During his confirmation hearing for HHS secretary, Robert F. Kennedy Jr. leaned in on his environmental credentials but refused to denounce President Donald Trump for calling climate change a hoax.

The administration’s request to delay a hearing was rejected, meaning it will have to defend a climate rule it wants to dismantle.

GOP lawmakers resisted the president’s suggestion FEMA might need to “go away,” but they support changes to the disaster response agency.

The Trump administration blamed increased vehicle prices on a Biden-era fuel economy rule. “It’s just way more complicated than that,” said one analyst.

The Metropolitan Water District of Southern California replaced Adel Hagekhalil with Deven Upadhyay after an investigation into claims of harassment.

Democratic legislators pushed for answers on Gov. Kathy Hochul’s delay of a climate funding rule and her $1 billion proposed one-time funding.

Lawmakers raised concerns about the speed of implementing state rules on transitioning heavy-duty vehicles off fossil fuels.

Efforts to improve the program have been limited by a 2012 law banning seed sharing, which farmers did to cut their production cost.

They worry President Donald Trump will back right-wing politicians who favor aggressive development in the Amazon.

When it comes to water use, most of us think of the water we drink. But industrial uses for things like manufacturing account for billions of gallons of water each day. For instance, making a single iPhone, by one estimate, requires more than 3,000 gallons.

Gradiant is working to reduce the world’s industrial water footprint. Founded by a team from MIT, Gradiant offers water recycling, treatment, and purification solutions to some of the largest companies on Earth, including Coca Cola, Tesla, and the Taiwan Semiconductor Manufacturing Company. By serving as an end-to-end water company, Gradiant says it helps companies reuse 2 billion gallons of water each day and saves another 2 billion gallons of fresh water from being withdrawn.

The company’s mission is to preserve water for generations to come in the face of rising global demand.

“We work on both ends of the water spectrum,” Gradiant co-founder and CEO Anurag Bajpayee SM ’08, PhD ’12 says. “We work with ultracontaminated water, and we can also provide ultrapure water for use in areas like chip fabrication. Our specialty is in the extreme water challenges that can’t be solved with traditional technologies.”

For each customer, Gradiant builds tailored water treatment solutions that combine chemical treatments with membrane filtration and biological process technologies, leveraging a portfolio of patents to drastically cut water usage and waste.

“Before Gradiant, 40 million liters of water would be used in the chip-making process. It would all be contaminated and treated, and maybe 30 percent would be reused,” explains Gradiant co-founder and COO Prakash Govindan PhD ’12. “We have the technology to recycle, in some cases, 99 percent of the water. Now, instead of consuming 40 million liters, chipmakers only need to consume 400,000 liters, which is a huge shift in the water footprint of that industry. And this is not just with semiconductors. We’ve done this in food and beverage, we’ve done this in renewable energy, we’ve done this in pharmaceutical drug production, and several other areas.”

Learning the value of water

Govindan grew up in a part of India that experienced a years-long drought beginning when he was 10. Without tap water, one of Govindan’s chores was to haul water up the stairs of his apartment complex each time a truck delivered it.

“However much water my brother and I could carry was how much we had for the week,” Govindan recalls. “I learned the value of water the hard way.”

Govindan attended the Indian Institute of Technology as an undergraduate, and when he came to MIT for his PhD, he sought out the groups working on water challenges. He began working on a water treatment method called carrier gas extraction for his PhD under Gradiant co-founder and MIT Professor John Lienhard.

Bajpayee also worked on water treatment methods at MIT, and after brief stints as postdocs at MIT, he and Govindan licensed their work and founded Gradiant.

Carrier gas extraction became Gradiant’s first proprietary technology when the company launched in 2013. The founders began by treating wastewater created by oil and gas wells, landing their first partner in a Texas company. But Gradiant gradually expanded to solving water challenges in power generation, mining, textiles, and refineries. Then the founders noticed opportunities in industries like electronics, semiconductors, food and beverage, and pharmaceuticals. Today, oil and gas wastewater treatment makes up a small percentage of Gradiant’s work.

