The U.S. tried to strong-arm nations into rejecting the measure. Now, the administration is pushing to make adoption harder.

His administration has stifled a green energy boom — but so have higher interest rates and supply chain bottlenecks.

Pollution from vehicles, power plants and wildfires soared last year, marking a global acceleration of carbon emissions, a U.N. report says.

Multnomah County attorneys defended their lead counsel after Chevron accused him of hiding his role in climate research papers.

The plaintiffs showed evidence of fossil fuels spurring climate change, but the court lacks authority to provide a fix, the judge said.

The commodities giant supplies beef, soybeans, sugar and coffee worldwide. But agriculture is also the country's highest-emitting sector.

The challenge is finding a solution that fits each member state with varying sources of energy and levels of wealth and industrial strength.

Britain should expect at least 2 degrees Celsius of warming by 2050 and 4 C by the end of the century, the U.K.'s climate adviser said.

The offsets must be verified by accredited bodies using international standards and can contribute to Indonesia’s own climate targets.

Say a person takes their French Bulldog, Bowser, to the dog park. Identifying Bowser as he plays among the other canines is easy for the dog-owner to do while onsite.

But if someone wants to use a generative AI model like GPT-5 to monitor their pet while they are at work, the model could fail at this basic task. Vision-language models like GPT-5 often excel at recognizing general objects, like a dog, but they perform poorly at locating personalized objects, like Bowser the French Bulldog.    

To address this shortcoming, researchers from MIT and the MIT-IBM Watson AI Lab have introduced a new training method that teaches vision-language models to localize personalized objects in a scene.

Their method uses carefully prepared video-tracking data in which the same object is tracked across multiple frames. They designed the dataset so the model must focus on contextual clues to identify the personalized object, rather than relying on knowledge it previously memorized.

When given a few example images showing a personalized object, like someone’s pet, the retrained model is better able to identify the location of that same pet in a new image.

Models retrained with their method outperformed state-of-the-art systems at this task. Importantly, their technique leaves the rest of the model’s general abilities intact.

This new approach could help future AI systems track specific objects across time, like a child’s backpack, or localize objects of interest, such as a species of animal in ecological monitoring. It could also aid in the development of AI-driven assistive technologies that help visually impaired users find certain items in a room.

“Ultimately, we want these models to be able to learn from context, just like humans do. If a model can do this well, rather than retraining it for each new task, we could just provide a few examples and it would infer how to perform the task from that context. This is a very powerful ability,” says Jehanzeb Mirza, an MIT postdoc and senior author of a paper on this technique.

Mirza is joined on the paper by co-lead authors Sivan Doveh, a graduate student at Weizmann Institute of Science; and Nimrod Shabtay, a researcher at IBM Research; James Glass, a senior research scientist and the head of the Spoken Language Systems Group in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL); and others. The work will be presented at the International Conference on Computer Vision.

An unexpected shortcoming

Researchers have found that large language models (LLMs) can excel at learning from context. If they feed an LLM a few examples of a task, like addition problems, it can learn to answer new addition problems based on the context that has been provided.

A vision-language model (VLM) is essentially an LLM with a visual component connected to it, so the MIT researchers thought it would inherit the LLM’s in-context learning capabilities. But this is not the case.

“The research community has not been able to find a black-and-white answer to this particular problem yet. The bottleneck could arise from the fact that some visual information is lost in the process of merging the two components together, but we just don’t know,” Mirza says.

The researchers set out to improve VLMs abilities to do in-context localization, which involves finding a specific object in a new image. They focused on the data used to retrain existing VLMs for a new task, a process called fine-tuning.

Typical fine-tuning data are gathered from random sources and depict collections of everyday objects. One image might contain cars parked on a street, while another includes a bouquet of flowers.

“There is no real coherence in these data, so the model never learns to recognize the same object in multiple images,” he says.

To fix this problem, the researchers developed a new dataset by curating samples from existing video-tracking data. These data are video clips showing the same object moving through a scene, like a tiger walking across a grassland.

