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.

In courts across the country, publishers and creators are suing large AI companies for training generative AI models on copyrighted works. It’s easy to see these cases as a David-and-Goliath showdown between authors and publishers fighting for a cut of Big Tech company profits. But this fight is actually much larger. It affects billions of people who aren’t represented in the case at all, including researchers, artists, activists, journalists, people with disabilities, and anyone who accesses and shares content online.   

That’s why EFF is stepping in to defend the public’s rights in these cases. Today, EFF filed an amicus brief in In re OpenAI, explaining how the court’s ruling on fair use could impact people worldwide. If fair use gets narrowed, it will impede work that benefits the public, like medical research and investigative journalism, and even limit access to privacy protection tools.

Let’s be clear: creators and workers deserve support as AI changes their fields, and Big Tech companies must be held accountable for the harms they cause. But giving copyright holders more control over the web won’t accomplish either of these goals. If the plaintiffs prevail in these cases, courts would have to rewrite copyright law in ways that hurt everyone except powerful media and tech companies.

Rewriting Fair Use Makes Everyone Worse Off

Copyright exists for one reason: to stimulate creativity and innovation in service of the greater public good. Unfortunately, the plaintiffs in this lawsuit are arguing to limit everyone’s fair use rights, in ways that would thwart the basic purpose of copyright. The harms of shrinking fair use will go far beyond generative AI, impacting everyone.

Initial speculation that AI models would be infringement machines, churning out works highly similar to those on which they were trained, has proven unfounded. And courts have already found fair use in cases where copyrighted works were used to train generative AI models, recognizing that training uses those works for a fundamentally different purpose.

Plaintiffs in these cases argue that, even if the use is transformative, using their works to train AI is still unfair, because the AI models could generate new, non-infringing works that might compete with plaintiffs’ own works. They also argue that scraping or downloading the data without permission—or in some cases, from websites containing infringing copies—was independently infringing and/or rendered any subsequent uses of that data unfair.

These arguments would put beneficial research and reporting at risk. First, if copying without permission rules out fair use, the law will never protect transformative uses that benefit us all. Researchers, journalists, and other watchdog groups gather information the same way AI companies do—but they do it to serve the public interest, and often to hold Big Tech itself accountable. For example, journalists and nonprofit researchers have used “crawling” or “scraping” to investigate potentially anticompetitive practices by tech companies, and research misinformation and hateful content posted on X. Their data acquisition techniques are virtually identical to those used by AI giants—but they lack those companies’ deep pockets.

Requiring users to show that copyrighted material was lawfully obtained would also threaten everyday internet use. As EFF’s brief explained, that requirement would “cast legal doubt on the billions of copies we collectively make every day through routine online activities like opening emails, clicking on an embedded link, visiting websites, sharing pictures, streaming music, and watching videos. All of this activity requires intermediate copying.”

Fair use is what lets us all use the internet without being compelled to track the source of every image, video, and article that we click on, and make sure the rights are all cleared.

Additionally, while rightsholders are understandably concerned about competition from AI generated works, copyright law exists to promote new creation, not protect authors who published first. Under the plaintiffs’ theory, no one could stand upon the shoulders of giants. Copyright could prevent people from writing new books inspired by themes in a favorite novel or genre, or prevent academic researchers from publishing literature reviews that distill insights from prior research—simply because readers might prefer those new publications over those that came first.

Finally, general-purpose AI models are neither designed nor usually used to create works targeted at any particular market. These models have been used to generate new insights and tools for medicine, science, journalism, art, and accessibility. For instance, an AI-powered tool called Be My AI uses Open AI’s GPT models to provide visual descriptions for people who are blind or have low vision. Generative tools have also accelerated scientific research, and researchers have used these tools to develop new medical treatments and medications for conditions previously thought to be incurable.

Mandatory Licensing Would Entrench Big Tech

Some creators and publishers argue that AI developers should just get a license. But requiring permission for every work used would give the largest companies massive advantages over small competitors and impede academic research. As Professor Lawrence Lessig recently noted:

If all training requires a license, then none but a handful of tech giants will be able to afford the costs of training. That’s not because authors are greedy but because the copyright system is the least efficient property system known to man.

Moreover, creators are unlikely to benefit from these licensing arrangements in many cases. Media and entertainment giants have exploited the imbalance between themselves and underpaid working artists for decades. Trying to help creators by giving them more rights under copyright is, as Cory Doctorow has written, like trying to help a bullied kid by giving them more lunch money for the bully to take. Artists aren’t getting good deals from record labels or publishers now; there’s no reason to think these companies, which already own the copyrights, will treat artists more fairly once they control the data that powers AI.

The Future of AI Is Too Important to Leave to Big Content or Big Tech

AI technologies will affect all of us, and copyright law is ill-equipped to address many of these consequences. The rules governing these technologies should be rules that serve the public—not just the small number of companies that can afford to litigate copyright battles for decades, or settle for billions.

Neat video:

The sighting, announced September 2, marks the first time anyone has spotted a live bigfin squid, which belongs to the Magnapinna genus, in the northeastern Pacific Ocean.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Blog moderation policy.

Developers of virtual private network (VPN) apps are reporting that their products are again being pulled from Apple’s App Store in Russia. This is an unacceptable crackdown on a vital tool to protect privacy and speech online.

Why? VPNs give people a way to control their own online activities. When you visit a website through a VPN, that website only sees the VPN server's location (well, IP address) rather than your actual location. It's like sending a letter through a P.O. box so the recipient doesn't know where you really live. It can also disguise where you’re accessing information from, which can make it possible to access information banned by your government.

This is an unacceptable crackdown on a vital tool to protect privacy and speech online.

Going after tools that give people the space to communicate and access information freely is a common tactic from the totalitarian playbook. This latest demand comes after several steps the Russian government’s taken to surveil speech specifically on Apple products. In 2024, the Moscow Times reported that Russia’s state media watchdog, Roskomnadzor, went after VPN apps on the Apple App Store, after banning access to Western social media platforms and blacklisting opposition media sites. Russia then blocked Apple’s FaceTime software in 2025, according to Apple Insider.

Bloomberg also recently reported that the Russian government has added Apple and Microsoft to its state surveillance registry. This gives Roskomnadzor access to local users’ correspondence, Apple Insider reported, and puts both companies in a position to turn over customer information to the country’s security services as required.

With government control encroaching on their devices and less access to tools that give them alternate methods, people in Russia are left with shrinking options to communicate online or find information that isn’t state-sanctioned.

While Russia’s actions offer a more extreme vision of what happens when the government tries to enforce speech rules by attacking vital privacy tools, it is far from the only government doing so. Several states in the U.S., bent on making it difficult to circumvent invasive age-gating laws, have suggested banning VPNs—either to access certain content or in some cases to simply ban them outright. EFF has publicly (and loudly) criticized these proposals, because going after VPNs is a dangerous step down the road of censorship.

Attacks on speech and privacy online are deeply troubling, no matter where they happen. What those in power may see as a “loophole” in their invasive laws is actually an expression of user control. We’ll continue to speak out when we see governments going after them to take that control away.

Five MIT faculty members and two additional alumni were recently named to the 2026 cohort of AI2050 Fellows. The honor is announced annually by Schmidt Sciences, Eric and Wendy Schmidt’s philanthropic initiative that aims to accelerate scientific innovation. 

Conceived and co-chaired by Eric Schmidt and James Manyika, AI2050 is a philanthropic initiative aimed at helping to solve hard problems in artificial intelligence. Within their research, each fellow will contend with the central motivating question of AI2050: “It’s 2050. AI has turned out to be hugely beneficial to society. What happened? What are the most important problems we solved and the opportunities and possibilities we realized to ensure this outcome?”

AI2050 Senior Fellow

Sendhil Mullainathan is the Peter de Florez Professor at MIT with dual appointments in the departments of Economics and Electrical Engineering and Computer Science who is also a principal investigator at the MIT Laboratory for Information and Decision Systems. His current research is at the intersection of algorithms and people, with a central focus on launching The Bike Shop@MIT, a new research center that works toward more human-centered AI through scalable bicycles for the mind, and innovative new gears for machine learning algorithms. Mullainathan is the co-founder of ideas42, a nonprofit to apply behavioral science; MIT's Abdul Latif Jameel Poverty Action Lab, which promotes the use of randomized control trials in development; and Dandelion Health, which combines proprietary AI tools with real-world patient data. He's the co-author of "Scarcity: Why Having Too Little Means So Much."

Among other awards, he is a recipient of the MacArthur “Genius Grant”; has been designated a “Young Global Leader” by the World Economic Forum; was labeled a “Top 100 Thinker” by Foreign Policy Magazine; and was named to the “Smart List: 50 people who will change the world” by Wired Magazine (U.K.). He serves on the board of the MacArthur Foundation, is affiliated with the NBER and BREAD, and is a member of the American Academy of Arts and Sciences. His AI2050 project focuses on the translation of patterns detectable by AI into testable theories.

