Meanwhile in Spain, 10 people were still unaccounted for Monday from a fire that ripped through a remote southern community last week.

Restored ecosystems in drylands can become increasingly self-sustaining over time, but they still require careful management and long-term monitoring.

President-elect Abelardo de la Espriella has pledged to revive Colombia’s oil sector and voiced support for fracking.

Tissue engineers are finding ways to grow living organs and tissues from cells, with the aim of replacing diseased and damaged counterparts in the body. Scientists have successfully grown artificial muscles, livers, kidneys, skin, and other tissues. But there’s been no reliable way to engineer precisely patterned networks of blood vessels, some of which can be finer than a human hair. 

Without a vascular network to deliver nutrients, any artificial tissues, no matter how life-like, can’t function. 

Now MIT engineers have found they can engineer and control the growth of blood vessels by mechanically stretching them. 

The team has built a human “blood vessel on a chip,” composed of a central artery made from human endothelial cells, that is embedded in a gel that also contains a small magnet. The researchers studied how the main artery responded as they jostled the gel back and forth using an external magnet to move the magnet embedded within the gel.

They found that the simple mechanical action of repeatedly jostling the artery stimulated the artery to sprout other, smaller capillaries. By changing the direction in which the artery is jostled or stretched, the researchers could redirect the growing new vessels. And stretching the artery by various degrees influenced how many more new vessels sprouted. 

Their results, reported in the Proceedings of the National Academy of Sciences, offer scientists a new way to engineer artificial blood vessels and program the patterns in which they grow. 

“Healthy tissues depend on organized blood vessel networks, but state-of-the-art protocols don't enable fabricating such networks within engineered tissues,” says Ritu Raman, associate professor of mechanical engineering at MIT and the study’s co-lead author. “The ability to program blood vessel growth with physical cues may enable reproducible and scalable fabrication of engineered tissues that can be implanted in the body to restore function after debilitating disease or injury.”

The study’s MIT co-authors include Sina Kheiri, Jessica Shah, Shashaank Venkatesh, and Roger Kamm, along with Peiyuan Chai and Ryan Flynn at Harvard University. 

“Moving is good”

Blood vessels are tricky to grow and control using conventional fabrication techniques. While 3D printers can produce vessels at the scale of major arteries and veins, the technology is not precise enough to print intricate networks of much finer, thread-like capillaries. Scientists have had some success with growing blood vessels from individual cells, by cultivating them in Petri dishes filled with nutrients and growth factors. But controlling how and where they grow remains a challenge. 

“You can try to pattern chemical cues, like growth factors, to direct where vessels grow, but you can’t do this very precisely,” Raman says. “We thus need other types of patternable cues that can help us build tissues with organized vessels.”

She and her students wondered whether they could grow and control new blood vessels using a protocol they previously developed to grow artificial muscles and nerves. In their previous works, the team engineered a small chip filled with a gel that they infused with nutrients and growth factors. They embedded a small magnet within the gel, and then carpeted the surface of the gel with live muscle or neuron cells. They then manipulated an external magnet to pull the embedded magnet, and the cell-covered gel, back and forth. This work revealed that mechanical “exercise,” pulling the cells back and forth, directly influenced how the cells grew. 

In their new work, the team used a similar setup to see if they could grow and control new blood vessels. 

The researchers built a “blood-vessel-on-a-chip,” smaller than a postage stamp, and filled it with a similar nutrient-rich gel containing a small magnet. They poked a thin tube lengthwise through the gel to create a hollow channel, and coated the channel with live endothelial cells, which naturally grow and fuse to form blood vessels in the body. Once the cells took on the channel’s shape, they started sprouting new, capillary-like vessels in the gel. 

Placing the device under a motorized stage fitted with small, suspended magnets, the researchers moved the magnets back and forth in different directions, and by various degrees, and observed whether and how blood vessels sprouted from the central artery in response. 

“The main takeaway is: Stretching the blood vessel back and forth seems to enhance the number of new capillaries that grow,” Raman says. 

If the main artery were simply left alone in the gel, it would grow some new vessels in random locations along its length. But when the artery was jostled, significantly more vessels sprouted. When the team used the magnets to stretch the gel back and forth, by 5 percent of the gel’s total width, many new vessels grew out from the main artery. When they stretched by 15 percent, fewer vessels sprouted, but those that did grew longer. And when the team changed the direction of stretching, the new vessels followed in response, taking turns and following the pattern of the team’s mechanical stimulation.

