Groups say there is a coordinated national effort to shield the fossil fuel industry from litigation.

A measure backed by Gov. Wes Moore (D) and statehouse leaders would slash an energy efficiency program in an effort to quickly lower utility bills. But critics say it's a shortsighted move.

The world’s third-highest emitter plans to boost green electricity and decouple economic growth from carbon emissions.

A major shipping choke point on the Red Sea could come under Iran-sponsored attack to further disrupt global energy supplies.

An energy research firm expects the conflict's effect on fuel markets to outlive the war.

But a FEMA associate administrator warned Congress on Wednesday that its disaster fund is "rapidly depleting" under the Department of Homeland Security shutdown.

The two sides agreed in principle to cut emissions from the oil and gas sector by 75 percent below 2014 levels by 2035.

The market for carbon credits has struggled to right itself after years of greenwashing allegations.

"We had no warning," said Honolulu's mayor as he toured the damage.

Named Coatlicue, after an Aztec deity, it will be used for climate predictions, energy planning and corruption prevention, among other issues.

Inside a giant autonomous warehouse, hundreds of robots dart down aisles as they collect and distribute items to fulfill a steady stream of customer orders. In this busy environment, even small traffic jams or minor collisions can snowball into massive slowdowns.

To avoid such an avalanche of inefficiencies, researchers from MIT and the tech firm Symbotic developed a new method that automatically keeps a fleet of robots moving smoothly. Their method learns which robots should go first at each moment, based on how congestion is forming, and adapts to prioritize robots that are about to get stuck. In this way, the system can reroute robots in advance to avoid bottlenecks.

The hybrid system utilizes deep reinforcement learning, a powerful artificial intelligence method for solving complex problems, to figure out which robots should be prioritized. Then, a fast and reliable planning algorithm feeds instructions to the robots, enabling them to respond rapidly in constantly changing conditions.

In simulations inspired by actual e-commerce warehouse layouts, this new approach achieved about a 25 percent gain in throughput over other methods. Importantly, the system can quickly adapt to new environments with different quantities of robots or varied warehouse layouts.

“There are a lot of decision-making problems in manufacturing and logistics where companies rely on algorithms designed by human experts. But we have shown that, with the power of deep reinforcement learning, we can achieve super-human performance. This is a very promising approach, because in these giant warehouses even a 2 or 3 percent increase in throughput can have a huge impact,” says Han Zheng, a graduate student in the Laboratory for Information and Decision Systems (LIDS) at MIT and lead author of a paper on this new approach.

Zheng is joined on the paper by Yining Ma, a LIDS postdoc; Brandon Araki and Jingkai Chen of Symbotic; and senior author Cathy Wu, the Class of 1954 Career Development Associate Professor in Civil and Environmental Engineering (CEE) and the Institute for Data, Systems, and Society (IDSS) at MIT, and a member of LIDS. The research appears today in the Journal of Artificial Intelligence Research.

Rerouting robots

Coordinating hundreds of robots in an e-commerce warehouse simultaneously is no easy task.

The problem is especially complicated because the warehouse is a dynamic environment, and robots continually receive new tasks after reaching their goals. They need to be rapidly redirected as they leave and enter the warehouse floor.

Companies often leverage algorithms written by human experts to determine where and when robots should move to maximize the number of packages they can handle.

But if there is congestion or a collision, a firm may have no choice but to shut down the entire warehouse for hours to manually sort the problem out.

“In this setting, we don’t have an exact prediction of the future. We only know what the future might hold, in terms of the packages that come in or the distribution of future orders. The planning system needs to be adaptive to these changes as the warehouse operations go on,” Zheng says.

The MIT researchers achieved this adaptability using machine learning. They began by designing a neural network model to take observations of the warehouse environment and decide how to prioritize the robots. They train this model using deep reinforcement learning, a trial-and-error method in which the model learns to control robots in simulations that mimic actual warehouses. The model is rewarded for making decisions that increase overall throughput while avoiding conflicts.

Over time, the neural network learns to coordinate many robots efficiently.

“By interacting with simulations inspired by real warehouse layouts, our system receives feedback that we use to make its decision-making more intelligent. The trained neural network can then adapt to warehouses with different layouts,” Zheng explains.

