brilliance and achievement.

“If part of Thomas’s legacy here at MIT is that we become a little more generous with our time, a little more humble about our accomplishments, and a little more deliberate about building communities in which other people can thrive and grow, that would be a legacy worthy of him.”

No one should have to think twice before criticizing the government online. But a mass surveillance program known as "Catch and Revoke" was created to make millions do just that. EFF and other defenders of free speech are fighting back. In our latest EFFector newsletter, we're talking about a key court win for our lawsuit challenging this unconstitutional surveillance program.

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For over 35 years, EFFector has been your guide to understanding the intersection of technology, civil liberties, and the law. This issue covers our Opt Out October guides for ditching Big Tech, a win in the fight against anti-VPN laws, and a court ruling allowing the lawsuit against "Catch and Revoke" to move forward.

Prefer to listen in? EFFector is now available on all major podcast platforms. This time we're talking to EFF's Lisa Femia about how the government is using AI to suppress protected speech online—and the important lawsuit she and others are working on to stop it. You can find the episode and subscribe on your podcast platform of choice:

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This is a current list of where and when I am scheduled to speak:

• I’m speaking at ATTENTION: Democracy, Rebuilt in Montreal, Canada. The event runs October 21–23, 2026, and my talk is on Wednesday, October 21.
• I’m speaking at the University of Toronto in Toronto, Ontario, Canada, at 5 PM ET on October 22, 2026.
• I’m speaking at [un]prompted in San Francisco, California. The event runs October 27–29, 2026, and my talk is at 11:35 AM PT on Thursday, October 29.
• I’m speaking at the Catalyst Summit in Brampton, Ontario, Canada, on Wednesday, November 18, 2026...

In 2021, Texas suffered the worst energy infrastructure failure in state history, following three severe winter storms in February. At least 246 people were killed as a result of the disruptions, which led to food, water, heat and electricity shortages.

If energy officials had had more time to prepare for the storms, for example by bringing in more trucks of natural gas to disrupted areas, damage from the event could have been mitigated. Unfortunately, weather forecasts beyond two weeks are notoriously unreliable, making for a tighter window for energy operators, farmers, and emergency response officials around the globe.

A new technique by a team including researchers at MIT significantly improves weather forecasting beyond two weeks. The researchers developed a machine-learning model that learns to correct errors in existing forecasting models to improve their output over the two-to-six-week time frame. When applied to a gold-standard forecasting model, it doubled forecasting accuracy over that time frame, placing first in a prominent international forecasting competition.

In a new paper in Nature, the researchers describe their approach and show how it enables forecasts that for the first time beat climatologic averages between two and six weeks.

“Not only does this do better on time averaged forecasts for this range, which is already very useful for farmers and members of the energy industry trying to match supply with demand, but it also improves predictions of extreme weather events, whether it’s flooding or cold spells or something else,” co-author and MIT Research Scientist Judah Cohen says. “It’s really important to predict both, but extreme events might be the most impactful part of this for society.” 

Joining Cohen on the paper are lead authors Hannah Guan, a Harvard College undergraduate; Soukayna Mouatadid, a University of Toronto postdoc; Genevieve Flaspohler SM ’18, PhD ’22; Paulo Orenstein, an associate professor at Brazil’s National Institute for Pure and Applied Mathematics; Jeremy Berman of the data company DTN; Jakob Schloer and Joshua Talib, researchers with the European Center for Medium-Range Weather Forecasts; and Alex Lu, Haiyu Dong, Jonathan Weyn, Zekun Ni, and Lester Mackey, all researchers at Microsoft.

Longer forecasts

In the 1970s, longtime MIT Professor Edward Lorenz came up with the idea of chaos theory to describe how tiny changes in initial weather conditions lead to completely different outcomes.