As the company expanded, it added technologies to its portfolio, patenting new water treatment methods around reverse osmosis, selective contaminant extraction, and free radical oxidation. Gradiant has also created a digital system that uses AI to measure, predict, and control water treatment facilities.

“The advantage Gradiant has over every other water company is that R&D is in our DNA,” Govindan says, noting Gradiant has a world-class research lab at its headquarters in Boston. “At MIT, we learned how to do cutting-edge technology development, and we never let go of that.”

The founders compare their suite of technologies to LEGO bricks they can mix and match depending on a customer’s water needs. Gradiant has built more than 2,500 of these end-to-end systems for customers around the world.

“Our customers aren’t water companies; they are industrial clients like semiconductor manufacturers, drug companies, and food and beverage companies,” Bajpayee says. “They aren’t about to start operating a water treatment plant. They look at us as their water partner who can take care of the whole water problem.”

Continuing innovation

The founders say Gradiant has been roughly doubling its revenue each year over the last five years, and it’s continuing to add technologies to its platform. For instance, Gradiant recently developed a critical minerals recovery solution to extract materials like lithium and nickel from customers’ wastewater, which could expand access to critical materials essential to the production of batteries and other products.

“If we can extract lithium from brine water in an environmentally and economically feasible way, the U.S. can meet all of its lithium needs from within the U.S.,” Bajpayee says. “What’s preventing large-scale extraction of lithium from brine is technology, and we believe what we have now deployed will open the floodgates for direct lithium extraction and completely revolutionized the industry.”

The company has also validated a method for eliminating PFAS — so-called toxic “forever chemicals” — in a pilot project with a leading U.S. semiconductor manufacturer. In the near future, it hopes to bring that solution to municipal water treatment plants to protect cities.

At the heart of Gradiant’s innovation is the founders’ belief that industrial activity doesn’t have to deplete one of the world’s most vital resources.

“Ever since the industrial revolution, we’ve been taking from nature,” Bajpayee says. “By treating and recycling water, by reducing water consumption and making industry highly water efficient, we have this unique opportunity to turn the clock back and give nature water back. If that’s your driver, you can’t choose not to innovate.”

Highly energetic explosions in the sky are commonly attributed to gamma-ray bursts. We now understand that these bursts originate from either the merger of two neutron stars or the collapse of a massive star. In these scenarios, a newborn black hole is formed, emitting a jet that travels at nearly the speed of light. When these jets are directed toward Earth, we can observe them from vast distances — sometimes billions of light-years away — due to a relativistic effect known as Doppler boosting. Over the past decade, thousands of such gamma-ray bursts have been detected.

Since its launch in 2024, the Einstein Probe — an X-ray space telescope developed by the Chinese Academy of Sciences (CAS) in partnership with European Space Agency (ESA) and the Max Planck Institute for Extraterrestrial Physics — has been scanning the skies looking for energetic explosions, and in April the telescope observed an unusual event designated as EP240408A. Now an international team of astronomers, including Dheeraj Pasham from MIT, Igor Andreoni from University of North Carolina at Chapel Hill, and Brendan O’Connor from Carnegie Mellon University, and others have investigated this explosion using a slew of ground-based and space-based telescopes, including NuSTAR, Swift, Gemini, Keck, DECam, VLA, ATCA, and NICER, which was developed in collaboration with MIT. 

An open-access report of their findings, published Jan. 27 in The Astrophysical Journal Letters, indicates that the characteristics of this explosion do not match those of typical gamma-ray bursts. Instead, it may represent a rare new class of powerful cosmic explosion — a jetted tidal disruption event, which occurs when a supermassive black hole tears apart a star. 

“NICER’s ability to steer to pretty much any part of the sky and monitor for weeks has been instrumental in our understanding of these unusual cosmic explosions,” says Pasham, a research scientist at the MIT Kavli Institute for Astrophysics and Space Research.

While a jetted tidal disruption event is plausible, the researchers say the lack of radio emissions from this jet is puzzling. O’Connor surmises, “EP240408a ticks some of the boxes for several different kinds of phenomena, but it doesn’t tick all the boxes for anything. In particular, the short duration and high luminosity are hard to explain in other scenarios. The alternative is that we are seeing something entirely new!”