It is designed to capture the long-term constraints and obstacles in each robot’s path, while also considering dynamic interactions between robots as they move through the warehouse.

By predicting current and future robot interactions, the model plans to avoid congestion before it happens.

After the neural network decides which robots should receive priority, the system employs a tried-and-true planning algorithm to tell each robot how to move from one point to another. This efficient algorithm helps the robots react quickly in the changing warehouse environment.

This combination of methods is key.

“This hybrid approach builds on my group’s work on how to achieve the best of both worlds between machine learning and classical optimization methods. Pure machine-learning methods still struggle to solve complex optimization problems, and yet it is extremely time- and labor-intensive for human experts to design effective methods. But together, using expert-designed methods the right way can tremendously simplify the machine learning task,” says Wu.

Overcoming complexity

Once the researchers trained the neural network, they tested the system in simulated warehouses that were different than those it had seen during training. Since industrial simulations were too inefficient for this complex problem, the researchers designed their own environments to mimic what happens in actual warehouses.

On average, their hybrid learning-based approach achieved 25 percent greater throughput than traditional algorithms as well as a random search method, in terms of number of packages delivered per robot. Their approach could also generate feasible robot path plans that overcame congestion caused by traditional methods.

“Especially when the density of robots in the warehouse goes up, the complexity scales exponentially, and these traditional methods quickly start to break down. In these environments, our method is much more efficient,” Zheng says.

While their system is still far away from real-world deployment, these demonstrations highlight the feasibility and benefits of using a machine learning-guided approach in warehouse automation.

In the future, the researchers want to include task assignments in the problem formulation, since determining which robot will complete each task impacts congestion. They also plan to scale up their system to larger warehouses with thousands of robots.

This research was funded by Symbotic.

Like many people who end up going into physics, Sophia Henneberg had a hard time, when she was young, choosing between that discipline and mathematics. Both subjects came easily to her, and she — unlike many of her peers — thought they were fun. Henneberg grew up in a small town in central Germany, and it was not until one week before applying to college that she decided on physics, reasoning that it would still give her the chance to do plenty of math, while also affording opportunities to connect with a broad range of applications. 

Midway through her undergraduate studies at Goethe University in Frankfurt, she started taking courses in plasma physics and almost instantly knew that she had found her niche. “Most of the visible material in the universe is in the form of hot, ionized gas called plasma, so studying that is really fundamental,” she says. “And there’s this amazing application, fusion, which has the potential to become an unlimited energy source.”

Early on, Henneberg resolved to try to make that potential a reality, and she’s been pursuing that goal at MIT since becoming the Norman Rasmussen Career Development Assistant Professor in the Department of Nuclear Science and Engineering in fall of 2025. Her research focus is on stellarators — a kind of fusion machine that has been overshadowed for many decades by another fusion device called the tokamak.  Both of these machines rely on magnetic confinement — using powerful magnetic fields to compress a plasma into a tiny volume causing some of the atoms within this dense cluster to fuse together, unleashing energy in the process. In the tokamak, the plasma assumes the shape of a donut. In a stellarator, the plasma is also contained within a rounded loop, only this one resembles a twisted donut.

As a PhD candidate at the University of York (in the United Kingdom), Henneberg studied the instabilities that can arise in tokamaks, where plasma temperatures often exceed 100 million degrees Celsius and currents induced within the plasma can attain speeds of roughly 100 kilometers per second. In such an ultra-extreme setting — more than six times hotter than the core of the sun — sudden surges of energy, leading to something akin to small-scale solar flares, can breach the magnetic cage enclosing the plasma, thereby disrupting the fusion process and possibly damaging the reactor itself. Henneberg started hearing about stellarators in her classes and, after a bit of research, she came to realize that “they could be much more stable if you design them in the right way.”

Striking a favorable balance

In 2016, she began a postdoctoral fellowship at the Max Planck Institute (MPI) for Plasma Physics in Greifswald, Germany, joining the Stellarator Theory Group. Greifswald may well have been the best place for her to carry out stellarator research, given that the world’s biggest and most advanced reactor of this type, Wendelstein 7-X (W7-X), was based there, and experiments were just starting in the year she arrived.

