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.

Daniel Solove argues in the Wall Street Journal (alternate link) that giving people control of their personal data is not an effective way to regulate privacy in this era. Instead, we need to hold companies accountable for their actions, similar to what we do with food and drug companies. Measures such as rigorous data minimization, fiduciary duties, liability for negligent or reckless technological design, liability for algorithms that cause harm, and multi-stakeholder review of technologies will be far more effective.

Paper.

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EFF and ARTICLE 19 have submitted joint comments to the European Commission on draft guidelines for the Digital Services Act’s trusted flagger mechanism. Having long advocated for a DSA that protects freedom of expression while preserving intermediary liability protections and the prohibition on general monitoring, we welcome the Commission's effort to provide practical guidance on how the trusted flagger system should operate. 

The DSA’s trusted flagger system can help platforms identify illegal content more efficiently. But if implemented poorly, it could also encourage over-removal of lawful speech, weaken due process, and give government authorities disproportionate influence over online expression. 

We support the Commission's focus on good practices and illustrative examples, rather than legal interpretations that could inadvertently steer platforms toward particular enforcement outcomes—and argue that the guidelines should include stronger safeguards to protect freedom of expression, due process, and the impartiality of the trusted flagger system. 

We also support the Commission's clarification that the DSA itself does not define "illegal content"; that determination must come from applicable national or EU law. Trusted flaggers submit prioritized notice, but platforms remain responsible for determining whether content is actually illegal. Platforms must therefore conduct careful, informed assessments and should not assume that a trusted flagger notice necessarily warrants restricting content. 

Our submission highlights several areas where the guidelines could be strengthened: 

• Cross-border assessments require caution. Platforms should not rely on a trusted flagger notice to assess legality across Member States, where national legal frameworks may differ. 

• Systemic risks extend beyond content moderation. The DSA's systemic risk framework should not rely too heavily on individual moderation decisions, but should also consider broader platform design choices, including recommender systems. 

• Law enforcement authorities should generally not be granted trusted flagger status. They already have statutory powers under Article 9 of the DSA, and combining those powers with trusted flagger status creates a risk that platforms may treat trusted flagger notices as de facto removal orders, undermining due process and the rule of law. 

• Civil society organizations play an essential role. Civil society organizations help identify illegal content and report human rights abuses, but the guidelines should also recognize that these organizations may face retaliation for their work and should be protected from abusive campaigns that threaten their independence. 

• Trusted flaggers should complement—not replace—existing partnerships. The new mechanism should not sideline existing trusted partnership programs, including collaborations with civil society organizations that do not or cannot hold trusted flagger status, especially those outside of the EU with valuable regional expertise.  
  

Read the full submission here:

Will a warming climate and changing weather patterns lead to more grid blackouts and other energy disruptions? Answering that question requires studying both regional climate forecasts and local energy systems, including emerging renewable generation, storage, transmission lines, and demand forecasts. The lack of such studies is one reason why energy developers and grid operators rarely consider climate change when deciding where to build their next project.

Now MIT researchers have created a way to make more climate-informed energy siting choices, and shown how it can be used to make energy systems more resilient and reduce blackouts. The researchers’ framework, described today in Nature Energy, combines fine-scale meteorology with detailed simulations of energy infrastructure. It shows how the location of new energy projects will play a significant role in meeting future demand in a changing climate.

The researchers applied their framework to decarbonized energy systems in New England and Texas, finding that energy systems designed for historic climate conditions could face up to a fivefold increase in energy shortfalls, potentially leading to blackouts, by 2050. Taking climate change into account when designing the system, conversely, improved the resilience of both regions’ energy systems at no or very little additional costs.

“As we mitigate climate change with renewables, we can also adapt to climate change by using future weather projections in our power system planning, and the extra costs of that adaptation are, at least in this study, not much,” says senior author Michael Howland, MIT’s Jeffrey Cheah Career Development Professor. “It’s different from other climate adaptation studies, where building a big seawall or other mitigation efforts are really expensive. In this case, if we’re smart when we design our power system decarbonization plans, it could cost almost nothing extra to simultaneously adapt to climate change.”

Joining Howland on the paper are first author Liying Qiu, a former MIT postdoc; Rahman Khorramfar and Shen Wang, current postdocs at MIT; and Saurabh Amin, MIT’s Edmund K. Turner Professor in Civil Engineering.

A better way to think about energy projects

The world’s energy systems are in a period of change. On the demand side, that change is driven by trends like the rising demand for artificial intelligence and the electrification of industries including transportation. On the supply side, that change is driven by the plummeting costs of renewable systems like solar and wind energy.

“That drop in costs has enabled the widespread deployment of renewables, because they’re the cheapest electricity-generation solution in many locations,” Howland explains. “At the same time, for the first time in more than a decade, electricity demand is starting to increase in the U.S.”

As low-cost variable renewable energy supplies increase, matching supply and demand throughout the day can become a harder problem for energy system operators. Adding to that complexity is the fact that renewables and energy demand are both influenced by weather and climate in different ways in different regions.

In the past, researchers have generally studied the impacts of climate change on individual technologies, for instance studying how it might change global wind and solar patterns. Other studies have considered the impact of climate change on states or other large areas, overlooking the specifics of regional energy systems. More recently, region-specific studies have been done but typically relied on low-resolution, global climate models.

“That’s what climate models are good at: giving you the global picture at coarse resolution,” Howland explains. “That limits insights for regional system planning and risk assessments.”

For their paper, the MIT researchers chose to study Texas and New England because they provided two different climate types and energy systems. The team used fine-scale meteorology models and considered the influence of climate change on weather-related energy failures.

