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.

When you picture a worker at a retail store, you probably think of someone at a cash register or helping a customer. But employees also spend a lot of their time combing through stockrooms and shop floors, fulfilling requests or online orders and generally trying to keep track of all their inventory.

Keeping track of inventory takes so much time, in part, because retailers don’t always know where everything is located. That’s why when you ask a store associate to check if they have a shirt in your size, it may take them 20 minutes to get back to you.

Cartesian is helping retailers keep track of inventory with a technology invented at MIT. The system uses wireless signals from radio frequency identification (RFID) tags attached to items to find their precise location in a store, from the stockroom to the shop floor.

Last year, Cartesian did a study with a retailer and found its platform delivered meaningful annual savings at the store level by streamlining inventory tracking, optimizing workflows, and improving customer experiences.

“The big problem we’re solving is that about 50 percent of working hours in retail stores go to managing inventory,” says co-founder Fadel Adib SM ’13, PhD ’17, an associate professor at MIT. “That is roughly a $15 billion problem in the U.S. alone. We use algorithms to decipher indoor locations using wireless signals. The core technology enables a new level of indoor localization.”

Cartesian is already deployed in more than 700 stores across 15 countries and is working with one of the world’s largest fashion groups, Inditex, which is the parent company to brands like ZARA, Pull&Bear, and Oysho.

Beyond retailers and warehouses, Cartesian’s platform could also improve indoor location tracking for manufacturers, logistics operators, and robotics companies.

“The broad vision for what we are doing is spatial AI,” says Adib. “Today, AI does extremely well in the digital world. Now it has to move into the physical world. That means allowing machines to perceive their environment in such a way that they can interact with it. That’s where spatial AI comes in and where Cartesian sits.”

From technology to product

Adib, who holds a joint appointment in MIT’s Media Lab and Department of Electrical Engineering and Computer Science, has been studying wireless signals at the Institute for more than 15 years, dating back to research during his master’s degree.

“My group today researches how to use wireless signals to sense the world in ways that were not possible before,” Adib says. “We develop the fundamental technology and then we build systems around them. Our goal is to see these systems deployed in the real world for impact.”

When Adib joined MIT’s faculty, the first project he worked on was indoor localization using RFID tags. Isaac Perper ’20, MEnG ’21 later joined his lab as a student, and together they developed machine-learning algorithms to process RFID data to translate them into location patterns, with an initial focus on helping robots locate RFIDs indoors.

In 2021, Adib went through the National Science Foundation’s I-Corps program, which challenges researchers to interview potential customers to find the right problems to solve with their technologies. That’s when he realized how big of a problem inventory management is for retailers.

Cartesian was officially founded by Adib and Perper in the beginning of 2023, after they received a small business award from the National Science Foundation. The pair worked with MIT’s Technology Licensing Office to license patents from Adib’s lab. They also received support from MIT’s Venture Mentoring Service.

“Our goal was to reduce the cost of the technology to make it scalable,” Adib recalls. “Isaac focused on simplifying the product, leveraging progress in machine learning, and making it fast. It was a lot of iterating and testing early on.”

Retail workers spend much of their time locating items for a number of reasons. They might get an online order to fulfill, need to restock store shelves, or get a customer inquiry about items in the back.

Stores differ in how they organize their inventory. Most separate items by categories in specific shelves and bins then use barcodes or inventory systems that tend to get outdated fast.

“It’s a big problem for stores because customers may just leave before asking an employee to look for their size, or customers may get frustrated and leave if it takes too long,” Adib says. “The associate also wastes time looking for items they could spend doing higher-value work.”

Cartesian’s platform works with retailers’ existing handheld RFID readers, which store associates already use to manage inventory. Each store installs Cartesian’s software into their existing inventory apps or uses a custom app for employees to access directly.

“The RFID readers are how stores tell what’s in stock and what’s out of stock,” Perper says. “We figured out a way to leverage the same scans they’re already using with the reader, put the data they generate into our machine-learning algorithms, and generate maps of where all the items are.”

Customers can build analytics on top of Cartesian’s technology to keep track of inventory levels, show customers maps of where each item is located, and create other services.

“They use our location intelligence platform and build different products on top,” Adib says. “We can work with any device, any store, any type of RFID. It’s a simple interface. All the sophisticated location algorithms sit in the cloud.”

Beyond retail

Cartesian signed its first big contract in 2025 and soon expanded to several hundred stores. One of Cartesian’s advantages is its ability to quickly scale. Perper says they can add a store in about one minute. Cartesian’s team doesn’t even have to travel to a new store to turn on its system if it’s already working with the company.

“It’s as simple as flipping a switch, preparing the data, and sending it to our customers,” Perper says. “One of our first big bets was, ‘Can we build this entirely on existing hardware?’ That bet is starting to pay off.”