AI2050 Early Career Fellows

Alex Damian is an assistant professor in the Department of Mathematics, holding an MIT Schwarzman College of Computing shared position with the Department of Electrical Engineering and Computer Science, who is also a principal investigator at the MIT Laboratory for Information and Decision Systems. His research focuses on the mathematical foundations of deep learning, with an emphasis on optimization dynamics and representation learning. He was previously a research fellow at the Kempner Institute at Harvard University. He received his PhD in applied and computational mathematics from Princeton University and his BS in mathematics from Duke University. His AI2050 project focuses on effective AI model training.

Bailey Flanigan is a Theodore T. Miller (1922) Career Development Assistant Professor at MIT, holding an MIT Schwarzman College of Computing shared position with the departments of Electrical Engineering and Computer Science and Political Science, and is also a principal investigator at the MIT Laboratory for Information and Decision Systems. She received her PhD in computer science from Carnegie Mellon University in 2024. In 2024-25, she was a Harvard Data Science Initiative Fellow, hosted at the Harvard Ash Center for Democratic Innovation. Her research combines tools from across disciplines (including social choice theory, game theory, algorithms, statistics, and survey methods) to advance political methodology and strengthen public participation in democracy. She is specifically interested in sampling algorithms, opinion measurement/preference elicitation, and the design of democratic innovations like deliberative minipublics and participatory budgeting. Her AI2050 project focuses on gathering structured public input that lawmakers can use when making complex decisions.

Yunha Hwang is the Samuel A. Goldblitch Career Development Assistant Professor at MIT, holding an MIT Schwarzman College of Computing shared position with the departments of Electrical Engineering and Computer Science and Biology. She is also a principal investigator at the MIT Laboratory for Information and Decision Systems. Hwang serves as the co-founder and chief scientist at Tatta Bio, a scientific nonprofit dedicated to advancing genomic AI for biological discovery. She received her PhD in biology from Harvard University and BS in computer science from Stanford University. Her research interests span machine learning for microbial evolution, biomanufacturing, and open science. Her AI 2050 project focuses on determining biological enzyme functions. 

Manish Raghavan is the Drew Houston (2005) Career Development Professor at the MIT Sloan School of Management, holding an MIT Schwarzman College of Computing shared position with the Department of Electrical Engineering and Computer Science. He is also a principal investigator at the MIT Laboratory for Information and Decision Systems. Before that, he was a postdoc at the Harvard Center for Research on Computation and Society. Raghavan received his bachelor’s degree in electrical engineering and computer science from the University of California at Berkeley, and PhD from Cornell University. His research interests lie in the application of computational techniques to domains of social concern, including algorithmic fairness and behavioral economics, with a particular focus on the use of algorithmic tools in the hiring pipeline. His AI2050 project focuses on mitigating negative effects, and strengthening positive aspects, of widespread AI reliance. 

MIT alumni Irene Chen PhD '22 and Allison Z. Koenecke '14 as well as former postdoc Juan Carlos Perdomo were also named AI2050 Early Career Fellows.

The Applied Research Training in Evaluation, Methods, and Impact Studies (ARTEMIS) program, launched by J-PAL North America, equips early-career researchers with high-level technical training and practical skills to design randomized evaluations (also referred to as a randomized controlled trials, or RCTs) on issues of poverty in the U.S. context. Fully funded and highly rigorous, the program's inaugural cohort of 20 participants — including PhD students through pre-tenure faculty — is receiving training, mentorship, and proposal development support through December. 

ARTEMIS is the newest offering within J-PAL North America’s Economics Pathways program, which broadens and strengthens the pipeline of economics researchers by providing support to aspiring scholars at every stage — from undergraduate interns to research staff and, now, academic researchers preparing to launch their first randomized evaluations.

ARTEMIS programming commenced this past June with the Summer Institute — an intensive weeklong course at MIT Endicott House in Dedham, Massachusetts, that focused on delivering technical content and building participants' practical skills and confidence to design and pursue funding for rigorous evaluations to address poverty in the United States. During the Summer Institute, participants had the opportunity to learn from researchers in the J-PAL network, including Gerald Daniels, associate professor of economics at Howard University, who served as a lecturer for the program. “ARTEMIS is an amazing program that bridges the gap for researchers who haven't previously had the opportunity to work with randomized evaluations,” notes Daniels. “It takes rigorous methodologies, like randomized evaluations, and makes them approachable, helping scholars translate their research ideas into high-impact evaluation designs." 

The inaugural cohort brought together early-career researchers from institutions across the United States, each tackling a research question addressing an issue related to poverty that is well-suited for a randomized evaluation. Their interests spanned the breadth of J-PAL’s work, touching on education, climate, health and labor markets, racial equity, and social protection — reflecting the wide range of policy questions that rigorous, evidence-based research can help answer. 

“I really appreciated being a part of the ARTEMIS Institute this summer,” says Felipe Juan, an ARTEMIS inaugural cohort participant. “I initially thought the randomized evaluation methodology would be out of reach for me, but the summer training helped to break down the practical steps of conducting an RCT and identify creative ways to approach experimental designs.” 

Bolstered by the intensive Summer Institute training, ARTEMIS participants are now engaging in six months of continued study design and proposal development support, including proposal webinars with J-PAL grants staff and office hours with senior technical staff who will advise on study design and proposal challenges. ARTEMIS participants will also participate in a research design workshop, where participants present their early-stage RCT designs and receive feedback from the broader J-PAL research network. Finally, participants will receive introductions to researchers in the J-PAL network with shared research interests to forge connections that may lead to future collaboration and opportunities. Through this sustained mentorship model, ARTEMIS is designed to help participants bridge the gap between developing a promising research idea and launching a funded, policy-relevant randomized evaluation. 

For early-career researchers, this hands-on, sustained support has already proven to be transformative. “Participating in ARTEMIS Summer Institute was an incredibly valuable experience. The combination of lectures by experts in the design and implementation of randomized controlled trials, hands-on workshops, and thoughtful feedback helped me sharpen my research questions and think more carefully about research design, implementation, statistical power, and meaningful stakeholder engagement,” says Hoyoung Yoo, an ARTEMIS 2026 participant. “I also greatly appreciated the opportunity to learn from J-PAL faculty and staff and exchange ideas with an inspiring group of fellow researchers. I left the training with a clearer path toward transforming an early-stage idea into a feasible, rigorous, and policy-relevant evaluation.” 

For many, the program’s value also extends beyond technical training to the community and long-term relationships it builds. “J-PAL North America’s support has continued well beyond the Summer Institute, which has been especially valuable, and the connections I made have already started shaping how I am approaching the work,” notes Tessie Krishna, an ARTEMIS 2026 participant. “I left with a clearer proposal, a wider network, and a real sense of belonging in the J-PAL community. I am grateful to the J-PAL team for creating something so thoughtful and welcoming.” 

By building the technical capacity and confidence of the next generation of researchers, ARTEMIS is advancing J-PAL’s global mission to reduce poverty by ensuring that policy is informed by rigorous, scientific evidence. Every individual trained through ARTEMIS is joining a growing network of researchers working to answer the most critical questions of today. 

ARTEMIS is made possible by grants from Arnold Ventures, Russell Sage Foundation, and the Alfred P. Sloan Foundation. Visit the J-PAL website for more information about ARTEMIS and the Economics Pathways program.

Global warming, the observed increase in the Earth’s average global surface temperature since preindustrial (1850-1900) times, currently stands at around 1.63 degrees Celsius. While this number is expected to continue to rise, local temperatures will not go up uniformly around the world. In regions where warming trends are most pronounced, local adaptation and resilience measures will be especially critical to minimizing risks to ecosystems, agricultural productivity, the built environment, human health, and economic well-being. 

To that end, a new open-access study in the journal Scientific Data identifies the highest-risk regions for warming within the United States. Using data from well-known, high-resolution climate and weather datasets (MERRA2 and ERA5) covering the years 1980-2022, researchers at the MIT Center for Sustainability Science and Strategy (CS3) report global warming-attributed trends in monthly-averaged maximum daily temperature for every U.S. state. 

The study finds that Alaska, Utah, New Mexico, Colorado, and Nevada have experienced the strongest average warming trends; the lowest occurred in Hawaii, Montana, North Dakota, South Dakota, and Arkansas. Across the country, warming trends were most pronounced during the summer and fall, particularly in September and November. Alaska has the highest historical maximum daily temperature warming (an average of 2.5 C of monthly-averaged maximum daily temperature increase per degree of global warming), while Hawaii has the lowest average historical warming, at 0.8 C. 

