California Governor Newsom has signed S.B. 524, a bill that begins the long process of regulating and imposing transparency on the growing problem of AI-written police reports. EFF supported this bill and has spent the last year vocally criticizing the companies pushing AI-generated police reports as a service. 

S.B.524 requires police to disclose, on the report, if it was used to fully or in part author a police report. Further, it bans vendors from selling or sharing the information a police agency provided to the AI. 

The bill is also significant because it required departments to retain all the various drafts of the report so that judges, defense attorneys, or auditors could readily see which portions of the final report were written by the officer and which portions were written by the computer. This creates major problems for police who use the most popular product in this space: Axon’s Draft One. By design, Draft One does not retain an edit log of who wrote what. Now, to stay in compliance with the law, police departments will either need Axon to change their product, or officers will have to take it upon themselves to go retain evidence of what each subsequent edit and draft of their report looked like. Or, police can drop Axon’s Draft One all together. 

EFF will continue to monitor whether departments are complying with this state law.

After Utah, California has become the second state to pass legislation that begins to address this problem. Because of the lack of transparency surrounding how police departments buy and deploy technology, it’s often hard to know if police departments are using AI to write reports, how the generative AI chooses to translate audio to a narrative, and which portions of reports are written by AI and which parts are written by the officers. EFF has written a guide to help you file public records requests that might shed light on your police department’s use of AI to write police reports. 

It’s still unclear if products like Draft One run afoul of record retention laws, and how AI-written police reports will impact the criminal justice system. We will need to consider more comprehensive regulation and perhaps even prohibition of this use of generative AI. But S.B. 524 is a good first step. We hope that more states will follow California and Utah’s lead and pass even stronger bills.

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

• I and Nathan E. Sanders will be giving a book talk on Rewiring Democracy at the Harvard Kennedy School’s Ash Center in Cambridge, Massachusetts, USA, on October 22, 2025 at noon ET.
• I and Nathan E. Sanders will be speaking and signing books at the Cambridge Public Library in Cambridge, Massachusetts, USA, on October 22, 2025 at 6:00 PM ET. The event is sponsored by Harvard Bookstore.
• I and Nathan E. Sanders will give a virtual talk about our book Rewiring Democracy on October 23, 2025 at 1:00 PM ET. The event is hosted by Data & Society...

Manufacturing better batteries, faster electronics, and more effective pharmaceuticals depends on the discovery of new materials and the verification of their quality. Artificial intelligence is helping with the former, with tools that comb through catalogs of materials to quickly tag promising candidates.

But once a material is made, verifying its quality still involves scanning it with specialized instruments to validate its performance — an expensive and time-consuming step that can hold up the development and distribution of new technologies.

Now, a new AI tool developed by MIT engineers could help clear the quality-control bottleneck, offering a faster and cheaper option for certain materials-driven industries.

In a study appearing today in the journal Matter, the researchers present “SpectroGen,” a generative AI tool that turbocharges scanning capabilities by serving as a virtual spectrometer. The tool takes in “spectra,” or measurements of a material in one scanning modality, such as infrared, and generates what that material’s spectra would look like if it were scanned in an entirely different modality, such as X-ray. The AI-generated spectral results match, with 99 percent accuracy, the results obtained from physically scanning the material with the new instrument.

Certain spectroscopic modalities reveal specific properties in a material: Infrared reveals a material’s molecular groups, while X-ray diffraction visualizes the material’s crystal structures, and Raman scattering illuminates a material’s molecular vibrations. Each of these properties is essential in gauging a material’s quality and typically requires tedious workflows on multiple expensive and distinct instruments to measure.

With SpectroGen, the researchers envision that a diversity of measurements can be made using a single and cheaper physical scope. For instance, a manufacturing line could carry out quality control of materials by scanning them with a single infrared camera. Those infrared spectra could then be fed into SpectroGen to automatically generate the material’s X-ray spectra, without the factory having to house and operate a separate, often more expensive X-ray-scanning laboratory.

The new AI tool generates spectra in less than one minute, a thousand times faster compared to traditional approaches that can take several hours to days to measure and validate.

“We think that you don’t have to do the physical measurements in all the modalities you need, but perhaps just in a single, simple, and cheap modality,” says study co-author Loza Tadesse, assistant professor of mechanical engineering at MIT. “Then you can use SpectroGen to generate the rest. And this could improve productivity, efficiency, and quality of manufacturing.”