“We’re finding that moving is good, which is always the takeaway of everything we do in our lab,” Raman says. “Mechanical forces play an important role in our bodies. That means that if you want to grow more or less vessels, or shorter or longer vessels, or vessels in certain directions, we now know how to do that.”

A gatekeeping gene

The researchers went a step further to investigate why blood vessels grow in response to mechanical forces. To do so, they looked to gene editing, and the role of one particular gene: Piezo1. 

Raman had recently attended a talk by molecular biologist Ardem Patapoutian. In 2021, Patapoutian received the Nobel Prize in Physiology or Medicine for his discovery of ion channels in cell membranes that open and close in response to mechanical pressure. These channels, named PIEZO1 and PIEZO2, act as a cell’s gatekeepers, controlling what goes in and what comes out of a cell. Both types of channels, Patapoutian found, are regulated by their respective genes, also named PIEZO1 and PIEZO2. 

After his talk, Raman showed Patapoutian her group’s experimental results, which showed a connection between blood vessel growth and mechanical stimulation. Patapoutian in turn proposed that the explanation could be the PIEZO1 channel; by mechanically exercising the central artery, Raman may have been stimulating ion channels in the artery’s cells to open, triggering new blood vessels to grow. 

To test this hypothesis, Raman looked to knock down the PIEZO1 gene. If this gene were less active, and fewer blood vessels grew as a result, then it would mean that blood vessels do indeed grow in response to mechanical stimulation, and specifically, through the activation of PIEZO1 ion channels. 

The team repeated their experiments, this time with endothelial cells that were genetically edited to suppress the PIEZO1 gene. Sure enough, they observed that significantly fewer new blood vessels sprouted, even as they mechanically exercised the central artery.

Now that the team has found a way to grow and control blood vessel growth, they plan to apply the protocol to grow organized networks of vessels to supply artificial organs and tissues. “We are now investigating how precisely patterning blood vessel growth can help improve muscle function,” says co-author Jessica Shah.

This work was supported, in part, by the U.S. Department of War Army Research Office Early Career Program and PECASE Grant, and a Department of War DURIP Program Grant.

Nature Climate Change, Published online: 14 July 2026; doi:10.1038/s41558-026-02699-6

The timescale on which river runoff reacts to glacier melt changes differs strongly between individual basins. Here we discuss how an article published in 2018 linked the buffering role of glaciers to future seasonal runoff losses, and how later work has extended this insight towards drought, water resources and the consequences for downstream societies.

Nature Climate Change, Published online: 14 July 2026; doi:10.1038/s41558-026-02695-w

Enhancing local adaptive capacity to reduce extreme weather-related health harms is essential in a warming world; understanding and sharing strategies that work helps to scale-up impact. Now a study focusing on China highlights the role of institutions, infrastructure and cities.

Professor Arthur Bahr has been named head of the MIT Literature Section, effective July 1. 

“Arthur is an exceptional scholar and a proven leader. I am confident that he will guide the unit with judgment, insight, and a deep commitment to its continued success,” says Agustín Rayo, the Kenan Sahin Dean of the School of Humanities, Arts, and Social Sciences. “I very much look forward to having him join the school’s leadership team.”

Bahr’s work blends formalist and materialist approaches to find literary resonance in the physical particularities of medieval manuscripts. He joined the MIT faculty in 2007 and helped lead the Ancient and Medieval Studies program in 2009-18 and 2022-23, working with colleagues from across the Institute to strengthen and expand the program. He has also been curriculum chair and undergraduate officer of the Literature Section.

“Lit@MIT has some of the world’s most innovative literary scholars and some of the Institute’s most dedicated teachers,” Bahr says. “It has also been my home for nearly 20 years, and I feel both humbled and energized by the opportunity to help shape its future. 

“Literature creates opportunities to slow down and reflect on what really matters, and in a fast-paced, increasingly automated world, those skills are more vital than ever,” he continues.

Bahr succeeds Associate Professor Sandy Alexandre, who served as head of the unit since July 2025.

Bahr is the author of “Chasing the Pearl-Manuscript: Speculation, Shapes, Delight” (University of Chicago Press, 2025); “Fragments and Assemblages: Forming Compilations of Medieval London” (University of Chicago Press, 2013); and co-editor of “Medieval English Manuscripts: Form, Aesthetics, and the Literary Text,” a special volume of The Chaucer Review (47.4, April 2013). His essays have appeared in ELH, Studies in the Age of Chaucer, Studies in Philology, and The Chaucer Review, among others. 