The authors expect that barring unforeseen local anomalies, these state-by-state temperature trends will continue unabated as ongoing global emissions from human activities continue to raise the average global temperature.

“Based on these results, we encourage decision-makers to assess states that have undergone the steepest increases in maximum and minimum daily temperatures, and identify where continued trends would indicate a rapid progression toward thresholds beyond which natural and managed resources, ecosystems, and infrastructure can function safely,” says MIT CS3 Deputy Director C. Adam Schlosser, a co-author of the study. “In such cases, triaging and adaptive measures would take a high priority. By directing resources to where they are needed most, state and regional planners could significantly reduce climate-related physical and financial risks.”

Unlike previous efforts to report temperature trends across the United States, this study aggregates average maximum, minimum, and mean daily temperature data at the state level for each month of the year, providing actionable information for planners in government, industry, and civil society.

People are peppering artificial intelligence chatbots everyday with questions, but when it comes to politics and the looming 2026 U.S. midterm elections, what role are AI platforms playing? Could an AI chatbot sway a voter to switch parties or re-examine their political beliefs? 

MIT researchers have built a new dashboard, called the LLM Election Observatory, to examine, in real-time, how popular AI chatbots are responding to questions about the 2026 midterm elections. The team behind the dashboard — led by Professor Adam Berinsky, Assistant Professor Chara Podimata, and Professor Charles Stewart III — has extensive experience studying voting and election administration, AI and misinformation, and how AI systems are impacting human decision making. In an effort to better understand how AI is shaping the election, they designed the dashboard to simulate how individuals across the country communicate with chatbots. 

The team tracks how different AI systems respond to the same fixed set of questions on a range of topics, including election certification, hot-button issues, result predictions, news coverage, and more. Every few days, the LLM Election Observatory team runs the questions through the chatbots again, monitoring how their responses vary as the election gets closer depending on user location, political party affiliation, identity and other factors. The goal of this work is not to merely understand how AI systems spread information, but also better comprehend how they are shaping political discourse and voter understanding of political issues across the country. 

MIT News chatted with Podimata, the Class of 1942 Career Development Professor, an assistant professor of operations research and statistics in the MIT Sloan School of Management, and a member of the Laboratory for Information and Decision Systems; and Berinsky, the Mitsui Professor and a professor in the Department of Political Science, about their work with the LLM Election Observatory, how AI could impact the midterms, and what voters should keep in mind when they interact with chatbots.

Q: What was the inspiration for this project and what are you hoping to learn through the LLM Election Observatory?

Podimata: People are increasingly using AI chatbots to find and make sense of political and election-related information. That makes it important to understand what information these systems actually provide, not just at one moment in time but throughout an election cycle. This is particularly challenging because the models themselves are continually getting updates, the political information inputs available to them are themselves changing, and their answers can depend on how a user asks a question or what information the user provides about themselves.

One basic problem is that we currently do not have a good historical record of this information environment. If an AI system gives a particular answer about an election today and gives a different answer several weeks later, it can be very difficult to reconstruct after the fact what voters would have seen at each point in time. One important goal of our project is simply to systematically document that environment as it develops and make the resulting data available to other researchers and to the public.

Ultimately, factual accuracy is only the first frontier. We absolutely need to understand whether chatbots provide accurate political information, but we also need to pay just as much attention to the subtler ways in which they may shape people’s understanding of political issues. Differences in framing, emphasis, uncertainty, and cited sources can matter enormously, even in the absence of outright misinformation.  

Q: Professor Podimata, you studied how LLMs shaped political discourse and information during the 2024 election. What were the main takeaways of that research and how did those findings inform this new project?

Podimata: The LLM Election Observatory builds on my previous work during the 2024 presidential election, which found evidence of meaningful variation in AI models’ responses over time and across prompt framings, as well as differences in how models characterized candidates and implicitly anticipated the election outcome. 

The 2026 midterms give us an opportunity to expand that work substantially. Midterm elections are particularly interesting because there are many state and local contests unfolding simultaneously, alongside broader national political narratives. Our new audit survey therefore asks both contest-specific questions about the U.S. House and Senate candidates, but also broader questions about voting information and other aspects of the election information environment.

Q: Why do you think it’s so important to be studying how chatbots are responding to political queries across different groups of users?

Podimata: We all build our own “microcosms” through our interactions with chatbots. And yet, we lack systematic ways of understanding how these individualized information environments are shaped by who we are or how they evolve over time as we and the information environment changes. 

For example, people may not realize that what their favorite chatbot tells them about a political issue may be quite different from what their friend’s favorite chatbot tells them about the exact same issue. And importantly, these differences matter even when the information being provided is factually correct. Chatbots may frame the same facts differently, emphasize different aspects of an issue, express different levels of uncertainty, or draw on entirely different sources, even when responding to identical queries between different users. As a result, two users may walk away with very different perceptions of the same underlying facts, without ever realizing that their experiences differed. 

In some ways, this brings back many of the concerns we (as researchers and society) have grappled with in the context of social media. On social media, users and researchers could, at least to some extent, observe the information circulating across different Facebook groups, Instagram accounts, or TikTok communities. With chatbots, these information environments are largely invisible to anyone outside the individual conversation. We cannot easily observe what different users are being told, let alone systematically compare those responses or track how they change over time. This is precisely why projects like the LLM Election Observatory are so important: By systematically collecting and publicly sharing chatbot responses, we can begin to make these otherwise invisible differences observable and open to scrutiny.

Q: What sorts of questions and AI responses are you tracking through the dashboard?

Podimata: There are several questions we ultimately want this dataset to help us study. For example: Do different models provide systematically different information for the same questions? Do their responses change over the course of the campaign? Does the answer a particular model gives change depending on how a user frames the question or describes themselves (e.g., does it change if the model gets informed that the user is a Democrat or a Republican)? And, finally, what are the sources that the models pull information from when answering election-related questions? 

Our public dashboard is an important part of the project. We want people both from and outside of academia to be able to see the data we are collecting as the election unfolds rather than only seeing the results months later in an academic paper. The dashboard therefore presents the underlying data and some preliminary descriptive analyses, and we plan to make the dataset available for other researchers to study as well. We expect the dashboard to evolve as the data and our methods evolve.

Through this dashboard, we wanted to build on the scientific methodology we developed in 2024 but also to make the research much more accessible and useful to the public while the election is actually unfolding. Not only that, but the addition of MIT political science experts in the team cannot be overstated; Adam and Charles have really helped expand the questions that we are asking in our audit.

Q: What do you wish voters across the country knew about AI chatbots when it comes to searching for election information?

Berinsky: People should be sure to remember that just because a chatbot sounds confident doesn’t mean it is accurate. AI systems are designed to answer the questions that people ask them in an authoritative voice even when they are wrong, incomplete, or leaning on a weak source. These answers are a reasonable place to start, but voters shouldn’t stop there.

Q: What are your findings thus far?

Podimata: We have not yet done the full, longitudinal analysis, so we are only going to talk about broad strokes here, but a few broad patterns seem to be visible in the 2026 data. Across the different user identities we present, we see differences in how the answers are presented, in which sources a model cites, and in tone — sometimes even when the underlying facts are the same. What we cannot yet say is how stable those differences are, how they move over the course of the election, or how a real person would read and react to them. Our future analyses will take this up. It also underscores why the continuous record of responses we are collecting matters more than any single snapshot that you might see in the days around the election.

Q: Professor Berinsky, your research has also shown that in some cases AI chatbots can be harnessed to help refute misinformation. Can you talk a bit about this?

Berinsky: In my undergraduate course on information and social media I have been talking for a while about how AI can cut both ways. On one hand, these tools can generate misleading claims in text, pictures, and video at a scale we have never seen. Basically, AI can help bad actors “flood the zone” with misinformation. On the other hand, some of the most promising recent work demonstrates that evidence-based dialogues with a chatbot can durably reduce belief in conspiracy theories and other beliefs that are not supported by the balance of publicly available evidence. 

AI has the potential to do such debunking at scale — something we have long struggled to do. The lesson from decades of misinformation research is that any technology (be it print, radio, or television in earlier eras) is not inherently good or bad for promoting truth. The devil is in the details in how it is built and deployed. Independent auditing like this project can help us assess the ultimate impact of such technologies.

Q: Can you talk a bit about your overall goal with this research? 

Podimata: AI is a transformative technology, and people understandably have very different expectations (and feelings) about what it will mean for society at large. All of us have people in our lives that are overly enthusiastic about AI, others that are deeply concerned, and many more that are somewhere in between trying to understand what this technology can and cannot do. Our goal here is not to begin from the assumption that AI is either good or bad for elections. Instead, we hope to shed some light onto one part of this transformation: what information major AI systems are actually giving people about an election as it unfolds.