“This study explores the joint, simultaneous impacts on multiple components of the energy system, similar to compound events studied in climate science,” Howland explains. “An extreme weather event can impact wind and solar generation and electricity demand all at the same time. Our hypothesis is that’s likely to be the biggest impact we’ll see from climate change on energy systems.”

The researchers also considered the impact of using climate change models to help site energy projects, looking out to 2050 because that’s the typical lifetime of wind and solar plants being built today. They found that locations that are best suited to provide the renewable wind and solar energy that the grid needs were meaningfully different in future climate conditions than in the historic climate.

The researchers found that climate change could increase energy failures by as much as 500 percent by 2050 if the siting did not consider future climate conditions. Such failures were driven primarily by the interaction between multiday renewable shortfalls and energy system design decisions like where to build solar farms and transmission lines.

“We are telling people where you put your wind and solar matters a lot for your ability to deliver energy when you need it,” Qiu explains. “We need to think more about the when and where of adding renewables rather than only focusing on adding overall capacity.”

In New England’s power system, the researchers found that energy supply disruptions caused by climate-related weather changes necessitate investment in solar capacity and transmission lines close to energy demand centers like cities. In Texas, energy disruption risks were primarily driven by transmission constraints.

The researchers found that climate-informed designs would prioritize adding wind farms in West Texas to better align with future demand patterns. The study assumes both regions will continue adding renewable capacity, thus the researchers concluded that Texas could improve the resilience of its grid at near-zero additional cost.

“We are showing that increasing energy resilience requires more than just spending more money,” Qiu says. “It primarily requires better and smarter planning.”

A new approach to adaptation

Howland says taking a broader view of climate change’s impact on energy systems helped his team get a clearer picture of blackout risks and other potential supply problems.

“On the individual power plant level, it’s not necessarily that climate change is a dominant uncertainty, so it really comes down to how all these energy system components and energy demand relate to each other,” Howland says. “That’s where we see the biggest impact of climate change, rather than on the level of individual wind or solar plants.”

Because the researchers used expensive, high-resolution models, Howland says their new model wouldn’t be practical for grid operators to use in their daily work today, but they hope to soon develop faster models that grid operators could use more easily.

“This study shows the opportunity and the need,” Howland says. “There are risks to not adapting our system, but if we do adapt our system, there could be big opportunities that are not costly. Now the key challenge is that we have to address the massive data and translation gap we have between meteorology and energy system planning and management. Right now, there’s too big of a divide between climate and weather modelers and power system practitioners. We want to continue to break that barrier down through interdisciplinary research.”

This work was supported by the MIT Climate Grand Challenges, the MIT Climate and Sustainability Consortium, and the MIT Energy Initiative Future Energy Systems Center.

Engineers often use vision-language models to produce new designs, such as for airplane or automobile components. To simulate how those components will perform in realistic situations, they’ll use tried-and-true computer-aided design (CAD) software to generate 3D models of those designs, which they can put through virtual crash or durability tests. 

Researchers from MIT and elsewhere have now developed a system that can teach a vision-language model to automatically convert 2D designs into CAD programs that are much more accurate and functional compared to other approaches, while using only a fraction of the computation.

By improving the performance and efficiency of AI-driven CAD generation, this technique could streamline the rapid prototyping process and reduce costs. It could also help engineers identify beneficial design choices they might otherwise overlook. 

The system generates new data based on the model’s abilities as it attempts to convert a 2D image into a CAD program. The framework corrects the model’s failures and incorporates them into a dataset with its successful solutions. 

It uses these data to teach the model how to fix specific mistakes and tackle tricky problems it would struggle with on its own.

“We want engineers to be able to point our framework at an underperforming CAD model, set a compute budget, and let the system take over — turning the model’s own mistakes into better training data,” says lead author Giorgio Giannone, a research affiliate in the Design Computation and Digital Engineering (DeCoDE) Lab at MIT and a principal research scientist on the AI Innovation Team at Red Hat.

He is joined on the paper by Anna Claire Doris, a mechanical engineering graduate student at MIT; Amin Heyrani Nobari, an MIT postdoc; Kai Xu of RedHat; and co-senior authors Akash Srivastava, director of Core AI at IBM and a principal investigator at the MIT-IBM Computing Research Lab; and Faez Ahmed, associate professor of mechanical engineering at MIT, leader of the DeCoDE Lab, and a principal investigator at the MIT-IBM Computing Research Lab. The research was recently presented at the International Conference on Machine Learning.

“Nearly every physical product around us, from airplanes to appliances, begins its life as a CAD model. Industry teams are eager for AI that can help speed-up the creation of these designs, but today's models often produce simple shapes inadequate for practice. What excites me about this work is that it gives many image-to-CAD-code models a way to improve themselves, learning from their own errors rather than waiting for more human-made data — and that brings trustworthy AI design tools much closer to everyday engineering,” says Ahmed.

Model-aware data

The researchers are working toward building vision-language models (VLMs) for CAD generation. These VLMs take a 2D image and some descriptive text, and output Python code that can be executed in a CAD software program to generate a 3D model of a physical object.

They studied the challenges of deploying existing VLMs for this task and determined the main bottleneck that limits their capabilities is the lack of diverse, high-quality CAD datasets to train them. 

To remedy this, they sought to create new data to teach a model how to perform CAD generation, using a process known as data augmentation.

In data augmentation, scientists typically create new data by randomly tweaking existing data to generate more samples, often by adjusting the color, size, and shape of objects in images. 

Instead, the MIT researchers built a data augmentation system called GIFT (which stands for Geometric Inference Feedback Tuning) that generates data designed to improve the performance of one VLM for a specific task.