The study’s lead author is former MIT postdoc Yanmin Zhu.

Beyond bonds

Tadesse’s interdisciplinary group at MIT pioneers technologies that advance human and planetary health, developing innovations for applications ranging from rapid disease diagnostics to sustainable agriculture.

“Diagnosing diseases, and material analysis in general, usually involves scanning samples and collecting spectra in different modalities, with different instruments that are bulky and expensive and that you might not all find in one lab,” Tadesse says. “So, we were brainstorming about how to miniaturize all this equipment and how to streamline the experimental pipeline.”

Zhu noted the increasing use of generative AI tools for discovering new materials and drug candidates, and wondered whether AI could also be harnessed to generate spectral data. In other words, could AI act as a virtual spectrometer?

A spectroscope probes a material’s properties by sending light of a certain wavelength into the material. That light causes molecular bonds in the material to vibrate in ways that scatter the light back out to the scope, where the light is recorded as a pattern of waves, or spectra, that can then be read as a signature of the material’s structure.

For AI to generate spectral data, the conventional approach would involve training an algorithm to recognize connections between physical atoms and features in a material, and the spectra they produce. Given the complexity of molecular structures within just one material, Tadesse says such an approach can quickly become intractable.

“Doing this even for just one material is impossible,” she says. “So, we thought, is there another way to interpret spectra?”

The team found an answer with math. They realized that a spectral pattern, which is a sequence of waveforms, can be represented mathematically. For instance, a spectrum that contains a series of bell curves is known as a “Gaussian” distribution, which is associated with a certain mathematical expression, compared to a series of narrower waves, known as a “Lorentzian” distribution, that is described by a separate, distinct algorithm. And as it turns out, for most materials infrared spectra characteristically contain more Lorentzian waveforms, while Raman spectra are more Gaussian, and X-ray spectra is a mix of the two.

Tadesse and Zhu worked this mathematical interpretation of spectral data into an algorithm that they then incorporated into a generative AI model.

“It’s a physics-savvy generative AI that understands what spectra are,” Tadesse says. “And the key novelty is, we interpreted spectra not as how it comes about from chemicals and bonds, but that it is actually math — curves and graphs, which an AI tool can understand and interpret.”

Data co-pilot

The team demonstrated their SpectroGen AI tool on a large, publicly available dataset of over 6,000 mineral samples. Each sample includes information on the mineral’s properties, such as its elemental composition and crystal structure. Many samples in the dataset also include spectral data in different modalities, such as X-ray, Raman, and infrared. Of these samples, the team fed several hundred to SpectroGen, in a process that trained the AI tool, also known as a neural network, to learn correlations between a mineral’s different spectral modalities. This training enabled SpectroGen to take in spectra of a material in one modality, such as in infrared, and generate what a spectra in a totally different modality, such as X-ray, should look like.

Once they trained the AI tool, the researchers fed SpectroGen spectra from a mineral in the dataset that was not included in the training process. They asked the tool to generate a spectra in a different modality, based on this “new” spectra. The AI-generated spectra, they found, was a close match to the mineral’s real spectra, which was originally recorded by a physical instrument. The researchers carried out similar tests with a number of other minerals and found that the AI tool quickly generated spectra, with 99 percent correlation.

“We can feed spectral data into the network and can get another totally different kind of spectral data, with very high accuracy, in less than a minute,” Zhu says.

The team says that SpectroGen can generate spectra for any type of mineral. In a manufacturing setting, for instance, mineral-based materials that are used to make semiconductors and battery technologies could first be quickly scanned by an infrared laser. The spectra from this infrared scanning could be fed into SpectroGen, which would then generate a spectra in X-ray, which operators or a multiagent AI platform can check to assess the material’s quality.

“I think of it as having an agent or co-pilot, supporting researchers, technicians, pipelines and industry,” Tadesse says. “We plan to customize this for different industries’ needs.”

The team is exploring ways to adapt the AI tool for disease diagnostics, and for agricultural monitoring through an upcoming project funded by Google. Tadesse is also advancing the technology to the field through a new startup and envisions making SpectroGen available for a wide range of sectors, from pharmaceuticals to semiconductors to defense.

As costs for diagnostic and sequencing technologies have plummeted in recent years, researchers have collected an unprecedented amount of data around disease and biology. Unfortunately, scientists hoping to go from data to new cures often require help from someone with experience in software engineering.