This story is interesting:

One Civic owner purchased a cheap aftermarket head unit off Amazon, and decided to have Claude work its magic on it, like it would a smart TV. What was reportedly discovered isn’t surprising, but is worth knowing about.

Among the pre-installed bloatware—­or spyware, if you’re being less charitable­—was a voice assistant equipped with WebView, an Android system component that lets apps display content off the web. This apparently always used about a third of processing power, even while idle, which isn’t great. There were also Baidu location services, a call recorder (!), a DAB radio client that is pretty much useless in the U.S., and Adobe Acrobat, presumably so your passenger can entertain themselves with PDFs on the go...

A nanoscale particle with a unique structure, developed at MIT, holds potential as a customizable delivery vehicle for personalized cancer vaccines.

The backbone of the vaccine is a modified, harmless bacteriophage called M13, which forms a nanofiber that can be coated with cancer antigens. By precisely controlling the density of those cancer proteins, the researchers found that they could create a vaccine that prevents the development of colon tumors in mice.

This type of delivery particle could be adapted to carry many different antigens, allowing for the creation of personalized vaccines to treat individual patients’ tumors, the researchers say.

“The great thing about this is that it’s modular and we could switch out different antigens on demand, so you could design it to be patient-specific and target a known antigen for a particular kind of cancer,” says Angela Belcher, the James Mason Crafts Professor of Biological Engineering and Materials Science and Engineering and a member of the Koch Institute.

Belcher is the senior author of the study, which appears today in the journal Advanced Healthcare Materials. Shengnan Huang, research scientist at the Koch Institute, is the paper’s lead author. Jack Rudrum, Huaiyao Peng, and Hao Nghi Luu, graduate students at the Koch Institute, are also authors of the paper.

Viral vaccine assembly

For more than 20 years, Belcher’s lab has been deploying the M13 bacteriophage in a variety of applications. This virus, which is harmless to humans, consists of a single strand of DNA coated in a protein shell, or capsid. 

Belcher has previously developed techniques for generating materials by engineering these viruses to self-assemble into nanoscale scaffolds. By coating these scaffolds with inorganic materials, they can create functional devices such as batteries, semiconductors, solar cells, and catalysts, with precise control over the electronic, optical, and magnetic properties of the materials.

More recently, Belcher’s lab has been developing methods to control features of the phage that could allow it to be repurposed for medical applications, including vaccine delivery. 

“We’re taking this toolkit that we’ve worked on and developed over decades and formatting it to be specific for vaccine therapeutics,” Belcher says. “Some of the lessons learned from making batteries and all these other things are now being fine-tuned to make personalized vaccines.”

The capsid that surrounds the M13 phage forms a long filament made from viral proteins. To create vaccines, the researchers modify one of these proteins, known as pVIII, by attaching an antigen to the end of the protein. This can be done by adding the gene for the antigen to the virus’s DNA.

Some of the lab’s previous work had suggested that changing the density of cancer antigens attached to the capsid could affect the strength of the resulting immune response. In the new study, Huang wanted to find a way to control that density. 

“The antigen is the specific target we want to initiate the immune response against, which means we have to ensure the right dose of antigen to make sure that we can have a strong immune response,” she says.

Huang discovered that she could increase the density of the modified pVIII proteins by adding one or two negatively charged amino acids to the protein sequence. The density could be further fine-tuned by adding a genetic mutation that slows down production of the antigen protein.

In this study, the researchers also figured out how to boost the phage’s own ability to act as a vaccine adjuvant, by changing the genetic sequence of the virus’s DNA. Most vaccines need to be delivered along with an adjuvant such as aluminum hydroxide, which helps to stimulate a strong immune response. However, the M13 phage can act as an adjuvant itself because single-stranded DNA activates a pathway called TLR9, which is part of the body’s innate immune system.

“We can control the exact length. We can control the exact antigen expression, density, pattern, the exact adjuvant amount. A lot of times with a vaccine, you’ll have to put a different adjuvant with it, but now it’s all genetically encoded,” Belcher says. 

Targeting tumors

Using these techniques, the researchers showed that they could design phages that express an antigen called SIINFEKL at densities ranging from 6 percent to 70 percent. SIINFEKL, a small protein that can be engineered into tumors, is often used as a model to test vaccine effectiveness.

The researchers tested two of these vaccines in mice, one with 6 percent density and one with 35 percent, and found that the one with 35 percent was the most protective. 

After vaccination with the higher-density particles, mice were much less likely to develop tumors when later exposed to melanoma cancer cells expressing the SIINFEKL antigen. These mice also generated more T cells targeting SIINFEKL. This is, in part, because dendritic cells, which help to activate T cells, were more likely to take up vaccines with higher antigen density, the researchers found.

In another set of experiments, the researchers created and tested a vaccine carrying a neoantigen, which is only present in the tumor cells. This protein, known as ADPGK, is specifically expressed by colon tumors in a mouse model of cancer. When given the higher density vaccine, these mice were much less likely to develop colon tumors.

In this study, the researchers tested the vaccine as a way to prevent cancer from developing, and in their previous work, they adapted it for use to treat mouse colon tumors that have already formed. In the future, they also hope to use it to combat other types of cancer.

“This interesting work adds another cog to improve the translational potential of the M13 phage platform for cancer immunotherapeutics,” says Alberto Danielli, an associate professor of molecular biology at the University of Bologna, who was not involved in the study. “I look forward to seeing whether this platform is also effective against established tumors and whether additional tumor antigens can be displayed with comparable molecular and immunological efficacy.”

This platform could be especially well-suited for personalized cancer vaccines, because many different antigens could be swapped into the vaccine.

“Because this is a modularly engineered platform, we can change the antigen we are interested in. For colon cancer, we use this specific antigen. For other types of cancer, we can just plug in other antigens and they can be assembled in this platform,” Huang says.

The research was funded, in part, by the Koch Institute Frontier Research Program, the Marble Center for Cancer Nanomedicine, and the U.S. Defense Advanced Research Projects Agency.

Nature Climate Change, Published online: 30 October 2026; doi:10.1038/s41558-026-02791-x

The long-term response of the Greenland and Antarctic ice sheets to warming is often studied using ice-only simulations. Here the authors use a coupled ice–atmosphere–ocean model and find that East Antarctica remains stable even under higher warming because changes in atmospheric circulation increase precipitation sensitivity.

In today’s digital world, the internet is an essential resource for young people to access information, explore community, and find themselves. But we know that this often comes alongside fears of experiencing online harms like doxxing, grooming, or bullying.

Trying to protect your family from everything all the time is impractical and exhausting, but some thoughtful planning can help families devise a workable plan that meets their needs and addresses their concerns.

That’s why this Digital Parent Week, we’re sharing easy tips on how parents and their children can work together to co-curate a digital safety plan that helps all members of the family enjoy the benefits of technology while protecting their online privacy and safety. 

Start with Open Conversations

Online safety begins with communication, not just restrictions. Talk with a young person about how they use the internet: their favourite apps, games, and social media platforms for example. Creating a supportive environment helps young people feel comfortable asking for help if something goes wrong online.

Together, you can think about:

1. What do we want to protect? e.g., I want to protect a young person’s privacy and mental health when they’re playing Roblox. They love Roblox and use it to communicate and hang out with their best friend, so we’d like to find a way to make it as safe as possible.
2. Who do we want to protect it from? e.g., messages from strangers, staying up too late playing, wasting money on pointless digital junk in games.
3. How bad are the consequences if we fail? e.g, losing some money on a scam or having creepy conversations with strangers that could escalate into something more nefarious or dangerous.
4. How likely is it that we will need to protect it? e.g., I don’t know the likelihood of a stranger messaging you on Roblox because I’m not sure how communication works on the platform, but I’ve heard from their friends that strangers message them all the time in some games.
5. How much trouble are we willing to go through to try to prevent potential consequences? e.g., I’m willing to learn from my child how the game works, play the game with my child, and learn how to work the parental controls. 
6. Who are our allies? e.g., my partner, our parents, as well as a young person’s best friends and their parents.

It's easy to think of a young person's experience on the internet as being totally disconnected and different from your own. But now is a great time to also interrogate your own internet habits, like the sorts of sites you engage with, tools you use, and privacy settings you've set. These questions are as valuable to help you as a parent as they are just an internet user.

Sometimes even just figuring out what conversations are worth having can take some effort, we've put together some ideas to get you started in our guide.

Make Privacy a Family Priority

Digital security training can feel overwhelming but this is why a community-oriented approach helps. In speaking with your family and friends, you can build out digital security practices that work for you. This makes it more likely that your privacy practices will become second nature to you and your loved ones.

Use secure messaging services for every communication: Choosing a secure service ensures that your privacy is protected from government and corporate surveillance. If you can convince your family to get on board, options like Signal provide the most extensive privacy protections out of the box through its use of end-to-end encryption and limited data collection. But we understand that sometimes it's easier to stick with whatever the family already uses for a group chat, like WhatsApp, Apple's iMessage, or Google Messages, so take the time to familiarize yourself with the limits of WhatsApp and RCS encryption.

