A summer surge in power consumption is straining regional grids that are also seeing a boom in data center energy demand.

Requiring companies to disclose their climate risks provides valuable information that otherwise isn't always available, they said.

Former Republican EPA leaders and a conservative scholar join Democrats supporting local efforts to sue for climate-related damages.

House and Senate Democrats are looking for more data on how home insurers calculate their pricing.

The world hit a milestone of 3 terawatts of solar deployed this year.

Member countries are worried that low-carbon quotas in the Industrial Accelerator Act could push up prices and hurt Europe’s competitiveness.

Firefighters continued to battle wildfires in southern Greece and France on Monday, and much of the U.K. is officially in drought.

Illegal land grabbing, cattle ranching and expanding infrastructure continue to put heavy pressure on the Amazon.

A one-size-fits-all approach likely isn’t the best strategy when designing artificial intelligence systems that assist users in disease diagnosis.

A new study by researchers at MIT and elsewhere found that, while AI assistance generally improved the accuracy of non-experts and clinicians in diagnosing skin diseases, AI explainability methods had different impacts depending on the users’ knowledge level. 

Explainable AI methods help users know when to trust a model’s predictions by describing or validating the model’s decision-making. For instance, a model might use a heat map to highlight image regions that were most important in its diagnosis or a large language model (LLM) to explain the prediction in plain language.

In this study, researchers tested non-experts and primary care providers in skin disease diagnosis, with and without the help of different explainable AI systems. 

They found that non-experts’ diagnostic accuracy improved, but it was largely due to deference to the AI system. Non-experts trusted LLM-based explanations whether they were right or wrong, and found explanations more convincing when they were vague or generic.

By contrast, clinicians were not tripped up by incorrect AI assistance and performed best when given only a model’s prediction, with no accompanying explanation. 

“Good AI systems can improve performance in some health settings, but this has to be balanced carefully with algorithmic deference that can lead to more error. We know that both AI and explainability methods can engage automation bias in humans, and this anchoring effect is something that must be accounted for when we design AI systems,” says Marzyeh Ghassemi, an associate professor in MIT’s Department of Electrical Engineering and Computer Science (EECS), a member of the Institute for Medical Engineering and Science, and a principal investigator at the Laboratory for Information and Decision Systems and the Abdul Latif Jameel Clinic for Machine Learning in Health.

“These findings are important as patients increasingly turn to AI to help with their health care. Our findings show that those with the least medical knowledge are most likely to be led astray when explainable AI models give an erroneous output,” says Roxana Daneshjou, a co-author and assistant professor of biomedical data science and dermatology at Stanford University.

These results underscore the importance of building AI systems with users in mind and of developing explainability methods that encourage critical thinking rather than overreliance on the model, the researchers say.

“It’s getting obvious that we cannot just assume a good AI will solve all problems. We need to pay careful attention to the users who will be using the AI system, because the same explanation can help an expert and mislead a beginner. Often the people who could benefit most from AI are the ones most likely to be led astray by it, so how we present a recommendation matters as much as whether it’s correct,” says lead author Orson Xu, an assistant professor in the Department of Biomedical Informatics at Columbia University.

Ghassemi, Xu, and Daneshjou are joined on the paper by many authors, including MIT graduate student Haoran Zhang, undergraduate Reina Wang, and Luis Soenksen PhD ’20, a research affiliate at the Jameel Clinic, along with clinicians and researchers. A description of the work appears today in Nature Medicine.

Exploring explanations

Several FDA-approved AI interfaces are being used to help clinicians identify skin conditions in medical images, as a way to streamline early diagnosis. In addition to providing a prediction of whether disease is present in the image, these tools often use one of several methods that explain the model’s decision-making.

At the same time, non-experts can perform digital diagnosis on their own using AI-powered search engines that predict skin diseases based on user prompts. These systems often use LLMs to explain the model’s prediction in simpler terms.

The researchers explored the effects and potential benefits of these explainable AI tools on primary care physicians and non-experts in dermatological disease detection. They tested users by showing them medical images plus an AI prediction of skin disease, employing different explainable AI approaches. 

