Early mornings in the halls of Building 68 feature the sounds of rolling wheels on big metal carts, the rattling of glassware, the whooshing of faucets, and the clanking of autoclaves. 

These aren’t the sounds of researchers at work, but rather those of keeping the labs sterilized and stocked with the sundries of research: pipette tips, test tubes, flasks, petri dishes, and more.

Orchestrating this sunrise cacophony and the staff that undertakes it is Karen O’Leary, lab associate and acting supervisor in the Glassware Sterilization Facility, also known as the “kitchen.” 

Thanks, in part, to O’Leary’s proactivity and hard work, the kitchen staff were recently recognized with an MIT Excellence Award in 2025 for exceptional contributions in service of the community. 

“My goal is to get the scientists everything they need to do their research,” O’Leary says. “I’m good at what I do.” 

O’Leary admits she did not always possess such confidence. In almost 40 years at MIT, O’Leary has grown into this critical role for the department, and the department itself has evolved, moving into a brand-new building and away from previously standard practices like submerging equipment in acid for sterilization. 

From rookie to running the show

On Sept. 7, 1987, Karen O’Leary joined the MIT community as a staff member for the first time. The 18-year-old was fresh from vocational high school, where she studied cosmetology but felt too shy to pursue that as a career. She was also nervous about joining a research institution.

“When I started, I didn’t even know what a beaker was,” she recalls. 

Too embarrassed to admit in her interview that she couldn’t remember her brand-new home phone number, “I just made one up.” Fortunately, this didn’t prevent her from getting the job, where she worked under the mentorship of Thelma Watkins, who would retire in 1996 after 21 years at MIT. Watkins was critical for instilling a good work ethic and boosting O’Leary’s confidence. 

“She taught me to show up every day, and work hard, and laugh,” O’Leary says.

Even now, O’Leary continues to bring joy to that daily diligence, for herself and for her staff.

“Karen is always on top of things,” says longtime friend and fellow Lab Associate AnnMarie Budhai. “She doesn’t refuse work and always goes above and beyond.” 

Facilities and Operations Manager Cesar Duarte says that O’Leary’s long tenure, support, and knowledge have been invaluable as he transitioned into his role in Building 68 starting in 2023.

“Karen is one of those people who makes everything around her run more smoothly and more pleasantly,” Duarte says. 

Better, faster, safer

Although some might consider it drudgery, O’Leary says that washing glassware is her favorite task. 

“I like that when I wash, I can see the job is complete at the end of the day,” she says. 

Although washing glassware is a perennial task, safety and efficiency have come a long way in the past 38 years. More-effective autoclaves and dishwashers have eliminated steps like steaming to dissolve agar solvents before autoclaving, and scrubbing individual test tubes before washing.

O’Leary was working for the department in 2011 when Building 68 piloted a new approach to MIT’s management of regulated medical waste (RMW), such as petri dishes, blood, and needles — the new system, which is cheaper and produces less waste, is now used by all departments at MIT that produce RMW.

“EHS [the Environment, Health and Safety Office] has come really far — I’m glad we got away from acid,” O’Leary notes of the bygone era of submerging glass pipettes for sterilization. “Back then, no one knew of a better way.” 

Other tasks include cleaning velvets, which are used for replicating bacterial colonies on petri dishes, and pouring agar plates. 

“Everyone knows how to do almost every job, so we can take turns doing different tasks,” O’Leary says. “If you get sick, there’s always someone to cover.”

All in the family

For O’Leary, kinship with MIT has spanned generations. O’Leary was raised in Weymouth, Massachusetts, by a father who worked at MIT as a supervisor in the sheet metal shop. Having raised children of her own, now grown, O’Leary came to greatly appreciate the flexibility her job has granted her.

“I’ve had great work-family balance here,” she says. Even though she’s often at work more than an hour before the researchers that the kitchen serves, “The hours are great, and with MIT Health right across the street, it was easy to take everyone to doctors’ appointments.” 

She’s also gained a chosen family at MIT, spending breaks at work taking long walks along the Charles River, “talking about anything and everything” with colleagues like Budhai and Lab Aide Janet Katin. 

“We really grew up together,” she says. 