According to Pasham, the Einstein Probe is just beginning to scratch the surface of what seems possible. “I’m excited to chase the next weird explosion from the Einstein Probe”, he says, echoing astronomers worldwide who look forward to the prospect of discovering more unusual explosions from the farthest reaches of the cosmos.

The National Academy of Sciences (NAS) recently announced that MIT Associate Professor Evelina Fedorenko will receive a 2025 Troland Research Award for her groundbreaking contributions toward understanding the language network in the human brain.

The Troland Research Award is given annually to recognize unusual achievement by early-career researchers within the broad spectrum of experimental psychology.

Fedorenko, an associate professor of brain and cognitive sciences and a McGovern Institute for Brain Research investigator, is interested in how minds and brains create language. Her lab is unpacking the internal architecture of the brain’s language system and exploring the relationship between language and various cognitive, perceptual, and motor systems. Her novel methods combine precise measures of an individual’s brain organization with innovative computational modeling to make fundamental discoveries about the computations that underlie the uniquely human ability for language.

Fedorenko has shown that the language network is selective for language processing over diverse non-linguistic processes that have been argued to share computational demands with language, such as math, music, and social reasoning. Her work has also demonstrated that syntactic processing is not localized to a particular region within the language network, and every brain region that responds to syntactic processing is at least as sensitive to word meanings.

She has also shown that representations from neural network language models, such as ChatGPT, are similar to those in the human language brain areas. Fedorenko also highlighted that although language models can master linguistic rules and patterns, they are less effective at using language in real-world situations. In the human brain, that kind of functional competence is distinct from formal language competence, she says, requiring not just language-processing circuits but also brain areas that store knowledge of the world, reason, and interpret social interactions. Contrary to a prominent view that language is essential for thinking, Fedorenko argues that language is not the medium of thought and is primarily a tool for communication.

Ultimately, Fedorenko’s cutting-edge work is uncovering the computations and representations that fuel language processing in the brain. She will receive the Troland Award this April, during the annual meeting of the NAS in Washington.

If you’ve watched cartoons like Tom and Jerry, you’ll recognize a common theme: An elusive target avoids his formidable adversary. This game of “cat-and-mouse” — whether literal or otherwise — involves pursuing something that ever-so-narrowly escapes you at each try.

In a similar way, evading persistent hackers is a continuous challenge for cybersecurity teams. Keeping them chasing what’s just out of reach, MIT researchers are working on an AI approach called “artificial adversarial intelligence” that mimics attackers of a device or network to test network defenses before real attacks happen. Other AI-based defensive measures help engineers further fortify their systems to avoid ransomware, data theft, or other hacks.

Here, Una-May O'Reilly, an MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) principal investigator who leads the Anyscale Learning For All Group (ALFA), discusses how artificial adversarial intelligence protects us from cyber threats.

Q: In what ways can artificial adversarial intelligence play the role of a cyber attacker, and how does artificial adversarial intelligence portray a cyber defender?

A: Cyber attackers exist along a competence spectrum. At the lowest end, there are so-called script-kiddies, or threat actors who spray well-known exploits and malware in the hopes of finding some network or device that hasn't practiced good cyber hygiene. In the middle are cyber mercenaries who are better-resourced and organized to prey upon enterprises with ransomware or extortion. And, at the high end, there are groups that are sometimes state-supported, which can launch the most difficult-to-detect "advanced persistent threats" (or APTs).

Think of the specialized, nefarious intelligence that these attackers marshal — that's adversarial intelligence. The attackers make very technical tools that let them hack into code, they choose the right tool for their target, and their attacks have multiple steps. At each step, they learn something, integrate it into their situational awareness, and then make a decision on what to do next. For the sophisticated APTs, they may strategically pick their target, and devise a slow and low-visibility plan that is so subtle that its implementation escapes our defensive shields. They can even plan deceptive evidence pointing to another hacker! 