They cut frames from these videos and structured the dataset so each input would consist of multiple images showing the same object in different contexts, with example questions and answers about its location.

“By using multiple images of the same object in different contexts, we encourage the model to consistently localize that object of interest by focusing on the context,” Mirza explains.

Forcing the focus

But the researchers found that VLMs tend to cheat. Instead of answering based on context clues, they will identify the object using knowledge gained during pretraining.

For instance, since the model already learned that an image of a tiger and the label “tiger” are correlated, it could identify the tiger crossing the grassland based on this pretrained knowledge, instead of inferring from context.

To solve this problem, the researchers used pseudo-names rather than actual object category names in the dataset. In this case, they changed the name of the tiger to “Charlie.”

“It took us a while to figure out how to prevent the model from cheating. But we changed the game for the model. The model does not know that ‘Charlie’ can be a tiger, so it is forced to look at the context,” he says.

The researchers also faced challenges in finding the best way to prepare the data. If the frames are too close together, the background would not change enough to provide data diversity.

In the end, finetuning VLMs with this new dataset improved accuracy at personalized localization by about 12 percent on average. When they included the dataset with pseudo-names, the performance gains reached 21 percent.

As model size increases, their technique leads to greater performance gains.

In the future, the researchers want to study possible reasons VLMs don’t inherit in-context learning capabilities from their base LLMs. In addition, they plan to explore additional mechanisms to improve the performance of a VLM without the need to retrain it with new data.

“This work reframes few-shot personalized object localization — adapting on the fly to the same object across new scenes — as an instruction-tuning problem and uses video-tracking sequences to teach VLMs to localize based on visual context rather than class priors. It also introduces the first benchmark for this setting with solid gains across open and proprietary VLMs. Given the immense significance of quick, instance-specific grounding — often without finetuning — for users of real-world workflows (such as robotics, augmented reality assistants, creative tools, etc.), the practical, data-centric recipe offered by this work can help enhance the widespread adoption of vision-language foundation models,” says Saurav Jha, a postdoc at the Mila-Quebec Artificial Intelligence Institute, who was not involved with this work.

Additional co-authors are Wei Lin, a research associate at Johannes Kepler University; Eli Schwartz, a research scientist at IBM Research; Hilde Kuehne, professor of computer science at Tuebingen AI Center and an affiliated professor at the MIT-IBM Watson AI Lab; Raja Giryes, an associate professor at Tel Aviv University; Rogerio Feris, a principal scientist and manager at the MIT-IBM Watson AI Lab; Leonid Karlinsky, a principal research scientist at IBM Research; Assaf Arbelle, a senior research scientist at IBM Research; and Shimon Ullman, the Samy and Ruth Cohn Professor of Computer Science at the Weizmann Institute of Science.

This research was funded, in part, by the MIT-IBM Watson AI Lab.

Nature Climate Change, Published online: 16 October 2025; doi:10.1038/s41558-025-02455-2

Impacts from a climate event can cascade through natural, anthropogenic and socio-economic systems. Here the authors assess cascading climate impacts on the EU and identify intervention points for adaptation related to water, livelihoods, agriculture, infrastructure and economy, and violent conflict.

Nature Climate Change, Published online: 16 October 2025; doi:10.1038/s41558-025-02462-3

Scenarios, generated by integrated assessment models in model intercomparison projects (MIPs), play a central role in climate decision-making. This Perspective discusses the challenges of the current approach and proposes a new MIP platform with a transparent and inclusive process.

A decade-plus collaboration between MIT’s AgeLab and the Toyota Motor Corporation is recognized as a key contributor to advancements in automotive safety and human-machine interaction. Through the AgeLab at the MIT Center for Transportation and Logistics (CTL), researchers have collected and analyzed vast real-world driving datasets that have helped inform Toyota’s vehicle design and safety systems.

Toyota recently marked the completion of its 100th project through the Collaborative Safety Research Center (CSRC), celebrating MIT’s role in shaping technologies that enhance driver-assistance features and continue to forge the path for automated mobility. A key foundation for the 100th project is CSRC’s ongoing support for MIT CTL’s Advanced Vehicle Technology (AVT) Consortium.