Cartesian’s models can also work with Wi-Fi and Bluetooth signals, which the company plans to use with customers in other verticals.

“Right now, we’re focused on applications in retail, but this technology has a lot of value in manufacturing, warehouses, and other locations,” Adib says.

Cartesian’s team aims to be deployed in tens of thousands of stores over the next year and then begin expanding beyond retail into industries like manufacturing and robotics.

“What’s most exciting about Cartesian to me is we’ve built a lot of the technology foundation, and now that we have the fundamentals in place, we hope to build specific application layers,” Perper says. “Then we can ask customers in different verticals about their problems and apply our technology in different ways to solve it.”

California lawmakers are again considering A.B. 412, a bill that would require AI developers to identify and disclose copyrighted works used to train generative AI systems.

The problem this year is the same as last year: it’s practically impossible to comply with this law. The bill demands information that often does not exist, and cannot realistically be obtained. 

EFF submitted an opposition letter to the California Senate Privacy Committee explaining why we continue to believe A.B. 412 is simply unworkable. To the extent developers do follow this law, it will have the effect of locking in the power of the largest companies in AI. 

A Burden That Can’t Be Met

A.B. 412 sounds simple: just have AI developers create and keep a list of all the registered copyrighted works they use in AI training. 

That may seem straightforward. In practice, it’s anything but. 

There is no machine-readable “list” of copyrighted works at the U.S. Copyright Office. And many copyright holders can get a copyright without even depositing a publicly viewable sample of the work—for example, software companies may register copyright on proprietary code without revealing it to the public. 

And on the open internet, copyright information is often incomplete, unavailable, or impossible to verify. One image may be registered with the copyright office, while the next is licensed under a free Creative Commons license (like the images that EFF creates), and the next is public domain. A message forum user might post an original story, photograph, or poem without any indication of ownership or registration status. 

The bill effectively asks developers to continuously cross-reference massive batches of online data against a copyright system that simply wasn’t designed to do so. If California passes A.B. 412, its impact will go far beyond the large AI companies we read about in the headlines. 

Not Just Big Tech

Supporters often frame this bill as a way to help creative workers have some leverage against Big Tech, but the bill reaches much further than the big AI companies. 

Its definition of “developer” extends to anyone who makes a generative AI model available to Californians. That includes indie developers tinkering with an existing model, open-source initiatives, nonprofits, and other non-commercial efforts. Recent amendments added exemptions for universities and government entities, which is important, but that still leaves out a vast swathe of non-commercial tech work that’s done by people without full-time jobs in government or academia. 

Large companies will hire compliance teams and lawyers to navigate these requirements. Smaller organizations and independent developers usually can’t. The result will be fewer opportunities for startups and new entrants. Faced with this massive compliance burden, some won’t even try. 

Courts Are Already Deciding These Questions

The bill is premised on the idea that copyright owners currently don’t have good remedies if they’re mistreated by AI companies. That simply isn’t true. And the growing wave of federal court filings in this space prove it. Content companies that want to sue tech companies, large or small, have no problem doing so. Those courts are still working through important questions about fair use and transformative use. Some courts have already concluded that many AI training activities qualify as fair use. Others continue to evaluate the issue.

California lawmakers should not rush to impose new state regulation while those questions remain unresolved. This is why copyright is governed at the federal level: both creators and fair users benefit from a single set of nationwide rules. 

At this point, the bill remains a solution in search of a problem. Rights holders already have powerful tools to protect their interests under existing federal law. What this bill adds isn’t clarity or transparency, but a costly and essentially impossible compliance burden that will discourage small developers and researchers. 

California has been able to support both artistic creativity and tech innovation for decades now.  But A.B. 412 does not strike the right balance. 

If you are a California resident and interested in speaking out about this bill, you can find and contact your representatives through this website. 

President Trump’s highly politicized appointment of an entirely unqualified acting Director of National Intelligence (DNI) underscores why the government’s warrantless mass spying power must be reformed. 

Congress now faces a deadline of Friday, June 12 to reauthorize Section 702 of the Foreign Intelligence Surveillance Act, an unconstitutional program rife with problems, loopholes, and compliance issues. Section 702 allows the National Security Agency to collect communications from targets overseas – including communications with Americans in the U.S. – and stores them in massive databases. The NSA then allows other agencies, including the Federal Bureau of Investigation, to access untold amounts of that information.  

Under current practice, the FBI can query and even read the U.S. side of that communication without a warrant. What’s more, victims won’t even know and have very few ways of finding out that their communications have been surveilled. EFF and other civil liberties advocates have been trying for years to know how data collected through Section 702 is used in domestic investigations and prosecutions.  