My research goal is to replicate this specific kind of offensive or attacking intelligence, intelligence that is adversarially-oriented (intelligence that human threat actors rely upon). I use AI and machine learning to design cyber agents and model the adversarial behavior of human attackers. I also model the learning and adaptation that characterizes cyber arms races.

I should also note that cyber defenses are pretty complicated. They've evolved their complexity in response to escalating attack capabilities. These defense systems involve designing detectors, processing system logs, triggering appropriate alerts, and then triaging them into incident response systems. They have to be constantly alert to defend a very big attack surface that is hard to track and very dynamic. On this other side of attacker-versus-defender competition, my team and I also invent AI in the service of these different defensive fronts. 

Another thing stands out about adversarial intelligence: Both Tom and Jerry are able to learn from competing with one another! Their skills sharpen and they lock into an arms race. One gets better, then the other, to save his skin, gets better too. This tit-for-tat improvement goes onwards and upwards! We work to replicate cyber versions of these arms races.

Q: What are some examples in our everyday lives where artificial adversarial intelligence has kept us safe? How can we use adversarial intelligence agents to stay ahead of threat actors?

A: Machine learning has been used in many ways to ensure cybersecurity. There are all kinds of detectors that filter out threats. They are tuned to anomalous behavior and to recognizable kinds of malware, for example. There are AI-enabled triage systems. Some of the spam protection tools right there on your cell phone are AI-enabled!

With my team, I design AI-enabled cyber attackers that can do what threat actors do. We invent AI to give our cyber agents expert computer skills and programming knowledge, to make them capable of processing all sorts of cyber knowledge, plan attack steps, and to make informed decisions within a campaign.

Adversarially intelligent agents (like our AI cyber attackers) can be used as practice when testing network defenses. A lot of effort goes into checking a network's robustness to attack, and AI is able to help with that. Additionally, when we add machine learning to our agents, and to our defenses, they play out an arms race we can inspect, analyze, and use to anticipate what countermeasures may be used when we take measures to defend ourselves.

Q: What new risks are they adapting to, and how do they do so?

A: There never seems to be an end to new software being released and new configurations of systems being engineered. With every release, there are vulnerabilities an attacker can target. These may be examples of weaknesses in code that are already documented, or they may be novel. 

New configurations pose the risk of errors or new ways to be attacked. We didn't imagine ransomware when we were dealing with denial-of-service attacks. Now we're juggling cyber espionage and ransomware with IP [intellectual property] theft. All our critical infrastructure, including telecom networks and financial, health care, municipal, energy, and water systems, are targets. 

Fortunately, a lot of effort is being devoted to defending critical infrastructure. We will need to translate that to AI-based products and services that automate some of those efforts. And, of course, to keep designing smarter and smarter adversarial agents to keep us on our toes, or help us practice defending our cyber assets.

Imagine a boombox that tracks your every move and suggests music to match your personal dance style. That’s the idea behind “Be the Beat,” one of several projects from MIT course 4.043/4.044 (Interaction Intelligence), taught by Marcelo Coelho in the Department of Architecture, that were presented at the 38th annual NeurIPS (Neural Information Processing Systems) conference in December 2024. With over 16,000 attendees converging in Vancouver, NeurIPS is a competitive and prestigious conference dedicated to research and science in the field of artificial intelligence and machine learning, and a premier venue for showcasing cutting-edge developments.

The course investigates the emerging field of large language objects, and how artificial intelligence can be extended into the physical world. While “Be the Beat” transforms the creative possibilities of dance, other student submissions span disciplines such as music, storytelling, critical thinking, and memory, creating generative experiences and new forms of human-computer interaction. Taken together, these projects illustrate a broader vision for artificial intelligence: one that goes beyond automation to catalyze creativity, reshape education, and reimagine social interactions.

Be the Beat 

“Be the Beat,” by Ethan Chang, an MIT mechanical engineering and design student, and Zhixing Chen, an MIT mechanical engineering and music student, is an AI-powered boombox that suggests music from a dancer's movement. Dance has traditionally been guided by music throughout history and across cultures, yet the concept of dancing to create music is rarely explored.