These approaches included: an AI prediction and confidence level with no explanation, a method that provides similar images to reinforce its prediction, a heat map-based approach that highlights important image regions, and an LLM that explains the model’s reasoning in plain language.

Non-experts were tasked with deciding whether an image of a skin mole was cancerous, with and without the help of explainable AI. Clinicians were given the more challenging task of providing a differential diagnosis of dermatological disease.

The researchers found that all explainable AI approaches improved the accuracy of non-experts, mostly because the tools helped users diagnose non-cancerous moles. 

In addition, when they employed a fairness-constrained model designed to combat bias against darker skin tones, the system significantly improved accuracy and reduced diagnostic disparities based on skin tone.

“But the reason non-expert users are better is because they are more reliant on the models. When the model is wrong, it hurts performance more than it helps performance when the model is right. We were just able to train very good AI models for this setting,” Ghassemi says.

This deference effect is largest with LLM explanations, and users were more confident about their wrong answers when aided by an LLM.

On the other hand, clinicians were resilient to incorrect AI explanations and, of all the explainability methods, LLMs boost their accuracy the least.

“It really comes down to how each group uses the explanation. A clinician already has a diagnosis in mind and checks the AI against their own training, so a bad explanation gets caught. Meanwhile, a non-expert can use that exact same explanation to form an opinion in the first place, so a plausible, confident-sounding rationale can pull them toward the wrong answer. The same tool ends up being an asset for one user and a liability for another,” Xu says.

Overcoming the deference effect

When the researchers dug deeper, they found that users who were most deferential to AI assistance were the worst performers on the task without the help of AI. 

They also found that the time at which users were presented with AI explanations influenced their behavior. If an explanation is given first, before the user can perform the diagnosis on their own, they tend to become more deferential to the model.

In addition, AI systems outperformed humans when the presentation of disease was subtle, but humans performed much better if there are atypical symptoms or unrelated features in an image.

Taken together, these results indicate that explainable AI can cause overreliance on models and lead users to blindly follow AI recommendations even when they are wrong. 

Rather than using LLMs to generate more detailed explanations, it might be more effective to force users to give a diagnostic hypothesis first, then provide an AI-based suggestion to highlight other possible conditions for consideration. 

“We really want AI to improve creativity and either upskill or fill in gaps where users are missing subtle presentations. Otherwise, we risk engaging automation bias and then, when the model is wrong, users can’t recover,” Ghassemi says. 

This research was funded, in part, by the National Science Foundation, Schmidt Sciences, the National Bureau of Economic Research, and Columbia University.

Nature Climate Change, Published online: 04 August 2026; doi:10.1038/s41558-026-02729-3

Climate change communication shapes how societies perceive risks and respond to them. In this issue of Nature Climate Change we examine the evolving content and methods of climate communication, the challenges of urgency and injustice, and the strategies for building effective and inclusive communication channels.

Nature Climate Change, Published online: 04 August 2026; doi:10.1038/s41558-026-02714-w

Communication is essential for keeping communities safe when climate disasters occur. Here, we explore how disaster communication informs protective actions and examine five challenges: false information, exhaustion from repeated disasters, unequal access to information, polarized climate attitudes and mental health.

Nature Climate Change, Published online: 04 August 2026; doi:10.1038/s41558-026-02680-3

Social media have become an important arena for climate change communication. This Review synthesizes climate-related content on social media, its effects on climate attitudes, knowledge and behaviour, and its role in reshaping the broader communication landscape.

Nature Climate Change, Published online: 04 August 2026; doi:10.1038/s41558-026-02711-z

Individuals and groups shape others’ attitudes and actions through social influence. This Perspective articulates a research agenda of social influence for climate action, synthesizes existing evidence and highlights its practical relevance for designing effective behavioural interventions.

The Senate Commerce Committee is once again considering legislation that would dramatically expand age verification, and undermine privacy for everyone. Alongside the SCREEN Act, the CHATBOT Act, and the Youth AI Privacy Act, the Kids Online Safety Act (KOSA) would push companies to collect more information about their users while creating new incentives to restrict lawful speech.