Consider the content posted online: It's easy to accidentally share personal information through photos, messages, and other content. Children and adults may unknowingly share personal information through photos, messages, location settings and app permissions. Take time together to review privacy settings, check which apps can access the camera or location, and discuss why personal details should be shared carefully. Make sure you have family agreement about when and where to post photos of each other or your home.

Create incident response plans: Developing a plan for if or when something bad happens is a good practice for anyone, but especially with younger people. It’s important to strategize with your family around what to do in the event of such things happening, and doing so before an incident occurs is much easier than when you’re presently facing a crisis.

Alongside this, simple steps like using strong and unique passwords on all devices, including those that are shared in the family, can make a real difference. Regardless of age, there’s no time like the present to get started on good security hygiene, and for many that starts with a password manager and two-factor authentication.

How to Think About Parental Controls

Many sites permit parents to control features such as personalization, algorithmic feed, autoplay, endless scroll, reactions, notifications, and time limits. Talk as a family about which features are important and useful and which are problematic. The conversations above around boundaries and digital literacy are a more effective way to prepare youth for accessing the internet. To develop true autonomy, young people need the privacy to discover the world for themselves without their parents monitoring the contents of every conversation they have.

Explore new apps together and revisit family rules as they grow. These decision points where you’re exploring with a young person what you’re both comfortable with can be good moments to “find your way to a yes” for whatever feature or tool or game they’re asking about. The questions they ask, along with your responses, provide a road map for what to look for in privacy controls that can get you to a place where everyone is comfortable engaging with that technology. 

Small Steps = Big Difference

There is no one size fits all to the tech questions and concerns a parent will face, and keeping young people safe online is not about removing every risk and monitoring every click. This Digital Parenting Week, you can start to focus on what’s important for your family and how to best achieve those goals. 

If you're still not sure where to start, installing EFF’s Privacy Badger on every computer in your household is a great starting point to protecting everyone's privacy.

For more detailed information on all these tips and more, visit EFF’s Online Privacy Guide for Parents and Youth.

You know it’s autumn when the scent of pumpkin spice latte begins to waft through the air at the Dunkin’ in the Stratton Student Center, and students in MIT’s Next House residence hall diligently begin to piece together the many parts needed to showcase Next Haunt — a combination of a Halloween haunted house and escape room, built in the basement of the hall.

Every fall since 2014, students — also known as hauntmasters — spend several weekends building the room and preparing puzzles, physical tasks, or word challenges to obtain a code to open locks that let participants escape the house. This year’s theme, titled “Where the Light Ends,” follows “Terror Under Destruction,” “Next Stop: MBTA,” and last year’s theme: “Get in the HOLE.”

Next Haunt is free and open to all MIT students, also known as “test subjects.” Participants typically spend around 20 minutes in small, dark, enclosed rooms, working with four to six teammates to find the clues they need to escape.

Along the way, they have to navigate zombies — played by student actors — who may shout at them or splash them with blood (fake) while they solve the puzzles. If a zombie touches a participant, they become “paralyzed” and can no longer move, but they can still communicate and relay information to their teammates.

Teams must work closely, strategize, delegate tasks, and communicate effectively as the clock counts down. The group with the quickest escape time gets bragging rights, and every group signs their names on a wooden board to show that they participated.

MIT junior Kiyoko Hayano, Next Haunt co-producer, says, “I became involved in Next Haunt during my first semester at MIT because I had experience with larger-scale painting. I split the build director’s role with two other freshmen, including Michelle Liang, our current assistant producer. I thought this would be a great opportunity to take a break from psets and work with my hands, and fell in love with the club.”

Safety first

Before the room is built and the puzzles and costumes are created, careful planning takes place to ensure the escape room is safe, fire-resistant, and passes an on-site inspection. MIT’s Campus Construction and Environment, Health, and Safety offices are involved from the start, and all students who partake in the build are required to attend safety orientation and tool training.

“We currently have 18 people on our executive team, and we have 12 outside volunteers come help over the first few weekends of Next Haunt build,” says Hayano. “We then run Next Haunt for six-and-a-half hours every night for two weekends at the end of October.”

Once the build is complete, several alumni volunteers conduct a “test run” to make sure the puzzles and clues are hard, but not impossible, and anyone who wants to leave the room before the allotted time can easily find the door out built with Velcro closures. 

Escaping by solving the puzzles is not easy, and not all participants are successful. Last year, only 16 percent of the 73 teams successfully got out of the room. The winning team escaped in just under 15 minutes.

Alumnus and previous Next Haunt producer Allen Lin ’26 notes, “I became involved with Next Haunt in my freshman year after being a test subject myself, and I loved the creative and building process so much I wanted to join. My favorite part was seeing all the teams go through the house, especially on the last day when we hosted past Haunt alumni. It was just as rewarding to see how much our test subjects enjoy the house as it was to share the many laughs and moments of camaraderie among my team. I’m excited to come back as an alum and see what the Next Haunt team built this year!”

Environmentally mindful

At the end of Next Haunt, the escape room is dismantled, and anything that can be repurposed safely is stored for the following year’s build. The executive crew then goes to a Boston-area escape room to celebrate their hard work.

“It’s a joy working collaboratively with students and connecting them to campus partners who lend their expertise as the build develops," says Milo Alto, manager of special projects, housing and residential services. “Students get very creative every year with the theme and costumes, but they also learn valuable leadership skills along the way — time management, delegation, working with peers, and implementation of a detailed plan. It’s also a great way to build community.”

Interested MIT students can sign up to help plan and work on the 2027 build.

MIT researchers have shown that they can accurately track muscle movement using tiny magnetic beads implanted in the muscle. These measurements could make it easier for people with certain types of amputations to control their prosthetic limbs.

For decades, researchers have been working to develop neuroprosthetic limbs, which use neural information from muscles remaining after an amputation to guide the prosthetic limb. Measurements of a muscle’s length, velocity and activation can be fed into the control algorithms that guide the limbs, allowing individuals with leg amputation to walk with a more natural gait, or those with arm amputation to manipulate objects more effectively.

Until now, these devices have relied on muscle activity measurements from electrodes placed on the surface of the skin. However, those measurements have not proven reliable enough to ensure safety while walking with a neuroprosthetic leg. 

The MIT team hopes that their new magnetic tracking system, which offers more accurate measurements, will enable commercial development of such limbs.

“For four decades, the standard methodology for neurally controlling prostheses has been to use skin electrodes to measure muscle activation. However, to control a prosthetic limb, joint position, torque, or impedance has to be controlled, and one cannot directly determine such mechanical signals using electrical activation alone,” says Hugh Herr, a professor of media arts and sciences, co-director of the K. Lisa Yang Center for Bionics at MIT, an associate member of MIT’s McGovern Institute for Brain Research, and the senior author of the new study.

In the new study, which appears today in Nature Sensors, the researchers demonstrated the accuracy of the magnetic measurements in three participants with below-knee amputations.

“This interface is minimally invasive, so it could be accessible to more people. You place two beads in the muscle and you have access to all of this rich information about the muscle dynamics,” says Christopher Shallal, an MIT graduate student in the Harvard-MIT Program in Health Sciences and Technology and one of the lead authors of the paper.

The paper’s other lead author is Cameron Taylor PhD ’20, an assistant professor of biomedical engineering at the University of North Carolina at Chapel Hill.

Tracking muscles

For many years, Herr’s lab has been developing human-machine interfaces that can be used to control an artificial limb in a way that makes it more like using a natural limb. Obtaining accurate measurements of how the user is moving the muscles at the amputation site is critical to the success of this type of prosthesis.

Electrodes on the surface of the skin can reveal the electrical activation of the muscle below, and those measurements can be converted to estimates of how much a muscle is contracting or flexing. Based on that input, a robotic controller generates commands that guide the prosthetic limb in the user’s intended direction. 

However, that measurement approach, known as electromyography, does not always provide accurate enough information. The electrical signal is not a perfect proxy for muscle dynamics; for example, when a muscle is held at a fixed length for several seconds, the signal fades. Additionally, the electrodes may pick up signals from muscles other than the intended target, and sweat can interfere with detection of the signal.

Last year, Herr and his colleagues reported a “bionic knee” prosthesis that uses electrodes implanted in the muscles to provide accurate measurements and feed them into a robotic controller. This approach proved successful in a small clinical study, but alongside that work, Herr’s team has also been working on a less invasive approach that relies on magnets instead of electrodes.

With this approach, known as magnetomicrometry, pairs of magnets are implanted into muscles. When a muscle contracts or stretches, external sensors placed on the skin can measure the change in distance between the magnets, allowing muscle length and velocity to be calculated.