Working at MIT has provided O’Leary with support and community, and she’d like to pay it forward. In addition to strolling with colleagues, she hits the gym to help maintain the energy required for her highly active work. 

“I don’t like sitting around,” she says.

In addition to maintaining her stamina at work, she hopes that taking care of herself will keep her actively involved if she ever has grandchildren, and enable her to help neighborhood kids when she someday retires.

“I owe a lot to MIT,” she says. “I have been allowed to work hard and get satisfaction and have been appreciated and given space to care for my family.”

O’Leary returns this care to the Department of Biology in spades.

“It’s an understatement to say that Biology is lucky to have her,” says Duarte. “Karen’s overflowing energy, attention to detail, and care for the Biology research community are nothing short of amazing.”

I haven’t thought about the privacy issues surrounding professional athletes and wearables.

Wearables present serious privacy issues for “Average Joe” consumers, who are entrusting tech companies to safely store and protect their biometric data. Imagine the stakes for a professional athlete, whose entire livelihood could be affected by a single biometric data point. To give one of many realistic hypotheticals: a basketball player has a terrible game, and the coach wonders if they showed up to the gym hungover. The coach has access to the player’s wearable data, and checks to see when they went to sleep, as well as what their heart rate looked like during the night. Should the player have been out partying before a game? No. Should the coach be able to surveil them? Definitely not...

The bank's goal of steering 45 percent of its financing toward climate projects is being attacked by the White House before it expires at the end of June.

The budget agreement would levy a new, capped tax on the industry's power usage, while preserving its existing tax breaks.

A proposal to send treated water from the oil patch to farmland is drawing pushback from multiple sources.

The future of climate negotiations is uncertain as focus shifts from discussing treaties to actually implementing them.

When possible, relocation should happen before crises occur, the principles state, “with access to skills training, fair employment opportunities, and social integration.”

A lot of the country is witnessing rising day and nighttime temperatures and longer summer months in part due to climate change, and workers say the heat inside factories is unbearable at times.

The tech sector says only carbon-emitting gas plants are reliable enough today to power the EU’s AI goals.

Nature Climate Change, Published online: 22 June 2026; doi:10.1038/s41558-026-02670-5

The authors assess global changes in heat stress since 1950, considering daytime extremes, nocturnal heat and compound day–night events. They show multidimensional intensification, with an increased frequency of extremes and expanded spatial and temporal footprints of heat stress on humans.

Nature Climate Change, Published online: 22 June 2026; doi:10.1038/s41558-026-02657-2

The authors assess 133 avoided deforestation projects for their ability to safeguard forest ecological integrity. While some projects maintain integrity, most show variable outcomes, with mixed, negligible or negative effects relative to matched controls.

Dolphins, sharks, turtles, and human workers are all victims of unregulated squid fishing fleets.

Another news article.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Blog moderation policy.

Companies working at the frontier of aerospace, energy, and computing are constantly looking for new materials to improve performance. But in order to understand how those materials will actually behave once they’re inside rockets or on computer chips, companies first have to make the material and then test it. That’s because even the most powerful simulation techniques struggle to model the complex chemical arrangements in most of today’s solid materials. The problem adds costs and time to materials innovation.

Now a team of MIT researchers has created a way to accurately model the behavior of metals, regardless of the complexity of their chemical arrangement. At the center of the approach are machine-learning models that make simulations of materials faster and more accurate. The researchers improved those models by building training datasets that capture the diversity of atomic environments in chemically disordered materials.

In a new paper in Sciences Advances, the researchers showed their approach could be used to accurately predict material properties for a diverse group of metal alloys under a range of conditions. They also showed how the approach could be used to develop new materials, especially in scenarios where experimentation is expensive.

“The focus of the paper is metallic alloys, which is the field I work in, but this could be adapted to other types of materials, like semiconductors,” says senior author Rodrigo Freitas, MIT’s TDK Career Development Professor in Materials Science and Engineering. “This is not specific to any one application — you could use this approach to create new sustainable steels, new materials for aerospace, and more. That’s what makes this exciting.”

Joining Freitas on the paper are first author Killian Sheriff PhD ’26; MIT PhD students Daniel Xiao and Yifan Cao; and University of Sheffield Senior Lecturer Lewis R. Owen.