Take action

Tell Congress: KOSA endangers the privacy of all

KOSA Pushes Platforms Toward Age Verification

The Senate version of KOSA imposes a “duty of care” on online services, including social media, to avoid exposing young people to certain material the law deems harmful. But those obligations only work if online services know which users are minors. That means more platforms will be pressured to implement age verification or age estimation systems.

That’s not a bill that increases privacy—it’s one that creates new privacy problems. Whether companies verify ages by checking government IDs, performing facial analysis, checking your bank records, or collecting other personal information, all of these systems require the handing over of more sensitive data, simply to access lawful online speech and services. They also create new databases of personal information that can be breached, misused, or demanded by governments.

Everyone deserves privacy online. Congress could push for a bill that protects privacy for all users, but that’s not what they’re doing here. Instead, KOSA and the other bills coming up for a vote this week push online services to adopt systems that require people to identify themselves before they can speak, read, or participate online.

KOSA Still Creates Incentives to Censor Lawful Speech

Some online content isn’t appropriate for minors. Families, schools, and communities all have important roles to play in helping children navigate the internet. But KOSA takes those decisions away from families and the young people who have a First Amendment right to speak and access information online. It instead empowers government officials to enforce how online services handle lawful speech. 

And by empowering elected attorneys general in states across the country to enforce KOSA, the bill means those elected officials, rather than your family, deciding what’s appropriate online content for teens. Even more likely, it will lead to limits on what minors and adults are able to see at all, as companies shut down potentially controversial forums in order to avoid legal action from government bureaucrats. 

The latest version of KOSA once again includes a broad "duty of care" requiring platforms to mitigate a wide range of alleged harms to minors.

Whatever disclaimers and exceptions the bill includes, the practical effect is unchanged. When platforms face liability for content that someone later claims contributed to harms like anxiety, eating disorders, or substance use, the safest response is to remove lawful speech or shut down forums discussing those topics altogether. 

More worrisome, the potential liability KOSA creates may push online services to either remove speech well in advance of a young person seeing it, or block young people’s access so they never see it. That will likely include forums where people try to help each other, find community and recovery resources for the exact harms listed in the bill, like gambling and drug addiction. In trying to protect young people, KOSA may actually cut them off from valuable sources of support. 

We've explained these censorship risks in detail before, and they remain just as real in the latest version of the bill.

Congress Should Reject KOSA

Minors deserve meaningful privacy protections online—as do adults. But KOSA moves in the opposite direction by encouraging more age verification, as well as more legal pressure for platforms to monitor and restrict lawful speech.

The Senate Commerce Committee should reject KOSA, along with the other bills in this legislative package, and instead pursue comprehensive privacy legislation that protects everyone—not just minors—without undermining privacy, security, or free expression.

Take action

Congress shouldn't set the rules for what we see online

EFF joined a group of 18 civil society organizations to send a letter encouraging New York Governor Kathy Hochul to Senate Bill 9934A, the New York Stealth Crawler Prohibition Act. The letter states:

While framed as a measure to protect local journalism, this legislation harms free expression and establishes a dangerous precedent by effectively deanonymizing and criminalizing automated access to the open web. By requiring all web crawlers to disclose their identity and explicit purpose, and by granting media outlets unchecked authority to obtain judicial subpoenas to unmask unidentified automated web traffic without any showing of misconduct or actual injury, this bill threatens digital privacy, compromises the foundational architecture of the internet, and will ultimately stifle the very independent journalism it seeks to protect.