Her main assignment at MPI was to work on stellarator optimization, figuring out the best way to design the reactor to meet the engineering and physics goals — a task not unlike that of tuning a car to achieve maximum fuel efficiency or, for a racecar, maximum speed. Henneberg’s interest in optimization continues to this day, remaining central to her research agenda at MIT.

“If you want to design a stellarator, there are two principal components you can look at,” she says. The first relates to the shape of the boundary, or cage, into which the plasma will ultimately be confined. This shape is constrained by magnetic fields that are generated, in turn, by a series of superconducting coils that might range in number anywhere from around 4 to 50. In stellarators, the coils tend to be bent rather than circular. That gives rise to twists in the magnetic fields, but it also makes the coils more complicated and likely more expensive. Henneberg has come up with ways to simplify the optimization process — one of which involves designing the plasma boundary and the shape of the coils in the same step rather than looking at them separately. 

“We’ve now reached the point where stellarator performances can exceed those of tokamaks, because we’re able to optimize them very well, but you have to put the effort in,” she says. “You can’t get good performance out of just any twisty donut.”

The best of both worlds

In a 2024 paper, Henneberg and her former Greifswald colleague, Gabriel Plunk, introduced the notion of a stellarator-tokamak hybrid reactor. The goal, they wrote, is both “simple and compelling: to combine the strengths of the two concepts into a single device” that outperforms either of the existing modes.

One of Henneberg’s major preoccupations at present is exploring ways of converting a tokamak into a stellarator that basically entails adding just a few coils — of the bent variety — that can be turned on or off. “This can be an easy way for people in the tokamak community to think more about the possible benefits of the stellarator,” she says. While no one has yet built a hybrid, at least one university has secured funding to do so.

Interest in stellarators has been steadily mounting in recent years, a fact that delights Henneberg. When she started working in this area almost a decade ago, the field of stellarator optimization was tiny and there were very few people she could converse with. There’s much more research going on today, which means that more ideas are coming out, along with some exciting results. Commercial interest is growing as well, and Henneberg has been in contact with several stellarator startup companies, including Type One Energy and Thea Energy in the United States and Proxima Fusion and Gauss Fusion in Germany.

“It seems to me that most new startups these days are focusing on stellarators,” Henneberg says. “With so many companies now entering the field, it can seem like the technical issues involved in fusion are already solved, but there are still many interesting open questions. I’m working on improved designs that advance both the physics and the economic feasibility.”

That’s where her students come in. She believes that one part of her role as an MIT professor is to train the next generation of stellarator experts — people who will help, for instance, to design effective coils that are easy to make, as well as to improve reactor performance overall. 

During her first term, she co-taught the renowned Fusion Design (22.63) course alongside MIT Professor Dennis Whyte. This course has had a remarkable influence on the fusion community, leading to nine published papers with over 1,000 citations and inspiring the creation of several companies. In the fall 2025 version of this course, students were charged with comparing designs for stellarators with machines that relied on a different way of confining the plasma called magnetic mirrors.

After just a few months at MIT, Henneberg has been impressed with her students, calling them “highly motivated and a lot of fun to work with.” She’s confident that her research group will soon be making progress.

She is also happy to be affiliated with MIT’s Plasma Science and Fusion Center, which is internationally recognized as a leading university laboratory in this field. “It’s great to have so many experts [primarily in tokamaks] in one place that I can work with and learn from,” Henneberg says. “Because of my interest in hybrid reactors, my research will really benefit from all the expertise here on the tokamak side.”

Each spring, river herring populations migrate from Massachusetts coastal waters to begin their annual journey up rivers and streams to freshwater spawning habitat. River herring have faced severe population declines over the past several decades, and their migration is extensively monitored across the region, primarily through traditional visual counting and volunteer-based programs. 

Monitoring fish movement and understanding population dynamics are essential for informing conservation efforts and supporting fisheries management. With the annual herring run getting underway this month, researchers and resource managers once again take on the challenge of counting and estimating the migrating fish population as accurately as possible. 

