MIT researchers have designed silicon structures that can perform calculations in an electronic device using excess heat instead of electricity. These tiny structures could someday enable more energy-efficient computation.

In this computing method, input data are encoded as a set of temperatures using the waste heat already present in a device. The flow and distribution of heat through a specially designed material forms the basis of the calculation. Then the output is represented by the power collected at the other end, which is thermostat at a fixed temperature.      

The researchers used these structures to perform matrix vector multiplication with more than 99 percent accuracy. Matrix multiplication is the fundamental mathematical technique machine-learning models like LLMs utilize to process information and make predictions.

While the researchers still have to overcome many challenges to scale up this computing method for modern deep-learning models, the technique could be applied to detect heat sources and measure temperature changes in electronics without consuming extra energy. This would also eliminate the need for multiple temperature sensors that take up space on a chip.

“Most of the time, when you are performing computations in an electronic device, heat is the waste product. You often want to get rid of as much heat as you can. But here, we’ve taken the opposite approach by using heat as a form of information itself and showing that computing with heat is possible,” says Caio Silva, an undergraduate student in the Department of Physics and lead author of a paper on the new computing paradigm.

Silva is joined on the paper by senior author Giuseppe Romano, a research scientist at MIT’s Institute for Soldier Nanotechnologies and a member of the MIT-IBM Watson AI Lab. The research appears today in Physical Review Applied.

Turning up the heat

This work was enabled by a software system the researchers previously developed that allows them to automatically design a material that can conduct heat in a specific manner.

Using a technique called inverse design, this system flips the traditional engineering approach on its head. The researchers define the functionality they want first, then the system uses powerful algorithms to iteratively design the best geometry for the task.

They used this system to design complex silicon structures, each roughly the same size as a dust particle, that can perform computations using heat conduction. This is a form of analog computing, in which data are encoded and signals are processed using continuous values, rather than digital bits that are either 0s or 1s.

The researchers feed their software system the specifications of a matrix of numbers that represents a particular calculation. Using a grid, the system designs a set of rectangular silicon structures filled with tiny pores. The system continually adjusts each pixel in the grid until it arrives at the desired mathematical function.

Heat diffuses through the silicon in a way that performs the matrix multiplication, with the geometry of the structure encoding the coefficients.

“These structures are far too complicated for us to come up with just through our own intuition. We need to teach a computer to design them for us. That is what makes inverse design a very powerful technique,” Romano says.

But the researchers ran into a problem. Due to the laws of heat conduction, which impose that heat goes from hot to cold regions, these structures can only encode positive coefficients. 

They overcame this problem by splitting the target matrix into its positive and negative components and representing them with separately optimized silicon structures that encode positive entries. Subtracting the outputs at a later stage allows them to compute negative matrix values.

They can also tune the thickness of the structures, which allows them to realize a greater variety of matrices. Thicker structures have greater heat conduction.

“Finding the right topology for a given matrix is challenging. We beat this problem by developing an optimization algorithm that ensures the topology being developed is as close as possible to the desired matrix without having any weird parts,” Silva explains.

Microelectronic applications

The researchers used simulations to test the structures on simple matrices with two or three columns. While simple, these small matrices are relevant for important applications, such as fusion sensing and diagnostics in microelectronics.     

The structures performed computations with more than 99 percent accuracy in many cases.

However, there is still a long way to go before this technique could be used for large-scale applications such as deep learning, since millions of structures would need to be tiled together. As the matrices become more complicated, the structures become less accurate, especially when there is a large distance between the input and output terminals. In addition, the devices have limited bandwidth, which would need to be greatly expanded if they were to be used for deep learning.

But because the structures rely on excess heat, they could be directly applied for tasks like thermal management, as well as heat source or temperature gradient detection in microelectronics.

“This information is critical. Temperature gradients can cause thermal expansion and damage a circuit or even cause an entire device to fail. If we have a localized  heat source where we don’t want a heat source, it means we have a problem. We could directly detect such heat sources with these structures, and we can just plug them in without needing any digital components,” Romano says.

Building on this proof-of-concept, the researchers want to design structures that can perform sequential operations, where the output of one structure becomes an input for the next. This is how machine-learning models perform computations. They also plan to develop programmable structures, enabling them to encode different matrices without starting from scratch with a new structure each time.

Today, we’re launching Encrypt It Already, our push to get companies to offer stronger privacy protections to our data and communications by implementing end-to-end encryption. If that name sounds a little familiar, it’s because this is a spiritual successor to our 2019 campaign, Fix It Already, a campaign where we pushed companies to fix longstanding issues.