GIFT develops an understanding of the model’s strengths and weaknesses by testing it. Then it uses this knowledge to generate data that could improve the model’s performance on the CAD generation problems it struggles to solve.

“We want to obtain data augmentation that is informed by the model itself,” Giannone says. 

Learning from mistakes

To do this, GIFT asks the model to generate code that solves a CAD generation problem multiple times in parallel. It checks the correctness of these guesses to understand how well the model can solve this problem.

“For a model, generating CAD query code that is almost correct is not that hard, but generating code that is perfectly correct and can be executed is much more challenging for a standard VLM,” Giannone says.

For guesses that are nearly correct, GIFT adjusts them to become successful solutions. It saves these “near-misses” and successful solutions in a new dataset that can teach the model how to overcome problems that would usually trip it up.

“If we sample the model 10 times and it generates 10 correct answers to the same problem, then there is not much for it to learn. We care about the in-between cases, where the model might only solve the problem 50 percent of the time,” he says.

Using these in-between cases allows GIFT to generate data augmentations that are both model-aware and task-aware. In addition, by incorporating multiple correct solutions to the same problem, the new data expand the model’s general knowledge of CAD code generation.

This automatic system does not require human intervention to correct the model’s mistakes.

GIFT creates data augmentations from a pre-trained VLM using a process known as inference-time scaling. This process allows a static model, which has already been trained, to generate better outputs without the high computational costs of retraining the entire model. 

Using inference-time scaling, the user can determine how much computation they want to use for GIFT, tailoring it to their time and budget constraints. 

GIFT outperformed several competing techniques, generating CAD programs that were more accurate while using only about 20 percent as much computation. The CAD models generated by VLMs using GIFT were better aligned with the shapes of ground-truth models.

“With GIFT, we started with geometry because with engineering problems, if the geometry of a 3D shape is not correct, nothing else will be correct, but there are many other aspects to consider,” Giannone says.

In the future, the researchers want to expand GIFT so the framework can teach models to generate CAD programs that improve the performance and manufacturability of 3D models. They also want to apply the system to larger models and more diverse CAD generation tasks.

This research was funded, in part, by the MIT-IBM Computing Research Lab. 

The California legislature has stepped back from a plan that would have expanded its age-gating law, removing language that could have compounded serious threats to users’ speech, privacy and security just to browse the internet. A.B. 1856, authored by Assemblymember Buffy Wicks, will now move forward through the legislature without its most problematic pieces.

EFF still believes the underlying law that A.B. 1856 amends, A.B. 1043, is unconstitutional. Signed into law in 2025 (and effective January of 2027), A.B. 1043 requires all operating systems and app stores to collect users’ ages, place them in various age brackets and then block young people from lawful speech and services depending on their age. We also believe that even though A.B. 1043 does not require age verification, the liability it creates for operating systems and app stores—including fining operating systems up to $7,500 per affected child for violating the law—will push those services to verify users’ ages. In practice, that could lead to more ID checks, more biometric scanning, more invasive data collection and risk of breach, and more barriers to adults’ and young people’s lawful speech.

However, we appreciate that the Legislature has abandoned its plan to expand this problematic age-gating  framework to browsers and websites. This would have significantly expanded this dangerous law before it even took effect. We thank the author and committee staff for recognizing these harms and not moving forward with this language. 

On top of that, EFF is pleased that an earlier amendment to A.B 1856 reduced the threat to the open-source community by exempting open-source operating systems. Given these changes, EFF has removed its opposition to A.B. 1856. We appreciate the author for listening to concerns from advocates, developers and others about the effect it would have on open-source development and also around expanding this problematic framework.

To be clear, we still believe the law passed last year threatens online anonymity, privacy, and security. A.B. 1043 is one of a troubling wave of proposals that encourage—or, in some cases, outright require—age verification. Our position on this is clear: no one should have to provide or verify their age to access the internet. Once users’ personal data is collected, it can easily be leaked, hacked, or misused. No matter the method, every age verification system demands that people hand over their sensitive and immutable personal information to link their offline identity to their online activity. That’s a bad deal for us all.

Age-gating mandates are reshaping the internet in ways that are invasive, dangerous, and deeply unnecessary. But users are not powerless! We can challenge these laws, protect our digital rights, and build a safer digital world for all internet users, no matter their ages. This resource hub can help—so explore, share, and join us in the fight for a better internet.

Millions of people are now designing their own personalized artificial intelligence companions, yet most have little idea how those creations will actually behave. In a new paper, MIT Media Lab Assistant Professor Pat Pataranutaporn and his graduate student researchers Anthony Baez and Sheer Karny introduce “neural transparency,” a tool that lets everyday users glimpse inside an AI’s neural network before their chatbot ever says a word. The work is being presented this week at the ACM Conference on Intelligent User Interfaces. 

In this interview, Pataranutaporn, who is the Asahi Broadcasting Corporation CD Professor of Media Arts and Sciences, explains what they found, why the stakes are higher than most users realize, and what genuinely transparent AI might look like in the future.

Q: Your paper introduces “neural transparency,” a way to let everyday users peek inside an AI’s neural networks before their chatbot ever says a word. Can you describe how that actually works, and why you focused on the design moment, rather than catching problems after a chatbot is already out in the wild?

A: Millions of people are now creating personalized AI chatbots and agents powered by large language models, turning them into collaborators, tutors, coaches, creative partners, and companions through simple text prompts. Yet most people have very little idea how those prompts will shape the AI’s behavior until they begin interacting with it. We wanted to change that.