Now, Watershed Bio is helping scientists and bioinformaticians run experiments and get insights with a platform that lets users analyze complex datasets regardless of their computational skills. The cloud-based platform provides workflow templates and a customizable interface to help users explore and share data of all types, including whole-genome sequencing, transcriptomics, proteomics, metabolomics, high-content imaging, protein folding, and more.

“Scientists want to learn about the software and data science parts of the field, but they don’t want to become software engineers writing code just to understand their data,” co-founder and CEO Jonathan Wang ’13, SM ’15 says. “With Watershed, they don’t have to.”

Watershed is being used by large and small research teams across industry and academia to drive discovery and decision-making. When new advanced analytic techniques are described in scientific journals, they can be added to Watershed’s platform immediately as templates, making cutting-edge tools more accessible and collaborative for researchers of all backgrounds.

“The data in biology is growing exponentially, and the sequencing technologies generating this data are only getting better and cheaper,” Wang says. “Coming from MIT, this issue was right in my wheelhouse: It’s a tough technical problem. It’s also a meaningful problem because these people are working to treat diseases. They know all this data has value, but they struggle to use it. We want to help them unlock more insights faster.”

No code discovery

Wang expected to major in biology at MIT, but he quickly got excited by the possibilities of building solutions that scaled to millions of people with computer science. He ended up earning both his bachelor’s and master’s degrees from the Department of Electrical Engineering and Computer Science (EECS). Wang also interned at a biology lab at MIT, where he was surprised how slow and labor-intensive experiments were.

“I saw the difference between biology and computer science, where you had these dynamic environments [in computer science] that let you get feedback immediately,” Wang says. “Even as a single person writing code, you have so much at your fingertips to play with.”

While working on machine learning and high-performance computing at MIT, Wang also co-founded a high frequency trading firm with some classmates. His team hired researchers with PhD backgrounds in areas like math and physics to develop new trading strategies, but they quickly saw a bottleneck in their process.

“Things were moving slowly because the researchers were used to building prototypes,” Wang says. “These were small approximations of models they could run locally on their machines. To put those approaches into production, they needed engineers to make them work in a high-throughput way on a computing cluster. But the engineers didn’t understand the nature of the research, so there was a lot of back and forth. It meant ideas you thought could have been implemented in a day took weeks.”

To solve the problem, Wang’s team developed a software layer that made building production-ready models as easy as building prototypes on a laptop. Then, a few years after graduating MIT, Wang noticed technologies like DNA sequencing had become cheap and ubiquitous.

“The bottleneck wasn’t sequencing anymore, so people said, ‘Let’s sequence everything,’” Wang recalls. “The limiting factor became computation. People didn’t know what to do with all the data being generated. Biologists were waiting for data scientists and bioinformaticians to help them, but those people didn’t always understand the biology at a deep enough level.”

The situation looked familiar to Wang.

“It was exactly like what we saw in finance, where researchers were trying to work with engineers, but the engineers never fully understood, and you had all this inefficiency with people waiting on the engineers,” Wang says. “Meanwhile, I learned the biologists are hungry to run these experiments, but there is such a big gap they felt they had to become a software engineer or just focus on the science.”

Wang officially founded Watershed in 2019 with physician Mark Kalinich ’13, a former classmate at MIT who is no longer involved in day-to-day operations of the company.

Wang has since heard from biotech and pharmaceutical executives about the growing complexity of biology research. Unlocking new insights increasingly involves analyzing data from entire genomes, population studies, RNA sequencing, mass spectrometry, and more. Developing personalized treatments or selecting patient populations for a clinical study can also require huge datasets, and there are new ways to analyze data being published in scientific journals all the time.

Today, companies can run large-scale analyses on Watershed without having to set up their own servers or cloud computing accounts. Researchers can use ready-made templates that work with all the most common data types to accelerate their work. Popular AI-based tools like AlphaFold and Geneformer are also available, and Watershed’s platform makes sharing workflows and digging deeper into results easy.

“The platform hits a sweet spot of usability and customizability for people of all backgrounds,” Wang says. “No science is ever truly the same. I avoid the word product because that implies you deploy something and then you just run it at scale forever. Research isn’t like that. Research is about coming up with an idea, testing it, and using the outcome to come up with another idea. The faster you can design, implement, and execute experiments, the faster you can move on to the next one.”