“He came up with this theoretical two-week limit where beyond that, we just can’t predict weather because the noise in the system grows so much that it overwhelms any signal you start with,” Cohen says. “That had a strong influence on the field that may have held people back from exploring forecasting beyond the two-week range. I never really believed that completely. In theory the atmosphere can create an infinite number of patterns, but it likes to fall into these favorite patterns over and over again. Once one thing happens, I think you can predict the domino that will fall after that, and the domino that will fall after that.”

Today, the best weather forecasts come from either dynamical models, which numerically simulate how the atmosphere and oceans evolve over time, or artificial intelligence models, which learn to make predictions from historical weather data. AI models have shown improvements in predicting weather at short ranges, but they’re even worse than dynamical models at longer time ranges.

Cohen, who is based in the MIT Department of Civil and Environmental Engineering, focuses on forecasting weather two to six weeks in advance, which is known as subseasonal forecasting. “That range is referred to as the ‘predictability desert,’ because in the short range you can capitalize on the initial conditions in the climate system and the atmosphere, and on the longer term you have these slow, varying boundary forces, like the super El Niño everyone’s focused on this winter,” Cohen says. “The subseasonal range can’t take advantage of either of those. In most cases forecasting in the subseasonal ranges is no different than going to a casino and rolling the dice.”

Cohen credits his co-author Lester Mackey, whom he has been collaborating with for more than a decade, with the initial idea of using machine learning to reduce errors in forecasting models.

“We built this wrapper — Lester calls it postprocessing,” Cohen says. “We know AI models have a lot of errors, so we thought maybe we can quantify those errors and remove them.”

While other forecasting systems do more simplistic error correction, the research team’s postprocessing layer uses AI to learn from a forecasting system’s mistakes based on historic data and shifts its forecasts. In that time range, forecasters use so-called probabilistic forecasts, where the likelihood of a range of possibilities if given.

“As more forecasts and data become available, our model adaptively improves its correction,” Cohen says.

To build the model, the researchers used MIT Engaging, a supercomputing cluster hosted by MIT’s Office of Research Computing and Data, but the resulting model does not require a supercomputer to run.

To put their model to the test, last year the research team submitted three models that included their post-processing layer to the European Center for Medium-Range Weather Forecasts (ECMWF), which is considered the best forecasting center in the world. Each season ECMWF holds forecasting competitions between models.

The submission that won first place combined ECMWF’s forecasting model with another, and then applied the postprocessing correction on top. That approach ended up doubling the original model’s predictive power in the subseasonal range. For the first time, the model offered an improvement over relying on climatologic averages.

“Up until now, no one had been able to show a model predicts weather better than long term averages for these subseasonal forecasts,” Cohen says. “They’ve pretty much all been worse than that — you could literally flip a coin and do better. People have questioned if it’s even worth investing in models at all for this time range. We consistently beat climatology averages with our models.”

The model also showed a statistically significant improvement in the ability to predict extreme weather.

“That’s one of the more important findings, because it’s so difficult to accomplish,” Cohen says. “When the ECMWF developed their own, non-AI, error-correction to their model, it got worse at predicting extreme weather. I think that’s because traditional error, or bias, correction is always trying to get to the mean, whereas extreme weather is on the fringe of probabilities.”

Preparing for disaster

In the Nature paper, the team showed their technique could be used to improve land forecasts in most regions across the globe. They also applied their bias correction technique to other models and showed it improved those as well, putting even lesser models on par with the ECMWF’s forecasts.

“What’s nice about our model is it’s an add-on wrapper,” Cohen explains. “We used it with the ECMWF’s dynamical model because that was considered the best, but it can be used with any model. It even improves models that are already bias corrected using another AI algorithm. So it shows a robust improvement. And it’s open source and low cost.”

Cohen says the meteorological office for the Republic of Kenya has already reached out about implementing the model to improve forecasting of the annual monsoon season to improve agricultural yields this winter. He hopes other groups will also use the model.

“This could help you decide when to fertilize around rain, when there’s a freeze coming to protect crops during winter harvests, or help you avoid droughts,” Cohen says. “It could also be useful for energy and emergency management. I’ve talked to energy officials who say, ‘If I’m told two days before that an ice storm’s coming, I don’t have enough time, but if I had two weeks, that could be enough to make adequate preparations.”