Real-world data, real-world impact

“AVT was conceptualized over a decade ago as an academic-industry partnership to promote shared investment in real-world, naturalistic data collection, analysis, and collaboration — efforts aimed at advancing safer, more convenient, and more comfortable automobility,” says Bryan Reimer, founder and co-director of AVT. “Since its founding, AVT has drawn together over 25 organizations — including vehicle manufacturers, suppliers, insurers, and consumer research groups — to invest in understanding how automotive technologies function, how they influence driver behavior, and where further innovation is needed. This work has enabled stakeholders like Toyota to make more-informed decisions in product development and deployment.”

“CSRC’s 100th project marks a significant milestone in our collaboration,” Reimer adds. “We deeply value CSRC’s sustained investment, and commend the organization’s commitment to global industry impact and the open dissemination of research to advance societal benefit.”

“Toyota, through its Collaborative Safety Research Center, is proud to be a founding member of the AVT Consortium,” says Jason Hallman, senior manager of Toyota CSRC. “Since 2011, CSRC has collaborated with researchers such as AVT and MIT AgeLab on projects that help inform future products and policy, and to promote a future safe mobility society for all. The AVT specifically has helped us to study the real-world use of several vehicle technologies now available.”

Among these technologies are lane-centering assistance and adaptive cruise control — widely-used technologies that benefit from an understanding of how drivers interact with automation. “AVT uniquely combines vehicle and driver data to help inform future products and highlight the interplay between the performance of these features and the drivers using them,” says Josh Domeyer, principal scientist at CSRC.

Influencing global standards and Olympic-scale innovation

Insights from MIT’s pedestrian-driver interaction research with CSRC also helped shape Toyota’s automated vehicle communication systems. “These data helped develop our foundational understanding that drivers and pedestrians use their movements to communicate during routine traffic encounters,” said Domeyer. “This concept informed the deployment of Toyota’s e-Palette at the Tokyo Olympics, and it has been captured as a best practice in an ISO standard for automated driving system communication.”

The AVT Consortium's naturalistic driving datasets continue to serve as a foundation for behavioral safety strategies. From identifying moments of distraction to understanding how drivers multitask behind the wheel, the work is guiding subtle but impactful design considerations.

“By studying the natural behaviors of drivers and their contexts in the AVT datasets, we hope to identify new ways to encourage safe habits that align with customer preferences,” Domeyer says. “These can include subtle nudges, or modifications to existing vehicle features, or even communication and education partnerships outside of Toyota that reinforce these safe driving habits.”

Professor Yossi Sheffi, director of MIT CTL, comments, “This partnership exemplifies the impact of MIT collaborative research on industry to make real, practical innovation possible.” 

A model for industry-academic collaboration

Founded in 2015, the AVT Consortium brings together automotive manufacturers, suppliers, and insurers to accelerate research in driver behavior, safety, and the transition toward automated systems. The consortium’s interdisciplinary approach — integrating engineering, human factors, and data science — has helped generate one of the world’s most unique and actionable real-world driving datasets.

As Toyota celebrates its research milestone, MIT reflects on a partnership that exemplifies the power of industry-academic collaboration to shape safer, smarter mobility.

Instead of catching you off-guard with a jump scare this Halloween season, EFF is here to catch you up on the latest digital rights news with our EFFector newsletter!

In this issue, we’re helping you take control of your online privacy with Opt Out October; explaining the UK’s attack on encryption and why it’s bad for all users; and covering shocking new details about an abortion surveillance case in Texas.

Prefer to listen in? Check out our audio companion, where EFF Security and Privacy Activist Thorin Klosowski explains how small steps to protect your privacy can add up to big changes.  Catch the conversation on YouTube or the Internet Archive.

LISTEN TO EFFECTOR

EFFECTOR 37.14 - 🎃 A FULL MONTH OF PRIVACY TIPS FROM EFF

Since 1990 EFF has published EFFector to help keep readers on the bleeding edge of their digital rights. We know that the intersection of technology, civil liberties, human rights, and the law can be complicated, so EFFector is a great way to stay on top of things. The newsletter is chock full of links to updates, announcements, blog posts, and other stories to help keep readers—and listeners—up to date on the movement to protect online privacy and free expression. 