Accelerating biology

Wang believes Watershed is helping biologists keep up with the latest advances in biology and accelerating scientific discovery in the process.

“If you can help scientists unlock insights not a little bit faster, but 10 or 20 times faster, it can really make a difference,” Wang says.

Watershed is being used by researchers in academia and in companies of all sizes. Executives at biotech and pharmaceutical companies also use Watershed to make decisions about new experiments and drug candidates.

“We’ve seen success in all those areas, and the common thread is people understanding research but not being an expert in computer science or software engineering,” Wang says. “It’s exciting to see this industry develop. For me, it’s great being from MIT and now to be back in Kendall Square where Watershed is based. This is where so much of the cutting-edge progress is happening. We’re trying to do our part to enable the future of biology.”

More than 300 million people worldwide are living with rare disorders — many of which have a genetic cause and affect the brain and nervous system — yet the vast majority of these conditions lack an approved therapy. Because each rare disorder affects fewer than 65 out of every 100,000 people, studying these disorders and creating new treatments for them is especially challenging.

Thanks to a generous philanthropic gift from Ana Méndez ’91 and Rajeev Jayavant ’86, EE ’88, SM ’88, MIT is now poised to fill gaps in this research landscape. By establishing the Rare Brain Disorders Nexus — or RareNet — at MIT's McGovern Institute for Brain Research, the alumni aim to convene leaders in neuroscience research, clinical medicine, patient advocacy, and industry to streamline the lab-to-clinic pipeline for rare brain disorder treatments.

“Ana and Rajeev’s commitment to MIT will form crucial partnerships to propel the translation of scientific discoveries into promising therapeutics and expand the Institute’s impact on the rare brain disorders community,” says MIT President Sally Kornbluth. “We are deeply grateful for their pivotal role in advancing such critical science and bringing attention to conditions that have long been overlooked.”

Building new coalitions

Several hurdles have slowed the lab-to-clinic pipeline for rare brain disorder research. It is difficult to secure a sufficient number of patients per study, and current research efforts are fragmented, since each study typically focuses on a single disorder (there are more than 7,000 known rare disorders, according to the World Health Organization). Pharmaceutical companies are often reluctant to invest in emerging treatments due to a limited market size and the high costs associated with preparing drugs for commercialization.

Méndez and Jayavant envision that RareNet will finally break down these barriers. “Our hope is that RareNet will allow leaders in the field to come together under a shared framework and ignite scientific breakthroughs across multiple conditions. A discovery for one rare brain disorder could unlock new insights that are relevant to another,” says Jayavant. “By congregating the best minds in the field, we are confident that MIT will create the right scientific climate to produce drug candidates that may benefit a spectrum of uncommon conditions.”

Guoping Feng, the James W. (1963) and Patricia T. Poitras Professor in Neuroscience and associate director of the McGovern Institute, will serve as RareNet’s inaugural faculty director. Feng holds a strong record of advancing studies on therapies for neurodevelopmental disorders, including autism spectrum disorders, Williams syndrome, and uncommon forms of epilepsy. His team’s gene therapy for Phelan-McDermid syndrome, a rare and profound autism spectrum disorder, has been licensed to Jaguar Gene Therapy and is currently undergoing clinical trials. “RareNet pioneers a unique model for biomedical research — one that is reimagining the role academia can play in developing therapeutics,” says Feng.

RareNet plans to deploy two major initiatives: a global consortium and a therapeutic pipeline accelerator. The consortium will form an international network of researchers, clinicians, and patient groups from the outset. It seeks to connect siloed research efforts, secure more patient samples, promote data sharing, and drive a strong sense of trust and goal alignment across the RareNet community. Partnerships within the consortium will support the aim of the therapeutic pipeline accelerator: to de-risk early lab discoveries and expedite their translation to clinic. By fostering more targeted collaborations — especially between academia and industry — the accelerator will prepare potential treatments for clinical use as efficiently as possible.

MIT labs are focusing on four uncommon conditions in the first wave of RareNet projects: Rett syndrome, prion disease, disorders linked to SYNGAP1 mutations, and Sturge-Weber syndrome. The teams are working to develop novel therapies that can slow, halt, or reverse dysfunctions in the brain and nervous system.