“Be the Beat” creates a space for human-AI collaboration on freestyle dance, empowering dancers to rethink the traditional dynamic between dance and music. It uses PoseNet to describe movements for a large language model, enabling it to analyze dance style and query APIs to find music with similar style, energy, and tempo. Dancers interacting with the boombox reported having more control over artistic expression and described the boombox as a novel approach to discovering dance genres and choreographing creatively.

A Mystery for You

“A Mystery for You,” by Mrinalini Singha SM ’24, a recent graduate in the Art, Culture, and Technology program, and Haoheng Tang, a recent graduate of the Harvard University Graduate School of Design, is an educational game designed to cultivate critical thinking and fact-checking skills in young learners. The game leverages a large language model (LLM) and a tangible interface to create an immersive investigative experience. Players act as citizen fact-checkers, responding to AI-generated “news alerts” printed by the game interface. By inserting cartridge combinations to prompt follow-up “news updates,” they navigate ambiguous scenarios, analyze evidence, and weigh conflicting information to make informed decisions.

This human-computer interaction experience challenges our news-consumption habits by eliminating touchscreen interfaces, replacing perpetual scrolling and skim-reading with a haptically rich analog device. By combining the affordances of slow media with new generative media, the game promotes thoughtful, embodied interactions while equipping players to better understand and challenge today’s polarized media landscape, where misinformation and manipulative narratives thrive.

Memorscope

“Memorscope,” by MIT Media Lab research collaborator Keunwook Kim, is a device that creates collective memories by merging the deeply human experience of face-to-face interaction with advanced AI technologies. Inspired by how we use microscopes and telescopes to examine and uncover hidden and invisible details, Memorscope allows two users to “look into” each other’s faces, using this intimate interaction as a gateway to the creation and exploration of their shared memories.

The device leverages AI models such as OpenAI and Midjourney, introducing different aesthetic and emotional interpretations, which results in a dynamic and collective memory space. This space transcends the limitations of traditional shared albums, offering a fluid, interactive environment where memories are not just static snapshots but living, evolving narratives, shaped by the ongoing relationship between users.

Narratron

“Narratron,” by Harvard Graduate School of Design students Xiying (Aria) Bao and Yubo Zhao, is an interactive projector that co-creates and co-performs children's stories through shadow puppetry using large language models. Users can press the shutter to “capture” protagonists they want to be in the story, and it takes hand shadows (such as animal shapes) as input for the main characters. The system then develops the story plot as new shadow characters are introduced. The story appears through a projector as a backdrop for shadow puppetry while being narrated through a speaker as users turn a crank to “play” in real time. By combining visual, auditory, and bodily interactions in one system, the project aims to spark creativity in shadow play storytelling and enable multi-modal human-AI collaboration.

Perfect Syntax

“Perfect Syntax,” by Karyn Nakamura ’24, is a video art piece examining the syntactic logic behind motion and video. Using AI to manipulate video fragments, the project explores how the fluidity of motion and time can be simulated and reconstructed by machines. Drawing inspiration from both philosophical inquiry and artistic practice, Nakamura's work interrogates the relationship between perception, technology, and the movement that shapes our experience of the world. By reimagining video through computational processes, Nakamura investigates the complexities of how machines understand and represent the passage of time and motion.

Last year the Earth exceeded 1.5 degrees Celsius of warming above preindustrial times, a threshold beyond which wildfires, droughts, floods, and other climate impacts are expected to escalate in frequency, intensity, and lethality. To cap global warming at 1.5 C and avert that scenario, the nearly 200 signatory nations of the Paris Agreement on climate change will need to not only dramatically lower their greenhouse gas emissions, but also take measures to remove carbon dioxide (CO2) from the atmosphere and durably store it at or below the Earth’s surface.

