Nature Climate Change, Published online: 12 November 2025; doi:10.1038/s41558-025-02481-0

The authors combine light intensity data with eddy covariance observations from 86 sites to show that artificial light at night increases ecosystem respiration and alters carbon exchange, with impacts shaped by diel cycles and seasonal dynamics.

This is why AIs are not ready to be personal assistants:

A new attack called ‘CometJacking’ exploits URL parameters to pass to Perplexity’s Comet AI browser hidden instructions that allow access to sensitive data from connected services, like email and calendar.

In a realistic scenario, no credentials or user interaction are required and a threat actor can leverage the attack by simply exposing a maliciously crafted URL to targeted users.

[…]

CometJacking is a prompt-injection attack where the query string processed by the Comet AI browser contains malicious instructions added using the ‘collection’ parameter of the URL...

Democratic election wins last week reignited arguments on how — or if — candidates should discuss climate change on the campaign trail.

The move comes amid speculation that DOE is preparing to issue emergency orders directing some retiring coal plants to stay open.

The move by Qcells came as U.S. authorities hold imported solar components to determine if they violate a slave labor law.

Colorado communities say local governments have a right to sue on behalf of residents, citing opioid and asbestos litigation.

Twelve states say the administration has put unreasonable conditions on their ability to secure grants to pay disaster responders.

Lawmakers are heading to Washington instead of Brazil to vote on reopening the government.

Australia and Turkey both want to host COP31, and if neither back down, the talks will be held in Germany.

Brazil's then-President Jair Bolsonaro falsely accused the Oscar-winning actor of funding nonprofit groups that Bolsonaro alleged were partly responsible for setting fires in the Amazon.

New York regulators signed an agreement allowing a Finger Lakes plant to keep operating despite opposition from environmental groups.

The move covers exports of gallium, germanium and antimony, which are used in advanced semiconductors and in military applications.

What can we learn about human intelligence by studying how machines “think?” Can we better understand ourselves if we better understand the artificial intelligence systems that are becoming a more significant part of our everyday lives?

These questions may be deeply philosophical, but for Phillip Isola, finding the answers is as much about computation as it is about cogitation.

Isola, the newly tenured associate professor in the Department of Electrical Engineering and Computer Science (EECS), studies the fundamental mechanisms involved in human-like intelligence from a computational perspective.

While understanding intelligence is the overarching goal, his work focuses mainly on computer vision and machine learning. Isola is particularly interested in exploring how intelligence emerges in AI models, how these models learn to represent the world around them, and what their “brains” share with the brains of their human creators.

“I see all the different kinds of intelligence as having a lot of commonalities, and I’d like to understand those commonalities. What is it that all animals, humans, and AIs have in common?” says Isola, who is also a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL).

To Isola, a better scientific understanding of the intelligence that AI agents possess will help the world integrate them safely and effectively into society, maximizing their potential to benefit humanity.

Asking questions

Isola began pondering scientific questions at a young age.

While growing up in San Francisco, he and his father frequently went hiking along the northern California coastline or camping around Point Reyes and in the hills of Marin County.

He was fascinated by geological processes and often wondered what made the natural world work. In school, Isola was driven by an insatiable curiosity, and while he gravitated toward technical subjects like math and science, there was no limit to what he wanted to learn.

Not entirely sure what to study as an undergraduate at Yale University, Isola dabbled until he came upon cognitive sciences.

“My earlier interest had been with nature — how the world works. But then I realized that the brain was even more interesting, and more complex than even the formation of the planets. Now, I wanted to know what makes us tick,” he says.

As a first-year student, he started working in the lab of his cognitive sciences professor and soon-to-be mentor, Brian Scholl, a member of the Yale Department of Psychology. He remained in that lab throughout his time as an undergraduate.

After spending a gap year working with some childhood friends at an indie video game company, Isola was ready to dive back into the complex world of the human brain. He enrolled in the graduate program in brain and cognitive sciences at MIT.

“Grad school was where I felt like I finally found my place. I had a lot of great experiences at Yale and in other phases of my life, but when I got to MIT, I realized this was the work I really loved and these are the people who think similarly to me,” he says.

Isola credits his PhD advisor, Ted Adelson, the John and Dorothy Wilson Professor of Vision Science, as a major influence on his future path. He was inspired by Adelson’s focus on understanding fundamental principles, rather than only chasing new engineering benchmarks, which are formalized tests used to measure the performance of a system.

A computational perspective

At MIT, Isola’s research drifted toward computer science and artificial intelligence.