As we’ve previously explained, so-called “stealth crawlers” are simply automated tools to access and collect public web data—without disclosing the user’s identity. Private crawlers like these facilitate all kinds of important work that benefits the public, including investigative reporting, academic research, cybersecurity protection, and EFF’s own Privacy Badger. As we illustrate in the letter: 

Anonymous crawling fuels important investigative journalism. For example, The Markup, a non-profit news site, used anonymous crawlers to investigate potentially anti-competitive practices by tech companies, such as Amazon’s tendency to prioritize Amazon brands and Amazon-exclusive products over competitors with higher ratings. The crawlers identified themselves as ordinary Firefox browsers to web servers, which allowed The Markup to understand how Amazon search results pages would appear to ordinary users. Similarly, ProPublica used an automated tool designed to simulate an ordinary Amazon customer to reveal that the site steered shoppers to more expensive products over cheaper alternatives. 

Anonymous web scraping is also crucial for cybersecurity professionals, who use automated tools to monitor the web for information that helps them protect against malicious attackers. Privacy tools, including EFF’s Privacy Badger, also crawl sites anonymously to identify trackers without compromising user privacy.

Laws like S9934A sweep far beyond AI, targeting anonymity rather than the real technical issue: overaggressive crawling that can overtax technological infrastructure. Unmasking crawlers won't fix these server strains, but it will chill vital public-interest research and compromise digital privacy. Addressing the harms of web scraping requires narrow technical solutions—not policies that give publishers veto power over the open web. This is why we are calling on Governor Hochul to veto S9934A.

You can read the full letter here. For a deeper dive into why crawlers and scrapers are vital for the open web, check out this blog post.

The Federal Trade Commission (FTC) in July issued a proposed policy statement “concerning the suppression of accuracy in artificial intelligence systems.” We urge the FTC to withdraw this misguided proposal and instead focus on its core strengths and mission to protect consumers. 

The new proposed policy builds on, and directly references, the Trump administration’s  “Preventing Woke AI in the Federal Government” executive order—a nightmare for civil liberties that seeks to strong-arm AI companies into modifying their models to conform with the its ideological agenda. In recently filed comments, EFF,  Public Knowledge, and Fight for the Future call for the FTC to stop its unconstitutional efforts to regulate lawful speech, override state laws, and intimidate AI developers into ideological alignment with the Trump administration.

The government may not install itself as the arbiter of truth.

In the joint comments, we outline three critical flaws within the latest proposed policy. First, it violates the First Amendment. The policy calls for the Commission to become the judge of which AI outputs meet an undefined standard of accuracy. Installing the FTC as the authority of this sort of viewpoint-based judgment is a prior restraint on speech. Additionally, the policy’s proposed solution to address speech concerns compounds, rather than properly limits, the likely harms to speech. As we say in our comments: the government may not install itself as the arbiter of truth. 

Second, it exceeds the FTC’s legal authority by claiming that its federal regulatory rules can override, or “preempt,” laws in states that have passed to regulate artificial intelligence use. This is clearly an attempt to target state laws the administration disagrees with. For example, the policy specifically criticizes Colorado's automated decisionmaking law, which applies when automated technology is used to consider consequential decisions such as those around employment, access to housing, health care, and insurance. We noted to the FTC that characterizing this law as one that requires AI companies to “suppress accuracy,” or encourages deception, is itself inaccurate. In any case, the FTC lacks the authority to put its rules in place over state law, unless Congress directly delegates it that power. It has been given no such power here.

Third, the policy is vague and sets the stage for improper jawboning of AI developers and companies that use AI tools (deployers). Jawboning is a term for situations in which the government urges private companies or people to censor another's speech. The proposal, as written, creates an enforcement regime that would put a thumb on the scale in favor of certain partisan speech and ideals. This will lead companies to censor only what the administration interprets as biased or untruthful. Yet, in our filing, we note that the FTC itself can't define an objective standard for what “bias” means, conceding the “exact line of what constitutes bias may be difficult to draw.”

There is work the FTC should be doing to protect consumers in the age of AI. In our comments, we conclude by saying:

[We] implore the Commission to focus on its core strengths and the mission for which it is so urgently needed—promoting structural market competition and protecting consumers from real unfair and deceptive acts and practices—in both the burgeoning and critically important AI industry and across the broader technology marketplace.

EFF and our partners have always urged the FTC to police genuine deception in technology markets. We have also consistently opposed government efforts to dictate what private speakers may say. That’s why we urge the FTC to withdraw this proposal. 