Bahr has been named a SHASS Faculty Fellow for the spring 2027 semester. His next project combines his interest in manuscripts with his training as a figure skating judge to explore analogies between sheets of parchment and sheets of ice, as sites of performance, inscription, and erasure.  

Bahr was named a MacVicar Faculty Fellow in 2015. He received the James A. (’48) and Ruth Levitan Award for Excellence in Teaching in 2012.

Bahr has served MIT as chair of the Committee on the Undergraduate Program from 2019 to 2021, and served on the pandemic-era Academic Policies and Regulations Team. He was also a subcommittee chair of the Education Group of Task Force 2021 and Beyond, and member of the subsequent Refinement and Implementation Committee on the Undergraduate Program. 

Bahr earned his undergraduate degree from Amherst College and his PhD in English Language and Literature from the University of California at Berkeley.

In May 2019, the government of Baltimore, Maryland, fell into chaos. Cybercriminals had locked the city out of many of its critical files and demanded payment to decrypt them. The city refused to pay ransom. The attack incapacitated a swath of services, including real estate transactions and bill payment, and recovery costs soared into the millions.

The syllabus of class 11.074/11.274 (Cybersecurity Clinic), a course in the MIT Department of Urban Studies and Planning (DUSP), includes a case study on Baltimore’s situation as an example of increasingly common ransomware attacks on municipal governments and other public agencies. To counter such threats, Lecturer Jungwoo Chun and Ford Professor of Urban and Environmental Planning Lawrence Susskind launched the MIT Cybersecurity Clinic in 2019. They have offered the course nearly every semester since.

Much like a legal or medical clinic, the course doubles as hands-on training for students and a pro-bono service to at-risk communities. After completing instructional modules and passing a certification exam, students are assigned in teams to a client. By the end of the semester, each team creates a report assessing the client’s vulnerabilities to cyberattack and recommending steps to improve protection. So far, the clinic has provided more than 40 assessments, confidential and free of charge, primarily for New England municipalities and health-care organizations.

In 2025, the FBI’s Internet Crime Complaint Center documented an average of 2,765 cyberattacks targeting Americans every day. When these attacks strike cities and towns, the fallout goes beyond finances, says Chun: “There’s a terrifying, cascading effect on every dimension of our lives.” 

In recent years, cyberattacks targeting the kinds of client communities served by MIT’s clinic have imperiled water supplies, impeded 911 and police services, and exposed citizens’ personal data.

Despite being gateways to essential infrastructure, many small municipalities and hospitals lack in-house staff trained in cybersecurity. Demand for such experts far exceeds supply in today’s labor market, and public sector budgets rarely can match the high salaries private companies offer qualified candidates.

According to Comparitech, from 2018 to 2024, there have been 525 ransomware attacks on U.S. government entities, approximately one every five days, leading to an estimated $1.09 billion in downtime costs.  

“Underfunded public and not-for-profit bodies need to follow a self-help pathway,” Susskind says. “There are many low-cost moves that these organizations can implement with a little coaching from a free-service clinic.”

Defensive social engineering

Some might be surprised to find a university cybersecurity program housed outside the computer science department. Chun is an applied social scientist with expertise in public policy and planning, and Susskind is a leading scholar of conflict resolution and consensus building. They call the approach they’ve developed for the clinic “defensive social engineering” to emphasize that cybersecurity isn’t solely a technical challenge.

Chun acknowledges that the rapid development of artificial intelligence has created alarming new tools for criminals — “now AI can not only identify the vulnerability, but do the attack itself, which is really scary” — and an ever-evolving menu of software claims to guard against these attacks. Accordingly, the course spends considerable time on the technical aspects of cybersecurity. “But at the end of the day,” Chun says, “the biggest attack vector is still through humans.”

The term “social engineering” commonly refers to ways cybercrime victims are manipulated into compromising security (for example, by sending money to a scammer, downloading malicious code, or disclosing sensitive information). Susskind and Chun’s concept of defensive social engineering is similarly grounded in human psychology. The approach emphasizes that cybersecurity must be part of everyone’s job, technical or otherwise.