“I still loved all those questions from cognitive sciences, but I felt I could make more progress on some of those questions if I came at it from a purely computational perspective,” he says.

His thesis was focused on perceptual grouping, which involves the mechanisms people and machines use to organize discrete parts of an image as a single, coherent object.

If machines can learn perceptual groupings on their own, that could enable AI systems to recognize objects without human intervention. This type of self-supervised learning has applications in areas such autonomous vehicles, medical imaging, robotics, and automatic language translation.

After graduating from MIT, Isola completed a postdoc at the University of California at Berkeley so he could broaden his perspectives by working in a lab solely focused on computer science.

“That experience helped my work become a lot more impactful because I learned to balance understanding fundamental, abstract principles of intelligence with the pursuit of some more concrete benchmarks,” Isola recalls.

At Berkeley, he developed image-to-image translation frameworks, an early form of generative AI model that could turn a sketch into a photographic image, for instance, or turn a black-and-white photo into a color one.

He entered the academic job market and accepted a faculty position at MIT, but Isola deferred for a year to work at a then-small startup called OpenAI.

“It was a nonprofit, and I liked the idealistic mission at that time. They were really good at reinforcement learning, and I thought that seemed like an important topic to learn more about,” he says.

He enjoyed working in a lab with so much scientific freedom, but after a year Isola was ready to return to MIT and start his own research group.

Studying human-like intelligence

Running a research lab instantly appealed to him.

“I really love the early stage of an idea. I feel like I am a sort of startup incubator where I am constantly able to do new things and learn new things,” he says.

Building on his interest in cognitive sciences and desire to understand the human brain, his group studies the fundamental computations involved in the human-like intelligence that emerges in machines.

One primary focus is representation learning, or the ability of humans and machines to represent and perceive the sensory world around them.

In recent work, he and his collaborators observed that the many varied types of machine-learning models, from LLMs to computer vision models to audio models, seem to represent the world in similar ways.

These models are designed to do vastly different tasks, but there are many similarities in their architectures. And as they get bigger and are trained on more data, their internal structures become more alike.

This led Isola and his team to introduce the Platonic Representation Hypothesis (drawing its name from the Greek philosopher Plato) which says that the representations all these models learn are converging toward a shared, underlying representation of reality.

“Language, images, sound — all of these are different shadows on the wall from which you can infer that there is some kind of underlying physical process — some kind of causal reality — out there. If you train models on all these different types of data, they should converge on that world model in the end,” Isola says.

A related area his team studies is self-supervised learning. This involves the ways in which AI models learn to group related pixels in an image or words in a sentence without having labeled examples to learn from.

Because data are expensive and labels are limited, using only labeled data to train models could hold back the capabilities of AI systems. With self-supervised learning, the goal is to develop models that can come up with an accurate internal representation of the world on their own.

“If you can come up with a good representation of the world, that should make subsequent problem solving easier,” he explains.

The focus of Isola’s research is more about finding something new and surprising than about building complex systems that can outdo the latest machine-learning benchmarks.

While this approach has yielded much success in uncovering innovative techniques and architectures, it means the work sometimes lacks a concrete end goal, which can lead to challenges.

For instance, keeping a team aligned and the funding flowing can be difficult when the lab is focused on searching for unexpected results, he says.

“In a sense, we are always working in the dark. It is high-risk and high-reward work. Every once in while, we find some kernel of truth that is new and surprising,” he says.

In addition to pursuing knowledge, Isola is passionate about imparting knowledge to the next generation of scientists and engineers. Among his favorite courses to teach is 6.7960 (Deep Learning), which he and several other MIT faculty members launched four years ago.

The class has seen exponential growth, from 30 students in its initial offering to more than 700 this fall.

And while the popularity of AI means there is no shortage of interested students, the speed at which the field moves can make it difficult to separate the hype from truly significant advances.

“I tell the students they have to take everything we say in the class with a grain of salt. Maybe in a few years we’ll tell them something different. We are really on the edge of knowledge with this course,” he says.

But Isola also emphasizes to students that, for all the hype surrounding the latest AI models, intelligent machines are far simpler than most people suspect.

“Human ingenuity, creativity, and emotions — many people believe these can never be modeled. That might turn out to be true, but I think intelligence is fairly simple once we understand it,” he says.

Even though his current work focuses on deep-learning models, Isola is still fascinated by the complexity of the human brain and continues to collaborate with researchers who study cognitive sciences.

All the while, he has remained captivated by the beauty of the natural world that inspired his first interest in science.

Although he has less time for hobbies these days, Isola enjoys hiking and backpacking in the mountains or on Cape Cod, skiing and kayaking, or finding scenic places to spend time when he travels for scientific conferences.