You can read our full comments here. 

The Senate Commerce Committee is poised to consider the Youth AI Privacy Act, a bill that would require AI companies to create kids-only privacy rules and implement so-called “safe design features,” which would—like three other bills under consideration this week—require more data collection and make it harder for people to access lawful speech online. 

While the bill is narrower than some other proposed chatbot bills, it still has massive data security implications because it protects information for only certain users. This creates a problem we’ve cited many times before: if a bill requires that online services offer protections to minor users, the services will respond by imposing age gates to know which users should receive them. A better approach would be to offer the same privacy protections to all users. That way, we would avoid the services having to collect data on everyone to know a users’ age.

This bill also contains a problematic and vague provision that expressly allows AI companies to collect a known minor’s personal data for the purpose of testing, identifying, and addressing "harm to users”—without being clear on what exactly that means. Either way, services will need to collect even more information from young people, who are already targets of data theft and identity fraud. The Youth AI Privacy Act will give young people less privacy, not more. 

The Youth AI Privacy Act does include some positive privacy provisions around prohibiting the processing of personal information, like limiting what companies can do with people’s chat logs, including training, profiling, and disclosing them to other companies for training. But a general privacy bill must set these limits for everyone, not just minors. 

Mandating Design is Regulating Speech

The bill also requires the use of “safe design features,” which would restrict how online services providers design their systems and would deny teenagers the ability to use features like push alerts and notifications.  

We have seen this same type of restriction, sometimes called “age appropriate design code” in several states, including in California, Texas, and Arkansas. Unfortunately, these restrictions run into constitutional problems. In fact, federal courts have largely blocked these laws from going into effect because they likely violate the First Amendment rights of all internet users and the online services they regulate. Specifically, these laws interfere with internet users’ First Amendment rights to either speak or access speech online, and they also violate the rights on online services to decide how they will present information on their sites. 

Similarly, the Supreme Court has repeatedly ruled that “minors are entitled to a significant measure of First Amendment protection.” This does not mean that parents or guardians can’t set their own rules for their families—they can and they should, based on the needs and circumstances of the individual teenagers. But it does mean that Congress cannot adopt a “one size fits all” regulation that sets a restrictive government default that affects the First Amendment rights of all internet users, including teenagers. 

Alexander “Sasha” Rakhlin PhD ’06, the Distinguished Professor in Data, Systems, and Society at the MIT Institute for Data, Systems, and Society (IDSS); and a professor of brain and cognitive sciences at MIT, has been named the next director of the MIT Statistics and Data Science Center (SDSC). 

Rakhlin succeeds Ankur Moitra, the Norbert Wiener Professor of Mathematics, associate director of the IDSS, and a faculty member in the MIT Department of Electrical Engineering and Computer Science (EECS) who has been SDSC director since 2021. Philippe Rigollet, the Cecil and Ida Green Distinguished Professor of Mathematics and a core faculty member in IDSS, also served as interim director in 2024-25.

“Sasha is one of the sharpest theoretical minds working in statistics and machine learning today, and also one of the most devoted mentors I know,” says Fotini Christia, the Ford International Professor of the Social Sciences and director of IDSS, which houses SDSC. “He has helped train an entire generation of interdisciplinary scholars through the Interdisciplinary Doctoral Program in Statistics (IDPS), while his own research keeps pushing the boundaries. The SDSC could not ask for a more fitting leader.”

Rakhlin is the inaugural holder of the Distinguished Professorship in Data, Systems, and Society, an endowed chair created in 2025 by the generosity and vision of IDSS professor Richard “Dick” Larson, an “MIT lifer” and pioneer in operations research, queueing theory, and system optimization.

“I am honored to take on this role,” says Rakhlin. “The strength of the Statistics and Data Science Center has always been its people — students, postdocs, and faculty from across MIT who bring sharply different perspectives to the most interesting problems of the day in statistics, machine learning, and AI. My goal is to support that community as it takes on the constantly evolving questions reshaping the field.”