These efforts will build new bridges to connect key stakeholders across the rare brain disorders community and disrupt conventional research approaches. “Rajeev and I are motivated to seed powerful collaborations between MIT researchers, clinicians, patients, and industry,” says Méndez. “Guoping Feng clearly understands our goal to create an environment where foundational studies can thrive and seamlessly move toward clinical impact.”

“Patient and caregiver experiences, and our foreseeable impact on their lives, will guide us and remain at the forefront of our work,” Feng adds. “For far too long has the rare brain disorders community been deprived of life-changing treatments — and, importantly, hope. RareNet gives us the opportunity to transform how we study these conditions, and to do so at a moment when it’s needed more than ever.”

This chilling paragraph is in a comprehensive Brookings report about the use of tech to deport people from the US:

The administration has also adapted its methods of social media surveillance. Though agencies like the State Department have gathered millions of handles and monitored political discussions online, the Trump administration has been more explicit in who it’s targeting. Secretary of State Marco Rubio announced a new, zero-tolerance “Catch and Revoke” strategy, which uses AI to monitor the public speech of foreign nationals and revoke visas...

States will have few options for fighting local air pollution if EPA repeals federal rules to limit vehicle emissions.

Climate finance still matters to much of the world, even if the United States has abandoned its leadership role.

The agency indicated that it could use the technology to sort and summarize thousands of public comments.

The state launched a new online tool to help raise homebuyers’ awareness about extreme-weather risks.

Despite those risks, banks and investors have yet to properly map out how climate-related losses will be distributed, the former official said.

A data scientist said a survey of Kenyan women suggests droughts and heat waves are linked with much higher levels of suicidal thoughts.

Prices are down almost 40 percent since the start of the year, weighed by a persistent oversupply and lagging demand.

Leading German firms are calling for carve-outs in Europe’s flagship emissions trading system.

Scientists at MIT and elsewhere have discovered extremely rare remnants of “proto Earth,” which formed about 4.5 billion years ago, before a colossal collision irreversibly altered the primitive planet’s composition and produced the Earth as we know today. Their findings, reported today in the journal Nature Geosciences, will help scientists piece together the primordial starting ingredients that forged the early Earth and the rest of the solar system.

Billions of years ago, the early solar system was a swirling disk of gas and dust that eventually clumped and accumulated to form the earliest meteorites, which in turn merged to form the proto Earth and its neighboring planets.

In this earliest phase, Earth was likely rocky and bubbling with lava. Then, less than 100 million years later, a Mars-sized meteorite slammed into the infant planet in a singular “giant impact” event that completely scrambled and melted the planet’s interior, effectively resetting its chemistry. Whatever original material the proto Earth was made from was thought to have been altogether transformed.

But the MIT team’s findings suggest otherwise. The researchers have identified a chemical signature in ancient rocks that is unique from most other materials found in the Earth today. The signature is in the form of a subtle imbalance in potassium isotopes discovered in samples of very old and very deep rocks. The team determined that the potassium imbalance could not have been produced by any previous large impacts or geological processes occurring in the Earth presently.

The most likely explanation for the samples’ chemical composition is that they must be leftover material from the proto Earth that somehow remained unchanged, even as most of the early planet was impacted and transformed.

“This is maybe the first direct evidence that we’ve preserved the proto Earth materials,” says Nicole Nie, the Paul M. Cook Career Development Assistant Professor of Earth and Planetary Sciences at MIT. “We see a piece of the very ancient Earth, even before the giant impact. This is amazing because we would expect this very early signature to be slowly erased through Earth’s evolution.”

The study’s other authors include Da Wang of Chengdu University of Technology in China, Steven Shirey and Richard Carlson of the Carnegie Institution for Science in Washington, Bradley Peters of ETH Zürich in Switzerland, and James Day of Scripps Institution of Oceanography in California.

A curious anomaly

In 2023, Nie and her colleagues analyzed many of the major meteorites that have been collected from sites around the world and carefully studied. Before impacting the Earth, these meteorites likely formed at various times and locations throughout the solar system, and therefore represent the solar system’s changing conditions over time. When the researchers compared the chemical compositions of these meteorite samples to Earth, they identified among them a “potassium isotopic anomaly.”