“Neural transparency” means giving people something like a brain scan for AI. Not because AI has a human brain, but because its neural network contains internal patterns that can hint at how it may behave before it speaks. In this work, my students Anthony Baez, Sheer Karny, and I combined insights from the fields of human-AI interaction and mechanistic interpretability to make those hidden patterns accessible to everyday users.

The basic idea is simple. First, we choose behaviors we care about, such as empathy, honesty, toxicity, hallucination, or sycophancy. Then, we compare the model’s internal activations when it is prompted to exhibit one trait versus its opposite. That difference becomes a kind of “behavior direction” inside the model. When a user writes a custom system prompt — the instructions that shape their chatbot’s personality before any conversation begins — we project the model’s internal activations onto those directions and translate the results into an intuitive visualization. In our case, this is a sunburst diagram that previews the chatbot’s likely personality traits before the user starts chatting with it.

We focused on the design moment because that is where prevention is possible. Today, people often discover problems only after the chatbot has already behaved in unintended ways. Our goal was to move from reactive correction to anticipatory design by helping people identify potential risks while they are still shaping the AI.

Q: Your study turned up something pretty striking: People consistently misjudge how their personalized AI will behave, overestimating the good traits and underestimating potentially harmful ones like sycophancy. What does that tell us about the risks baked into how millions of people are currently building AI companions, and why is that blind spot so hard to close?

A: I often joke that if AI showed up looking like the Terminator, it would be much easier for us to know what to do. The real challenge is that AI often appears as a warm friend, coach, tutor, or companion. That makes it difficult to recognize when something is going wrong.

Our study suggests that people have a blind spot when designing personalized AI. People often think they know how their chatbot will behave, but in our study they incorrectly predicted its personality on 11 of the 15 traits we measured. That highlights the need for tools that help people better understand AI before they start using it.

This matters because some behaviors that feel helpful in the moment may not be healthy over time. In previous research, we documented cases of psychological harm associated with interactions with AI chatbots. An LLM [large language model] that constantly validates your opinions or never challenges your thinking can reinforce harmful decisions, unhealthy beliefs, or emotional dependency. Psychology has long shown that people are naturally drawn to affirmation, so designing AI is not only a technical challenge, but also a psychological one.

The deeper issue is that today’s AI systems remain largely black boxes: Even experts cannot always predict how a system prompt will shape an AI’s behavior over a long conversation. As AI companions become part of everyday life, we need tools that help people understand what they are building before they begin using it. AI should be supportive without becoming blindly agreeable, personalized without becoming manipulative, and transparent enough that people can make informed choices.

Q: One of your most interesting findings is that the visualization significantly increased user trust but didn’t actually change how people designed their chatbots. What will it take to close that gap, and where do you see tools like this heading as AI companions become more deeply embedded in people’s everyday lives?

A: I actually think this is one of the most interesting findings in the paper, because it shows that transparency alone is not enough. People appreciated being able to see inside the model and reported greater trust in the system, but simply presenting information did not fundamentally change how they designed their AI companions.  

In our followup work, which is currently available as a preprint, we are studying how a model’s internal neural representation changes over the course of a multi-turn conversation rather than remaining fixed from the initial prompt. We are already seeing promising results. By visualizing how these internal representations drift over time, people become significantly better at recognizing and anticipating changes in AI behavior, and are less likely to become overconfident in their understanding of the chatbot. AI companions are dynamic systems that evolve as they interact with us, so understanding those internal changes is an important next step. Nevertheless, this is still a very young research area. 

Looking further ahead, I believe these kinds of transparency tools could become as commonplace as nutrition labels are for food. As AI becomes deeply woven into education, health care, work, and personal relationships, people should be able to understand not only what an AI can do, but how it may influence their thinking, emotions, and behavior. That kind of transparency is essential if we want AI to genuinely help people flourish.

Oura Rings, Garmin GPS fitness watches, Apple Watches, Whoop bands—every year, more and more tech devices are promising to monitor our health and fitness, guide us toward healthier living, and provide useful health metrics to take to our doctors. But few of these tools provide the sorts of privacy and security promises we demand from all technology, let alone tech that captures personal health data. It’s time they step up and start providing transparency reports and stronger encryption options.

Surveys suggest that around 40 percent of people in the United States own some sort of commercially available wearable health device. Despite being marketed as health devices, they have no special health-related privacy protections that one might hope for. The companies who make these devices can and do collect an abundance of data, and many of them share that data with third-parties for marketing or to influence insurance rates, or use it for their own purposes, like training artificial intelligence models.

Health data is increasingly an important part of law enforcement or government investigations. Wearable data has been critical in a number of cases, where information about heart rate and steps was used to determine the whereabouts of individuals. And the surveillance company Penlink calls fitness trackers and wearables an “overlooked source” for law enforcement since they tend to show movement patterns and changes in heart rates. Law enforcement can try to get access to this data through subpoenas or warrants. 

There are many potential privacy issues with these sorts of devices, including whether the companies who make them share or sell information to third-parties. But here we are choosing to focus on two facets we’re concerned with around health data itself: 1) whether the company shares information with law enforcement and governments and 2) if they offer end-to-end encryption, which means the company itself can’t access that health data to begin with.

Reading through dozens of product review sites we narrowed our research in on ten companies that seem to make the majority of recommended consumer health products on the market:

• Amazfit
• Apple
• Coros
• Garmin
• Google (including Fitbit)
• Hume
• Oura
• Polar
• Suunto
• Whoop 

We reviewed each company’s public facing policies, then emailed them to confirm those findings. Here’s what we found.