A team of researchers from the Woodwell Climate Research Center, MIT Sea Grant, the MIT Computer Science and Artificial Intelligence Lab (CSAIL), MIT Lincoln Laboratory, and Intuit explored a new monitoring method using underwater video and computer vision to supplement citizen science efforts. The researchers — Zhongqi Chen and Linda Deegan from the Woodwell Climate Research Center, Robert Vincent and Kevin Bennett from MIT Sea Grant, Sara Beery and Timm Haucke from MIT CSAIL, Austin Powell from Intuit, and Lydia Zuehsow from MIT Lincoln Laboratory — published a paper describing this work in the journal Remote Sensing in Ecology and Conservation this February. 

The open-access paper, “From snapshots to continuous estimates: Augmenting citizen science with computer vision for fish monitoring,” outlines how recent advancements in computer vision and deep learning, from object detection and tracking to species classification, offer promising real-world solutions for automating fish counting with improved efficiency and data quality. 

Traditional monitoring methods are constrained by time, environmental conditions, and labor intensity. Volunteer visual counts are limited to brief daytime sampling windows, missing nighttime movement and short migration pulses, when hundreds of fish pass by within the span of a few minutes. While technologies like passive acoustic monitoring and imaging sonar have advanced continuous fish monitoring under certain conditions, the most promising and low-cost option — manual review of underwater video — is still labor-intensive and time-consuming. With the growing demand for automated video processing solutions, this study presents a scalable, cost-effective, and efficient deep learning-based system for reliable automated fish monitoring. 

The team built an end-to-end pipeline — from in-field underwater cameras to video labeling and model training — to achieve automated, computer vision-powered fish counting. Videos were collected from three rivers in Massachusetts: the Coonamessett River in Falmouth, the Ipswich River (Ipswich), and the Santuit River in Mashpee. 

To prepare the training dataset, the team selected video clips with variations in lighting, water clarity, fish species and density, time of day, and season to ensure that the computer vision model would work reliably across diverse real-world scenarios. They used an open-source web platform to manually label the videos frame-by-frame with bounding boxes to track fish movement. In total, they labeled 1,435 video clips and annotated 59,850 frames. 

The researchers compared and validated the computer vision counts with human video reviews, stream-side visual counts, and data from passive integrated transponder (PIT) tagging. They concluded that models trained on diverse multi-site and multi-year data performed best and produced season-long, high-resolution counts consistent with traditionally established estimates. Going one step further, the system provided insights into migration behavior, timing, and movement patterns linked to environmental factors. Using video from the 2024 Coonamesset River migration, the system counted 42,510 river herring and revealed that upstream migration peaked at dawn, while downstream migration was largely nocturnal, with fish utilizing darker, quieter periods to avoid predators.

With this real-world application, the researchers aim to advance computer vision in fisheries management and provide a framework and best practices for integrating the technology into conservation efforts for a wide range of aquatic species. “MIT Sea Grant has been funding work on this topic for some time now, and this excellent work by Zhongqi Chen and colleagues will advance fisheries monitoring capabilities and improve fish population assessments for fisheries managers and conservation groups,” Vincent says. “It will also provide education and training for students, the public, and citizen science groups in support of the ecologically and culturally important river herring populations along our coasts.”

Still, continued traditional monitoring is essential for maintaining consistency in long-term datasets until fisheries management agencies fully implement automated counting systems. Even then, computer vision and citizen science should be seen as complementary. Volunteers will be necessary for camera maintenance and for contributing directly to the computer vision workflow, from video annotation to model verification. The researchers envision that integrating citizen observations and computer vision-generated data will help create a more comprehensive and holistic approach to environmental monitoring.

This work was funded by MIT Sea Grant, with additional support provided by the Northeast Climate Adaptation Science Center, an MIT Abdul Latif Jameel Water and Food Systems seed grant, the AI and Biodiversity Change Global Center (supported by the National Science Foundation and the Natural Sciences and Engineering Research Council of Canada), and the MIT Undergraduate Research Opportunities Program.

Little Is Known About AI That Could Affect Millions of Seniors' Care

SAN FRANCISCO – The Electronic Frontier Foundation (EFF) today filed a Freedom of Information Act (FOIA) lawsuit against the Centers for Medicare & Medicaid Services (CMS) seeking records about a multi-state program that is using AI to evaluate requests for medical care.