And while he looks forward to exploring new questions in his lab at MIT, Isola can’t help but contemplate how the role of intelligent machines might change the course of his work.

He believes that artificial general intelligence (AGI), or the point where machines can learn and apply their knowledge as well as humans can, is not that far off.

“I don’t think AIs will just do everything for us and we’ll go and enjoy life at the beach. I think there is going to be this coexistence between smart machines and humans who still have a lot of agency and control. Now, I’m thinking about the interesting questions and applications once that happens. How can I help the world in this post-AGI future? I don’t have any answers yet, but it’s on my mind,” he says.

Nature Climate Change, Published online: 11 November 2025; doi:10.1038/s41558-025-02483-y

Dissolved oxygen concentrations are expected to decline with rising water temperatures under climate change. This study projects declining oxygen levels for most rivers globally and an increase in hypoxic days by the end of the century, with implications for ecosystem and fish health.

Danna Freedman is seeking the early adopters.

She is the faculty director of the nascent MIT Quantum Initiative, or QMIT. In this new role, Freedman is giving shape to an ambitious, Institute-wide effort to apply quantum breakthroughs to the most consequential challenges in science, technology, industry, and national security.

The interdisciplinary endeavor, the newest of MIT President Sally Kornbluth’s strategic initiatives, will bring together MIT researchers and domain experts from a range of industries to identify and tackle practical challenges wherever quantum solutions could achieve the greatest impact.

“We’ve already seen how the breadth of progress in quantum has created opportunities to rethink the future of security and encryption, imagine new modes of navigation, and even measure gravitational waves more precisely to observe the cosmos in an entirely new way,” says Freedman, the Frederick George Keyes Professor of Chemistry. “What can we do next? We’re investing in the promise of quantum, and where the legacy will be in 20 years.”

QMIT — the name is a nod to the “qubit,” the basic unit of quantum information — will formally launch on Dec. 8 with an all-day event on campus. Over time, the initiative plans to establish a physical home in the heart of campus for academic, public, and corporate engagement with state-of-the-art integrated quantum systems. Beyond MIT’s campus, QMIT will also work closely with the U.S. government and MIT Lincoln Laboratory, applying the lab’s capabilities in quantum hardware development, systems engineering, and rapid prototyping to national security priorities.

“The MIT Quantum Initiative seizes a timely opportunity in service to the nation’s scientific, economic, and technological competitiveness,” says Ian A. Waitz, MIT’s vice president for research. “With quantum capabilities approaching an inflection point, QMIT will engage students and researchers across all our schools and the college, as well as companies around the world, in thinking about what a step change in sensing and computational power will mean for a wide range of fields. Incredible opportunities exist in health and life sciences, fundamental physics research, cybersecurity, materials science, sensing the world around us, and more.”

Identifying the right questions

Quantum phenomena are as foundational to our world as light or gravity. At an extremely small scale, the interactions of atoms and subatomic particles are controlled by a different set of rules than the physical laws of the macro-sized world. These rules are called quantum mechanics.

“Quantum, in a sense, is what underlies everything,” says Freedman.

By leveraging quantum properties, quantum devices can process information at incredible speed to solve complex problems that aren’t feasible on classical supercomputers, and to enable ultraprecise sensing and measurement. Those improvements in speed and precision will become most powerful when optimized in relation to specific use cases, and as part of a complete quantum system. QMIT will focus on collaboration across domains to co-develop quantum tools, such as computers, sensors, networks, simulations, and algorithms, alongside the intended users of these systems.

As it develops, QMIT will be organized into programmatic pillars led by top researchers in quantum including Paola Cappellaro, Ford Professor of Engineering and professor of nuclear science and engineering and of physics; Isaac Chuang, Julius A. Stratton Professor in Electrical Engineering and Physics; Pablo Jarillo-Herrero, Cecil and Ida Green Professor of Physics; William Oliver, Henry Ellis Warren (1894) Professor of Electrical Engineering and Computer Science and professor of physics; Vladan Vuletić, Lester Wolfe Professor of Physics; and Jonilyn Yoder, associate leader of the Quantum-Enabled Computation Group at MIT Lincoln Laboratory.

While supporting the core of quantum research in physics, engineering, mathematics, and computer science, QMIT promises to expand the community at its frontiers, into astronomy, biology, chemistry, materials science, and medicine.

“If you provide a foundation that somebody can integrate with, that accelerates progress a lot,” says Freedman. “Perhaps we want to figure out how a quantum simulator we’ve built can model photosynthesis, if that’s the right question — or maybe the right question is to study 10 failed catalysts to see why they failed.”