End-to-end encryption is the best way we have to protect our conversations and data. It ensures the company that provides a service cannot access the data or messages you store on it. So, for secure chat apps like WhatsApp and Signal, that means the company that makes those apps cannot see the contents of your messages, and they’re only accessible on your and your recipients. When it comes to data, like what’s stored using Apple’s Advanced Data Protection, it means you control the encryption keys and the service provider will not be able to access the data.  

We’ve divided this up into three categories, each with three different demands:

• Keep your Promises: Features that the company has publicly stated they’re working on, but which haven’t launched yet.
• Facebook should use end-to-end encryption for group messages
• Apple and Google should deliver on their promise of interoperable end-to-end encryption of RCS
• Bluesky should launch its promised end-to-end encryption for DMs
• Defaults Matter: Features that are available on a service or in app already, but aren’t enabled by default.
• Telegram should default to end-to-end encryption for DMs
• WhatsApp should use end-to-end encryption for backups by default
• Ring should enable end-to-end encryption for its cameras by default
• Protect Our Data: New features that companies should launch, often because their competition is doing it already.
• Google should launch end-to-end encryption for Google Authenticator backups
• Google should offer end-to-end encryption for Android backup data
• Apple and Google should offer an AI permissions per app option to block AI access to secure chat apps

What is only half the problem. How is just as important.

What Companies Should Do When They Launch End-to-End Encryption Features

There’s no one-size fits all way to implement end-to-end encryption in products and services, but best practices can support the security of the platform with the transparency that makes it possible for its users to trust it protects data like the company claims it does. When these encryption features launch, companies should consider doing so with:

• A blog post written for a general audience that summarizes the technical details of the implementation, and when it makes sense, a technical white paper that goes into further detail for the technical crowd.
• Clear user-facing documentation around what data is and isn’t end-to-end encrypted, and robust and clear user controls when it makes sense to have them.
• Data minimization principles whenever feasible, storing as little metadata as possible.

Technical documentation is important for end-to-encryption features, but so is clear documentation that makes it easy for users to understand what is and isn’t protected, what features may change, and what steps they need to take to set it up so they’re comfortable with how data is protected.

What You Can Do

When it’s an option, enable any end-to-end encryption features you can, like on Telegram, WhatsApp, and Ring.

For everything else, let companies know that these are features you want! You can find messages to share on social media on the Encrypt It Already website, and take the time to customize those however you’d like. 

In some cases, you can also reach out to a company directly with feature requests, which all the above companies, except for Google and WhatsApp, offer in some form. We recommend filing these through any service you use for any of the above features you’d like to see:

• Apple’s feedback page
• Bluesky’s support
• Facebook’s feedback form

As for Ring and Telegram, we’ve already made the asks and just need your help to boost them. Head over to the Telegram bug and suggestions and upvote this post, and Ring’s feature request board and boost this post.

End-to-end encryption protects what we say and what we store in a way that gives users—not companies or governments—control over data. These sorts of privacy-protective features should be the status quo across a range of products, from fitness wearables to notes apps, but instead it’s a rare feature limited to a small set of services, like messaging and (occasionally) file storage. These demands are just the start. We deserve this sort of protection for a far wider array of products and services. It’s time to encrypt it already!

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EFF has long warned about the dangers of the “real-time bidding” (RTB) system powering nearly every ad you see online. A proposed class-action settlement with Google over their RTB system is a step in the right direction towards giving people more control over their data. Truly curbing the harms of RTB, however, will require stronger legislative protections.

What Is Real-Time Bidding?

RTB is the process by which most websites and apps auction off their ad space. Unfortunately, the milliseconds-long auctions that determine which ads you see also expose your personal information to thousands of companies a day. At a high-level, here’s how RTB works:

1. The moment you visit a website or app with ad space, it asks an ad tech company to determine which ads to display for you. This involves sending information about you and the content you’re viewing to the ad tech company.
2. This ad tech company packages all the information they can gather about you into a “bid request” and broadcasts it to thousands of potential advertisers. 
3. The bid request may contain information like your unique advertising ID, your GPS coordinates, IP address, device details, inferred interests, demographic information, and the app or website you’re visiting. The information in bid requests is called “bidstream data” and typically includes identifiers that can be linked to real people. 
4. Advertisers use the personal information in each bid request, along with data profiles they’ve built about you over time, to decide whether to bid on the ad space. 
5. The highest bidder gets to display an ad for you, but advertisers (and the adtech companies they use to buy ads) can collect your bidstream data regardless of whether or not they bid on the ad space.   