“Magnets can be tracked wirelessly through the body, so you can get very specific muscle signals and measure the mechanical output of the muscle without needing to be extremely invasive,” Shallal says.

Better control

In a 2021 study led by Taylor, Herr’s lab first demonstrated the magnet-based technique in animal models. The new Nature Sensors study extends the approach to humans with long-term magnetic implants. For the study, the researchers recruited three participants who have below-knee amputations.

All of these participants had a type of amputation that creates what’s called an agonist-antagonist myoneural interface (AMI). With this surgical procedure, muscle pairs are reconnected during surgery so that they still dynamically communicate with each other within the residual limb. This sensory feedback, called proprioception, helps the wearer of the prosthesis to decide how to position and move their prosthetic limb. 

Participants had magnets implanted in up to four muscles of the ankle joint. The surgeries were performed at Brigham and Women’s Hospital in collaboration with plastic surgeon Matthew Carty, a co-author of the study. The beads are 3 millimeters in diameter, about the size of a sesame seed. The magnets are made from an alloy of neodymium, iron, and boron, and are coated with gold and a polymer called parylene C to make them biocompatible. The magnetic signals can be read from a depth of up to 6 centimeters from the skin’s surface, using a customized external sensor that is worn over the skin, positioned over the muscles that are being tracked. 

Each of the participants in this study also had electrodes implanted into the same muscles that received magnetic beads. This allowed the researchers to compare the accuracy of three different types of measurements: magnetomicrometry, electromyography with implanted electrodes, and electromyography with electrodes on the surface of the skin.

During the experiments, the participants were not wearing a prosthetic limb but were asked to perform tasks such as moving a cursor on a screen to a particular target using their muscle movements. Using each of the three measurement techniques, the signals generated by the muscle movements were translated into control signals that, in this case, moved the cursor but in the future could be fed into the controller of a robotic ankle-foot prosthesis.  

“The cursor on the screen represented the degrees of freedom that they would control on a robotic ankle-foot prosthesis. This virtual neuroprosthetic task allowed us to compare the performance across different neural interfaces, and our results suggest that the magnet-based approach can offer more accurate and stable control,” Shallal says.

The researchers are now performing a larger study in which they are evaluating how well people can control prosthetic limbs using measurements from the magnetic beads. 

The research was funded, in part, by the Yang Tan Collective at MIT as well as a National Science Foundation Graduate Research Fellowship.

A user who is frustrated with changes a social media company made to their favorite messaging platform, like the introduction of an annoying AI chatbot, can’t do much to address the situation, short of leaving the platform.

Now, MIT researchers are offering an alternative path.

They’ve created Social.Wiki, a system that allows users to easily edit any social application made within Social.Wiki, and choose which of the edits made by others they want to see. 

For example, a user could change a messaging site on Social.Wiki to remove an AI chatbot and instead add a restaurant voting feature so their group chat can finally decide where they are going out for dinner.

“The infrastructure that we are all dependent on online is very rigid. It is based on these one-size-fits-all applications that we don’t have a lot of power to change. This project is about trying to imagine what a different web would look like where anyone can participate,” says Theia Henderson, an electrical engineering and computer science (EECS) graduate student, and lead author of a new paper on Social.Wiki.

The researchers envision a world in which, if a user is unhappy with their social app, they can edit it to change it however they like — without getting cut off from their social connections.

The platform enables users with no coding expertise to create a range of websites, including social sites like photo sharing networks, discussion forums, and dating apps. It handles all back-end work, eliminating the need for creators to set up servers or arrange databases.

Social.Wiki employs a decentralized infrastructure that allows users to retain ownership of their data as they create, edit, and navigate social websites within the platform. It also includes a “data guard” that protects user data from malicious or buggy applications. 

Through case studies and user tests, the researchers demonstrated the breadth of applications people can create with Social.Wiki, including a rideshare app where riders propose the prices they are willing to pay and a collaborative canvas for creating art together.

While Social.Wiki is still in its early days, the researchers plan to continue refining the platform as they deploy it to a wider audience. 

“We want to empower the creation of far more social applications that meet the needs of unusual small groups. Each group is unusual, but everyone is in unusual groups. Everybody has their own needs for social applications that we want to be able to address,” says David Karger, professor of EECS, a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL), and senior author of the paper.

Henderson and Karger are joined on the paper by EECS graduate students Carmel Schare and Ana Dodik; Clemens Klokmose, a professor of computer science at Aarhus University in Denmark; Ziv Epstein, a postdoc in the MIT Media Lab; and David D. Clark, an MIT research scientist. The paper will be presented at the ACM Symposium on User Interface Software and Technology in November.

Letting users choose

Social.Wiki is a Wikipedia-like system in which users can navigate to different web applications or create their own web application within the Social.Wiki ecosystem. Each website has an “edit” button that allows any user to change the content of the site, from the text color on a message board to the pricing algorithm in a ride-sharing app.

Clicking the “edit” button in Social.Wiki opens a page that lets users view and edit the source code manually or prompt an AI assistant to make changes to the site. 

This platform builds on the researchers’ prior work on Graffiti, a web framework with a decentralized infrastructure that makes building personalized applications easier. 

With Social.Wiki, the researchers sought a collaboratively editable system that would allow anyone to revise web applications to be ideally suited for their needs and the needs of groups they belong to. 

To enable this structure, Social.Wiki relies on a strategy the researchers call “plural governance.” Instead of a central moderator deciding which content users will see, each Social.Wiki user can choose the rules that determine which edits they will see on Social.Wiki webpages.

Users can also identify “trusted editors,” so they will see edits from individuals or organizations they consider reliable.

For instance, a user could customize their rules, or “governance policy,” so they only see edits to a site that have been endorsed by at least two of their friends. When the Social.Wiki browser loads a site for a user, it applies the governance policy that user selected to decide which version of the site to present.

For example, based on each user’s choices and edits, the system might show one user a ride-sharing app with a uniform pricing algorithm while another user might see the same app with a dynamic pricing algorithm.

“No website is tied to a particular machine. This means we have no centralized ownership over the websites or the underlying infrastructure,” Henderson explains.

Security and creativity

In this decentralized infrastructure, user data are stored separately from the websites that access it, which helps keep data secure.

Social.Wiki also employs a data guard, which prompts a user to review each request from a website that is attempting to access their data.

“In Social.Wiki, these websites are held in common and anybody can edit them, but the data guard makes sure that each user stays in control of what happens to their data,” Karger adds.

The researchers evaluated Social.Wiki through several case studies. For one, they developed a messaging platform that allows participants to modify the chat’s styling and interface without affecting the experience of others. In another, they built a collaborative ride-sharing app that enables riders to adjust the fare they want to pay using a transparent “living wage” calculator.

They also sponsored a hackathon in which users built Social.Wiki applications.

Participants developed a range of creative sites, including a housing co-op communication tool that organizes chats on a map of the co-op, as well as a garden-themed microblogging site where posts are “planted” as flowers.

“Even the little bit that has been done so far with Social.Wiki demonstrates that there is so much more that could be done with social apps if users could specialize them to meet a particular group’s needs,” Karger says.

Moving forward, the researchers are working to deploy Social.Wiki to a broader audience and gather feedback from more diverse user groups. They also want to make technical upgrades to enable end-to-end encryption on private message data and provide better developer tools.

This work was funded, in part, by Schmidt Sciences and the National Science Foundation (NSF). 

For decades, scientists have been able to look at individual moments in the development of an embryo: a snapshot of a developing brain, a picture of how the heart takes shape, or a glimpse of cells beginning to form the nervous system.

But what if instead of taking snapshots, they could watch the entire process unfold?

A new study out of the lab of MIT Professor Jonathan Weissman, a member of the Whitehead Institute for Biomedical Research and a Howard Hughes Medical Institute investigator, offers one of the most detailed views yet of how a mammal is built, cell by cell.

By following the family history of over a million cells as a mouse embryo develops, the researchers have created a kind of developmental blueprint, or movie, showing how cells divide and ultimately become the tissues and organs in the body.

This open-access work, published Oct. 8 in the journal Cell, in collaboration with Zachary Smith’s group at Yale University and the Max Planck Institute for Molecular Genetics, provides a comprehensive view of a process that has long been studied in pieces: how a single fertilized egg gives rise to hundreds of different kinds of cells, arranged in the right places and at the right times to build an animal.

“This is really a view of how the information in the genome plays out over space and time to go from linear DNA to a three-dimensional animal,” Weissman says.

The map could have broad implications for understanding birth defects and cancer. Many birth defects arise when cells change course as tissues and organs are being assembled. A detailed map of typical development could help researchers pinpoint where and when those processes go awry. In cancer, the same approach could help scientists trace when cells acquire traits such as the ability to spread or resist treatment. 