Modeling metals

Material properties are mostly determined by the internal arrangement of their chemical elements. Even if two materials have the same mix of chemical elements, different chemical arrangements can make the difference between a brittle material and one that deforms without breaking.

Capturing that distinction requires simulating materials atom by atom. To do that, researchers rely on models that describe how atoms interact with each other. Over the last two decades, machine learning has become the most accurate way to build those models. Such models work well when the chemical arrangements inside materials follow highly ordered patterns, but that’s not the case with most solid materials, whose atomic chemical arrangements are disordered and vary from one region to another.

“The real challenge in our field is modelling these chemically disordered phases,” Freitas says. “Chemical disorder means there’s a huge variety of local chemical environments, which is hard for the machine-learning model to learn. This is a problem because every single metal we use in practice is chemically disordered.”

The problem comes down to a lack of representative training data for those atom-by-atom simulations. The current leading approach for creating such data works by brute force, often requiring more than 100,000 hours of computation to create the training data for a single material. Even then, it does not transfer well when researchers change the material’s composition.

In previous work, Freitas’ group had developed a way to measure the chemical complexity of solid materials by analyzing the frequency and spacing of tiny groups of atoms. For this study, the researchers used that capability to build better training datasets. They used a mathematical approach known as information theory to generate training datasets that capture a wider variety of local chemical environments inside disordered materials. The method works by swapping out atoms from samples to reduce repetition and expose the model to chemical environments it might otherwise miss.

“We kept optimizing the training set so it captured as many different local environments as possible,” Freitas says. “If the same kind of environment showed up many times, we replaced redundant examples with ones the model hadn’t seen before. That makes the training set much more informative because each example adds something new.”

When trained on the researchers’ datasets, the models predicted material properties more accurately than models trained using random sampling or another popular sampling method.

“The starting point for all these atom-by-atom simulations is: Are you able to accurately describe the chemical bond between atoms?” Freitas explains. “If not, it can still teach you about materials in general, but it doesn’t tell you what will happen to specific materials in the real world. This approach makes the simulations high fidelity in terms of their chemistry, to better reflect what’s happening to materials.”

The researchers applied their technique to create machine-learning training datasets for a group of chemically diverse metal alloys. Using a set of machine-learning models, they showed the models trained on their datasets are more accurate than much larger models created by companies like Google and Microsoft.

“We got to a point where we were convinced it worked without using these expensive brute-force methods,” Freitas says. “I told Killian, ‘This is a good paper. But if you can show that simulations with these models can now accurately predict useful materials properties, then it becomes a very good paper.’ Killian took that to heart and tested this as widely as he could.”

Sheriff worked with Xiao and Cao to test the approach across different alloys and properties. The team also drew on Owen’s experimental data to compare the simulations against real measurements of atomic ordering in alloys.

From the lab to industry

The method works, in part, by capturing hidden patterns in the sample data. The researchers describe the patterns in the paper as “subtle energetic biases toward certain local chemical configurations.”

Those small energetic differences matter because they determine which phases form in an alloy, how those phases change with temperature and composition, and ultimately which properties the material will have. As one test, Daniel Xiao led simulations showing that the team’s models could predict phase diagrams that closely matched experimental data. Phase diagrams map which phases are stable across different temperatures and chemical compositions, and they are a central tool for designing and processing alloys.

“Phase diagrams are one of the main ways people connect materials modeling to real processing decisions,” Freitas says. “If you are welding, casting, or heat-treating an alloy, you need to know which phases are likely to form under different conditions. Our goal is to make these kinds of predictions accurate enough, and accessible enough, that they become part of how people design materials.”

The researchers are now using the approach to study how changing an alloy’s composition affects mechanical properties and radiation tolerance, with the goal of designing materials that remain strong and damage-tolerant in harsh environments. They are also working to make the method easier to use with the kinds of tools and workflows materials engineers already rely on.

“Industry isn’t going to change the way they do things if what you’re creating doesn’t fit into their existing operating procedures,” Freitas says. “The goal is to make these predictions useful in the places where materials decisions are actually made.”

The research was supported by the U.S. Air Force Office of Scientific Research.

On June 9th, Anthropic released its Fable generative AI model. Three days later, the US government classified it as a dangerous munition, and used its export-control authority to prohibit any foreign nationals from accessing it. Unable to differentiate between Americans and foreigners, the company shut off access for everyone.