"Tasking an algorithm with making determinations about treatment can create unwarranted—and even discriminatory—delays or denials of necessary medical care," said Kit Walsh, EFF’s Director of AI and Access-to-Knowledge Legal Projects. "Given these serious risks, the public requires transparency that it hasn't gotten. We're suing to get badly needed answers about how Medicare's AI experiment works."

Announced by CMS Administrator Dr. Mehmet Oz last year, the pilot program known as WISeR (Wasteful and Inappropriate Service Reduction) uses AI to assess prior authorization requests from Medicare beneficiaries. Previously rare in original Medicare, prior authorization requires medical providers to obtain advance approval from a patient’s health insurer before delivering certain treatments or services as a condition of coverage.

Unfortunately, there is little information about how the AI algorithms used in WISeR work, including what training data they rely on. It remains unclear whether WISeR has any safeguards against systemic flaws such as algorithmic bias, privacy violations, and wrongful denials of care.

Healthcare experts, care providers, and lawmakers have all raised alarms that WISeR may cause serious harm to patients by relying on AI unless it has the necessary safeguards. Despite this widespread criticism, WISeR was rolled out in six states in January, potentially affecting as many as 6.4 million Medicare beneficiaries, according to one estimate.

By design, WISeR incentivizes contracted companies to deny prior approval against the best interests of patients. Vendors are compensated, in part, on the volume of healthcare services they deny and are entitled to as much as 20 percent of the associated savings. Just weeks after WISeR's launch, hospitals and other health care providers started reporting delays in care approval, communication gaps, and administrative strain.

Earlier this year, EFF submitted a FOIA request to CMS asking for records related to WISeR. Among other records, the request sought agreements with software vendors participating in WISeR; records related to any tests for accuracy, bias, or hallucinations in vendors' technology; and records related to any audits, monitoring, or evaluation of WISeR and participating vendors. To date, CMS has not provided any of these records to EFF. EFF's FOIA lawsuit asks for their immediate processing and release.

"The public has a right to know more about the algorithms driving decisions around their healthcare," said Tori Noble, Staff Attorney at EFF. "Without greater transparency, patients, providers, and policymakers will continue to be left in the dark.”

EFF thanks Stanford Law School's Juelsgaard Intellectual Property & Innovation Clinic for their help in preparing this lawsuit.

For the complaint: https://www.eff.org/document/complaint-eff-v-cms-medicare-wiser-foia

Tags: transparencyartificial intelligence and machine learningautomated decision-makingContact:  ToriNobleStaff Attorneytori@eff.org

Batteries that use solid metal as their charge-carrying electrolyte could potentially be a safer and far more energy-dense alternative to lithium-ion batteries. However, these solid-state batteries have been plagued by the formation of metallic cracks called dendrites that cause them to short circuit.

The problem has so far prevented such batteries from becoming a major player in energy storage. But now, research from MIT could finally help engineers find a way to get past this hurdle.

For decades, many researchers have treated dendrites as largely the result of mechanical stress — like cracks that form on the sidewalk when a tree root grows underneath. But MIT engineers have discovered the exact opposite: Faster dendrite growth was associated with lower stress levels in a commonly used battery electrolyte material. Using a new technique that allowed them to directly measure the stress around growing dendrites, the researchers found cracks formed at stress levels as low as 25 percent of what would be expected under mechanical stress alone.

The experiments, published in Nature today, instead revealed another culprit: chemical reactions caused by high electrical currents that weaken the electrolyte and make it more susceptible to dendrite growth. Researchers had previously proposed that such reactions cause dendrite growth, but the new study provides the first experimental data on the interplay between chemical and mechanical stress in dendrite formation.

“Direct measurement techniques allowed us to see how tough the material is as we cycle the cell,” says Cole Fincher, the paper’s first author and an MIT PhD student in materials science and engineering. “What we saw was that if you just test the ceramic electrolyte on the benchtop, it’s about as tough as your tooth. But during charging, it gets a lot weaker — closer to the brittleness of a lollipop.”

The findings reveal why developing stronger electrolytes alone hasn’t solved the decades-old dendrite problem. It also points to the importance of developing more chemically stable materials to finally fulfill the promise of high-density solid-state batteries.