Our advocacy to reform Section 702 has been consistent across administrations, including when the federal Intelligence Community was run by people with experience in the relevant agencies. In fact, the 2004 law creating the position of DNI – which coordinates America’s 18 spy agencies – requires those who hold it to have “extensive national security expertise.” 

Enter Bill Pulte. 

Trump on Tuesday named Pulte – currently director of the Federal Housing Finance Agency (FHFA) and chairman of Fannie Mae and Freddie Mac – to replace current DNI Tulsi Gabbard, who announced her resignation last month. Pulte lacks any intelligence, military, or congressional experience.  

“William has deep experience managing the most sensitive matters in America, the safety and soundness of the Markets, and over 10 Trillion Dollars at Fannie Mae/Freddie Mac, a substantial increase from where it was just 12 months ago,” Trump wrote on his Truth Social platform.

Pulte isn't a qualified intelligence administrator. He does, however, seem to be unquestioningly loyal to President Trump and willing to use his position to attack and smear the President’s political foes.   

Because Trump named him acting DNI, Pulte isn’t subject to Senate confirmation. And under the Vacancies Act, Pulte could remain in the role for about seven months. 

This is particularly concerning because of Pulte’s history of using private information held by the government as a political weapon. In his FHFA role, he has accused several of the President’s political foes and targets – including New York State Attorney General Letitia James, U.S. Sen. Adam Schiff, D-Calif., and Federal Reserve governor Lisa Cook – of mortgage fraud based on private data held by his agency.  

All these targets and others have denied wrongdoing. A federal criminal complaint filed against James in Virginia imploded after a judge found prosecutor Lindsey Halligan had been unlawfully appointed, and prosecutors twice failed to convince a grand jury to indict James. Pulte’s accusations against Schiff, Cook, and others have not led to criminal charges. 

Pulte also used his FHFA pulpit to attack then-Federal Reserve Chair Jerome Powell and dismantle internal oversight. 

Pulte isn't a qualified intelligence administrator. He does, however, seem to be unquestioningly loyal to President Trump and willing to use his position to attack and smear the President’s political foes. As acting DNI, Pulte would have access to every scrap of classified information the Intelligence Community holds, and under Section 702, that includes massive amounts of information about Americans. 

Even lawmakers who are typically friendly to the intelligence community acknowledge that this is a disaster in the making. U.S. Sen. Mark Warner, D-Va., who is the Senate Intelligence Committee’s ranking Democrat, told NPR that Pulte has "no experience in the military, no experience in Congress, no experience in the intel community or law enforcement" and was chosen because he is "100% loyal to doing anything and everything President Trump demands." 

And Senate Majority Leader John Thune, R-S.D., told reporters “we don’t need a weaponized” national intelligence director. Asked about fears that Pulte might pursue Trump’s political opponents, Thune said: “We need professionals there.” 

Congress already has had trouble reauthorizing Section 702 as Freedom Caucus Republicans and many Democrats joined forces to demand reforms including the common-sense requirement that federal agencies get a probable cause warrant from a judge before searching any data involving Americans. Pulte’s appointment exemplifies why no administration should have the power granted by Section 702 without the independent judicial review required in seeking a warrant. 

Governments must not adopt emerging and powerful AI technologies without also adopting strong and clear safeguards to protect Constitutional rights, EFF Senior Policy Analyst Dr. Matthew Guariglia testified today to the House Homeland Security Subcommittee on Cybersecurity and Infrastructure Protection. 

During the hearing on “The AI Security Landscape: How Frontier Models, Agentic AI, and AI Coding Tools Are Reshaping Cybersecurity and Critical Infrastructure Resilience,” he explained that he use of generative AI for the purposes of mass government surveillance would supercharges unconstitutional violations of civil liberties. He also highlighted how government secrecy, in addition to the black box of for-profit proprietary technology, prevents the public and lawmakers from knowing when AI models make mistakes, including errors that seriously impact the cybersecurity of critical infrastructure and the lives of individuals.  

“AI also has a track record of getting things wrong—from false citations on legal briefs to a major AI mistake that sent DHS recruits to the field without proper training. There are likely more consequential examples that we do not even know about because of classification that would prevent a more thorough accounting," he said in his opening remarks.

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“At this level the question is not how do we rein in AI, it’s how do we rein in the agencies that would unleash AI on the American public,” Matthew said in response to a question by Subcommittee Ranking Member Delia Ramirez, D-Ill.  

You can read his full testimony as prepared here. 

Carbon capture is an important climate change mitigation strategy, but it faces technological barriers and can be energy-intensive and expensive. To help make necessary advances in this area, a team of MIT researchers, with support from the MIT Climate and Sustainability Consortium (MCSC), are exploring energy-efficient and scalable alternatives to conventional carbon dioxide (CO2) capture methods. 

