Nature Climate Change, Published online: 17 October 2025; doi:10.1038/s41558-025-02446-3

The Southern Ocean carbon sink is predicted to decline under climate change. This study explores why this is yet to be seen in observations, finding that recent surface freshening increases stratification and traps the CO2-rich water in the subsurface layer, which prevents atmospheric outgassing.

Nature Climate Change, Published online: 17 October 2025; doi:10.1038/s41558-025-02453-4

Damages such as crevasses or cracks can be early indicators of ice shelf weakening. Here, the authors quantify changes in damage structures in Antarctic ice sheets, which show sensitivity to warming

Two leading experts in the field of carbon capture and sequestration (CCS) — Howard J. Herzog, a senior research engineer in the MIT Energy Initiative, and Niall Mac Dowell, a professor in energy systems engineering at Imperial College London — explore methods for removing carbon dioxide already in the atmosphere in their new book, “Carbon Removal.” Published in October, the book is part of the Essential Knowledge series from the MIT Press, which consists of volumes “synthesizing specialized subject matter for nonspecialists” and includes Herzog’s 2018 book, “Carbon Capture.”

Burning fossil fuels, as well as other human activities, cause the release of carbon dioxide (CO2) into the atmosphere, where it acts like a blanket that warms the Earth, resulting in climate change. Much attention has focused on mitigation technologies that reduce emissions, but in their book, Herzog and Mac Dowell have turned their attention to “carbon dioxide removal” (CDR), an approach that removes carbon already present in the atmosphere.

In this new volume, the authors explain how CO2 naturally moves into and out of the atmosphere and present a brief history of carbon removal as a concept for dealing with climate change. They also describe the full range of “pathways” that have been proposed for removing CO2 from the atmosphere. Those pathways include engineered systems designed for “direct air capture” (DAC), as well as various “nature-based” approaches that call for planting trees or taking steps to enhance removal by biomass or the oceans. The book offers easily accessible explanations of the fundamental science and engineering behind each approach.

The authors compare the “quality” of the different pathways based on the following metrics:

Accounting. For public acceptance of any carbon-removal strategy, the authors note, the developers need to get the accounting right — and that’s not always easy. “If you’re going to spend money to get CO2 out of the atmosphere, you want to get paid for doing it,” notes Herzog. It can be tricky to measure how much you have removed, because there’s a lot of CO2 going in and out of the atmosphere all the time. Also, if your approach involves, say, burning fossil fuels, you must subtract the amount of CO2 that’s emitted from the total amount you claim to have removed. Then there’s the timing of the removal. With a DAC device, the removal happens right now, and the removed CO2 can be measured. “But if I plant a tree, it’s going to remove CO2 for decades. Is that equivalent to removing it right now?” Herzog queries. How to take that factor into account hasn’t yet been resolved.

Permanence. Different approaches keep the CO2 out of the atmosphere for different durations of time. How long is long enough? As the authors explain, this is one of the biggest issues, especially with nature-based solutions, where events such as wildfires or pestilence or land-use changes can release the stored CO2 back into the atmosphere. How do we deal with that?

Cost. Cost is another key factor. Using a DAC device to remove CO2 costs far more than planting trees, but it yields immediate removal of a measurable amount of CO2 that can then be locked away forever. How does one monetize that trade-off?

Additionality. “You’re doing this project, but would what you’re doing have been done anyway?” asks Herzog. “Is your effort additional to business as usual?” This question comes into play with many of the nature-based approaches involving trees, soils, and so on.

Permitting and governance. These issues are especially important — and complicated — with approaches that involve doing things in the ocean. In addition, Herzog points out that some CCS projects could also achieve carbon removal, but they would have a hard time getting permits to build the pipelines and other needed infrastructure.

The authors conclude that none of the CDR strategies now being proposed is a clear winner on all the metrics. However, they stress that carbon removal has the potential to play an important role in meeting our climate change goals — not by replacing our emissions-reduction efforts, but rather by supplementing them. However, as Herzog and Mac Dowell make clear in their book, many challenges must be addressed to move CDR from today’s speculation to deployment at scale, and the book supports the wider discussion about how to move forward. Indeed, the authors have fulfilled their stated goal: “to provide an objective analysis of the opportunities and challenges for CDR and to separate myth from reality.”

At an age when many kids prefer to play games on their phones, 11-year-old Vivan Mirchandani wanted to explore physics videos. Little did he know that MIT Open Learning’s free online resources would change the course of his life. 

Now, at 16, Mirchandani is well on his way to a career as a physics scholar — all because he forged his own unconventional educational journey.

Nontraditional education has granted Mirchandani the freedom to pursue topics he’s personally interested in. This year, he wrote a paper on cosmology that proposes a new framework for understanding Einstein’s general theory of relativity. Other projects include expanding on fluid dynamics laws for cats, training an AI model to resemble the consciousness of his late grandmother, and creating his own digital twin. That’s in addition to his regular studies, regional science fairs, Model United Nations delegation, and a TEDEd Talk.