Why Is Real-Time Bidding Harmful?

A key vulnerability of real-time bidding is that while only one advertiser wins the auction, all participants receive data about the person who would see their ad. As a result, anyone posing as an ad buyer can access a stream of sensitive data about billions of individuals a day. Data brokers have taken advantage of this vulnerability to harvest data at a staggering scale. Since bid requests contain individual identifiers, they can be tied together to create detailed profiles of people’s behavior over time.

Data brokers have sold bidstream data for a range of invasive purposes, including tracking union organizers and political protesters, outing gay priests, and conducting warrantless government surveillance. Several federal agencies, including ICE, CBP and the FBI, have purchased location data from a data broker whose sources likely include RTB. ICE recently requested information on “Ad Tech” tools it could use in investigations, further demonstrating RTB’s potential to facilitate surveillance. RTB also poses national security risks, as researchers have warned that it could allow foreign states to obtain compromising personal data about American defense personnel and political leaders.

The privacy harms of RTB are not just a matter of misuse by individual data brokers. RTB auctions broadcast torrents of personal data to thousands of companies, hundreds of times per day, with no oversight of how this information is ultimately used. Once your information is broadcast through RTB, it’s almost impossible to know who receives it or control how it’s used. 

Proposed Settlement with Google Is a Step in the Right Direction

As the dominant player in the online advertising industry, Google facilitates the majority of RTB auctions. Google has faced several class-action lawsuits for sharing users’ personal information with thousands of advertisers through RTB auctions without proper notice and consent. A recently proposed settlement to these lawsuits aims to give people more knowledge and control over how their information is shared in RTB auctions.

Under the proposed settlement, Google must create a new privacy setting (the “RTB Control”) that allows people to limit the data shared about them in RTB auctions. When the RTB Control is enabled, bid requests will not include identifying information like pseudonymous IDs (including mobile advertising IDs), IP addresses, and user agent details. The RTB Control should also prevent cookie matching, a method companies use to link their data profiles about a person to a corresponding bid request. Removing identifying information from bid requests makes it harder for data brokers and advertisers to create consumer profiles based on bidstream data. If the proposed settlement is approved, Google will have to inform all users about the new RTB Control via email. 

While this settlement would be a step in the right direction, it would still require users to actively opt out of their identifying information being shared through RTB. Those who do not change their default settings—research shows this is most people—will remain vulnerable to RTB’s massive daily data breach. Google broadcasting your personal data to thousands of companies each time you see an ad is an unacceptable and dangerous default. 

The impact of RTB Control is further limited by technical constraints on who can enable it. RTB Control will only work for devices and browsers where Google can verify users are signed in to their Google account, or for signed-out users on browsers that allow third-party cookies. People who don't sign in to a Google account or don't enable privacy-invasive third-party cookies cannot benefit from this protection. These limitations could easily be avoided by making RTB Control the default for everyone. If the settlement is approved, regulators and lawmakers should push Google to enable RTB Control by default.

The Real Solution: Ban Online Behavioral Advertising

Limiting the data exposed through RTB is important, but we also need legislative change to protect people from the online surveillance enabled and incentivized by targeted advertising. The lack of strong, comprehensive privacy law in the U.S. makes it difficult for individuals to know and control how companies use their personal information. Strong privacy legislation can make privacy the default, not something that individuals must fight for through hidden settings or additional privacy tools. EFF advocates for data privacy legislation with teeth and a ban on ad targeting based on online behavioral profiles, as it creates a financial incentive for companies to track our every move. Until then, you can limit the harms of RTB by using EFF’s Privacy Badger to block ads that track you, disabling your mobile advertising ID (see instructions for iPhone/Android), and keeping an eye out for Google’s RTB Control.

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Lawmakers in Washington are once again focusing on kids, screens, and mental health. But according to Congress, Big Tech is somehow both the problem and the solution. We're diving into the latest attempt to control how kids access the internet and more with our latest EFFector newsletter.

Since 1990, EFFector has been your guide to understanding the intersection of technology, civil liberties, and the law. This latest issue tracks what to do when you hit an age gate online, explains why rent-only copyright culture makes us all worse off, and covers the dangers of law enforcement purchasing straight-up military drones.

Prefer to listen in? In our audio companion, EFF Senior Policy Analyst Joe Mullin explains what lawmakers should do if they really want to help families. Find the conversation on YouTube or the Internet Archive.