Conventional amine scrubbing, which is the current standard for CO2 capture, is energy-intensive and difficult to scale, limiting its impact despite the urgent need to reduce carbon emissions and upgrade CO2 into valuable products. In a new article published in Nature Energy, MIT researchers — graduate students Fang-Yu Kuo of the Department of Chemical Engineering, and Gi Hyun Byun of the Department of Mechanical Engineering (MechE); Professor Betar Gallant of MechE; and former MCSC postdoctoral Impact Fellows Glen Junor and Akachukwu Obi — investigate a promising alternative to these conventional CO2 capture methods. Their findings could move the needle on achieving efficient and flexible carbon capture and removal.

In their paper, the team explores an alternative, electrochemically mediated CO2 capture (EMCC). This approach enables electrification of CO2 separation — driven ideally by renewables — but currently faces challenges, such as relying on sorbents that require highly reducing potentials, where oxygen reduction side reactions become significant. This can compromise both efficiency and long-term performance. To tackle this shortcoming of EMCC, the MIT team researched whether N-heterocyclic imines (NHIs) is a useful new class of EMCC sorbent.

“NHIs have shown promise in recent years as CO2 sorbents because of the ease of NHI molecular modifications for tuning basicity,” says Fang-Yu Kuo. “Our work translates these NHIs for the first time into the EMCC application space, and demonstrates that NHI-based sorbents can be modulated electrochemically for CO2 separation by a unique separation mechanism that avoids the need of applying highly reducing potentials.”

The team’s initial research establishes a novel bis(NHI) structure that can enable a theoretical CO2 modulation of two molecules per electron during cell operation. The initial published result also indicates that with further molecular engineering of bis(NHI) structures to strengthen CO2 binding affinity, the bis(NHI) could operate in more diverse electrolyte environments, opening new possibilities to optimize system performance in terms of electron efficiency, energy efficiency, and operational flexibility.

“A critical future direction of our work involves gaining deeper mechanistic insight into the stability and degradation pathways of the bis(NHI) radical cation,” says Kuo. “Understanding these pathways will inform the rational design of next-generation bis(NHI) molecules, enabling longer operational lifetimes and enhanced cycling durability for practical deployment.”

Meta has deployed facial recognition code to millions of their always-on surveillance glasses, according to new reporting by Wired. EFF’s Threat Lab was able to confirm that the facial recognition code is present through static analysis of the application. 

This dangerous new Meta functionality stores faceprints as a series of 2,048 numbers uniquely representing the positioning of a person’s facial features. When this feature is activated, it will convert every new face in the sightlines of the surveillance glasses into a series of numbers, and compare it to all the existing faceprints in the user’s database.

Wired and EFF confirmed that the code is present and active, though not yet exposed to consumers. Another researcher confirmed that when they manually added a face to the app database by connecting the phone to a computer in debug mode and issuing a few commands, the glasses would subsequently detect that face when it came into view. 

Meta has already paid $650 million to settle a BIPA lawsuit challenging mass facial recognition of every photo posted to its platform, a feature which it has since shut down. 

Despite the billions of reasons not to, Meta seems to have created the capacity to turn their customers into a distributed surveillance machine. This is just one more reason to think twice before buying or using Meta’s surveillance glasses. 

Considering that Meta previously wrote in an internal document that they want to launch facial recognition “during a dynamic political environment where many civil society groups that we would expect to attack us would have their resources focused on other concerns," this invasive new feature doesn't come as a surprise. But Meta's surveillance plans won't escape public scrutiny that easily, and we'll be watching if this feature is rolled out to the public. 

MIT, in collaboration with Georgia State University and a growing network of educational institutions, has announced expanded work under PATH (Pathways for AI Training and Hiring) — a multiyear initiative designed to scale effective, affordable, industry-aligned AI training for entry-level and current workers, with a particular focus on transforming community colleges into engines powering an AI-enabled workforce for the nation. 

“In the era of AI, economic opportunity and mobility will increasingly depend on whether people can develop practical, industry-relevant AI skill sets and mindsets, not just familiarity with tools,” says Cynthia Breazeal, principal investigator (PI) of PATH and professor of media arts and sciences at MIT. “That means combining hands-on, work-learn experiences with strong technical foundations and the responsible design, professional, and human skills that employers are looking for.”

To make that possible, the initiative is building state-based hubs anchored by research universities and community colleges. Each hub works with regional employers to design curricula that reflect local industry needs. The program also provides professional development for instructors and develops modular, open educational materials that institutions can adapt and share.