The map could also inform stem cell research by revealing the sequence of cell states that leads to mature, functional cell types.

In the 1980s, scientists ventured into similar territory by painstakingly following the development of Caenorhabditis elegans, a tiny worm whose transparent body contains only about 1,000 cells. Using microscopy, they could trace how individual cells divided and where their descendants ended up.

But achieving something similar in a mammal was a challenge on a vastly different scale. Mammalian embryos contain far more cells, develop inside the uterus — out of sight — and do not follow a completely predetermined script.

The new study tackles this challenge using a technology developed in the Weissman Lab called PEtracer. The system acts somewhat like a flight recorder inside a cell. PEtracer makes small, heritable changes to DNA that are passed down as cells divide. By reading those changes later, scientists can work backward to reconstruct which cells came from which ancestors, essentially building a family tree for the cells in the developing embryo.

In this work, the researchers combined these family trees with information about what genes were turned on in each cell and where these cells were located in the embryo. They analyzed over a million cells from mouse embryos collected at multiple points during development, allowing them to follow not just where cells ended up, but when they began heading toward particular fates.

A new study from Jay Shendure’s group at the University of Washington, published concurrently in the journal Science, uses a related approach called DNA Typewriter to track cell divisions in developing mouse embryos and build a record of their cellular history. 

Both studies provide a much more comprehensive look at mammalian development than existed previously. The Weissman team, however, studied embryos at closely spaced stages of development, looking at multiple embryos at each stage. This gave them a more detailed picture of how development unfolds and allowed them to ask how consistently cells follow the same paths from one embryo to another. 

With this detailed timeline, the researchers could also determine when cells commit to particular fates, how many early cells go on to form a tissue, and how they ultimately contribute to the developing organ.

While earlier studies might focus on one cell population, this approach puts those individual stories into the context of the entire developing animal — a perspective that is particularly useful for understanding disease. 

Cancer, for example, is not a single event, but a process in which cells acquire new characteristics over time, including the ability to spread or resist treatment. A lineage map could allow researchers to trace when those characteristics emerge and how cells change as they acquire them.

“Development and cancer both depend on decisions cells make about what they will become,” says Luke Koblan, a postdoc in the Weissman Lab and co-first author of the paper. “By tracing how cells develop in mouse embryos, we hope to better understand how cancer cells change in ways that help them spread or resist treatment.”

The work could also provide a new guide for stem cell research. Scientists have spent decades trying to coax stem cells into becoming mature, functional versions of particular cell types for research or potential therapies. But reproducing the precise conditions in an embryo is difficult.

“If we could have a step-by-step recipe for how to create every cell type in the body,” says graduate student and co-first author William Colgan, “that would be incredibly powerful for stem cell biology.”

Weissman compares the work to the Human Genome Project: knowing the sequence of the human genome gave scientists the ingredients — but not the instructions for how those ingredients are used — to build an organism.

“The genome is like the ingredient list for a four-course meal,” he says. “It tells you what the ingredients are, but it doesn’t tell you which ones you use when, how much you use, or where you put them. Without the recipe, it’s impossible to know how those ingredients can become the meal.”

This new map begins to fill in the details of that missing recipe. The result is a resource that researchers can use to explore mammalian development in a fundamentally new way. Rather than studying development as a collection of separate processes, scientists can now begin to see how those processes fit together into one unfolding story. 

In the most recent update to Apple’s devices, iOS 27.2, the company has introduced a new setting to “Exclude Apps” from Siri AI, fulfilling one of our asks from our Encrypt It Already campaign earlier this year.

One of our main concerns with new AI features on devices is how those features interact with end-to-end encrypted content, particularly with secure messaging apps. Many AI features require cloud processing, and despite privacy protections engineered into these systems, the content of secure messaging apps may leave a user’s device without their knowledge. That changes the privacy concerns around using those tools. In a perfect world, users would be able to put a barrier up to ensure data from certain apps doesn’t leave the device. Here was our initial ask:

You should be able to tell the operating system’s AI to not access an app, even if that comes at the “cost” of missing out on some features. The setting should be straightforward and easy to understand in ways the Gemini and Apple Intelligence controls currently are not.

At launch, we found the settings to control Siri AI’s app access behavior pretty confusing. You had to toggle several options to block Siri AI off from accessing an app, or disable Siri AI entirely. Turning off Siri AI and reverting to Siri Classic is, for now, still a good option if you’d prefer to use the older version.

The most recent update is far more clear and does more to protect your data. And, just as expected, it comes at the cost of some features. That’s a good thing, actually. The features not working shows that the new setting is effective. 

Here’s how to add apps to the exclusion list, once you’ve updated to 27.2:

• Open Settings > Siri > Excluded Apps
• Tap “Add Apps” and select any apps you’d like to add to the exclusion list

Now Siri AI cannot access the contents of the app. Excluding an app also means you may lose some features. For example, if you exclude the Apple Messages app, you will no longer be able to have Siri AI read or respond to messages for you. Even the “onscreen awareness” feature, where you invoke Siri and ask it for information about what you’re currently looking at, will not work anymore. For example, if you exclude Signal, you cannot invoke Siri and ask it to discern any information in the Signal app. You’ll just get these messages:

What’s the State of This on Android?

This update from Apple is also a good excuse to check in on the process on Android. It has improved a little, but is not as straightforward as Apple’s new settings, and lacks the ability to set the screen context feature per-app. But you do have some control over what Gemini can access: 

• Open up the Gemini app
• Tap your user photo, then Settings > Personal Intelligence > Connected Apps
• Revoke access to any apps you do not want Gemini to have access to

This process seems to work well enough for some data and we like that it’s opt in, but it’s not exactly intuitive. For example, Signal doesn’t appear there because Signal doesn’t support Gemini’s app access feature at all. That means Gemini can’t access the content of Signal directly, which is good. 

But Gemini can read notifications if you’ve enabled the “Device Assistance” Connected App (which you likely have, since that also enables basic features, like setting a timer—check under Settings > Personal Intelligence > Connected Apps), and it can use screen context unless you’ve disabled the Device controls > Screen context > “Use screen and app data” option in the Gemini Settings. These two features may access the contents of the Signal app or notifications, if you choose to use them. We’d prefer a more hard-lined approach that reduces the chances of user error here: Google should give users the ability to turn these features off per app.

Apple clearly understands that some users want stronger controls over what Siri AI can and cannot access. But we think they should go further and offer the option to only use “on device” AI features, similar to what Samsung offered with its Galaxy AI. We’re happy to see this change and think all operating systems need to consider similar controls moving forward.

A federal appeals court should curb the government’s abuse of grand jury subpoenas to unmask social media users who criticize lawless immigration enforcement.

That is the message from the ACLU, EFF, and other civil liberties and trade groups who filed a friend-of-the-court brief in a sealed case in the U.S. Court of Appeals for the District of Columbia.

The case involves the government’s investigation of an anonymous Reddit user who commented on a post about an immigration officer in Minnesota. The Department of Homeland Security (DHS) first issued an administrative subpoena to Reddit to unmask the user. When the user challenged the subpoena in court on free speech and statutory grounds (as many others have), DHS withdrew it. But then the government obtained a separate grand jury subpoena—a subpoena issued by a prosecutor to gather evidence to present to a grand jury before any criminal charges are filed—for much of the same information. The user then challenged the grand jury subpoena too, and the case is currently being appealed.

The right to speak anonymously is essential. It preserves privacy and shields critics from government retaliation. In the past year, the Department of Justice (DOJ) has issued grand jury subpoenas to social media companies for their users’ personal data. This is frequently done for improper purposes and without really showing that social media users have committed any crime. These subpoenas have the effect of intimidating and chilling critics, as immigration authorities have engaged in invasive, unconstitutional, and sometimes violent conduct.

As the amicus brief makes clear, courts should not defer to the government’s claims regarding the legitimacy of using grand jury subpoenas to unmask internet users. Instead, the subpoenas must receive proper First Amendment scrutiny. Like many other types of legal processes, grand jury subpoenas are issued by federal officials without prior court approval. And they too can be abused to harass or retaliate against people critical of the government.

The amicus brief was filed in early September and recently unsealed. The brief was signed by the American Civil Liberties Union of DC, EFF, the Center for Individual Rights, the Foundation for Individual Rights and Expression, the Cato Institute, and NetChoice.

It is notable that NetChoice signed the amicus brief. It is a trade group that represents many companies who have received similar subpoenas including Reddit, Google, Meta, PayPal, among others. EFF has called on these companies to do more to defend their users’ speech and privacy rights in the face of these subpoenas.