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EFFECTOR 38.2 - ✍️ THE BILL TO HAND PARENTING TO BIG TECH

Want to stay in the fight for privacy and free speech online? Sign up for EFF's EFFector newsletter for updates, ways to take action, and new merch drops. You can also fuel the fight to protect people from these data breaches and unlawful surveillance when you support EFF today!

Keeril Makan has been appointed vice provost for the arts at MIT, effective Feb. 1. In this role, Makan, who is the Michael (1949) and Sonja Koerner Music Composition Professor at MIT, will provide leadership and strategic direction for the arts across the Institute.

Provost Anantha Chandrakasan announced Makan’s appointment in an email to the MIT community today.

“Keeril’s record of accomplishment both as an artist and an administrative leader makes him exceedingly qualified to take on this important role,” Chandrakasan wrote, noting that Makan “has repeatedly taken on new leadership assignments with skill and enthusiasm.”

Makan’s appointment follows the publication last September of the final report of the Future of the Arts at MIT Committee. At MIT, the report noted, “the arts thrive as a constellation of recognized disciplines while penetrating and illuminating countless aspects of the Institute’s scientific and technological enterprise.” Makan will build on this foundation as MIT continues to strengthen the role of the arts in research, education, and community life.

As vice provost for the arts, Makan will provide Institute-wide leadership and strategic direction for the arts, working in close partnership with academic leaders, arts units, and administrative colleagues across MIT, including the Office of the Arts; the MIT Center for Art, Science and Technology; the MIT Museum; the List Visual Arts Center; and the Council for the Arts at MIT. His role will focus on strengthening connections between artistic practice, research, education, and community life, and on supporting public engagement and interdisciplinary collaboration.

“At MIT, the arts are a vital way of thinking, making, and convening,” Makan says. “As vice provost, my priority is to support and strengthen the extraordinary artistic work already happening across the Institute, while listening carefully to faculty, students, and staff as we shape what comes next. I’m excited to build on MIT’s distinctive, only-at-MIT approach to the arts and to help ensure that artistic practice remains central to MIT’s intellectual and community life.”

Makan says he will begin his new role with a period of listening and learning across MIT’s arts ecosystem, informed by the Future of the Arts at MIT report. His initial focus will be on understanding how artistic practice intersects with research, education, and community life, and on identifying opportunities to strengthen connections, visibility, and coordination across MIT’s many arts activities.

Over time, Makan says he will work with the arts community to advance MIT’s long-standing commitment to artistic excellence and experimentation, while supporting student participation and public engagement in the arts. He said his approach will “emphasize collaboration, clarity, and sustainability, reflecting MIT’s distinctive integration of the arts with science and technology.”

Makan came to MIT in 2006 as an assistant professor of music. From 2018 to 2024, he served as head of the Music and Theater Arts (MTA) Section in the School of Humanities, Arts, and Social Sciences (SHASS). In 2023, he was appointed associate dean for strategic initiatives in SHASS, where he helped guide the school’s response to recent fiscal pressures and led Institute-wide strategic initiatives.

With colleagues from MTA and the School of Engineering, Makan helped launch a new, multidisciplinary graduate program in music technology and computation. He was intimately involved in the project to develop the new Edward and Joyce Linde Music Building (Building 18), a state-of-the-art facility that opened in 2025. 

Makan was a member of the Future of the Arts at MIT Committee and chaired a working group on the creation of a center for the humanities, which ultimately became the MIT Human Insight Collaborative (MITHIC), one of the Institute’s strategic initiatives. Since last year, he has served as MITHIC’s faculty lead. Under his leadership, MITHIC has awarded $4.7 million in funding to 56 projects across 28 units at MIT, supporting interdisciplinary, human-centered research and teaching.

Trained initially as a violinist, Makan earned undergraduate degrees in music composition and religion from Oberlin and a PhD in music composition from the University of California at Berkeley.

A critically-acclaimed composer, Makan is the recipient of a Guggenheim Fellowship and the Luciano Berio Rome Prize from the American Academy in Rome. His music has been recorded by the Kronos Quartet, the Boston Modern Orchestra Project, and the International Contemporary Ensemble, and performed at Carnegie Hall, the Lincoln Center for the Performing Arts, and Tanglewood. His opera, “Persona,” premiered at National Sawdust and was performed at the Isabella Stewart Gardner Museum in Boston and by the Los Angeles Opera. The Los Angeles Times described the music from “Persona” as “brilliant.”

Makan succeeds Philip Khoury, the Ford International Professor of History, who served as vice provost for the arts from 2006 before stepping down in 2025. Khoury will return to the MIT faculty following a sabbatical.

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