The work was supported, in part, by the U.S. National Science Foundation, the Carlos Chagas Filho Foundation for Research Support, and the European Union.

Matthew Green has been writing a rather pessimistic series of posts about the potential for AI systems to vastly outperform humans in cryptanalysis of both symmetric and public-key ciphers.

I am more optimistic than he is. That is, I have stronger faith in the over-engineering cryptographers tend to do. I am trying to write this up in more detail.

Another IoT cyberattack, this one in France:

Hackers gained control of the software that regulates the temperatures of the refrigeration units, forcing some to warm to 50C (120F) overnight, according to one supermarket chain affected.

[…]

Normally, supermarket freezers go through a short period of defrosting overnight, in which temperatures rise slightly. But the hackers ordered the units to heat up and stay hot, although Danfoss did not confirm any reports about just how warm the fridges and freezers could become in that process.

Danfoss said the affected cold units were older models whose security software could not be updated to ward off a risk they had identified...

Nature Climate Change, Published online: 14 October 2026; doi:10.1038/s41558-026-02764-0

Climate citizens’ assemblies are gaining traction worldwide as a way to involve the public in climate governance. Now a study demonstrates that although these assemblies propose more ambitious recommendations than existing climate policy, these recommendations vary across contexts and have limited impact on policy decisions.

Nature Climate Change, Published online: 14 October 2026; doi:10.1038/s41558-026-02777-9

As the Rio Conventions mature from negotiating commitments towards delivering them, strengthening implementation has become an urgent priority. We argue for greater cross-convention learning within the context of institutional architecture to accelerate progress on climate, biodiversity and land degradation.

Nature Climate Change, Published online: 14 October 2026; doi:10.1038/s41558-026-02780-0

Species hybridization belongs on the climate change agenda

Nature Climate Change, Published online: 14 October 2026; doi:10.1038/s41558-026-02755-1

Deliberative citizen participation, such as climate assemblies, has been promoted in climate governance. This study finds that, although assemblies proposed more ambitious recommendations than existing climate policy, these recommendations rarely translated into actual policy decisions.

Hiroshi Ishii, the Jerome B. Wiesner Professor of Media Arts and Sciences at MIT and associate director of the MIT Media Lab, recently received the 2026 Industrial Designers Society of America Special Achievement Award for “...pioneering work that has greatly influenced human-computer interaction and design.”

For over 30 years, Ishii has explored how the digital and physical worlds can merge more meaningfully. Working with students, MIT colleagues, and international collaborators through his Tangible Media group, Ishii has pursued research marked by intellectual tension and artistic vision. He and his collaborators have produced truly original concepts including Tangible User Interfaces, which are interactive systems where physical objects, rather than screens, mice, or keyboards, are used to control and interact with digital information; and Tangible Bits, introduced at CHI ‘97, which established how people engage with digital information through physical objects. His later concepts, Radical Atoms and TeleAbsence, inspire researchers and designers worldwide.

“Beyond his research, Ishii has made lasting contributions to the global design community through ACM CHI [the Association for Computing Machinery Conference on Human Factors in Computing Systems], TEI [conference on Tangible, Embedded and Embodied Interaction], and UIST [User Interface Software and Technology], and by mentoring generations of designers and researchers,” said Professor Leila Ensaniat in her Industrial Designers Society of America Awards committee chair introduction.

Ishii's approach shows how bringing the arts, science, engineering, and design together creates innovations with real-world applications. His models now influence product development across industries, shaping how millions of people interact with information and each other.

As artificial intelligence becomes more and more powerful, its ability to handle human-like tasks is undeniable. Artificial neural networks can learn to recognize images, use language, or write computer code. Like the brain’s circuits, they reorganize themselves as they learn, adjusting the strengths of their connections to improve their performance.

The question for many neuroscientists is: What can these machine systems teach them about how the brain learns? And what kinds of models are likely to be the most informative?