“There’s a large community of researchers that are constantly trying to discover and design better solid electrolytes to enable the solid-state battery,” says senior author Yet-Ming Chiang, MIT’s Kyocera Professor of Materials Science and Engineering. “This study provides guidance in those efforts. We discovered a new mechanism by which these dendrites grow, allowing us to explore ways to design around it to make solid-state batteries successful.”

Joining Fincher and Chiang on the paper are MIT PhD student Colin Gilgenbach; Thermo Fisher Scientific scientists Christian Roach and Rachel Osmundsen; MIT.nano researcher Aubrey Penn; MIT Toyota Professor in Materials Processing W. Craig Carter; MIT Kyocera Professor of Materials Science and Engineering James LeBeau; University of Michigan Professor Michael Thouless; and Brown University Professor Brian W. Sheldon.

Measuring stress

Dendrites have presented a major roadblock to battery development since the 1970s. One reason lithium-ion batteries have become ubiquitous while other approaches have stalled is that their commonly used graphite anodes are less susceptible to dendrite formation. That’s a shame because solid-state batteries that use lithium metal as an anode and a solid electrolyte could theoretically store far more energy in the same sized package with less weight. They could thus enable longer-lasting phones and laptops, or electric cars with double the range of today’s options.

“There’s no more energy-dense form of lithium than lithium metal,” Chiang says. “But the dendrite problem has limited progress with solid-state batteries.”

Lithium metal is soft like taffy. Fincher, who has been studying the dendrite problem in the labs of Chiang and Carter, says one puzzle is how such a soft material can penetrate into the hard electrolyte materials being explored for use in solid-state batteries.

“The ceramics that have been used in these applications are stiff, like a coffee mug, so it’s been hoped that solid-state batteries would stop this relatively soft dendrite from growing,” Fincher explains.

Believing that mechanical stress causes dendrites, scientists have worked to develop stronger electrolytes that can withstand more mechanical stress. Some researchers have proposed that chemical reactions play a role in dendrite formation, but how those reactions worked with mechanical stress was not known.

For their Nature study, the researchers set out to directly observe mechanical and chemical changes in a commonly used solid-state electrolyte material as dendrites grew. Solid-state batteries are typically organized like a sandwich, which makes it hard to look inside the middle electrolyte layer. For their first experiment, the researchers developed a special solid-state battery cell in which the ceramic layers can be observed from the side, allowing the researchers to watch dendrite growth occurring in the electrolyte.

The researchers also used a measurement technique called birefringence microscopy to precisely measure the stress around the dendrite, which Fincher developed as part of his PhD thesis.

“It works the same way as polarized sunglasses when you look at something like a windshield,” Fincher explains of the technique. “When light comes through, residual stresses in the glass enable light of some orientations to pass faster than others, and that can give rise to observable rainbow patterns. These patterns can be used to measure stress.”

The technique gave the researchers a way to both visualize and quantify stress around actively growing dendrites for the first time, leading to the unexpected findings.

“Normally you would expect that the faster a dendrite grows, the more stress it creates,” Chiang says. “Instead, we observed exactly the opposite. The faster it grew, the lower the stress around it, meaning the solid electrolyte is breaking under a lower stress, and therefore it’s been embrittled.”

In fact, the dendrites grew at stress levels far weaker than expected. Fincher describes the weaker electrolyte as electrochemically corroded.

“Imagine you test a piece of glass one day, and the next day it’s only a quarter as strong,” Chiang says. “It was very surprising.”

Led by LeBeau, the researchers then cooled the electrolyte to extremely low temperatures and applied a powerful imaging technique called cryogenic scanning transmission electron microscopy that allowed them to study the area around the dendrite on nearly atomic scales. The imaging revealed that the passage of ionic current through the material had caused chemical reactions that made it more brittle.

“The electric current drives the flow of lithium ions through the solid electrolyte,” Chiang explains. “That causes a highly concentrated flow of lithium ions at the dendrite tip. We believe that leads to a chemical reduction of the material compound, which leads to its decomposition into new phases. You start with a crystalline phase of the electrolyte, then there’s a volume contraction after the deposition that is consistent with the embrittlement we see.”