Mirchandani started down this path between the ages of 10 and 12, when he decided to read books and find online content about physics during the early Covid-19 lockdown in India. He was shocked to find that MIT Open Learning offers free course videos, lecture notes, exams, and other resources from the Institute on sites like MIT OpenCourseWare and the newly launched MIT Learn.

“My first course was 8.01 (Classical Mechanics), and it completely changed how I saw physics,” Mirchandani says. “Physics sounded like elegance. It’s the closest we’ve ever come to have a theory of everything.”

Experiencing “real learning”

Mirchandani discovered MIT Open Learning through OpenCourseWare, which offers free, online, open educational resources from MIT undergraduate and graduate courses. He says MIT Open Learning’s “academically rigorous” content prepares learners to ask questions and think like a scientist.

“Instead of rote memorization, I finally experienced real learning,” Mirchandani says. “OpenCourseWare was a holy grail. Without it, I would still be stuck on the basic concepts.”

Wanting to follow in the footsteps of physicists like Sir Isaac Newton, Albert Einstein, and Stephen Hawking, Mirchandani decided at age 12 he would sacrifice his grade point average to pursue a nontraditional educational path that gave him hands-on experience in science.

“The education system doesn’t prepare you for actual scientific research, it prepares you for exams,” Mirchandani says. “What draws me to MIT Open Learning and OpenCourseWare is it breaks the old model of education. It’s not about sitting in a lecture hall, it’s about access and experimentation.”

With guidance from his physics teacher, Mirchandani built his own curriculum using educational materials on MIT OpenCourseWare to progress from classical physics to computer science to quantum physics. He has completed more than 27 online MIT courses to date.

“The best part of OpenCourseWare is you get to study from the greatest institution in the world, and you don’t have to pay for it,” he says.

Innovating in the real world

6.0001 (Introduction to Computer Science and Programming Using Python) and slides from 2.06 (Fluid Dynamics) gave Mirchandani the foundation to help with the family business, Dynamech Engineers, which sells machinery for commercial snack production. Some of the recent innovations he has assisted with include a zero-oil frying technology that cuts 300 calories per kilogram, a gas-based heat exchange system, and a simplified, singular machine combining the processes of two separate machines. Using the modeling techniques he learned through MIT OpenCourseWare, Mirchandani designed how these products would work without losing efficiency.

But when you ask Mirchandani which achievement he is most proud of, he’ll say it’s being one of 35 students accepted for the inaugural RSI-India cohort, an academic program for high school students modeled after the Research Science Institute program co-sponsored by MIT and the Center for Excellence in Education. Competing against other Indian students who had perfect scores on their board exams and SATs, he didn’t expect to get in, but the program valued the practical research experience he was able to pursue thanks to the knowledge he gained from his external studies.

“None of it would have happened without MIT OpenCourseWare,” he says. “It’s basically letting curiosity get the better of us. If everybody does that, we’d have a better scientific community.”

Should you have to think twice before posting a protest flyer to your Instagram story? Or feel pressure to delete that bald JD Vance meme that you shared? Now imagine that you could get kicked out of the country—potentially losing your job or education—based on the Trump administration’s dislike of your views on social media. 

That threat to free expression and dissent is happening now, but we won’t let it stand. 

"...they're not just targeting individuals—they're targeting the very idea of freedom itself."

The Electronic Frontier Foundation and co-counsel are representing the United Automobile Workers (UAW), Communications Workers of America (CWA), and American Federation of Teachers (AFT) in a lawsuit against the U.S. State Department and Department of Homeland Security for their viewpoint-based surveillance and suppression of noncitizens’ First Amendment-protected speech online.  The lawsuit asks a federal court to stop the government’s unconstitutional surveillance program, which has silenced citizens and noncitizens alike. It has even hindered unions’ ability to associate with their members. 

• Press Release
• Full Complaint in UAW v. State Department 

"When they spy on, silence, and fire union members for speaking out, they're not just targeting individuals—they're targeting the very idea of freedom itself,” said UAW President Shawn Fain. 

The Trump administration has built this mass surveillance program to monitor the constitutionally protected online speech of noncitizens who are lawfully present in the U.S. The program uses AI and automated technologies to scour social media and other online platforms to identify and punish individuals who express viewpoints the government considers "hostile" to "our culture" and "our civilization".  But make no mistake: no one should be forced to conform to the views of the state. 

The Foundation of Democracy 

Your free expression and privacy are fundamental human rights, and democracy crumbles without them. We have an opportunity to fight back, but we need you.  EFF’s team of lawyers, activists, researchers, and technologists have been on a mission to protect your freedom online since 1990, and we’re just getting started.

Donate and become a member of EFF today. Your support helps protect crucial rights, online and off, for everyone.