“Artificial intelligence is shaping every sector of the economy, and the United States will need far more people who understand how to build with these technologies and apply them responsibly,” says MIT President Sally Kornbluth. “Through PATH, MIT RAISE is using our convening power to bring community colleges, industry, research universities, and government together to build human-centered AI pathways that lead to shared prosperity. When research universities contribute their expertise to expand access and economic mobility, we strengthen both the nation’s workforce and our collective capacity for innovation.”

Unlike many large-scale online training efforts, PATH emphasizes in-person, collaborative learning. Students work in teams to address real problems brought by industry collaborators. These projects mirror the kinds of challenges graduates will face in the workplace, helping them build technical skills alongside the judgment, communication, collaboration, and ethical awareness that employers increasingly value.

The initiative’s first two hubs launched earlier this year in Massachusetts and Georgia.

“As PIs for the Georgia PATH hub, we are very excited with the significant early momentum, with over 1,000 GSU students enrolled in PATH courses,” says Arun Rai, regents’ professor, Howard S. Starks Distinguished Chair, and director of the Center for Digital Innovation at Georgia State University (GSU), with Balasubramaniam Ramesh, regents’ professor and the George E. Smith Eminent Scholar’s Chair at GSU. “Our curriculum, co-designed with MIT RAISE and spanning AI foundations, data science, deep learning, and agentic AI systems, is now being shared with partner institutions including Georgia Gwinnett College, GSU Perimeter College, and Clark Atlanta University. By leveraging the University System of Georgia’s FinTech Academy to expand work-based learning opportunities, we are building a collaborative ecosystem that rapidly advances the state’s AI workforce capabilities and creates tangible, job-ready skills for our diverse student population.” 

GSU President Brian Blake says, “Our collaboration with MIT reflects a shared commitment to strengthening the nation’s AI talent pipeline. Georgia State University brings a distinctive strength to this effort — the ability to prepare students from all backgrounds for AI-enabled careers at scale. By combining academic rigor with strong industry partnerships and work-based learning, we are translating advances in AI into practical skills and expanding access to opportunities in this transformative era.”

In Massachusetts, students at Quinsigamond Community College are participating in Data Science in Action, a course that introduces AI-enabled data analysis and engineering. The class includes a hands-on Action Lab, modeled after experiential learning programs at the MIT Sloan School of Management. David Birnbach, lecturer at MIT Sloan, leads the design framework for the PATH Action Labs. Working with industry partners, students tackle real data challenges while building portfolio projects and professional connections. 

Beyond individual courses, PATH is building clearer pathways for students to turn AI learning into real job opportunities. Through industry-informed micro-credentials and a shared set of workforce skills, students will gain practical abilities that employers are actually looking for, along with the human skills needed to succeed at work, like communication, problem-solving, and collaboration. 

The MIT skills taxonomy team, led by Katerina Bagiati in collaboration with Professor Tom Malone from the MIT Sloan Center for Collective Intelligence, is mapping the skills and roles emerging in AI across fields such as financial technology (fintech), information technology, and business operations, with plans to expand into areas such as health care, manufacturing, and creative media. The goal is to help students build skills that are relevant, recognized, and directly connected to growing career paths.

The initiative is supported by a grant to MIT from Google.org, which is helping MIT and its collaborators build a multi-state network for AI workforce development.

“MIT’s PATH initiative offers a blueprint for expanding opportunity in the age of AI,” says Shanika Hope, director of Google.org. “By connecting research universities, community colleges, and industry partners, it helps translate innovation into real jobs and sustainable career pathways.”

PATH is led by Breazeal, who has brought together a cross-MIT team with expertise in AI literacy, workforce pedagogy, educator professional development, open education, research, and the future of work. Breazeal is a professor and director of the MIT RAISE Initiative. Eric Klopfer, director of the STEP Lab and co-director of the MIT RAISE Initiative, serves as a co-PI on this award. The GSU leadership team includes PIs Arun Rai and Balasubramaniam Ramesh.

Hackers are convincing Meta’s AI support chatbot to let them take over other peoples’ accounts:

A video posted on X showed the step-by-step process to hack someone’s Instagram account. The hacker allegedly used a VPN to spoof the targets’ presumed location to avoid triggering Instagram’s automated account protections. Then, the hacker opened a chat with Meta AI Support Assistant and asked the bot to add a new email address to the target’s account. The chatbot can be seen sending a verification code to the email address provided by the hacker; the hacker then shares the verification code with the chatbot, which prompts the chatbot to show a button to “Reset Password.” The hacker enters a new password and takes over the victim’s account...

The agency's regulatory rollback for grocery refrigerators has sparked a backlash from U.S. manufacturers.

The National Center for Atmospheric Research is the latest example of how the Trump administration’s efforts to chain saw the federal government can happen too fast for the courts or Congress to counter.