Toward better batteries

The experiment was done on one of the most stable electrolytes used in solid-state batteries, making the researchers confident the findings will carry over to other electrolyte materials.

“This tells us we have to look for electrolyte materials that are even more stable, especially when in contact with lithium metal, which chemically speaking is very reducing,” Chiang says. “This will help direct the search for new materials.”

For instance, Chiang says now that they understand more about the chemical changes causing embrittlement, researchers could explore materials that actually get tougher as cracks grow.

The researchers say it will take more work to figure out what electrochemical reactions are taking place to make the electrolyte so much weaker. But they say their approach for directly observing stresses could also help improve materials for use in devices like fuel cells and electrolyzers.

The work was supported by the center for Mechano-Chemical Understanding of Solid Ionic Conductors, a Department of Energy Engineering Frontiers Research Center, the National Science Foundation, and Fincher’s Department of Defense Science and Engineering Graduate Fellowship, and was carried out using MIT.nano facilities.

After years of tech industry experiments, smartglasses with embedded cameras and microphones have finally gone mainstream. And, disturbingly, sometimes it's not just their owners who are watching what these devices record. In this week's EFFector newsletter, we're taking a closer look at the privacy implications of Meta Ray-Bans, and sharing all the latest in the fight for privacy and free speech online.

JOIN OUR NEWSLETTER

For over 35 years, EFFector has been your guide to understanding the intersection of technology, civil liberties, and the law. This week's issue covers EFF's new executive director; how publishers blocking the Internet Archive threaten the web's historical record; and why you should think twice before buying or using Meta’s Ray-Bans.

Prefer to listen in? EFFector is now available on all major podcast platforms. This week, we're chatting with EFF Security and Privacy Activist Thorin Klosowski about smartglasses and privacy. And don't miss the EFFector news quiz. You can find the episode and subscribe on your podcast platform of choice: 

%3Ciframe%20height%3D%22200px%22%20width%3D%22100%25%22%20frameborder%3D%22no%22%20scrolling%3D%22no%22%20seamless%3D%22%22%20src%3D%22https%3A%2F%2Fplayer.simplecast.com%2Fc139744a-aad2-4d31-8b5e-84764a13bf2f%3Fdark%3Dfalse%22%20allow%3D%22autoplay%22%3E%3C%2Fiframe%3E Privacy info. This embed will serve content from simplecast.com

   

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 against online surveillance when you support EFF today!

This is the first installment of a blog series reflecting on the global digital legacy of the 2011 Arab uprisings.

A new generation of protesters, raised on social media and often fluent in the tools of digital dissent, has taken to the streets in recent months and years. In Bangladesh, Iran, Togo, France, Uganda, Nepal, and more than a dozen other countries, young people have harnessed digital tools to mobilize at scale, shape political narratives, and sustain movements that might once have been easier to ignore or suppress.

The tools at their disposal are vast, allowing them to coordinate quickly and turn local grievances into visible, transnational moments of dissent. But each new tactic is met in turn: governments now implement draconian regulations and deploy sophisticated surveillance systems, content manipulation, and automated censorship to pre-empt, predict, and punish collective action. 

This cycle of digital empowerment and repression is not new. In many ways, its roots can be traced to the 2011 uprisings that rippled across the Middle East and North Africa. Often referred to as the “Arab Spring,” these movements didn’t just reshape politics…they transformed how we talk about the internet, and how governments respond in times of protest, crisis, and conflict. Fifteen years later, the legacy of that moment still defines the terms of resistance and control in the digital age.

At the time, we were sold the comforting narrative that the internet would help bring about democracy, that connectivity itself was revolutionary, and that Silicon Valley’s products—particularly social media platforms—were aligned with the people. It was a narrative that tech executives were sometimes happy to amplify and certain Western governments were happy to believe. 

But the same networks that helped protesters to organize and broadcast their demands beyond their own borders laid the groundwork for new forms of repression. Over the years, the same tools that were once celebrated as tools of dissent have become instruments for tracking, harassing, and prosecuting dissenters.

This series examines the digital legacy of the 2011 uprisings that shook the region: how governments refined censorship and surveillance after 2011, how platforms alternately resisted and enabled those efforts, and how a new generation of civil society has pushed back.