Isotopes are slightly different versions of an element that have the same number of protons but a different number of neutrons. The element potassium can exist in one of three naturally-occurring isotopes, with mass numbers (protons plus neutrons) of 39, 40, and 41, respectively. Wherever potassium has been found on Earth, it exists in a characteristic combination of isotopes, with potassium-39 and potassium-41 being overwhelmingly dominant. Potassium-40 is present, but at a vanishingly small percentage in comparison.

Nie and her colleagues discovered that the meteorites they studied showed balances of potassium isotopes that were different from most materials on Earth. This potassium anomaly suggested that any material that exhibits a similar anomaly likely predates Earth’s present composition. In other words, any potassium imbalance would be a strong sign of material from the proto Earth, before the giant impact reset the planet’s chemical composition.

“In that work, we found that different meteorites have different potassium isotopic signatures, and that means potassium can be used as a tracer of Earth’s building blocks,” Nie explains.

“Built different”

In the current study, the team looked for signs of potassium anomalies not in meteorites, but within the Earth. Their samples include rocks, in powder form, from Greenland and Canada, where some of the oldest preserved rocks are found. They also analyzed lava deposits collected from Hawaii, where volcanoes have brought up some of the Earth’s earliest, deepest materials from the mantle (the planet’s thickest layer of rock that separates the crust from the core).

“If this potassium signature is preserved, we would want to look for it in deep time and deep Earth,” Nie says.

The team first dissolved the various powder samples in acid, then carefully isolated any potassium from the rest of the sample and used a special mass spectrometer to measure the ratio of each of potassium’s three isotopes. Remarkably, they identified in the samples an isotopic signature that was different from what’s been found in most materials on Earth.

Specifically, they identified a deficit in the potassium-40 isotope. In most materials on Earth, this isotope is already an insignificant fraction compared to potassium’s other two isotopes. But the researchers were able to discern that their samples contained an even smaller percentage of potassium-40. Detecting this tiny deficit is like spotting a single grain of brown sand in a bucket rather than a scoop full of of yellow sand.

The team found that, indeed, the samples exhibited the potassium-40 deficit, showing that the materials “were built different,” says Nie, compared to most of what we see on Earth today.

But could the samples be rare remnants of the proto Earth? To answer this, the researchers assumed that this might be the case. They reasoned that if the proto Earth were originally made from such potassium-40-deficient materials, then most of this material would have undergone chemical changes — from the giant impact and subsequent, smaller meteorite impacts — that ultimately resulted in the materials with more potassium-40 that we see today. 

The team used compositional data from every known meteorite and carried out simulations of how the samples’ potassium-40 deficit would change following impacts by these meteorites and by the giant impact. They also simulated geological processes that the Earth experienced over time, such as the heating and mixing of the mantle. In the end, their simulations produced a composition with a slightly higher fraction of potassium-40 compared to the samples from Canada, Greenland, and Hawaii. More importantly, the simulated compositions matched those of most modern-day materials.

The work suggests that materials with a potassium-40 deficit are likely leftover original material from the proto Earth.

Curiously, the samples’ signature isn’t a precise match with any other meteorite in geologists’ collections. While the meteorites in the team’s previous work showed potassium anomalies, they aren’t exactly the deficit seen in the proto Earth samples. This means that whatever meteorites and materials originally formed the proto Earth have yet to be discovered.

“Scientists have been trying to understand Earth’s original chemical composition by combining the compositions of different groups of meteorites,” Nie says. “But our study shows that the current meteorite inventory is not complete, and there is much more to learn about where our planet came from.”

This work was supported, in part, by NASA and MIT.

In a letter sent to Microsoft at the end of last month, EFF and five other civil society organizations—Access Now, Amnesty International, Human Rights Watch, Fight for the Future, and 7amleh—called on the company to cease any further involvement in providing AI and cloud computing technologies for use in Israel’s ongoing genocide against Palestinians in the Gaza Strip.

EFF also sent updated letters to Google and Amazon renewing our calls for each company to respond to the serious concerns we raised with each of them last year about how they are fulfilling their respective human rights promises to the public. Neither Google nor Amazon has responded substantively. Amazon failed to even acknowledge our request, much less provide any transparency to the public. 

Microsoft Takes a Positive Step Against Surveillance

On September 25, Microsoft’s Vice Chair & President reported that the company had “ceased and disabled a set of services” provided to a unit within the Israel Ministry of Defense. The announcement followed an internal review at the company after The Guardian reported on August 6 that the IDF is using Azure for the storage of data files of phone calls obtained through broad or mass surveillance of civilians in Gaza and the West Bank.