The far-reaching measure also would launch a study of the state’s 2050 carbon-neutrality target — a move that could set the stage for its demise.

The country's climate pollution climbed 2 percent this year because wind and solar power weren't integrated into the grid.

The South Texas city is trying to navigate a potential water shortage that could affect refineries and residents.

A combination of climate change, urbanization and inflation mean disasters are getting more costly when they hit.

The findings underscore the economic toll of climate change and the opportunities it creates for companies that help customers mitigate and adapt to its effects.

With the center of the storm at sea, the cyclone posed no immediate threat to land.

The Japan Meteorological Agency issued flood warnings in several areas in Japan, urging people living along rivers and in vulnerable areas to move to higher ground.

When Sonya Atalay conducted her doctoral research, she studied pottery in Çatalhöyük, a remarkable ancient site in Turkey. It’s one of the world’s earliest known urban settlements, flourishing by at least 7000 B.C.E.

Yet even as Atalay was conducting field research and writing her doctoral thesis, she was scrutinizing standard archaeological practices, believing the discipline to be in need of an update. Indeed, it’s an issue she had been grappling with going back to her undergraduate days, when she first went to a dig site near Rome.

“When I started doing archaeological work, the local people were labor,” says Atalay, now a professor at MIT. “They came, they cleaned your clothes, they cleaned the dig house, they weren’t thought of as having important connections with the archaeology, and that really bothered me.” 

Surely, she believed, a culture producing the remarkable things worth studying is worth including in that research process, too. As she says, given “their place-based knowledge, it seemed like we should be talking to people about their heritage. They’re the ones who live on or near sites. I started thinking about what archaeology could look like if it included local communities in a meaningful way.” 

Atalay completed her dissertation while continuing to examine how researchers could alter their approach. She has since published articles and books about the subject, worked to introduce new research practices, and today, as an MIT professor, is a leader in the growing field of community-based archaeology, building partnerships between researchers and local residents.

Among other things, Atalay is the director and principal investigator of the Center for Braiding Indigenous Knowledges and Science (CBIKS), a National Science Foundation-backed project that helps train scholars and implement community-oriented work. She is convinced that community-oriented work creates better outcomes in many fields. 

“A community-based approach is highly applicable beyond archaeology and anthropology, outside of the social sciences,” Atalay says. “I think there’s a lot for engineers or designers or folks in a lot of different fields to learn by involving community members in the research process.”

Atalay joined MIT with tenure in 2024, where she is a professor in MIT’s Anthropology Section.

Roll me away

Atalay grew up in Michigan, not far from Detroit, where she was the first person from her family to go to college. Growing up, she hoped to be a physician. 

“I wanted to be a doctor. That’s what I thought I was going to do,” Atalay says. “I wanted to be a pediatrician.” 

But she also developed an interest in ancient history, something she can date to a precise moment. A 4th grade teacher named Barbara Eisman would give Atalay extra reading when Atalay would finish homework early. One day, Eisman produced a book about ancient Greece and Rome. 

“I remember thinking, this is amazing, discovering things I never knew existed,” Atalay says. “And that stuck with me.”

By the time Atalay enrolled at the University of Michigan, she was still planning to become a doctor. But as an undergraduate, she enjoyed taking archaeology electives to such an extent that she simply changed career paths. 

“I loved it and just got so into it,” Atalay says. And Michigan even provided opportunities for undergraduate fieldwork near Rome, although that meant Atalay had to dig deep to finance her first trip to an archaeological site. 

“I worked at a nightclub and put myself through college by bartending,” Atalay says. “I had a motorcycle, so I was tooling around Ann Arbor. Then I sold my motorcycle to buy the plane ticket to go to Rome so I could take part in the archaeological fieldwork.”

“Relationships are the task”

After graduating, Atalay was accepted into the graduate program for anthropology at the University of California at Berkeley, where she earned an MA and then, in 2003, her PhD. While Atalay’s doctoral research focused on the ancient pottery at Çatalhöyük, she maintained a steady interest in helping archaeology evolve. 

And increasingly, she started drawing on her own observations about fieldwork in the U.S., too. Atalay is Native American, and she recognized the same patterns of exclusion and archaeological extraction being applied to the historical study of Native American societies. 

One additional influence in shaping Atalay’s thinking was the North American Graves Protection and Repatriation Act (NAGPRA), passed by the U.S. federal government in 1990. It requires federal institutions to return human remains, sacred objects, and other cultural materials to Native Americans. Seeing the law enacted reinforced to Atalay that progress in this domain is possible. 

“The push for that act was really about Indigenous people standing up for sovereignty. To return what was wrongfully taken and to carry out research in an ethical way moving forward, there has to be trust and partnerships built,” Atalay says. While observing advocates trying to get NAGPRA passed, she adds, “I learned a lot from them.”