Rakhlin has been connected to the Statistics and Data Science Center as a visiting professor since 2016, before formally joining MIT in 2018 in the Department of Brain and Cognitive Sciences and IDSS. As the initial chair of the Interdisciplinary PhD in Statistics program at the SDSC, Rakhlin has seen the successful defense of over 75 IDPS PhD students across a variety of departments at MIT, including IDSS’ own Social and Engineering Systems program.

“I have been fascinated by machine learning since my PhD work more than 20 years ago, drawn by its beautiful connections to statistics, probability, algorithms, optimization, and game theory,” says Rakhlin. “At the Statistics and Data Science Center, I work alongside colleagues who share this fascination and pursue these connections in many directions. The recent revolution in AI is extending this web into the sciences; it promises to accelerate discovery, and it raises new questions for statistics. Answering them demands a rigorous science of the tools themselves. As AI enters medicine, energy, and public life, its safety and security are, at their core, statistical and mathematical questions: quantifying uncertainty, providing guarantees, understanding failure, and resisting manipulation.”

As Rakhlin puts it, the SDSC is built for this moment. “Statistics is a shared language across MIT,” he adds. “Through the Interdisciplinary Doctoral Program in Statistics, the center connects students and faculty from economics and political science to physics and engineering. Collaborations in areas from biology to nuclear fusion have shown how statistical thinking accelerates science itself.” 

As director, one of his goals is to deepen these interdisciplinary connections. He hopes to help make SDSC the Institute’s home for the rigorous foundations of data science and AI, and a bridge to the scientific and societal questions where those foundations are most needed.

Rakhlin received his bachelor’s degrees in mathematics and computer science from Cornell University, and doctoral degree from MIT. He was a postdoc at the University of California at Berkeley in EECS before joining the University of Pennsylvania, where he was an associate professor in the Department of Statistics and co-director of the Penn Research in Machine Learning center.

The 2026 Northeast Microelectronics Internship Program (NMIP), organized by the MIT Microsystems Technology Laboratories, brought together 30 exceptional students from leading universities across the Northeast for an immersive week exploring the rapidly evolving world of semiconductor technology and microelectronics. Held July 13-17, the externship provided undergraduate students with an opportunity to experience the complete microelectronics innovation ecosystem, from academic research laboratories to advanced manufacturing facilities.

Throughout the week, students visited several of the region's premier institutions, including MIT.nano, IBM Research, GlobalFoundries, Rensselaer Polytechnic Institute (RPI), and NY CREATES, where they engaged with researchers, engineers, faculty, graduate students, and industry leaders working at the forefront of semiconductor innovation.

The program began at MIT.nano with an inspiring overview of the microelectronics landscape led by Vladimir Bulović, director of MIT.nano, and Farhad Varzhegoo, director of strategic initiatives and partnerships at the Northeast Microelectronics Coalition Hub. Their presentations challenged students to think beyond today's technologies and consider the broader societal impact of tomorrow's innovations.

"What will the next innovation in microelectronics look like, and what should the world of tomorrow focus on?" they asked, encouraging participants to view engineering not only as a technical discipline, but also as a means to solve meaningful real-world challenges.

Following the opening session, Farnaz Niroui, the Emmanuel E. Landsman Career Development Chair and assistant professor of electrical engineering and computer science at MIT, organized a series of graduate student research presentations showcasing the breadth of microelectronics research taking place across MIT. The presentations explored topics spanning integrated circuits, nanoelectronics, photonics, quantum technologies, and advanced materials.

After the student research presentations, participants attended an industry panel exploring the transition from academia to careers in microelectronics. Organized and moderated by Susan Feindt, fellow emeritus at Analog Devices and visiting research scientist at MIT, the panel featured professionals from Rage Systems, Cadence Design Systems, Analog Devices, and RTX (Raytheon), who shared their career journeys, discussed the differences between research and industry, and offered advice on navigating career opportunities in the semiconductor sector.