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To help mitigate climate change, companies are using bioreactors to grow algae and other microorganisms that are hundreds of times more efficient at absorbing CO2 than trees. Meanwhile, in the pharmaceutical industry, cell culture is used to manufacture biologic drugs and other advanced treatments, including lifesaving gene and cell therapies.

Both processes are hampered by cells’ tendency to stick to surfaces, which leads to a huge amount of waste and downtime for cleaning. A similar problem slows down biofuel production, interferes with biosensors and implants, and makes the food and beverage industry less efficient.

Now, MIT researchers have developed an approach for detaching cells from surfaces on demand, using electrochemically generated bubbles. In an open-access paper published in Science Advances, the researchers demonstrated their approach in a lab prototype and showed it could work across a range of cells and surfaces without harming the cells.

“We wanted to develop a technology that could be high-throughput and plug-and-play, and that would allow cells to attach and detach on demand to improve the workflow in these industrial processes,” says Professor Kripa Varanasi, senior author of the study. “This is a fundamental issue with cells, and we’ve solved it with a process that can scale. It lends itself to many different applications.”

Joining Varanasi on the study are co-first authors Bert Vandereydt, a PhD student in mechanical engineering, and former postdoc Baptiste Blanc.

Solving a sticky problem

The researchers began with a mission.

“We’ve been working on figuring out how we can efficiently capture CO2 across different sources and convert it into valuable products for various end markets,” Varanasi says. “That’s where this photobioreactor and cell detachment comes into the picture.”

Photobioreactors are used to grow carbon-absorbing algae cells by creating tightly controlled environments involving water and sunlight. They feature long, winding tubes with clear surfaces to let in the light algae need to grow. When algae stick to those surfaces, they block out the light, requiring cleaning.

“You have to shut down and clean up the entire reactor as frequently as every two weeks,” Varanasi says. “It’s a huge operational challenge.”

The researchers realized other industries have similar problem due to many cells’ natural adhesion, or stickiness. Each industry has its own solution for cell adhesion depending on how important it is that the cells survive. Some people scrape the surfaces clean, while others use special coatings that are toxic to cells.

In the pharmaceutical and biotech industries, cell detachment is typically carried out using enzymes. However, this method poses several challenges — it can damage cell membranes, is time-consuming, and requires large amounts of consumables, resulting in millions of liters of biowaste.

To create a better solution, the researchers began by studying other efforts to clear surfaces with bubbles, which mainly involved spraying bubbles onto surfaces and had been largely ineffective.

“We realized we needed the bubbles to form on the surfaces where we don’t want these cells to stick, so when the bubbles detach it creates a local fluid flow that creates shear stress at the interface and removes the cells,” Varanasi explains.

Electric currents generate bubbles by splitting water into hydrogen and oxygen. But previous attempts at using electricity to detach cells were hampered because the cell culture mediums contain sodium chloride, which turns into bleach when combined with an electric current. The bleach damages the cells, making it impractical for many applications.

“The culprit is the anode — that’s where the sodium chloride turns to bleach,” Vandereydt explained. “We figured if we could separate that electrode from the rest of the system, we could prevent bleach from being generated.”

To make a better system, the researchers built a 3-square-inch glass surface and deposited a gold electrode on top of it. The layer of gold is so thin it doesn’t block out light. To keep the other electrode separate, the researchers integrated a special membrane that only allows protons to pass through. The set up allowed the researchers to send a current through without generating bleach.

To test their setup, they allowed algae cells from a concentrated solution to stick to the surfaces. When they applied a voltage, the bubbles separated the cells from the surfaces without harming them.

The researchers also studied the interaction between the bubbles and cells, finding the higher the current density, the more bubbles were created and the more algae was removed. They developed a model for understanding how much current would be needed to remove algae in different settings and matched it with results from experiments involving algae as well as cells from ovarian cancer and bones.