Over time, Atalay went on to serve multiple terms on the commission overseeing NAGPRA, first appointed by President George W. Bush and then President Barack Obama. Ultimately, her perspective has been fed by many sources, converging on similar themes.   

“I was really uncomfortable with how local people weren’t involved with studies of their own heritage,” Atalay says. “So I started thinking about what would it look like to truly partner with communities to plan and carry out research. And that’s how I started my first book, trying to set up a model for how to do ethical work in partnership with communities.”

That book, “Community-Based Archaeology: Research with, by, and for Indigenous and Local Communities” was published by the University of California Press in 2012. In her work, Atalay has focused on a range of specific practices, from research development to fieldwork methods and protecting intellectual property rights for Indigenous people. But the starting point for any work, she emphasizes, is relationship-building and the creation of mutual trust. 

“I tell students, ‘Relationships are the task,’” Atalay says. “I know you want to get in there and carry out fieldwork, but the relationships are everything. Sitting down and talking and sharing life stories and developing trust. Those relationships move at the speed of trust. And that takes time to develop. That’s the key piece. And that’s going to lead to good research outcomes.”

Stronger together

After receiving her PhD, Atalay had postdocs at UC Berkeley as well as Stanford University, then joined the faculty at Indiana University. In 2012, Atalay moved to the University of Massachusetts at Amherst, before joining MIT two years ago.

Currently Atalay is working on multiple projects. As director of CBIKS, she is running an organization with eight research “hubs,” where nearly 100 affiliated scholars are working with over 50 Indigenous communities to establish partnerships that advance environmental, and scientific research projects. 

In some cases, the scholars are involved in familiar-seeming archaeological work, while other center projects involve topics such as enhancing salmon farming, clam cultivation, or returning native seeds from museums to tribes in the Southwest, where elders still retain knowledge for their appropriate use and care.

“Our team members across multiple disciplines are learning from each other,” Atalay says. “So archaeologists and heritage management scholars are talking to environmental scientists and team members who study seeds and agriculture.” The NSF sometimes refers to this as ”convergence science.” The center’s name uses the metaphor of braiding, to represent the ways different strands of knowledge can be woven together to form a sturdy whole.

“With braiding, each of the strands retains its integrity, and they’re stronger when they’re brought together,” says Atalay. She is also currently working on another book project, “Braiding Knowledges,” about how the community-based approach can enhance and strengthen research within universities; it is under contract with the University of Arizona Press. 

At MIT, Atalay adds, she is delighted by the range of students who have started taking her classes, begun thinking about applications to all kinds of projects, and who in turn may end up leading innovative, community-oriented projects of their own.

“I would encourage anyone, no matter what field they’re in, to think about working with a community,” Atalay says. “What we’re learning isn’t just about working with Indigenous communities. It’s applicable outside of anthropology, outside of the social sciences. There is a lot you can learn and contribute to society by carrying out research this way, in any number of fields.”

In 2026, the hype for artificial intelligence agents is louder than ever before. These semi-autonomous programs can “think” and execute well-defined tasks in areas like customer service and software development, typically using language models (LMs). But fields like medical diagnosis and scientific discovery require them to inquire about a vast range of solutions in uncertain environments, which LMs struggle with.

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Harvard University’s School of Engineering and Applied Sciences (SEAS) peered deeper into LMs to understand their main issues in high-stakes settings. Their test: “Battleship,” a classic guessing game that’s helped cognitive scientists study how humans seek information. 

CSAIL and SEAS scholars added a twist by reframing the game around asking and answering natural language questions. In their “Collaborative Battleship” game, one participant is a “captain” who inquires about where hidden ships are, while their teammate plays the “spotter” by responding to those questions in real-time.

The researchers first had over 40 humans play the game together, collecting their questions and yes-no answers to build the “BattleshipQA” dataset. These results were a helpful point of comparison when the team tested state-of-the-art LMs (like GPT-5) and smaller models (like Llama 4 Scout) on their game. Without training the models beforehand, they found that top LMs can “beat” humans at “Battleship” — that is, complete the game in fewer turns — but smaller systems are far less rational.

The chief issue was that many models are simply not adept at coming up with useful questions. To get LMs to inquire in ways that reveal more information about hidden ships, the researchers gave each model a Monte Carlo inference strategy, which carefully measures the likelihood of different options being correct with each response. The result: AI models that can beat regular players at “Battleship,” regardless of scale.

Perhaps the most striking results were Llama 4 Scout’s gains. As a relatively small LM, it only beat humans 8 percent of the time. But with refinements to its inference strategy, the model reached a “Battleship” win rate of 82 percent versus humans. This careful and efficient style of asking questions also enabled the model to outpace a frontier model (GPT-5), while operating at around 1 percent of its cost.