“It’s about people knowing what to do, people making the right choices,” says Chun. “It’s helping them use the resources and budget they have now on things that can be long-lasting, rather than just spending on the latest antivirus software.”

“Students with computer science backgrounds are surprised by the importance we attach to helping clients build organizational capacity,” says Susskind. “Students need to understand the leadership dynamics in their client communities. The IT director can’t just do what she or he wants. They depend on the local government for their budget. They need approval to hire new staff.”

On the other hand, Susskind says, students from planning or social science backgrounds often study smart city innovations without learning much about the technologies needed to manage the associated risks. And there are aspects of AI and advanced system design — along with cyber law and other topics critical to cybersecurity — that engineering students may not learn in their other courses. The Cybersecurity Clinic aims to round out the knowledge of students from every discipline. The course aims to broaden those students’ knowledge, too, by inviting at least half a dozen guest speakers each semester from industry, other universities and MIT academic departments, industry, and/or relevant public agencies.

This past spring, for example, the lineup of lecturers included Dan Ricci, the founder of Industrial Data Works, on the modeling of risk in energy systems within budget-constrained environments; Gus Serino, president of I&C Secure Inc., on operational-technology cybersecurity for industrial control systems; and representatives from the MassCyberCenter and the Cybersecurity Infrastructure Security Agency providing overviews of their respective state- and federal-level organizations’ programs and initiatives.

“There are highly specialized things to learn, especially about the ways AI is changing cybersecurity, that we need help teaching,” Susskind says. “The rate at which the field of cybersecurity is changing means that most academics will have a very hard time keeping up.”

A roadmap for improvement

Clinic students spend the first four weeks of the semester preparing for field assignments. A series of online modules, supplemented by class discussion, outline the scope and nature of cyberattacks against critical urban infrastructure; review the 23 risk areas most relevant to their type of clients; and provide guidance for each step of the assessment process. This includes simulations of tricky client interactions. What if clients don’t take students seriously, or fail to provide the necessary information? What if they argue to receive a more positive assessment than the facts warrant?

“I’ve never ever had a class that prepared us for such realistic scenarios before,” says Diego Contreras, a rising senior majoring in computer science and engineering who completed the course this spring.

The modules culminate in an exam students must pass on their first try to receive a field assignment. For the remainder of the semester, they’ll receive continued support via weekly class meetings and get faculty input on their drafted reports, but the onus is on students to coordinate their team’s activities and build client trust.

“You represent MIT, and that is quite the responsibility,” Contreras says. “This course has given me people skills I wouldn’t have developed in any other context.”

“The most delicate aspect of the project was balancing our assessment findings,” says Zev Moore ’26, who took the class last fall as a senior studying mathematical economics and finance. “Our approach was to provide important feedback while simultaneously validating the positive security measures our client already had in place, which ensured our report felt like a collaborative roadmap for improvement.”

Certain key recommendations show up in the majority of reports. For example, clients are advised to inventory all hardware and software tied into their network and track who has access; patch software and back up data regularly; require multi-factor authentication and frequent password updates; train employees not to open attachments from unknown parties; prepare an attack response plan that clarifies lines of authority and includes the organization’s stance on paying ransoms; and only use vendors with good cybersecurity hygiene.

“None of these items is costly,” Susskind says. “Together, they will probably avoid 80 percent or more of the possible cost and danger of cyberattacks.”

Spreading the model

To date, more than 120 students have completed the full course at MIT. The online modules that prepare students for certification are freely available to the public as a massive open online course on MITx called Cybersecurity for Critical Urban Infrastructure, which has attracted tens of thousands of learners. The modules are also used by universities with their own cybersecurity clinics — a growing cohort, thanks in part to a consortium (with 61 member institutions and counting) co-founded by MIT in 2021 with the University of California at Berkeley, Indiana University, and the University of Alabama.

Most student teams wrap up client work after finalizing their recommendations; a few have volunteered to stay on after semester’s end to advise on implementation. In either case, Susskind and Chun check in periodically with clients for at least two years following each engagement.

“We often hear of the vulnerability assessment report serving as the organization's blueprint for their short-term, mid-term, and long-term agenda to be more prepared for future attacks,” says Chun. “We primarily work with IT directors or chief technology officers, and many of them have been telling us post-engagement that they shared the MIT report with the city or town leadership and were able to convince them they needed extra budget or a specific line item. They were using the student report as leverage to say, ‘it’s not just me saying it. We have a credible team who dedicated their time and these are the findings.’