Past analyses of the climate mitigation potential, costs, benefits, and drawbacks of different carbon dioxide removal (CDR) options have focused primarily on three strategies: bioenergy with carbon capture and storage (BECCS), in which CO2-absorbing plant matter is converted into fuels or directly burned to generate energy, with some of the plant’s carbon content captured and then stored safely and permanently; afforestation/reforestation, in which CO2-absorbing trees are planted in large numbers; and direct air carbon capture and storage (DACCS), a technology that captures and separates CO2 directly from ambient air, and injects it into geological reservoirs or incorporates it into durable products. 

To provide a more comprehensive and actionable analysis of CDR, a new study by researchers at the MIT Center for Sustainability Science and Strategy (CS3) first expands the option set to include biochar (charcoal produced from plant matter and stored in soil) and enhanced weathering (EW) (spreading finely ground rock particles on land to accelerate storage of CO2 in soil and water). The study then evaluates portfolios of all five options — in isolation and in combination — to assess their capability to meet the 1.5 C goal, and their potential impacts on land, energy, and policy costs.

The study appears in the journal Environmental Research Letters. Aided by their global multi-region, multi-sector Economic Projection and Policy Analysis (EPPA) model, the MIT CS3 researchers produce three key findings.

First, the most cost-effective, low-impact strategy that policymakers can take to achieve global net-zero emissions — an essential step in meeting the 1.5 C goal — is to diversify their CDR portfolio, rather than rely on any single option. This approach minimizes overall cropland and energy consumption, and negative impacts such as increased food insecurity and decreased energy supplies.

By diversifying across multiple CDR options, the highest CDR deployment of around 31.5 gigatons of CO2 per year is achieved in 2100, while also proving the most cost-effective net-zero strategy. The study identifies BECCS and biochar as most cost-competitive in removing CO2 from the atmosphere, followed by EW, with DACCS as uncompetitive due to high capital and energy requirements. While posing logistical and other challenges, biochar and EW have the potential to improve soil quality and productivity across 45 percent of all croplands by 2100.

“Diversifying CDR portfolios is the most cost-effective net-zero strategy because it avoids relying on a single CDR option, thereby reducing and redistributing negative impacts on agriculture, forestry, and other land uses, as well as on the energy sector,” says Solene Chiquier, lead author of the study who was a CS3 postdoc during its preparation.

The second finding: There is no optimal CDR portfolio that will work well at global and national levels. The ideal CDR portfolio for a particular region will depend on local technological, economic, and geophysical conditions. For example, afforestation and reforestation would be of great benefit in places like Brazil, Latin America, and Africa, by not only sequestering carbon in more acreage of protected forest but also helping to preserve planetary well-being and human health.

“In designing a sustainable, cost-effective CDR portfolio, it is important to account for regional availability of agricultural, energy, and carbon-storage resources,” says Sergey Paltsev, CS3 deputy director, MIT Energy Initiative senior research scientist, and supervising co-author of the study. “Our study highlights the need for enhancing knowledge about local conditions that favor some CDR options over others.”

Finally, the MIT CS3 researchers show that delaying large-scale deployment of CDR portfolios could be very costly, leading to considerably higher carbon prices across the globe — a development sure to deter the climate mitigation efforts needed to achieve the 1.5 C goal. They recommend near-term implementation of policy and financial incentives to help fast-track those efforts.

The Department of Justice is investigating a lobbying firm representing ExxonMobil for hacking the phones of climate activists:

The hacking was allegedly commissioned by a Washington, D.C., lobbying firm, according to a lawyer representing the U.S. government. The firm, in turn, was allegedly working on behalf of one of the world’s largest oil and gas companies, based in Texas, that wanted to discredit groups and individuals involved in climate litigation, according to the lawyer for the U.S. government. In court documents, the Justice Department does not name either company...

President Donald Trump's sudden move to freeze federal grants is hitting states where people are struggling to recover from wildfires and hurricanes.

The president's first week brought tough talk on cutting climate and clean energy spending. This week brought chaos.

Maryland, New York and Vermont were looking to start carbon markets to address climate change. Their efforts face obstacles and delays.

Newly confirmed DOT Secretary Sean Duffy directed the National Highway Traffic Safety Administration to make good on President Donald Trump's pledge to slow or stop the shift to electric vehicles.