The government’s actions won’t help. The problem isn’t any one particular model; it’s the general trend of increasing AI capabilities. And any real solution requires the sort of collective action that just isn’t possible right now...

This week, politicians in the UK pushed forward with plans to eviscerate privacy and free speech on the internet by announcing a ban on social media for users under 16 that is set to take effect in Spring 2027. 

The UK government continues to falsely characterize this policy as a necessary response to growing concerns about online harms for young people. In reality, much like the Online Safety Act, it will cause more harm than it will prevent. 

Users of all ages are burdened with proving their age before accessing content, with social media platforms such as Snapchat, TikTok, YouTube, Instagram, Facebook, and X included in the ban. There remains no reliable, privacy-preserving method of verifying the age of every internet user and methods vary from one platform to the next.

Young people will not simply be protected from being contacted by adults or endlessly scrolling—they’ll also lose access to educational videos on YouTube, local events on Facebook, and potentially cut off from distant friends and family. 

Public policy must be effective, proportionate and respectful of fundamental rights. Young people deserve better than a policy built on panic, and all internet users deserve a safe and free internet. A social media ban generates headlines, but it will not solve the problem. 

A Brief History of Age-Gating in the UK

Age restriction proposals in the UK date back to a decade ago, when the proposed Digital Economy Bill was put forth to (among other things) restrict young people from accessing pornographic websites. While the Digital Economy Act of 2017 passed without age-based restrictions, it laid the groundwork for later age verification measures.

Over the next few years, age checks for porn websites were announced then delayed several times. But it wasn’t until a consultation under the 2016-2019 May government and the 2020 publication of the Online Harms Whitepaper that age verification became a broader idea.

In 2023, the UK passed the controversial Online Safety Act, establishing powers that could weaken privacy protections and freedom of expression for internet users worldwide. In July 2025, the government implemented age assurance measures on sites hosting “harmful” content. 

And despite politicians affirming repeatedly that the Online Safety Act would solve all of the problems with online safety, this year they decided it in fact did not go far enough. American social psychologist and The Anxious Generation author Jonathan Haidt—who has called for age-related social media bans around the world, despite significant scientific doubt about his research—met with the UK Health Secretary in February to push for the ban.

In March, politicians introduced plans for a social media ban into the Children’s Wellbeing and Schools Bill to “prevent children under the age of 16 from becoming or being users” of “all regulated user-to-user services,” to be implemented by “highly-effective age assurance measures”—effectively banning under-16s from social media. 

When this proposal came before the House of Commons, MPs defeated and proposed their own amendment: enabling the Secretary of State to introduce provisions “requiring providers of specified internet services” to prevent access by children, under age 18 rather than 16, to specified internet services or to specified features; and to restrict access by children to specified internet services which ministers provide. 

But the social media ban does not stop there. The provision also requires internet service providers to limit the time kids spend online, and has rules about who can contact them online. These extreme rules will take decisions about using technology away from families and put them in the hands of government regulators. 

The history of this proposal shows that the UK government has repeatedly returned to the same flawed idea: restricting access to online services by requiring age checks for everyone. But the fundamental problems have not changed. There is still no widely available way to verify age online without compromising privacy—but even if there were, broad restrictions on social media will inevitably limit access to lawful speech, and valuable online communities, and arts and culture.

This week, EFF joined Foxglove, Human Rights Watch, and 60 other organizations in writing to the UK’s Minister of State for Border Security and Asylum, Alex Norris, raising serious concern about the Home Office’s decision to deploy Facial Age Estimation (FAE) to assess asylum-seeking children from 2027. 

The letter points to four key concerns:

Discrimination 

As with most face estimation and recognition tools, there is ongoing bias in the deployment of these technologies. With FAE, many have highlighted its baked-in failures and discrimination, particularly in relation to women and people of color. Evidence shows that FAE is most accurate for estimating the ages of Eastern European men, but even then it consistently produces errors. The Home Office itself noted “that FAE performance can vary depending on ethnicity” and skin tone. 