“It's really a humbling experience,” Chun adds, “when some of our past clients reach out to us again after some time to say: ‘Now we have different people, we just purchased new equipment. Can we do this all over again?’”

Robots walking down the street, surrounded by astounded onlookers, is an increasingly common sight. But these machines aren’t yet the do-it-all assistants you’d want working in a kitchen or factory, and a major bottleneck is data. Much like humans, robots learn best by experience. The challenge is that it’s labor-intensive and time-consuming to physically teach these machines so many actions across different settings. 

“One natural idea is to use simulation as a training ground. While there has been significant progress over the last few years in the physics engines that power robotics simulators, one of the remaining challenges has been creating sufficiently rich and diverse simulation content to capture the complexity of the real world,” says Russ Tedrake, the Toyota Professor of Electrical Engineering and Computer Science (EECS), Aeronautics and Astronautics, and Mechanical Engineering at MIT, and a principal investigator at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL).

It turns out that AI agents, or semi-autonomous programs that “think” and complete well-defined tasks, could help produce the lifelike virtual settings that robots need. The new “SceneSmith” system developed by researchers at MIT CSAIL and Toyota Research Institute uses three agents to piece together the objects, walls, and overall look of a 3D scene. Its recreations of indoor spaces such as restaurants, bedrooms, and hotels are more realistic and detailed than prior systems, helping robots practice skills and try out different ways of doing tasks before they’re powered on. In turn, engineers save time on real-world testing.

The agents have a sense of how everyday places are supposed to look because they each call on a multi-modal system called a vision-language model (VLM), specifically the state-of-the-art VLM GPT-5.2. It’s trained on lots of text and images from the internet to handle more visual prompts. This advanced model gives each agent a sort of spatial knowledge: First, a “designer” agent generates the elements of a scene, then a “critic” advises whether it looks realistic, and finally, an “orchestrator” manages their back-and-forth, deciding when the design is done. Once the three VLMs wrap up their creative collaboration, the scene is ready to load directly into physics simulation software.

“We’ve found that the system can construct 3D scenes the way a human designer would,” says MIT EECS PhD student Nicholas Pfaff, a CSAIL researcher and a lead author on a paper with Tedrake presenting the work. “We made over 1,300 scenes using a leading VLM that has internet-scale priors, and it made insanely creative and diverse arrangements. I hadn’t taught the system to do that in the prompts; it just improvised.”

Talk to my agent

Thanks to VLM agents, you can ask SceneSmith to do things like “generate a garage with a car, a workbench, tires stacked in the corner, and a ladder against the wall,” and get a virtual playground rich with objects a robot can tinker with. These rooms are decorated with up to six times more items per scene than prior methods, making them great for helping robots learn skills such as putting a cup in the sink, placing fruit on plates, and moving a soda can from a shelf to a table.

With so many rich virtual environments handy, you can evaluate whether your robot is ready for deployment without so much trial and error in the physical world. The researchers tested out different action plans (also called “policies”) in SceneSmith’s digital worlds, generating 100 unique spaces in the process. A VLM agent evaluated each attempt, and it found the robot’s plans were faulty, with the machine often failing at its chores. Humans agreed with the model’s verdicts over 99 percent of the time, which could help roboticists weed out flawed approaches in simulation before a robot moves in the real world.

But how realistic are these virtual worlds, really? It can be difficult to prove outright, so the researchers approached the question from several angles. The most telling test: they dropped a pretrained robot policy — an AI controller trained largely on real-world data, which had never seen a SceneSmith scene — into the generated environments. In one test, users told the system to “take the apple from the bowl and place it onto the cutting board,” and the simulated robot did exactly that. If the scenes didn’t closely resemble the real settings the policy had learned from, it simply wouldn’t have worked. 

The team also teleoperated robots through the virtual spaces, guiding them to open cabinets, put away bottles, and navigate between rooms. Their experiments revealed that the environments hold up under sustained physical interaction, expanding beyond visual inspection.

Behind the scenes

The agents that SceneSmith uses each have a well-defined role in the generative process, fleshing out scenes in stages. They essentially create a floor plan and bring it to life. 