Transparency Reports Are Few and Far Between

Companies should provide transparency reports of how often they provide data to the government, including information about whether it’s an official demand or an unofficial request. We have been calling on tech companies to publish transparency reports for a long time, but the practice is still rare across the industry. That’s especially true with fitness gadgets. 

Only two of the companies we surveyed, Apple and Google (which also owns Fitbit), currently publish transparency reports. Apple, Google, and Whoop promise to notify users of law enforcement requests in publicly available documentation. 

Oura now does too, after an update to their privacy policy in June 2026 that was perhaps prompted by a series of requests from journalist Zack Whittaker. In that same update and in an email to us, Oura promises that it is “actively evaluating ways to provide greater visibility into how we handle these requests, like through a transparency report.” This is promising, and we hope the company agrees that transparency reports are the best option moving forward. 

Any company that handles data that’s of interest to law enforcement and governments owes it to their users to publish transparency reports and, when legally possible, notify users when that data is requested.

Similarly, Suunto does not currently publish transparency reports, but in an email reply to our questions the company did express an openness to potentially doing so, stating, “We continuously evaluate our transparency practices and may publish additional information, such as a transparency report, in the future if we believe it would provide meaningful value for users and support our data protection efforts.” We hope they do, as these sorts of reports are a useful metric for all of us to better understand if and when our data can potentially be accessed by law enforcement.

We could not find instances where the other companies publicly state a policy around notification or transparency reports, and no others replied to our email questions.

Any company that handles data that’s of interest to law enforcement and governments owes it to their users to publish transparency reports and, when legally possible, notify users when that data is requested. This is especially true of personal health data, which can reveal our movements, and be used to infer details about what we’re doing at any given moment.

End-to-End Encrypted Data Is Far Too Rare of a Feature

End-to-end encryption is a method to ensure that your personal data is only accessible by you, and not the company who makes the device and manages the cloud storage. End-to-end encryption is usually used to refer to message encryption in communication apps, like Signal or WhatsApp, but can also refer to data storage. For example, many password managers use end-to-end encryption, and Ring implemented it for its cameras after we pushed for it. There’s no reason it can’t be offered for wearables too.

In the case of health data from wearable devices, it’s a way to store data in the cloud so that information can be synced and backed up between your device and an app on your phone in a way where only your devices can access it. 

Support for end-to-end encryption is more rare than transparency reports. 

The Apple Watch, at least with data that’s stored in the Health app, is the only popular fitness wearable that supports end-to-end encryption, and it’s enabled by default for all users (you are required to have two-factor authentication enabled as well, but that is also on by default for most accounts).

However, Apple Watch owners should remember that this protection is only for data stored in the Apple Health app. If you use other apps on your watch, or choose to share data with third-parties, like Strava, or if you’re sharing data with other wearables, like an Oura ring, that data is likely not end-to-end encrypted by the third-party company. 

Support for end-to-end encryption is more rare than transparency reports. 

And that’s it. Apple is the only one. No other popular consumer health wearable offers end-to-end encryption for the data it collects and stores online. Not Google. Not Garmin. Not Oura. Most of these companies instead offer encryption in transit and at rest, but this means those companies can still see and use your data. This is the industry standard, but it doesn’t have to be.

Another option would be more robust local-storage options. Some devices we looked at, like a handful of Garmin and Polar watches, can operate on the watch itself without syncing data to the cloud, but some models are limited in capability and cannot sync to an app without storing data online. More robust options for limiting the data to just the wearable and the phone app it's synced to would be a privacy improvement. For example, the Apple Watch has the option to disable iCloud sharing in Apple Health, which will keep the data only on your phone. It’s the only wearable we found that offers this feature without using a third-party app like Gadgetbridge or by physically connecting the wearable to a computer with a USB cable and transferring activity files over manually.

The general lack of local-only options or end-to-end encryption is a major privacy oversight, especially when you consider these devices collect heart rate, track sleep, and can log your location while also calculating a variety of health metrics supposedly intuiting everything from anxiety to your fitness “age.”

We understand that it’s technically more difficult to implement end-to-end encryption than other sorts of cloud storage, and comes with some limitations that may affect a user’s experience with a product. It also makes certain types of AI-related features harder to implement, since they’d typically need to work on-device (either in the app or the wearable device itself). Because of that, we believe an option for end-to-end encryption or local-only storage of the data collected by a wearable is the least companies can do. This way, those who want to use these devices can do so with the choice to either accept some privacy risks, or choose a more locked down option.

What’s Next

If you’re a user of a fitness wearable from any of the companies we’ve reached out to, or any other one, don’t be shy in asking for these sorts of features. In the rare cases a company offers a feature request page, use it—like for Garmin, Polar, Suunto, and Whoop. And when those types of outlets aren’t offered, don’t shy away from general contact pages, like those offered by Amazfit and Oura, or on community subreddits.

The companies that make these wearables, whether they’re designed for fitness or health, need to improve. At the bare minimum, companies need to publish transparency reports detailing how often they receive requests from law enforcement and commit to notifying users whenever that happens. 

It’s also well past the time for more companies to offer end-to-end encryption for the health data they’re storing. We acknowledge that this may be a trade-off for some features, like social networking features, but it should be up to users to decide if they’re willing to make those trade-offs. This level of privacy is an appealing feature that benefits users in myriad ways and more companies can set themselves apart by committing to this level of privacy.

Health data is some of the most personal data we produce, and most wearables companies are behind the times when it comes to basic privacy practices and transparency. Now’s the time to improve those practices.

Susumu Tonegawa, the Picower Professor of Biology and Neuroscience at MIT and a Nobel laureate, died July 11 at the age of 86.