This investigation by The Guardian, +972 Magazine, and Local Call also revealed the extent to which Israel’s military intelligence unit in question, Unit 8200, has used Microsoft’s Azure cloud infrastructure and AI technologies to process intercepted communications and power AI-driven targeting systems against Palestinians in Gaza and the West Bank—potentially facilitating war crimes and acts of genocide.

Microsoft’s actions are a positive step, and we urge its competitors Google and Amazon to, at the very least, do the same, rather than continuing to support and facilitate mass surveillance of Palestinians in Gaza and the West Bank.  

The Next Steps

But this must be the starting point, and not the end. Our joint letter therefore calls on Microsoft to provide clarity around:

1. What further steps Microsoft will take to suspend its business with the Israeli military and other government bodies where there is evidence indicating that business is contributing to grave human rights abuses and international crimes.
2. Whether Microsoft will commit to publishing the review findings in full, including the scope of the investigation, the specific entities and services under review, and measures Microsoft will take to address adverse human rights impacts related to its business with the Israeli military and other government bodies.
3. What steps Microsoft has taken to ensure that its current formal review thoroughly investigates the use of its technologies by the Israeli authorities, in light of the fact that the same law firm carried out the previous review and concluded that there was no evidence of use of Microsoft’s Azure and AI technologies to target or harm people in Gaza.
4. Whether Microsoft will conduct an additional human rights review, or incorporate a human rights lens to the current review.
5. Whether Microsoft has applied any limited access restrictions to its AI technologies used by the IDF and Israeli government to commit genocide and other international crimes. 
6. Whether Microsoft will evaluate the “high-impact and higher-risk uses” of its evolving AI technology deployed in conflict zones.
7. How Microsoft is planning to provide effective remedy, including reparations, to Palestinians affected by any contributions by the company to violations of human rights by Israel.

Microsoft’s announcement of an internal review and the suspension of some of its services is long overdue and much needed in addressing its potential complicity in human rights abuses. But it must not end here, and Microsoft should not be the only major technology company taking such action.  

EFF, Access Now, Amnesty International, Human Rights Watch, Fight for the Future, and 7amleh provided a deadline of October 10 for Microsoft to respond to the questions outlined in the letter. However, Microsoft is expected to send its written response by the end of the month, and we will publish the response once received.

Read the full letter to Microsoft here.

My latest book, Rewiring Democracy: How AI Will Transform Our Politics, Government, and Citizenship, will be published in just over a week. No reviews yet, but can read chapters 12 and <a href=https://newpublic.substack.com/p/2ddffc17-a033-4f98-83fa-11376b30c6cd”>34 (of 43 chapters total).

You can order the book pretty much everywhere, and a copy signed by me <a href=”https://www.schneier.com/product/rewiring-democracy-hardcover/’>here.

Please help spread the word. I want this book to make a splash when it’s public. Leave a review on whatever site you buy it from. Or make a TikTok video. Or do whatever you kids do these days. Is anyone a SlashDot contributor? I’d like the book to be announced there...

Two years ago, Americans anxious about the forthcoming 2024 presidential election were considering the malevolent force of an election influencer: artificial intelligence. Over the past several years, we have seen plenty of warning signs from elections worldwide demonstrating how AI can be used to propagate misinformation and alter the political landscape, whether by trolls on social media, foreign influencers, or even a street magician. AI is poised to play a more volatile role than ever before in America’s next federal election in 2026. We can already see how different groups of political actors are approaching AI. Professional campaigners are using AI to accelerate the traditional tactics of electioneering; organizers are using it to reinvent how movements are built; and citizens are using it both to express themselves and amplify their side’s messaging. Because there are so few rules, and so little prospect of regulatory action, around AI’s role in politics, there is no oversight of these activities, and no safeguards against the dramatic potential impacts for our democracy...

For decades, synthetic biologists have been developing gene circuits that can be transferred into cells for applications such as reprogramming a stem cell into a neuron or generating a protein that could help treat a disease such as fragile X syndrome.

These gene circuits are typically delivered into cells by carriers such as nonpathogenic viruses. However, it has been difficult to ensure that these cells end up producing the correct amount of the protein encoded by the synthetic gene.