Let’s say you wanted to create a scene similar to the first floor of a house. The “designer” VLM would start with a general layout, which the “critic” reviews, and then the “orchestrator” signs off. The agents repeat this approach for each step: adding furniture, placing objects on walls and then ceilings, and finally, dropping in objects that robots can manipulate. For example, the VLMs can add cabinets that the robots can open and close — an articulated item, which prior baselines didn’t often have.

At each stage, the second VLM ensures the scene is practical, advising that a bathtub is removed from a living room, for example. The third VLM ensures a high-quality scene is generated, even taking the design process a few turns back if the visuals aren’t up to par. Once the three VLMs wrap up their creative collaboration, the mechanics of the physical world are added via simulation software.

With a sound understanding of how rooms should look, where objects should be placed, and real-world physics, SceneSmith has a noticeable edge over prior methods. Compared to scene-generation baselines such as “HSM” and “Holodeck,” SceneSmith made environments with more objects, including a private office, a pottery store, and even a Minecraft-themed gaming room.

SceneSmith was also a favorite among over 200 users. They found the system’s visuals to be more realistic over 90 percent of the time. They also observed that, generally speaking, it followed prompts more closely than other approaches did. In other words, it was the best at generating the virtual playgrounds users actually wanted to see.

A system of many talents

Realism, diversity, and richness are all strong suits for SceneSmith, even when it comes to generating individual 3D objects. You can prompt it to create a rolling serving cart, and it’ll make a 2D image that it then turns into a detailed model with physical properties like mass, friction, and inertia.

Such a detailed process does come with a speed trade-off, though. It can take multiple hours to produce a single scene because the agents are creating and closely scrutinizing each object. With more computing power, the system could see dramatic increases in efficiency. CSAIL engineers are also hoping to expand to deformable objects (like sponges), should extensive 3D libraries become available.

“SceneSmith represents a significant advance in this regard by providing an agentic framework for generating simulation-ready indoor environments just from a simple text prompt,” says Jeremy Binagia, an applied scientist at Amazon Robotics who wasn’t involved in the research. “It advances the state of the art in several ways, including pushing the limits of the density of objects in the simulated environment, ensuring that all of the objects are physically accurate (versus just being visually realistic), and creating assets that are not constrained to a fixed library, since they can be generated via text-to-3D.”

Pfaff and Tedrake wrote the paper with Thomas Cohn SM ’24, an MIT PhD student and CSAIL researcher; and Toyota Research Institute roboticists Sergey Zakharov and Rick Cory SM ’08, PhD ’10. Their work was supported, in part, by Amazon, the U.S. Office of Naval Research, the Toyota Research Institute, and the U.S. National Science Foundation.

The team presented their findings as a spotlight at last week’s International Conference on Machine Learning. 

Legal intern Suzanne Castillo co-authored this post.

Playstation’s decision to kill physical game discs is the latest attack on our diminishing rights to access and engage with culture digitally. Rent-seeking corporations and negligent lawmakers share the blame–and they can do better. 

We’ve seen the same playbook used in the move to digital distribution of  film, TV, and music: draw in customers with the convenience of a digital download, then limit physical access and move the goalpost on what it actually means to “own” a piece of media. The end goal is to turn the customer into a renter, stuck making regular subscription payments for access. Gamers are right to sound the alarm, and we must take this moment to fight for digital ownership before it’s too late.

Disk Space Invaders

Depriving gamers of physical discs leads to another obvious and immediate cost: data.  Unlike other digital media like film and TV, video games require a ton of storage. Access to high speed internet is still abysmal in the US, making the high-speeds needed for digital game downloads a luxury some of us may take for granted. For many, a modern game can take days and exceed their data caps. 

This made physical discs, particularly for the biggest AAA titles, a logical choice that also largely spared gamers from losing traditional ownership rights. With physical disks, the cost of storing the game was included in the purchase.

Own or Be Pwned

Limiting customers to digital copies also pushes gamers further into rent-only copyright culture.

Physical media comes with a "right of first sale," which means you can lawfully share, resell, alter, or destroy your own copy of a copyrighted work. This right has also helped protect the emergence of alternative community servers, and emulator addition of online play to games from the dial up era.

But courts have held that digital media doesn't carry the same right, meaning no such protection is afforded to digital purchases. Your ability to freely share games with friends or pass them on to family members becomes totally subject to the whims of the distributor. 

So, for example, a digital-only approach effectively guts the second-hand market for games. Saving some money with a used game and recouping the costs by reselling are no longer an option. Even with steep discounts and holiday sales, this raises the minimum cost of engaging with the medium at all.