In a paper published Oct. 9 in the journal PNAS, scientists at MIT’s McGovern Institute for Brain Research and K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center argue that a crucial metric for the biological plausibility of an artificial neural network’s approach to learning is the network’s ability to organize itself, without the guiding hand of a developer. They show that it is possible for an artificial neural network to build its own learning machinery. With some simple instructions about how and when connections should change, a group of randomly connected units can reshape itself into an effective information-processing network, learning from the data it is trained on.

Biological learning

“Current AI technology is working very well, but it doesn’t necessarily use a learning algorithm that is used by the brain,” says Qianli Liao, a K. Lisa Yang ICoN Center Postdoctoral Fellow in the labs of MIT professors and McGovern Institure members Tomaso Poggio and Mark Harnett, and co-first author of the PNAS paper with MIT Research Scientist Liu Ziyin and graduate student Yulu Gan. Liao explains that engineers design the rules about how connections change as an artificial neural network processes training data and receives feedback about its mistakes. 

When it comes to training AI to do its job, the approach is powerful. But Liao and his colleagues caution that networks designed in this way may not offer much insight into biological learning. “The brain doesn't have an external designer or architect,” Liao says. “The brain has to organize itself. And we need to know how simple rules are going to make the brain organize.”

The rules that reshape neural connections as an animal learns may be different in different parts of the brain, or they may change as the brain becomes more mature. Neuroscientists are working to understand how those rules work and how they might be disrupted by aging or disease — a vital step toward developing new therapies and prevention strategies. Liao points to the development of brain-computer interfaces, which aim to restore communication when the brain’s language abilities are lost or let paralyzed patients control neuroprosthetic limbs, as one potential application.

“None of these can work without knowing the rules behind neuron-neuron interactions,” he says. “That’s not achievable by just designing a circuit that works very well.” An artificial neural network’s value to neuroscience should instead be judged by the extent to which it can organize itself — not just its connections, but also the machinery that adjusts those connections.

Self-organized artificial learning

Working with Poggio and Hartnett, Liao, Ziyin, and Gan set out to develop and validate a self-organizing computational network capable of learning. Liao explained that the network’s structure begins with multiple layers of randomly interconnected neuron-like units. Within each layer, every neuron-like component is subject to the same rules about how connections should be adjusted. “They just interact with each other using that rule, and they develop some type of behavior,” he says.

The team’s network implements the kind of simple rules thought to rewire brain circuits as humans and other animals learn from their experiences. In both the brain and the machine network, for example, “neurons that fire together wire together.” In other words, when two connected neurons are activated at the same time, the connection between them strengthens. 

The team showed that a self-organizing network governed by rules like these can learn to recognize patterns and classify images. They explain that in their model, learning is enabled by self-assembling motifs that emerge according to simple rules, then serve as building blocks for functional circuits. As their network learned, these motifs reorganized themselves using an optimization strategy similar to the approach built into most modern machine learning systems, called gradient descent, in which connections are gradually adjusted in response to errors in order to improve performance.

Most modern AI implements gradient descent through some form of the back-propagation algorithm, which neuroscientists say is unlikely to be used by the brain. The team compared its self-organizing network to networks designed to learn in this way and found that it performed at a similar level.

Connecting brains and machines

Poggio says finding a biological parallel to back-propagation could trigger significant progress in both AI and neuroscience. 

“If the synaptic motifs we propose are, in fact, used by the brain to do what backpropagation does for artificial neural networks, then we will have a strong connection at the level of fundamental mechanisms between artificial and natural networks — between machines and brains,” he says.

Liao says a self-organizing network does not necessarily learn the same way the brain does — but it’s a critical starting point for biological plausibility. 

“Self-organization is the first high-level framework,” he says. “Once we are good with self-organization as a measure, then we can talk about next thing.”

While the group’s main objective is better understanding the brain, Liao says that knowledge could ultimately improve machine learning systems, too, enabling engineers to develop networks that perform better or in more human-like ways.