To overcome that obstacle, MIT engineers have designed a new control mechanism that allows them to establish a desired protein level, or set point, for any gene circuit. This approach also allows them to edit the set point after the circuit is delivered.

“This is a really stable and multifunctional tool. The tool is very modular, so there are a lot of transgenes you could control with this system,” says Katie Galloway, an assistant professor in Chemical Engineering at MIT and the senior author of the new study.

Using this strategy, the researchers showed that they could induce cells to generate consistent levels of target proteins. In one application that they demonstrated, they converted mouse embryonic fibroblasts to motor neurons by delivering high levels of a gene that promotes that conversion.

MIT graduate student Sneha Kabaria is the lead author of the paper, which appears today in Nature Biotechnology. Other authors include Yunbeen Bae ’24; MIT graduate students Mary Ehmann, Brittany Lende-Dorn, Emma Peterman, and Kasey Love; Adam Beitz PhD ’25; and former MIT postdoc Deon Ploessl.

Dialing up gene expression

Synthetic gene circuits are engineered to include not only the gene of interest, but also a promoter region. At this site, transcription factors and other regulators can bind, turning on the expression of the synthetic gene.

However, it’s not always possible to get all of the cells in a population to express the desired gene at a uniform level. One reason for that is that some cells may take up just one copy of the circuit, while others receive many more. Additionally, cells have natural variation in how much protein they produce.

That has made reprogramming cells challenging because it’s difficult to ensure that every cell in a population of skin cells, for example, will produce enough of the necessary transcription factors to successfully transition into a new cell identity, such as a neuron or induced pluripotent stem cell.

In the new paper, the researchers devised a way to control gene expression levels by changing the distance between the synthetic gene and its promoter. They found that when there was a longer DNA “spacer” between the promoter region and the gene, the gene would be expressed at a lower level. That extra distance, they showed, makes it less likely that transcription factors bound to the promoter will effectively turn on gene transcription.

Then, to create set points that could be edited, the researchers incorporated sites within the spacer that can be excised by an enzyme called Cre recombinase. As parts of the spacer are cut out, it helps bring the transcription factors closer to the gene of interest, which turns up gene expression.

The researchers showed they could create spacers with multiple excision points, each targeted by different recombinases. This allowed them to create a system called DIAL, that they could use to establish “high,” “med,” “low” and “off” set points for gene expression.

After the DNA segment carrying the gene and its promoter is delivered into cells, recombinases can be added to the cells, allowing the set point to be edited at any time.

The researchers demonstrated their system in mouse and human cells by delivering the gene for different fluorescent proteins and functional genes, and showed that they could get uniform expression across the a population of cells at the target level.

“We achieved uniform and stable control. This is very exciting for us because lack of uniform, stable control has been one of the things that's been limiting our ability to build reliable systems in synthetic biology. When there are too many variables that affect your system, and then you add in normal biological variation, it’s very hard to build stable systems,” Galloway says.

Reprogramming cells

To demonstrate potential applications of the DIAL system, the researchers then used it to deliver different levels of the gene HRasG12V to mouse embryonic fibroblasts. This HRas variant has previously been shown to increase the rate of conversion of fibroblasts to neurons. The MIT team found that in cells that received a higher dose of the gene, a larger percentage of them were able to successfully transform into neurons.

Using this system, researchers now hope to perform more systematic studies of different transcription factors that can induce cells to transition to different cell types. Such studies could reveal how different levels of those factors affect the success rate, and whether changing the transcription factors levels might alter the cell type that is generated.

In ongoing work, the researchers have shown that DIAL can be combined with a system they previously developed, known as ComMAND, that uses a feedforward loop to help prevent cells from overexpressing a therapeutic gene.

Using these systems together, it could be possible to tailor gene therapies to produce specific, consistent protein levels in the target cells of individual patients, the researchers say.

“This is something we’re excited about because both DIAL and ComMAND are highly modular, so you could not only have a well-controlled gene therapy that’s somewhat general for a population, but you could, in theory, tailor it for any given person or any given cell type,” Galloway says.

The research was funded, in part, by the National Institute of General Medical Sciences, the National Science Foundation, and the Institute for Collaborative Biotechnologies.

Nature Climate Change, Published online: 13 October 2025; doi:10.1038/s41558-025-02438-3

The rise of generative AI presents both risks and opportunities for shaping climate discourse. New findings suggest it can help lower climate scepticism and bolster support for climate action.