During its annual meeting on Oct. 19, the National Academy of Medicine (NAM) announced election of 100 new members, including MIT faculty member Michael Yaffe and six MIT alumni: Steven Allen Abrams ’79; Susan L. Ettner PhD ’91; Diane Medved Harper ’80, SM ’82; Albert Icksang Ko ’81; Jennifer Lewis ScD ’91; and Alexander Marson PhD ’08.

Election to the academy is considered one of the highest honors in the fields of health and medicine and recognizes individuals who have demonstrated outstanding professional achievement and commitment to service.

Michael Yaffe, David H. Koch (1962) Professor of Science, is the founding director of the MIT Center for Precision Cancer Medicine, as well as a professor in the MIT departments of Biology and Biological Engineering and a member of the MIT Koch Institute for Integrative Cancer Research. He is also an attending surgeon and intensivist at the Beth Israel Deaconess Medical Center, where he specializes in injury and surgical oncology, and was recently appointed as senior advisor to the director of the National Cancer Institute (NCI) for the NCI Intramural Research Program. 

The NAM recognized Yaffe for “the discovery of phospho Ser/Thr-binding domains in proteins and how they regulate signaling pathways controlling the cell cycle or in response to stress.” His contributions in this area were pivotal to “the emergence of systems biology and bioinformatic approaches to map motifs across the kinome and characterize biochemical principles that determine kinase-substrate selectivity.” 

Yaffe’s work in cell signaling more broadly seeks to understand how signaling pathways that control DNA damage responses, survival, cell cycle progression, and immune activation are integrated at the molecular and systems levels to control tumor outcomes after treatment, and to translate discoveries into more effective cancer drug combinations and treatment strategies.

Yaffe earned his doctoral degree in biophysical chemistry and his medical degree from Case Western Reserve University. Before joining the MIT faculty in 2000, he completed residencies in both general surgery at University Hospitals of Cleveland and surgical oncology at New England Deaconess Hospital, a fellowship in surgical critical care at Harvard Medical School’s Harvard-Longwood Critical Care Program, and a postdoctoral fellowship with Lewis Cantley at Harvard Medical School. Yaffe is a decorated veteran of the war in Afghanistan and retired as a colonel from the U.S. Army Reserve Medical Corps in 2021. He is also the scientific editor-in-chief of Science Signaling, an elected member of the Association of American Physicians, and a 2021 MacVicar Faculty Fellow.  

MIT alumni who were elected to the NAM for 2026 are affiliated with the following departments:

• Steven Allen Abrams ’79 is an alumnus of the Department of Biology.
• Susan L. Ettner PhD ’91 is an alumna of the Department of Economics.
• Diane Medved Harper ’80, SM ’82 is an alumna of the departments of Chemical Engineering and Materials Science and Engineering.
• Albert Icksang Ko ’81 is an alumnus of the departments of Chemistry and Biology.
• Jennifer Lewis ScD ’91 is an alumna of the Department of Materials Science and Engineering.
• Alexander Marson PhD ’08 is an alumnus of the Department of Biology.

Established originally as the Institute of Medicine in 1970 by the National Academy of Sciences, the National Academy of Medicine addresses critical issues in health, science, medicine, and related policy, and inspires positive actions across sectors.

“I am thrilled to welcome these outstanding health and medicine leaders into the National Academy of Medicine,” said NAM President Monica M. Bertagnolli. “Their extraordinary talent and commitment to advancing evidence-based guidance and solutions for our country’s greatest health-care challenges will strengthen our efforts to improve health for all.”

One of the main ways that the APOE4 gene variant, the biggest genetic risk factor for late onset Alzheimer’s disease, undermines brain circuit function is by disrupting the process of insulating neurons with a fatty sheath that optimizes their electrical transmission. A new study led by neuroscientists at the Picower Institute for Learning and Memory at MIT provides several lines of evidence to implicate a specific molecular pathway in that disruption, and uses that insight to demonstrate an intervention for preventing it in lab experiments.

The study, published Oct. 15 in Cell Stem Cell, shows that APOE4 leads to a suppression of “Wnt” signaling in the oligodendrocyte cells that provide neurons with a fatty insulation, called myelin. When Wnt signaling is deficient, they showed, the cells accumulate droplets of lipids rather than deploying fats to properly myelinate the neurons’ axon branches. When the researchers restored Wnt signaling in APOE4-carrying oligodendrocytes, myelination rebounded, both in human cell cultures and in mice modeling Alzheimer’s.

The research team, led by Picower Professor Li-Huei Tsai and study lead author Leyla Akay PhD ’25, a former graduate student in Tsai’s lab, discovered Wnt’s key role via multiple methods, including a screen in which they used CRISPR gene editing to systematically knock out each protein-coding gene in the genome, one by one, in cultures of human APOE4-carrying oligodendrocytes. The screen showed that when genes promoting Wnt activity in oligodendrocytes were lacking, lipid droplets accumulated, and that when genes suppressing Wnt were lacking, lipid droplets were, too.

“This proof of principle of doing CRISPR screens for genetic modifiers of APOE4-associated lipid accumulation in a central nervous system cell type is very exciting,” says Tsai, who also directs MIT’s Aging Brain Initiative. “I think this type of screen not only provides new insights into cellular pathways contributing to lipid metabolism defects, but really also has the potential to provide new targets for drug development.”

For want of Wnt

In 2022, Tsai’s Lab, led by Akay, Djuna Von Maydell PhD ’25, Jose Davila-Velderrain, and Joel Blanchard (also a co-author of the new study), showed that APOE4 caused lipid droplet accumulation in oligodendrocytes and upset myelination. But, Akay says, that droplet accumulation could happen for many reasons. The new study sought to dig deeper into the underlying molecular mechanisms.

“This CRISPR screen allowed us an unbiased way to test every single possibility,” Akay says. “What kind of lipid enzymes are involved in this process? And what are upstream pathways that could be targeted?”

In parallel with the CRISPR screen, the team also analyzed the chromatin accessibility of the DNA in human oligodendrocyte cell cultures and in postmortem human brain samples, in each case comparing APOE4-carrying samples to ones carrying the neutral APOE3 variant. This analysis of ATAC-seq data allowed them to assess which genes were most accessible for expression versus which ones were locked down from transcription. The chromatin analysis showed that genes promoting Wnt signaling were less accessible in APOE4 carriers.

“There was a striking overlap between ATAC-seq and CRISPR screen results,” Tsai says. “Both pointed to a dysregulation of the Wnt signaling pathway.” 

Toward a therapy

Further analysis, including direct observations in cells at the lab bench, revealed how Wnt signaling became depressed in cultured APOE4 oligodendrocytes. Two proteins, beta-catenin and GSK3b, each compete to regulate Wnt — beta-catenin promotes it, and GSK3b suppresses it. The team found that in APOE4 oligodendrocytes compared to APOE3 ones, there was reduced beta-catenin expression and excessive GSK3b expression.

The team then tested interventions that manipulate GSK3b activity. In human APOE3 oligodendrocyte cultures, they showed that increasing GSK3b with a chemical called Dif3 increased lipid droplets. In APOE4 oligodendrocytes, they showed that inhibiting GSK3b with a chemical called CHIR99021 reduced droplets. In more complex “organoid” cultures combining APOE4 oligodendrocytes with neurons, they found that inhibiting GSK3b increased myelination and promoted the growth of neural axons.

The tests suggested that GSK3b inhibition could be a proof-of-concept therapeutic strategy for restoring Wnt signaling and myelination. As a final test, they treated mice carrying APOE4 and genetically modeling Alzheimer’s tau pathology with doses of CHIR99021. The treated mice exhibited a reduction in lipid droplets in the hippocampus of the brain, a key memory-making region. Moreover, they also showed increased myelination there, compared to untreated controls.

In addition to highlighting suppression of Wnt signaling, the CRISPR screen and chromatin analysis provided key clues about the molecular mechanisms by which loss of Wnt alters how oligodendrocytes metabolize lipids. Akay and Tsai say they are eager to follow up on those clues. They are also interested in investigating GSK3b’s linkage to tau phosphorylation, a major feature of Alzheimer’s pathology.

In addition to Tsai, Akay, and Blanchard, the paper’s other authors are Anna Bright, Daiyun Dong, Ethan Smith, Robert Hurt, Carles Boix, Kate Louderback, Janine Medrano, Dingcheng Sun, Haonan Lin, Oisín King, Gwyneth Welch, Hiba Nawaid, Emre Agbas, Alan Jiang, Adele Bubnys, and Ji-Xin Cheng.

The National Institutes of Health, the Robert and Renee E. Belfer Family Foundation, the Manthripragada McManama Alzheimer's Fund, the CAMA Seed Fund, the J. Crayton Pruitt Foundation, Joseph P. DiSabato and Nancy Sakamoto, and the Freedom Together Foundation provided funding for the study.

Raw data from the study is available on the Open Science Framework. Akay and Tsai have applied for a patent on the CRISPR screen results.