Gov. Tina Kotek, a Democrat, said up to 700 state workers could be fired because lawmakers failed to increase the cost of gasoline by 3 cents.

The new deadline to stop the data products will be July 31.

Brazil struggles to reassure countries that hotels will be available and affordable at November’s pivotal climate conference in Belém.

The Danish presidency has less than three months to reach a deal on the EU’s next emissions-cutting targets.

Within a year, GOSAT-GW will start distributing data with much higher resolution to users around the world, including NOAA.

Starlink has been adopted by criminal organizations to coordinate logistics, make payments and receive alerts about police raids.

Nature Climate Change, Published online: 01 July 2025; doi:10.1038/s41558-025-02372-4

Literature produced inconsistent findings regarding the links between extreme weather events and climate policy support across regions, populations and events. This global study offers a holistic assessment of these relationships and highlights the role of subjective attribution.

A new imaging technique developed by MIT researchers could enable quality-control robots in a warehouse to peer through a cardboard shipping box and see that the handle of a mug buried under packing peanuts is broken.

Their approach leverages millimeter wave (mmWave) signals, the same type of signals used in Wi-Fi, to create accurate 3D reconstructions of objects that are blocked from view.

The waves can travel through common obstacles like plastic containers or interior walls, and reflect off hidden objects. The system, called mmNorm, collects those reflections and feeds them into an algorithm that estimates the shape of the object’s surface.

This new approach achieved 96 percent reconstruction accuracy on a range of everyday objects with complex, curvy shapes, like silverware and a power drill. State-of-the-art baseline methods achieved only 78 percent accuracy.

In addition, mmNorm does not require additional bandwidth to achieve such high accuracy. This efficiency could allow the method to be utilized in a wide range of settings, from factories to assisted living facilities.

For instance, mmNorm could enable robots working in a factory or home to distinguish between tools hidden in a drawer and identify their handles, so they could more efficiently grasp and manipulate the objects without causing damage.

“We’ve been interested in this problem for quite a while, but we’ve been hitting a wall because past methods, while they were mathematically elegant, weren’t getting us where we needed to go. We needed to come up with a very different way of using these signals than what has been used for more than half a century to unlock new types of applications,” says Fadel Adib, associate professor in the Department of Electrical Engineering and Computer Science, director of the Signal Kinetics group in the MIT Media Lab, and senior author of a paper on mmNorm.

Adib is joined on the paper by research assistants Laura Dodds, the lead author, and Tara Boroushaki, and former postdoc Kaichen Zhou. The research was recently presented at the Annual International Conference on Mobile Systems, Applications and Services.

Reflecting on reflections

Traditional radar techniques send mmWave signals and receive reflections from the environment to detect hidden or distant objects, a technique called back projection.

This method works well for large objects, like an airplane obscured by clouds, but the image resolution is too coarse for small items like kitchen gadgets that a robot might need to identify.

In studying this problem, the MIT researchers realized that existing back projection techniques ignore an important property known as specularity. When a radar system transmits mmWaves, almost every surface the waves strike acts like a mirror, generating specular reflections.

If a surface is pointed toward the antenna, the signal will reflect off the object to the antenna, but if the surface is pointed in a different direction, the reflection will travel away from the radar and won’t be received.

“Relying on specularity, our idea is to try to estimate not just the location of a reflection in the environment, but also the direction of the surface at that point,” Dodds says.

They developed mmNorm to estimate what is called a surface normal, which is the direction of a surface at a particular point in space, and use these estimations to reconstruct the curvature of the surface at that point.

Combining surface normal estimations at each point in space, mmNorm uses a special mathematical formulation to reconstruct the 3D object.

The researchers created an mmNorm prototype by attaching a radar to a robotic arm, which continually takes measurements as it moves around a hidden item. The system compares the strength of the signals it receives at different locations to estimate the curvature of the object’s surface.

For instance, the antenna will receive the strongest reflections from a surface pointed directly at it and weaker signals from surfaces that don’t directly face the antenna.

Because multiple antennas on the radar receive some amount of reflection, each antenna “votes” on the direction of the surface normal based on the strength of the signal it received.

“Some antennas might have a very strong vote, some might have a very weak vote, and we can combine all votes together to produce one surface normal that is agreed upon by all antenna locations,” Dodds says.