The inevitable conclusion of the move to digital-only purchases is to lock gamers into  subscription models, making their access totally dependent on the distributor— or, several distributors, as we’ve seen with major TV and movie streamers. A handful of companies actually own the games, and your only option is to regularly pay for fractured libraries of games you may never play and will never truly own.

Achievement Locked

Since digital games are easy to copy, distributors and publishers argue that they are in an arms race against piracy. The irony is that law-abiding customers consistently suffer collateral damage. 

Most digital distributors lock down the content they offer with restrictive user agreements and digital rights management (DRM) software. DRM software, in particular, imposes onerous controls on the game — like forcing internet connection for single player games or modifications that harm performance — and can even introduce serious privacy and security concerns. Any gamer or researcher in the US who wants to reduce this burden by removing or modifying that DRM risks a lawsuit, thanks to Section 1201 of the Digital Millennium Copyright Act (DMCA). This federal law makes it illegal to alter DRM software, and is a beloved tool for companies trying to restrict how we can lawfully use our purchases — whether it’s a copy of the newest tractor simulator or a literal tractor. 

And since much of this DRM is tied to user accounts, ownership of a game is also revocable and modifiable for any number of reasons outside of your control. Error in your subscription payment? Your account got hacked? Licensing deal falls through with a major publisher? Developers want to kill the game in an update? All of this can limit or change your ability to access the game long after your so-called “purchase.”

Level-up Ownership

Policymakers can and should work to restore our ownership rights for the digital age. 

That starts with legal protections ensuring that the same rights that apply to physical media apply to digital media. Next up? Reform Section 1201 of the DMCA to clarify that it does not forbid fair uses.  

At the state level, we need meaningful consumer protections. Some promising models include California’s AB 1921, which would clarify what customers are actually paying for on digital storefronts and ensure some protections for maintaining discontinued games. The gaming industry has done its best to kill the bill, including claiming that private community servers are illegal. 

If you bought it, you should own it, and EFF will continue working to mitigate some of the worst harms of the DMCA 1201, defending modders, and fighting deceptive licensing that makes culture less free.

This essay was written with Nathan E. Sanders, and originally appeared in The Guardian.

Opposition to AI data centers has emerged as a primary theme in US politics, one that—surprisingly—doesn’t fall along party lines. We applaud people coming together for constructive debate on any issue, and agree that communities need to evaluate whether any economic benefits these data centers bring is worth their costs. Still, we worry that a focus on data centers obscures the larger impacts of AI on people’s lives: the concentration of power of AI companies, and their widespread political and financial influence...

Wind and solar generated more power than coal and nuclear during the first half of the year. Will that trend continue?

A green group has asked a state court to scrap a permit for a facility designed to power artificial intelligence.

California residents can expect the El Niño to bring both high wildfire and flood risk this year.

While New York pushed back a near-term climate goal, Gov. Kathy Hochul touted the state’s solar deployment, a major new transmission line and plans to boost nuclear power to achieve its 2050 net-zero target.

Temperatures will be 15 to 25 degrees Fahrenheit warmer than normal in many areas, including at night, officials said.

The boats are commonly made of carbon fiber, fiberglass and PVC foam, which take a lot of energy to produce in processes that emit carbon pollution.

Officials warn a deal with South American nations could undercut British farmers.

Europe is the world’s fastest-warming continent, with temperatures increasing twice as fast as the global average since the 1980s, according to officials.

With the exploding popularity of generative artificial intelligence, many open-source models are now available online for anyone to adapt for their task, such as generating product renderings in a certain artistic style. 

But these models also find their way into the hands of nefarious actors who may optimize them to produce illegal content, like hate speech or child sexual abuse material (CSAM). This is a growing problem — the National Center for Missing and Exploited Children received more than 1.5 million reports of AI-generated CSAM in 2025, an increase from 67,000 in 2024.

Engineers usually test AI for harmful capabilities by prompting the model and inspecting its outputs, but this is impossible for CSAM, since it is illegal in the U.S to generate such content, regardless of intent.

To avoid this dilemma and improve AI safety, a team of MIT scientists, led by graduate student Vinith Suriyakumar and associate professors Ashia Wilson and Marzyeh Ghassemi, joined forces with researchers from Thorn to develop a new auditing approach that determines whether a model can produce CSAM, without prompting it. Thorn is a child safety nonprofit whose mission is to transform how children are protected from sexual abuse and exploitation in the digital age.