"Over the years, the same tools that were once celebrated as tools of dissent have become instruments for tracking, harassing, and prosecuting dissenters."

When Tunisian fruit vendor Mohamed Bouazizi set himself on fire on December 17, 2010, after repeated harassment by local officials, he could not have known the chain reaction his act would spark. After nearly twenty-three years in power, President Zine El Abidine Ben Ali faced a public fed up with repression. Protests spread across Tunisia, ultimately forcing him to flee.

In his final speech, Ben Ali promised reforms: a freer press and fewer internet restrictions. He left before either materialized. For Tunisians, who had lived for years under normalized censorship both online and off, the promises rang hollow.

At the time, Tunisia’s internet controls were among the most restrictive in the world. Reporting by the exiled outlet Nawaat documented a sophisticated filtering regime: DNS tampering, URL blocking, IP filtering, keyword censorship. Yet despite that machinery, Tunisians built a resilient blogging culture, often relying on circumvention tools to push information beyond their borders. When protests began—and before international media caught up—they were ready.

Eleven days after Ben Ali fled, Egyptians took to the streets. International headlines rushed to label it a “Twitter revolution,” mistaking a tool for a movement. Egypt’s government drew a similar conclusion. On January 26, authorities blocked Twitter and Facebook. The next day, they shut down the internet almost entirely, a foreshadowing of what we’d see fifteen years later in Iran.

As Egyptians fought to free their country from President Hosni Mubarak’s autocratic rule, protests swept across the region to Bahrain, where demonstrators gathered at the Pearl Roundabout before facing a brutal crackdown; to Syria, where early calls for reform spiraled into one of the most devastating conflicts of the century; to Morocco, where the February 20 Movement pushed for constitutional change. Outside of the region, movements took shape in Spain, Greece, Portugal, Iceland, the United States, and beyond.

In each context, digital platforms helped circulate images, testimonies, and tactics across borders. They created visibility—and, in turn, inspired a playbook. Governments watched not only their own populations but one another, quickly learning how to disrupt networks, identify organizers, and seize back control of the narrative.

Cause and Effect

To be clear, the internet didn’t create these movements. Decades of repression, corruption, labor organizing, and grassroots activism did. Later research confirmed what many in the region already understood: digital tools helped people share information and coordinate action, but they were neither the spark nor the engine of revolt.

But regardless, the myth of the “Twitter revolution” had consequences. The breathless coverage, and rapid policy reactions that followed shaped state strategy around the world. Governments across the region and well beyond invested heavily in surveillance technologies, developed new legal mechanisms, increased their own social media presence, and found ways to influence platforms. Internet blackouts, once rare, became a normalized tool of crisis response. And companies were forced into increasingly public decisions about whether to resist state pressure or comply.

When it comes to the internet, the legacy of the 2011 uprisings that swept the region and beyond is a story about power: how states moved to consolidate control online, how platforms—often under pressure—have narrowed the space for dissent, and how civil society has been forced to evolve to defend it.

This five-part series will take a deeper look at how the internet as a space for dissent and for hope has changed over the past fifteen years throughout the region and well beyond.  

Sen. Ron Wyden is warning us of an abuse of Section 702:

Wyden took to the Senate floor to deliver a lengthy speech, ostensibly about the since approved (with support of many Democrats) nomination of Joshua Rudd to lead the NSA. Wyden was protesting that nomination, but in the context of Rudd being unwilling to agree to basic constitutional limitations on NSA surveillance. But that’s just a jumping off point ahead of Section 702’s upcoming reauthorization deadline. Buried in the speech is a passage that should set off every alarm bell:

There’s another example of secret law related to Section 702, one that directly affects the privacy rights of Americans. For years, I have asked various administrations to declassify this matter. Thus far they have all refused, although I am still waiting for a response from DNI Gabbard. I strongly believe that this matter can and should be declassified and that Congress needs to debate it openly before Section 702 is reauthorized. In fact, ...

The court found three legal challenges from Maryland cities and one county against oil companies are "far afield" from matters of local or state law.

Climate expert, speaker and author Kate Marvel cited "uncertainty" about NASA climate research as President Donald Trump cuts programs and cancels a lease.