Tonegawa was a renowned molecular biologist who wielded his keen insight in a variety of fields, including immunology and neuroscience. In the early 1980s, Tonegawa discovered how the immune system generates its incredible diversity of antibodies — a breakthrough that earned him the Nobel Prize in Physiology or Medicine in 1987.

Following that landmark achievement, he turned his attention to neuroscience, where his work has helped to reveal how the brain stores memories as traces called “engrams.”

An MIT faculty member for more than 40 years, Tonegawa also served as the founding director of MIT’s Picower Institute for Learning and Memory and director of the RIKEN Brain Science Institute of Japan, and was a Howard Hughes Medical Institute Investigator.

“Few scientists have reshaped our understanding of biology as profoundly as Susumu Tonegawa,” says Myriam Heiman, director of the Picower Institute. “His intellectual fearlessness, extraordinary creativity, and relentless pursuit of fundamental questions opened entirely new frontiers in both immunology and neuroscience. His influence on science and on the people who had the privilege of working alongside him is immeasurable.” 

Drawn to molecular biology

Born in Nagoya, Japan, Tonegawa spent his early years moving between rural towns, due to his father’s job as an engineer for a textile company. When it was time for him to go to high school, his parents sent him to a school in Tokyo, where he became interested in chemistry.

He was admitted to the University of Kyoto to study chemistry, and while there, he was drawn to the nascent field of molecular biology. He began his graduate studies at the Institute for Virus Research at the University of Kyoto, but after only a couple of months, his advisor, Professor Itaru Watanabe, suggested that he apply to a school in the United States, which had more advanced molecular biology programs.

Tonegawa took that advice and was accepted at the University of California at San Diego, where he studied how a virus called phage lambda controls gene transcription. After earning his PhD in 1968, he went on to a postdoc in a lab at the Salk Institute.

In that lab, Tonegawa began studying gene expression of a virus known as SV40. However, his U.S. visa was set to expire at the end of 1970, so he soon headed for a position at the newly established Basel Institute for Immunology in Switzerland. 

At the time, Tonegawa had little background in immunology, but he soon became fascinated by the 100-year-old question of “antibody diversity” — how the body’s immune system is able to generate hundreds of millions of antibodies from a relatively small set of genes. (The entire human genome contains about 20,000 genes.) That antibody diversity is what allows the immune system to recognize so many pathogens, including those it has never seen before.

With colleagues in Basel, Tonegawa discovered that each antibody protein is not encoded by its own gene — instead, genes for different components of the antibody can be randomly recombined to generate limitless combinations.

In 1987, Tonegawa was a solo recipient of the Nobel Prize for discovering that process, known as V(D)J gene rearrangement. In announcing the award, the Nobel committee noted that Tonegawa’s discoveries “explain the genetic background allowing the enormous richness of variation amongst antibodies. Beyond deeper knowledge of the basic structure of the immune system these discoveries will have importance in improving immunological therapy of different kinds, such as for instance the enforcement of vaccinations and inhibition of reactions during transplantation.”

From antibodies to engrams

In the early 1980s, after his groundbreaking antibody discoveries, Tonegawa began to feel the urge to turn to new research directions. He also wanted to return to the United States, so in 1981, he accepted the offer of a professorship at MIT’s Center for Cancer Research (today known as the Koch Institute for Integrative Cancer Research). There, he began working on T cells and contributed to scientists’ understanding of how T cells are able to generate a large diversity of T-cell receptors.

While at the CCR, he also began to study questions in neuroscience. As he told an interviewer from the Picower Institute in 2022, he was always in search of new scientific endeavors to keep him interested in his work.

“When I decided to become a scientist, my criteria of what to do was whether the scientific problem I got to solve was interesting or not. Whether I’m curious our not. I didn’t think about other things like, Could it be too risky? Can I really develop my career by venturing into the field I am not familiar with? That never occurred to me. I just followed my curiosity and instinct,” Tonegawa said in an interview published in the summer 2022 Picower Institute newsletter.

In 1994, he was chosen as the founding director for MIT’s Center for Learning and Memory, which became the Picower Institute for Learning and Memory in 2002. Tonegawa continued to serve as the center’s director until the end of 2006. 

Professor Li-Huei Tsai, who succeeded Tonegawa as the Picower Institute’s director, calls working alongside Tonegawa “one of the greatest honors of my career.”

“His passion, boundless energy, and unwavering pursuit of the fundamental mechanisms underlying memory were contagious, inspiring generations of neuroscientists to join and advance the field. Today, we lost a giant. His scientific legacy will continue to shape neuroscience for years to come, and he will be deeply missed by all of us,” she says.

Over the past two decades, Tonegawa’s lab has made significant discoveries in the field of memory research. In 2013, he and his colleagues reported that they had identified “engrams” in the brain’s hippocampus. These engrams consist of episodic memories — memories of experiences — that are stored in specific groups of hippocampal cells. Engrams encode elements including objects, space, and time, linked to a specific experience.

At that time, the researchers also found that it was possible to implant “false memories” in mice by using optogenetics to reactivate an existing engram while the animals formed a new memory. This prompted the mice to associate a new location with the memory of an event that had actually happened in a different location.

Later work from Tonegawa’s lab showed that engrams extend beyond the hippocampus and are stored across a widely distributed complex that spans many brain circuits. More recently, he had been working on engrams of “knowledge memory” to decipher the fundamental mechanism of abstract memory. His recent work also delved into how the emotional associations of memories are encoded, and how the brain maintains a timeline of chronological events.