On top of this improvement, the researchers shrank the gap between humans and LMs in answering questions. While GPT-5 was a reliable spotter that helped models finish games faster, smaller systems had a bad habit of giving the wrong answers about where ships were hidden. The models saw an accuracy boost of 15 percent on average when they began converting questions into code that explicitly tells them how to verify their answers (for example, having the model run a quick search of an area when asked if a ship was there). 

“Today’s language models are primarily optimized to answer complex queries, but it’s less clear whether they learn to ask good questions for themselves,” says MIT PhD student and CSAIL researcher Gabriel Grand SM ’23, who is a lead author on a paper about the work. “Our work shows that asking informative questions depends on the ability to predict and simulate the world. We find that when we give agents access to a ‘world model,’ they ask better questions and make discoveries more efficiently.”

A sea change for LMs

The team’s first focus was getting LMs to ask better questions. By implementing Monte Carlo inference strategies, the LMs reason about potential guesses as individual particles. The ones that appear more valid with each answer from the spotter would be weighted more heavily, sort of like game balls that inflate or deflate each turn. With this more calculated, adaptive approach, the captain could make inquiries that extracted considerably more info from the spotter.

The scientists then turned to the widely used programming language Python to help out AI spotters. Each question the captain asked was automatically converted into an encoded command. For example, a question like, “Is there a ship in column one that spans two rows?” turns into instructions for the spotter LM to search the area in question and assess how wide the digital game piece is. By giving the model clear directions in a language it understands particularly well, each system gave correct answers considerably more often. The lightweight system GPT-4o-mini saw a nearly 30 percent performance bump, for instance, and even the large model Claude 4 Opus jumped about eight points.

“The field has seen a lot of success from ‘auto-formalization’ strategies, in which LMs generate code to verify their solutions,” says senior author Jacob Andreas, an MIT electrical engineering and computer science associate professor and CSAIL principal investigator. “What I find most exciting about this work is that it opens up the possibility of using these techniques to generate better solutions in the first place, by improving LMs’ exploration and information gathering capabilities. We are excited to scale this work up from scientific domains to applications like coding and mathematical problem-solving.”

Let’s play something else

But how would this approach fare in other board games? The team tested their newly equipped LMs at “Guess Who?”, where large and small models skillfully whittled down 100 options to correctly guess which hidden character had been chosen. Llama 4 Scout was successful 30 percent of the time, but after Grand and his colleagues’ tweaks, it completed the task on over 72 percent of its runs. Meanwhile, GPT-4o leapt from 62 percent to 90 percent. GPT-5 was the spotter in each game to ensure questions were answered as accurately as possible.

While LMs have made promising progress in both games, there’s room for improvement. For instance, the models still struggle to answer complex questions, compared to humans. OpenAI researcher, recent Harvard graduate, and coauthor Valerio Pepe adds that “GPT-5 can beat your average ‘Battleship’ player, and gets a hair better with our methods. However, expert players are still hard to beat for all models, unlike in chess, where even top players don’t succeed against AI systems.”

The researchers’ findings show that AI agents have untapped potential in “needle-in-a-haystack” discovery — navigating a massive space of options to find a rare solution to scientific challenges. While improved information-seeking skills would make them excellent research assistants with, say, identifying a compound’s molecular structure, the researchers caution that “Collaborative Battleship” is a somewhat simple test bed. They’d like to test LMs in more complex settings, where the systems have to consider far more options.

Grand also plans to have humans and AI models collaborate to study whether they work better together. The models might also benefit from a bit of fine-tuning on game simulations, and with more computing power, LMs would have more advanced inference capabilities to predict how a game will evolve. 

“As AI systems become more agentic, the hardest problems turn out to be social ones: tracking common ground, resolving misunderstandings, and adapting to different partners over time,” says Robert Hawkins, assistant professor of linguistics at Stanford University, who wasn’t involved in the paper. “This work elegantly captures these phenomena in a controlled collaborative setting, and makes a compelling case that the real bottleneck for AI agents isn’t just the calculation of optimal questions, but the pragmatic reasoning needed to make the most of their answers.”

Grand and Pepe wrote the paper with two CSAIL principal investigators: MIT Associate Professor Jacob Andreas and MIT Professor Joshua Tenenbaum. Their work was supported, in part, by the MIT Siegel Family Quest for Intelligence, the MIT-IBM Watson AI Lab, the FinTechAI@CSAIL initiative, a Sloan Research Fellowship, Intel, the Air Force Office of Scientific Research, the Defense Advanced Research Projects Agency, the Office of Naval Research, and the National Science Foundation. They showcased their paper as an oral presentation at the International Conference on Learning Representations (ICLR) in April.