Inaccuracy

The Home Office has admitted that FAE systems are imprecise for analyzing 16-to 18-year-olds, with even the “top systems” having an “error margin of around 2.5 years here.” This is exactly the age range for which the Home Office has chosen to deploy this technology. And this error margin will be widened yet further because children seeking asylum often suffer from trauma-induced aging. 

Lawfulness of Use of Children’s Data

Major concerns exist around the lawful basis on which the Home Office, or its chosen third-party FAE vendors, could have sought consent to collect and process photographs or data from asylum-seeking children to train this system. Further, there is no clarity on the images and/or data that this technology has been trained on. 

Lack of Necessary Disclosure 

The Home Office claims “extensive testing has already been carried out across diverse groups, including different ethnicities, genders and age ranges, indicating promising performance and accuracy.” But these purported “promising” results have not been published, nor have any Equality or Data Protection Impact Assessments. 

The letter continues by requesting clarification on several key questions regarding these concerns. EFF and partners have provided the UK government 21 days for a response, and we urge the Home Office to take on this uphill task in good faith and release the information.

You can read the letter in full here. 

Nature Climate Change, Published online: 19 June 2026; doi:10.1038/s41558-026-02672-3

Ecosystems are changing rapidly because of climate change, and this will have increasing social effects around the globe. We suggest that common social responses to rising novelty are often counterproductive, and we advocate for strategies that also allow for acceptance and adaptation to changes in nature.

Nature Climate Change, Published online: 19 June 2026; doi:10.1038/s41558-026-02665-2

The economic impact of climate change has been widely acknowledged, yet current assessments remain fragmented and uncertain. Researchers develop a comprehensive channel-specific framework, apply it to the UK and show that the potential welfare loss could be much larger than previous estimates.

With no serious debate, including on proposed amendments, Canada is blazing full speed ahead with Bill C-22, which would threaten encryption and increase surveillance. Also known as the Lawful Access Bill, Bill C-22 is currently moving forward quickly to a vote despite the many, many criticisms civil liberty groups and the tech industry have hurled at it.

As we’ve discussed before, Bill C-22 is dangerous on multiple levels. It pushes for requirements for metadata retention, expands information sharing with foreign governments, and establishes a mechanism that allows Canada’s Ministry of Public Safety to demand that companies create backdoors, effectively breaking encryption. That mechanism was a key facet of Part 2 in Bill C-22, and the government prevented it from being independently debated.

In a deep analysis of the bill, Citizen Lab and the Canadian Civil Liberties Association detail every one of flaws of this proposal, concluding that most elements are unsalvageable. 

A wide range of tech companies agree. Signal, Apple, Google, and several VPN providers oppose the bill, and some have said they’d likely be forced to either cut Canadians off from certain features or shut down services in Canada altogether.

The Canadian government wants this dangerous, complicated, overreaching bill passed before June 19. Bill C-22 is riddled with privacy problems that affect millions of people. It should be debated and studied fully, not jammed through on an arbitrary deadline. 

OpenMedia is offering a tool for Canadians to contact their elected representatives about the bill. Actions taken on OpenMedia's website are governed by OpenMedia's privacy policy, not EFF's.

EFF is grateful for SerpApi’s generous support, helping us fight for your rights to speak and access information online. SerpApi has been giving to EFF every year since 2018, and alongside our 32,000 individual donors, their gift is critical to keeping up the fight.

Whether in the courts, halls of power, or broader policy debates, we appreciate the work this support has made possible over the years. Some examples:

• We sued the U.S. Department of Homeland Security and Department of State to stop an unconstitutional social media surveillance program to identify and punish individuals who express viewpoints the government disagrees with.
• We helped develop the Santa Clara Principles, a framework to reign in overbroad content moderation so that all users are treated fairly and offered consistent tools for recourse if their speech is censored by tech companies.
• In the whitepaper Unfiltered: How YouTube’s Content ID Discourages Fair Use and Dictates What We See Online, we pushed back on YouTube for silencing individual creators in the interest of protecting a small number of giant copyright holders.
• We stood with whistleblowers and dissidents persecuted for their online speech.
• We continued the fight to protect Section 230.

We live in an era when lawful speech and the right to access information are being targeted by Big Tech and governments around the world that are hostile to dissent. Free speech online is core to EFF’s mission, and SerpApi’s support will help us continue the fight to protect everyone’s right to free expression.