In addition to the Nobel Prize, Tonegawa received many other awards, including the Albert and Mary Lasker Award for Basic Research in 1987, the Bristol-Myers Award for Distinguished Achievement in Cancer Research in 1986, and the David M. Bonner Lifetime Achievement Award from the University of California at San Diego in 2010. He was also known for training many scientists who are now leaders in the field of neuroscience.

Tonegawa was a longtime fan of the Boston Red Sox, and in May 2004, he had the opportunity to throw out the ceremonial first pitch at Fenway Park, as part of the team’s tribute to the Boston area’s scientific and medical communities.

He is survived by his wife, Mayumi Tonegawa ’92, two children, Hidde Tonegawa ’09 and Hanna Tonegawa, and two grandchildren. He was predeceased by a son, Satto Tonegawa. 

Following a private funeral, his ashes will be buried in Kyoto, Japan.

The effort to age gate the internet is back in Washington—and now it has a new name. Recently passed by the House of Representatives, the KIDS Act is a sprawling package of proposals to control what we can see and say online. Supporters claim the KIDS Act is needed to protect minors online. But if lawmakers really want to make the internet safer, why are they encouraging more surveillance instead of protecting our privacy? We dive into this question with our EFFector newsletter.

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For over 35 years, EFFector has been your guide to understanding the intersection of technology, civil liberties, and the law. This issue covers a victory for location privacy in the Supreme Court, disturbing developments in the militarization of domestic drones, and a controversial Congressional bill to control what we can see and say online.

Prefer to listen in? EFFector is now available on all major podcast platforms. This time, we're chatting with EFF Senior Policy Analyst Joe Mullin on what would happen to the open internet if the KIDS Act becomes law. You can find the episode and subscribe on your podcast platform of choice:

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Concrete is the most widely used building material on Earth, and producing it is one of the largest single sources of carbon emissions. One promising way to reduce its environmental footprint is to 3D-print concrete, laying it down bead by bead like a giant icing-piping robot. This process eliminates the labor-intensive formwork of pouring it into molds, and places the material only where a structure needs it.

But many of the most efficient designs created by computers are impossible for today’s printers to build. Engineers use a technique called topology optimization to find the strongest structure that uses the least amount of material. But those mathematically ideal designs, with their intricate, spider-web shapes, don’t account for the physical limitations of large-scale concrete printers with their thick nozzles, limited turning, and need to print in one continuous motion.

Now a team of MIT researchers has developed a way to close that gap. Their framework, described in a new article in Additive Manufacturing, bakes a printer’s real fabrication limits directly into the optimization, so the design that comes out is one a machine can build and print with little or no manual redesign. They demonstrated it by designing, printing, and load-testing a 2.3-meter concrete bridge and found that today’s printing hardware, not the concrete itself, limits how light a structure can be.

“We were finding a lot of cracks you can fall through when it comes to translating these super-optimal designs into manufacturable designs,” says co-first author Hajin Kim-Tackowiak PhD ’26, a postdoc in MIT’s Department of Civil and Environmental Engineering (CEE). “Those cracks were like chasms.”

Designing for what can be built

To pin down the constraints, the team worked with the people who run the large-scale printing machines at Autodesk’s facility in Boston.

“They pointed at some of our sharp angles, and they went, 'I don't feel safe printing something like that,'” Kim-Tackowiak recalls.

Those conversations surfaced three key limitations: how thick each printed bead must be, how sharply the nozzle can turn, and the need to print in a single continuous line. The researchers translated each constraint directly into the mathematical rules of their framework.

Existing 3D-printed structures are typically produced with older methods that optimize the shape first, and then require “a massive amount of post-processing,” taking days to run, Kim-Tackowiak explains. By contrast, the team’s framework generated fully printable designs in about two minutes on a laptop. When the team needed to slightly reduce the bridge’s size on the day of printing, they simply reran the optimization and had an updated design five to 10 minutes later. 

“Reaching that speed at all is recent,” says co-first author Zane Schemmer, a PhD student in CEE. The math the method relies on, mixed-integer optimization, was long considered too hard to use. “You go back five, 10 years ago, the solver we used, even three years ago, could not solve these problems,” he says. “This field has been avoided, because everyone thinks that’s not an avenue we can go down. But with new algorithms and resources, it’s becoming a way we can start to frame problems.”

A bridge reveals the real limitation

To validate the framework, the researchers went back to Autodesk’s facility to print a 2.3-meter-long concrete bridge.

“The bridge took about 30 minutes to make and was built from off-the-shelf mortar,” says senior author Josephine Carstensen, the Gilbert W. Winslow (1937) Career Development Professor in Civil Engineering.

In testing, the roughly 900-pound structure held more than 2,000 pounds spread across it with virtually no measurable bending, closely matching the team’s simulations.

But the test also revealed the study’s biggest surprise. “What we found was our result was super over-engineered,” Kim-Tackowiak says. “From zero to 200,000 pounds, your design is entirely driven by these 'can I build it or not' constraints. And then, after 200,000 pounds, you can start to think about the physics.” In other words, the limits of current printing technology, not the strength of concrete, were dictating how efficient the structure could be.

A roadmap for better printers

Because the framework finds the mathematically best possible design, the researchers could measure exactly how much each hardware limitation costs in material.

“With mixed-integer optimization, we can find the global optimum, the best solution there is, as opposed to just a good solution,” Carstensen says. “Because we know we’re finding the best solution out there, we can also quantify: If we had a machine that could do other things, what would that mean for how much material we’re using?”

The single biggest lever was the width of the printed bead. The bridge used a 4 centimeter bead. The analysis showed a machine that was able to lay a 1cm bead could cut material use by as much as 76 percent while staying “well within safety margins,” Carstensen says. The result surprised her. “I thought the continuous path would be the problem, the one that had the highest effect,” she says. “But it wasn’t. It was the bead width.”