“Mammalian cells are orders of magnitude more sensitive than algae cells, but even with those cells, we were able to detach them with no impact to the viability of the cell,” Vandereydt says.

Getting to scale

The researchers say their system could represent a breakthrough in applications where bleach or other chemicals would harm cells. That includes pharmaceutical and food production.

“If we can keep these systems running without fouling and other problems, then we can make them much more economical,” Varanasi says.

For cell culture plates used in the pharmaceutical industry, the team envisions their system comprising an electrode that could be robotically moved from one culture plate to the next, to detach cells as they’re grown. It could also be coiled around algae harvesting systems.

“This has general applicability because it doesn’t rely on any specific biological or chemical treatments, but on a physical force that is system-agnostic,” Varanasi says. “It’s also highly scalable to a lot of different processes, including particle removal.”

Varanasi cautions there is much work to be done to scale up the system. But he hopes it can one day make algae and other cell harvesting more efficient.

“The burning problem of our time is to somehow capture CO2 in a way that’s economically feasible,” Varanasi says. “These photobioreactors could be used for that, but we have to overcome the cell adhesion problem.”

The work was supported, in part, by Eni S.p.A through the MIT Energy Initiative, the Belgian American Educational Foundation Fellowship, and the Maria Zambrano Fellowship.

Computational neuroscientist and singer/songwriter Kimaya (Kimy) Lecamwasam, who also plays electric bass and guitar, says music has been a core part of her life for as long as she can remember. She grew up in a musical family and played in bands all through high school.

“For most of my life, writing and playing music was the clearest way I had to express myself,” says Lecamwasam. “I was a really shy and anxious kid, and I struggled with speaking up for myself. Over time, composing and performing music became central to both how I communicated and to how I managed my own mental health.”

Along with equipping her with valuable skills and experiences, she credits her passion for music as the catalyst for her interest in neuroscience.

“I got to see firsthand not only the ways that audiences reacted to music, but also how much value music had for musicians,” she says. “That close connection between making music and feeling well is what first pushed me to ask why music has such a powerful hold on us, and eventually led me to study the science behind it.”

Lecamwasam earned a bachelor’s degree in 2021 from Wellesley College, where she studied neuroscience — specifically in the Systems and Computational Neuroscience track — and also music. During her first semester, she took a class in songwriting that she says made her more aware of the connections between music and emotions. While studying at Wellesley, she participated in the MIT Undergraduate Research Opportunities Program for three years. Working in the Department of Brain and Cognitive Sciences lab of Emery Brown, the Edward Hood Taplin Professor of Medical Engineering and Computational Neuroscience, she focused primarily on classifying consciousness in anesthetized patients and training brain-computer interface-enabled prosthetics using reinforcement learning.

“I still had a really deep love for music, which I was pursuing in parallel to all of my neuroscience work, but I really wanted to try to find a way to combine both of those things in grad school,” says Lecamwasam. Brown recommended that she look into the graduate programs at the MIT Media Lab within the Program in Media Arts and Sciences (MAS), which turned out to be an ideal fit.

“One thing I really love about where I am is that I get to be both an artist and a scientist,” says Lecamwasam. “That was something that was important to me when I was picking a graduate program. I wanted to make sure that I was going to be able to do work that was really rigorous, validated, and important, but also get to do cool, creative explorations and actually put the research that I was doing into practice in different ways.”

Exploring the physical, mental, and emotional impacts of music

Informed by her years of neuroscience research as an undergraduate and her passion for music, Lecamwasam focused her graduate research on harnessing the emotional potency of music into scalable, non-pharmacological mental health tools. Her master’s thesis focused on “pharmamusicology,” looking at how music might positively affect the physiology and psychology of those with anxiety.

The overarching theme of Lecamwasam’s research is exploring the various impacts of music and affective computing — physically, mentally, and emotionally. Now in the third year of her doctoral program in the Opera of the Future group, she is currently investigating the impact of large-scale live music and concert experiences on the mental health and well-being of both audience members and performers. She is also working to clinically validate music listening, composition, and performance as health interventions, in combination with psychotherapy and pharmaceutical interventions.