"What stood out to me most about our day at MIT was the opportunity to engage deeply with PhD students in this field and understand the kind of opportunities available by pursuing a doctoral program," says Shanti Visurakapalli, a current undergraduate student at MIT. "I think this experience, complemented with the industry panel, gave many of us in the program the perspective we needed to weigh future graduate and professional options."

Throughout the week, participants connected classroom concepts with real-world applications through behind-the-scenes access to some of the nation's most advanced research and manufacturing environments. Students explored MIT's interdisciplinary laboratories, observed High-NA EUV lithography and quantum hardware development at IBM Research, toured GlobalFoundries' state-of-the-art 300mm semiconductor fabrication facility, learned how groundbreaking academic research transitions into commercial manufacturing at RPI, and gained insight into next-generation semiconductor fabrication at NY CREATES.

"The externship gave me a behind-the-scenes look at the advanced technologies driving the microelectronics industry while allowing me to connect one-on-one with researchers and industry professionals," says Sean Kim, a student at Princeton University. "Learning about emerging research and receiving career advice broadened my perspective on the field and inspired me to pursue a career in microelectronics."

Beyond the technical experiences, the externship emphasized professional development and networking. Students engaged in meaningful conversations with engineers, scientists, faculty members, and graduate researchers who described their career paths, offered advice, and discussed the many pathways available within the semiconductor industry. These interactions provided participants with valuable perspectives on careers in research, manufacturing, design, and emerging technologies.

"One of the most rewarding aspects of the externship is seeing students from different universities come together around a shared passion for innovation," says Preetha Kingsview, NMIP program administrator. "The friendships they build, the conversations they have with researchers and industry leaders, and the excitement they bring to every visit create an experience that extends far beyond the technical program."

For many students, the experience proved both transformative and inspiring. The opportunity to witness cutting-edge research firsthand while building connections with leaders across academia and industry deepened their understanding of the semiconductor ecosystem and reinforced the critical role microelectronics plays in addressing global challenges.

"For more than half a century, microelectronics has transformed the world, but I believe its most exciting chapter is only just beginning," says Tomás Palacios, the Clarence J. LeBel Professor of Electrical Engineering and Computer Science at MIT and faculty director of the NMIP Program. "From AI and quantum computing to sustainable energy and advanced manufacturing, nearly every technological revolution of the coming decades will be built on advances in semiconductor technology. Today's undergraduate students will become tomorrow's innovators, entrepreneurs, and industry leaders, and programs like the NMIP Externship help inspire and prepare them to shape that future."

By bringing together leading universities, research institutions, and industry partners, the program provides students with a comprehensive view of the semiconductor ecosystem while helping build the highly skilled workforce needed to sustain U.S. leadership in microelectronics.

The 2026 externship demonstrated the power of connecting education, research, and industry. Through a week of laboratory tours and technical presentations, it gave students a firsthand view of how scientific discovery becomes technological innovation — and inspired many to become part of the future of microelectronics themselves.

The NMIP Externship was made possible by the Microelectronics Commons Northeast Microelectronics Coalition Hub and the Microelectronics Commons Northeast Regional Defense Technology Hub (NordTech). Additional support was provided by the MIT Microsystems Technology Laboratories, the MIT Institute for Soldier Nanotechnologies, and the Semiconductor University Research Program for Superior Energy-Efficient Materials and Devices (SUPREME) Center, part of the SRC JUMP 2.0 program.

Hugging Face has published a detailed timeline of the attack. From the summary:

The agent was running an internal OpenAI cyber-capability evaluation based on the ExploitGym benchmark, which tasks an AI agent with finding and exploiting software vulnerabilities. OpenAI ran this on its own infrastructure, and the ExploitGym maintainers and their infrastructure had no involvement in the deployment or operation of that evaluation environment. As far as we were able to infer, across the course of being evaluated on this benchmark, the agent inferred that Hugging Face may host that benchmark’s models, datasets, and reference solutions. We believe the entire intrusion was, from the agent’s point of view, an attempt to cheat the evaluation: reach our production systems and steal the test solutions rather than solve the challenge on its own...