“We are going to figure out what real problems exist that we could approach with quantum tools, and work toward them in the next five years,” she adds. “We are going to change the forward momentum of quantum in a way that supports impact.”

The MIT Quantum Initiative will be administratively housed in the Research Laboratory of Electronics (RLE), with support from the Office of the Vice President for Research (VPR) and the Office of Innovation and Strategy.

QMIT is a natural expansion of MIT’s Center for Quantum Engineering (CQE), a research powerhouse that engages more than 80 principal investigators across the MIT campus and Lincoln Laboratory to accelerate the practical application of quantum technologies.

“CQE has cultivated a tremendously strong ecosystem of students and researchers, engaging with U.S. government sponsors and industry collaborators, including through the popular Quantum Annual Research Conference (QuARC) and professional development classes,” says Marc Baldo, the Dugald C. Jackson Professor in Electrical Engineering and director of RLE.

“With the backing of former vice president for research Maria Zuber, former Lincoln Lab director Eric Evans, and Marc Baldo, we launched CQE and its industry membership group in 2019 to help bridge MIT’s research efforts in quantum science and engineering,” says Oliver, CQE’s director, who also spent 20 years at Lincoln Laboratory, most recently as a Laboratory Fellow. “We have an important opportunity now to deepen our commitment to quantum research and education, and especially in engaging students from across the Institute in thinking about how to leverage quantum science and engineering to solve hard problems.”

Two years ago, Peter Fisher, the Thomas A. Frank (1977) Professor of Physics, in his role as associate vice president for research computing and data, assembled a faculty group led by Cappellaro and involving Baldo, Oliver, Freedman, and others, to begin to build an initiative that would span the entire Institute. Now, capitalizing on CQE’s success, Oliver will lead the new MIT Quantum Initiative’s quantum computing pillar, which will broaden the work of CQE into a larger effort that focuses on quantum computing, industry engagement, and connecting with end users.

The “MIT-hard” problem

QMIT will build upon the Institute’s historic leadership in quantum science and engineering. In the spring of 1981, MIT hosted the first Physics of Computation Conference at the Endicott House, bringing together nearly 50 physics and computing researchers to consider the practical promise of quantum — an intellectual moment that is now widely regarded as the kickoff of the second quantum revolution. (The first was the fundamental articulation of quantum mechanics 100 years ago.)

Today, research in quantum science and engineering produces a steady stream of “firsts” in the lab and a growing number of startup companies.

In collaboration with partners in industry and government, MIT researchers develop advances in areas like quantum sensing, which involves the use of atomic-scale systems to measure certain properties, like distance and acceleration, with extreme precision. Quantum sensing could be used in applications like brain imaging devices that capture more detail, or air traffic control systems with greater positional accuracy.

Another key area of research is quantum simulation, which uses the power of quantum computers to accurately emulate complex systems. This could fuel the discovery of new materials for energy-efficient electronics or streamline the identification of promising molecules for drug development.

“Historically, when we think about the most well-articulated challenges that quantum will solve,” Freedman says, “the best ones have come from inside of MIT. We’re open to technological solutions to problems, and nontraditional approaches to science. In many respects, we are the early adopters.”

But she also draws a sharp distinction between blue-sky thinking about what quantum might do, and the deeply technical, deeply collaborative work of actually drawing the roadmap. “That’s the ‘MIT-hard’ problem,” she says.

The QMIT launch event on Dec. 8 will feature talks and discussions featuring MIT faculty, including Nobel laureates and industry leaders.

Encryption can protect data at rest and data in transit, but does nothing for data in use. What we have are secure enclaves. I’ve written about this before:

Almost all cloud services have to perform some computation on our data. Even the simplest storage provider has code to copy bytes from an internal storage system and deliver them to the user. End-to-end encryption is sufficient in such a narrow context. But often we want our cloud providers to be able to perform computation on our raw data: search, analysis, AI model training or fine-tuning, and more. Without expensive, esoteric techniques, such as secure multiparty computation protocols or homomorphic encryption techniques that can perform calculations on encrypted data, cloud servers require access to the unencrypted data to do anything useful...

Oil companies are once again pushing the Supreme Court to stop a flood of cases that could force them to pay billions.

Scientists from leading institutions offered evidence of worsening impacts as leaders meet for the COP30 climate summit.

The analysis also examines water use and offers a road map for lower emissions through states with considerable renewable resources.

A new study involving risk modelers shows that mangrove forests protect Florida's coast and could help reduce property-insurance rates.