Their technique examines how the inner workings of a model have been adapted, but it never generates an output. By examining hidden representations, it can reliably infer whether a model has been specialized to produce harmful imagery.

When tested, the auditing procedure identified model variations that had been specialized to generate CSAM with 100 percent accuracy. A hosting platform could use this technique to flag unsafe models and quickly remove them or prevent them from being uploaded in the first place.

“This unlocks a new avenue for platforms that host open-source models and for law enforcement to actually test whether a model is capable of generating CSAM. Before, we had no way of measuring this. It was a huge blind spot that some people were taking advantage of. Now, we can address an AI safety problem that is having severe negative impacts,” says Vinith Suriyakumar, an MIT electrical engineering and computer science (EECS) graduate student and lead author of a paper on this technique.

Suriyakamur and Wilson, the Lister Borthers Career Develop Professor in EECS and a principal investigator in the Laboratory for Information and Decision Systems (LIDS), are joined on the paper by Lena Stempfle, an MIT postdoc; Ghassemi, an associate professor in EECS and a member of the Institute of Medical Engineering Sciences (IMES) and LIDS; and others at Boston University and Thorn. The paper was be presented as a spotlight at the “Trustworthy AI for Good” workshop at the International Conference on Machine Learning.

Auditing adaptations

Recent techniques have made it easier for users to specialize a generative AI model for their task through a process known as fine-tuning. 

Rather than retraining the entire model on a task-specific dataset, individuals can utilize an algorithm called low-rank adaptation (LoRA) to specialize the model in a more efficient manner.

This has led to a wave of new generative AI model variants for a variety of purposes, like producing watercolor images that mimic an artistic movement. But it has also enabled malicious actors to create models that can generate high-quality CSAM and other harmful imagery.

To audit a model, engineers typically prompt it for harmful content and check its outputs, but this manual auditing procedure is not scalable. In addition, repeatedly generating heinous images can have negative psychological impacts on human evaluators. 

This evaluation method quickly falls apart when testing CSAM, which is illegal to generate for any purpose in the U.S. and many other international jurisdictions.

“We are in this very difficult situation where, based on the law itself, we cannot use the de facto means of evaluation. We had to throw out the entire toolkit and take a different approach,” Suriyakumar says.

After learning about this conundrum, the researchers joined forces with Thorn, to address this issue.

A nongenerative solution

Instead of focusing on outputs, the researchers targeted the modifications a LoRA algorithm makes during fine-tuning. 

Their technique probes these modifications, called LoRA adaptors, to determine whether a model has been specialized for a harmful capability, without generating an output.

Using a technique called Gaussian probing, the researchers feed the model a set of random data points and analyze how it manipulates those data within its multilayer internal structure. 

“We never run the model all the way to the end or prompt the model, so we never generate images,” Suriyakumar explains.

The researchers capture those modifications at multiple time points within the model’s inner structure and average them to summarize how the LoRA adaptor changed the model’s computation. They found these responses to be a strong signal of how a model had been specialized.

They tested their method on variations of three types of models, comparing the results to ground-truth data from LoRA adaptors known for generating CSAM, other harmful images, and safe content. 

Their method was 100 percent accurate in identifying models that had been adapted to generate CSAM. 

“There is a huge bucket of child safety concerns with AI, and these are real concerns that need to be addressed. A lot of children are being harmed by AI deepfakes. We’ve shown that Gaussian probing can be a very useful tool, and we hope the research community really pours more attention into this problem,” Wilson says.

Importantly, their technique is scalable and would be relatively inexpensive to implement. Since thousands of model variations are published online every month, scalability is key to help auditors remove harmful adaptations before they are widely distributed.

Gaussian probing is also more robust than some other auditing techniques, since a nefarious actor would need to carefully alter the inner workings of the base model to avoid detection.

In the future, the researchers want to evaluate their technique on a larger set of model variations and explore whether Gaussian probing can detect harmful capabilities in base models before they are adapted.

“Now we have a technological approach to partially address this concern. So much effort was poured into this collaboration, which enabled us to tackle a really hard problem that is harming so many children, nationally and around the world. Hopefully, we can have a transformative impact in this area,” Ghassemi says.

This work was supported, in part, by the Bridgewater AIA Labs Research Fellowship.