The result is a roadmap for printer-makers showing that modest hardware improvements could unlock large gains in efficiency and cut concrete’s carbon footprint.

Part of what made the bridge possible is that every piece is in compression. “With concrete, it’s really good when you push on it, really bad when you pull on it,” Schemmer says. “We're able to guarantee that every piece of concrete that you see is in compression, there’s no part that’s being pulled on.”

The savings come not only from using less material, but from skipping molds entirely, an advantage that grows for one-off shapes. Carstensen sees early promise in disaster relief, “You can quickly put up new infrastructure without needing to make formwork.”

The bridge’s compression-only nature showed itself dramatically after testing. It had held more than 2,000 pounds without budging, but when a worker lifted one corner a few inches to sweep beneath it, it broke. The failure wasn’t a design flaw so much as a demonstration of the principle behind it: Concrete is weak when pulled, and the lift put parts of the bridge in tension they were never meant to carry. “It’s optimal in one way, but it’s definitely not optimal in every way,” Kim-Tackowiak says.

That points to the team’s next step of reinforced concrete. “We know a pure concrete structure is not necessarily going to be the most optimal thing, so we’re moving it more into the world we live in today, which is reinforced concrete,” Kim-Tackowiak says, “though working out how to feed rebar into a printed concrete structure,” she adds, “is proving its own challenge.”

The work was funded by the National Science Foundation and supported by the MIT Center for Advanced Production Technologies. Joining Kim-Tackowiak, Schemmer, and Carstensen on the paper are co-authors Pittipat Wongsittikan, a PhD student in the MIT Building Technology Architecture program, and Jackson Jewett MEng ’18, PhD ’25, a former MIT postdoc.

It’s a fact of life that the electrical activity of neurons will vary during repetitions of the same task, even when the ultimate outcome is the same. A new study shows that a lot of ongoing fluctuations in the brain’s activity could be explained by the influence local electric fields exerted on the neurons, a phenomenon called “ephaptic coupling.” The finding, published in Cerebral Cortex, adds to evidence that the brain’s electric fields act as important control signals for underlying brain function.

“The brain is a rollicking sea of electrical influences,” says study co-author Earl K. Miller, Picower Professor of Neuroscience in The Picower Institute for Learning and Memory and MIT’s Department of Brain and Cognitive Sciences. “But the traditional view of brain function focuses only on the spiking and synaptic connections among individual neurons. Now, there is growing evidence for electric field effects. For instance, in this study we show that neural variability is explained by how ephaptic effects are influencing neural activity.”

In 2022 and 2023, Miller and fellow author Dimitris Pinotsis, associate professor at City St George’s, University of London, published several studies showing that local electric fields in the brain’s cortex not only reflected the information neurons were processing better than any individual neuron did, but also that the fields actively helped to organize the underlying neural spiking that executes that processing. Like an orchestra conductor, the electric waves can conduct crowds of neurons so that they are “playing the same tune.” They further theorize that fields physically exert influence on the structure of the brain via cytoelectric coupling, in which the fields alter the cytoskeleton of neurons, optimizing them to oscillate in synchrony.

Because electric fields can be manipulated, Miller and Pinotsis argue in the new study that understanding how they influence momentary brain function could open the door to therapeutic interventions designed to improve it when it is faltering in disease. It would be difficult to adjust every neural connection, but ephaptic coupling suggests that intervening at the level of electric fields could accomplish that therapeutic end, the researchers say.

“Properly devised electric field manipulations could help patients rewire faulty circuits,” Pinotsis and Miller wrote.

In the duo’s prior studies, they analyzed signals averaged over time, documenting that in general, even though local (or “mesoscale”) electric fields in the cortex arise from the electrical activity of individual neurons, the field ultimately represents and coordinates their function. Think of it this way: Neurons are like individual citizens, and the electric fields are their government. Once the citizens establish a government with their individual votes, they are then subject to and unified by the laws the government creates and enforces. 

In the new study, the team asked whether mesoscale electric fields not only provide this ephaptic influence overall during working memory tasks, but also trial by trial. After all, that’s closer to the timescale of actual brain operations that matter both for healthy function and in disease. 

So the scientists looked anew at the data they recorded as animals played a simple video game. The animals were shown a dot in one of six positions around a screen. After the dot disappeared, the animals had to hold its former position in memory because to succeed in the game and earn a reward, they had to glance when cued to indicate the direction where the dot had appeared. Meanwhile, the scientists recorded neural electrical spiking and more collective local field potentials. Using that information, they calculated the local prevailing electric field at each moment.

In their statistical analysis of the data, they made several findings. One, as expected, was that neural activity varied sometimes quite widely trial by trial during the task. Another, using a mathematical technique called Granger Causality, showed that the direction of influence between the electric field and the neural activity was strongly in favor of the field. In other words, in the coupling between the two, the fields were dominant.

“We found that electric fields that emerge from neural activity, captured with LFPs [local field potentials], turn around and influence this activity in a top-down fashion (ephaptic coupling),” the researchers wrote.

Moreover, the team’s modeling and calculations showed that the strength of the ephaptic coupling between the field and the neural activity was proportional to the variations in the LFP power — another sign that the fields influenced the neural activity.

“The larger the variability, the more evident the top-down organizing effects,” the researchers wrote. “The emerging picture is that electric fields serve as control parameters.”

The U.K. Medical Council, the U.S. Army Research Office, the U.S. Office of Naval Research, the Freedom Together Foundation, and the Picower Institute funded the study.