Her recent work, in collaboration with Professor Anna Huang’s Human-AI Resonance Lab, assesses the emotional resonance of AI-generated music compared to human-composed music; the aim is to identify more ethical applications of emotion-sensitive music generation and recommendation that preserve human creativity and agency, and can also be used as health interventions. She has co-led a wellness and music workshop at the Wellbeing Summit in Bilbao, Spain, and has presented her work at the 2023 CHI conference on Human Factors in Computing Systems in Hamburg, Germany and the 2024 Audio Mostly conference in Milan, Italy. 

Lecamwasam has collaborated with organizations near and far to implement real-world applications of her research. She worked with Carnegie Hall's Weill Music Institute on its Well-Being Concerts and is currently partnering on a study assessing the impact of lullaby writing on perinatal health with the North Shore Lullaby Project in Massachusetts, an offshoot of Carnegie Hall’s Lullaby Project. Her main international collaboration is with a company called Myndstream, working on projects comparing the emotional resonance of AI-generated music to human-composed music and thinking of clinical and real-world applications. She is also working on a project with the companies PixMob and Empatica (an MIT Media Lab spinoff), centered on assessing the impact of interactive lighting and large-scale live music experiences on emotional resonance in stadium and arena settings.

Building community

“Kimy combines a deep love for — and sophisticated knowledge of — music with scientific curiosity and rigor in ways that represent the Media Lab/MAS spirit at its best,” says Professor Tod Machover, Lecamwasam’s research advisor, Media Lab faculty director, and director of the Opera of the Future group. “She has long believed that music is one of the most powerful and effective ways to create personalized interventions to help stabilize emotional distress and promote empathy and connection. It is this same desire to establish sane, safe, and sustaining environments for work and play that has led Kimy to become one of the most effective and devoted community-builders at the lab.”

Lecamwasam has participated in the SOS (Students Offering Support) program in MAS for a few years, which assists students from a variety of life experiences and backgrounds during the process of applying to the Program in Media Arts and Sciences. She will soon be the first MAS peer mentor as part of a new initiative through which she will establish and coordinate programs including a “buddy system,” pairing incoming master’s students with PhD students as a way to help them transition into graduate student life at MIT. She is also part of the Media Lab’s Studcom, a student-run organization that promotes, facilitates, and creates experiences meant to bring the community together.

“I think everything that I have gotten to do has been so supported by the friends I’ve made in my lab and department, as well as across departments,” says Lecamwasam. “I think everyone is just really excited about the work that they do and so supportive of one another. It makes it so that even when things are challenging or difficult, I’m motivated to do this work and be a part of this community.”

Apple is now offering a $2M bounty for a zero-click exploit. According to the Apple website:

Today we’re announcing the next major chapter for Apple Security Bounty, featuring the industry’s highest rewards, expanded research categories, and a flag system for researchers to objectively demonstrate vulnerabilities and obtain accelerated awards.

1. We’re doubling our top award to $2 million for exploit chains that can achieve similar goals as sophisticated mercenary spyware attacks. This is an unprecedented amount in the industry and the largest payout offered by any bounty program we’re aware of ­ and our bonus system, providing additional rewards for Lockdown Mode bypasses and vulnerabilities discovered in beta software, can more than double this reward, with a maximum payout in excess of $5 million. We’re also doubling or significantly increasing rewards in many other categories to encourage more intensive research. This includes $100,000 for a complete Gatekeeper bypass, and $1 million for broad unauthorized iCloud access, as no successful exploit has been demonstrated to date in either category. ...

Kevin Guthrie operates "Alligator Alcatraz." Some emergency managers worry that could scare Hispanic residents away from seeking help in a hurricane or other disaster.

The sharply worded ruling came after the judge said administration officials ignored his earlier decision barring Federal Emergency Management Agency from forcing states to participate in immigration enforcement activities.

Information from the Defense Meteorological Satellite Program has grown spotty, some researchers say.