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Viewpoint-based Online Surveillance of Permanent Residents and Visa Holders Violates First Amendment, Lawsuit Argues

NEW YORK—The United Automobile Workers (UAW), Communications Workers of America (CWA), and American Federation of Teachers (AFT) filed a lawsuit today against the Departments of State and Homeland Security for their viewpoint-based surveillance and suppression of protected expression online. The complaint asks a federal court to stop this unconstitutional surveillance program, which has silenced and frightened both citizens and noncitizens, and hampered the ability of the unions to associate with their members and potential members. The case is titled UAW v. State Department.

Since taking power, the Trump administration has created a mass surveillance program to monitor constitutionally protected speech by noncitizens lawfully present in the U.S. Using AI and other automated technologies, the program surveils the social media accounts of visa holders with the goal of identifying and punishing those who express viewpoints the government doesn't like. This has been paired with a public intimidation campaign, silencing not just noncitizens with immigration status, but also the families, coworkers, and friends with whom their lives are integrated.

As detailed in the complaint, when asked in a survey if they had changed their social media activity as a result of the Trump administration's ideological online surveillance program, over 60 percent of responding UAW members and over 30 percent of responding CWA members who were aware of the program said they had. Among noncitizens, these numbers were even higher. Of respondents aware of the program, over 80 percent of UAW members who were not U.S. citizens and over 40 percent of CWA members who were not U.S. citizens said they had changed their activity online.

Individual union members reported refraining from posting, refraining from sharing union content, deleting posts, and deleting entire accounts in response to the ideological online surveillance program. Criticism of the Trump administration or its policies was the most common type of content respondents reported changing their social media activity around. Many members also reported altering their offline union activity in response to the program, including avoiding being publicly identified as part of the unions and reducing their participation in rallies and protests. One member even said they declined to report a wage theft claim due to fears arising from the surveillance program.

Represented by the Electronic Frontier Foundation (EFF), Muslim Advocates (MA), and the Media Freedom & Information Access Clinic (MFIA), the UAW, CWA, and AFT seek to halt the program that affects thousands of their members individually and has harmed the ability of the unions to organize, represent, and recruit members. The lawsuit argues that the viewpoint-based online surveillance program violates the First Amendment and the Administrative Procedure Act.

"The Trump administration's use of surveillance to track and intimidate UAW members is a direct assault on the First Amendment—and an attack on every working person in this country," said UAW President Shawn Fain. "When they spy on, silence, and fire union members for speaking out, they're not just targeting individuals—they're targeting the very idea of freedom itself. The right to protest, to organize, to speak without fear—that's the foundation of American democracy. If they can come for UAW members at our worksites, they can come for any one of us tomorrow. And we will not stand by and let that happen."

"Every worker should be alarmed by the Trump administration’s online surveillance program," said CWA President Claude Cummings Jr. "The labor movement is built on our freedoms under the First Amendment to speak and assemble without fear retaliation by the government. The unconstitutional Challenged Surveillance Program threatens those freedoms and explicitly targets those who are critical of the administration and its policies. This policy interferes with CWA members’ ability to express their points of view online and organize to improve their working conditions."

"Free speech is the foundation of democracy in America," said AFT President Randi Weingarten. "The Trump administration has rejected that core constitutional right and now says only speech it agrees with is permitted—and that it will silence those who disagree. This suit exposes the online surveillance tools and other cyber tactics never envisioned by the founders to enforce compliance with the administration’s views. It details the direct harms on both the target of these attacks and the chilling effect on all those we represent and teach."

"Using a variety of AI and automated tools, the government can now conduct viewpoint-based surveillance and analysis on a scale that was never possible with human review alone," said EFF Staff Attorney Lisa Femia. "The scale of this spying is matched by an equally massive chilling effect on free speech."

"The administration is hunting online for an ever-growing list of disfavored viewpoints," said Golnaz Fakhimi, Legal Director of Muslim Advocates. "Its goal is clear: consolidate authoritarian power by crushing dissent, starting with noncitizens, but certainly not ending there. This urgent lawsuit aims to put a stop to this power grab and defend First Amendment freedoms crucial to a pluralistic and democratic society."

"This case goes to the heart of the First Amendment," said Anthony Cosentino, a student in the Media Freedom & Information Access Clinic. "The government can’t go after people for saying things it doesn’t like. The current administration has ignored that principle, developing a vast surveillance apparatus to find and punish people for their constitutionally protected speech. It is an extraordinary abuse of power, creating a climate of fear not seen in this country since the McCarthy era, especially on college campuses. Our laws and Constitution will not allow it."

For the complaint: https://www.eff.org/document/uaw-v-dos-complaint

For more about the litigation: https://eff.org/cases/united-auto-workers-v-us-department-state

Contacts:
Electronic Frontier Foundation: press@eff.org
Muslim Advocates: golnaz@muslimadvocates.org

CNN has a great piece about how cryptocurrency ATMs are used to scam people out of their money. The fees are usurious, and they’re a common place for scammers to send victims to buy cryptocurrency for them. The companies behind the ATMs, at best, do not care about the harm they cause; the profits are just too good.

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.