In addition, because mmNorm estimates the surface normal from all points in space, it generates many possible surfaces. To zero in on the right one, the researchers borrowed techniques from computer graphics, creating a 3D function that chooses the surface most representative of the signals received. They use this to generate a final 3D reconstruction.

Finer details

The team tested mmNorm’s ability to reconstruct more than 60 objects with complex shapes, like the handle and curve of a mug. It generated reconstructions with about 40 percent less error than state-of-the-art approaches, while also estimating the position of an object more accurately.

Their new technique can also distinguish between multiple objects, like a fork, knife, and spoon hidden in the same box. It also performed well for objects made from a range of materials, including wood, metal, plastic, rubber, and glass, as well as combinations of materials, but it does not work for objects hidden behind metal or very thick walls.

“Our qualitative results really speak for themselves. And the amount of improvement you see makes it easier to develop applications that use these high-resolution 3D reconstructions for new tasks,” Boroushaki says.

For instance, a robot can distinguish between multiple tools in a box, determine the precise shape and location of a hammer’s handle, and then plan to pick it up and use it for a task. One could also use mmNorm with an augmented reality headset, enabling a factory worker to see lifelike images of fully occluded objects.

It could also be incorporated into existing security and defense applications, generating more accurate reconstructions of concealed objects in airport security scanners or during military reconnaissance.

The researchers want to explore these and other potential applications in future work. They also want to improve the resolution of their technique, boost its performance for less reflective objects, and enable the mmWaves to effectively image  through thicker occlusions.

“This work really represents a paradigm shift in the way we are thinking about these signals and this 3D reconstruction process. We’re excited to see how the insights that we’ve gained here can have a broad impact,” Dodds says.

This work is supported, in part, by the National Science Foundation, the MIT Media Lab, and Microsoft.

Imagine that you want to know the plot of a movie, but you only have access to either the visuals or the sound. With visuals alone, you’ll miss all the dialogue. With sound alone, you will miss the action. Understanding our biology can be similar. Measuring one kind of data — such as which genes are being expressed — can be informative, but it only captures one facet of a multifaceted story. For many biological processes and disease mechanisms, the entire “plot” can’t be fully understood without combining data types.

However, capturing both the “visuals and sound” of biological data, such as gene expression and cell structure data, from the same cells requires researchers to develop new approaches. They also have to make sure that the data they capture accurately reflects what happens in living organisms, including how cells interact with each other and their environments.

Whitehead Institute for Biomedical Research and Harvard University researchers have taken on these challenges and developed Perturb-Multimodal (Perturb-Multi), a powerful new approach that simultaneously measures how genetic changes such as turning off individual genes affect both gene expression and cell structure in intact liver tissue. The method, described in Cell on June 12, aims to accelerate discovery of how genes control organ function and disease.

The research team, led by Whitehead Institute Member Jonathan Weissman and then-graduate student in his lab Reuben Saunders, along with Xiaowei Zhuang, the David B. Arnold Professor of Science at Harvard University, and then-postdoc in her lab Will Allen, created a system that can test hundreds of different genetic modifications within a single mouse liver while capturing multiple types of data from the same cells.

“Understanding how our organs work requires looking at many different aspects of cell biology at once,” Saunders says. “With Perturb-Multi, we can see how turning off specific genes changes not just what other genes are active, but also how proteins are distributed within cells, how cellular structures are organized, and where cells are located in the tissue. It’s like having multiple specialized microscopes all focused on the same experiment.”

“This approach accelerates discovery by both allowing us to test the functions of many different genes at once, and then for each gene, allowing us to measure many different functional outputs or cell properties at once — and we do that in intact tissue from animals,” says Zhuang, who is also a Howard Hughes Medical Institute (HHMI) investigator.

A more efficient approach to genetic studies

Traditional genetic studies in mice often turn off one gene and then observe what changes in that gene’s absence to learn about what the gene does. The researchers designed their approach to turn off hundreds of different genes across a single liver, while still only turning off one gene per cell — using what is known as a mosaic approach. This allowed them to study the roles of hundreds of individual genes at once in a single individual. The researchers then collected diverse types of data from cells across the same liver to get a full picture of the consequences of turning off the genes.

“Each cell serves as its own experiment, and because all the cells are in the same animal, we eliminate the variability that comes from comparing different mice,” Saunders says. “Every cell experiences the same physiological conditions, diet, and environment, making our comparisons much more precise.”

“The challenge we faced was that tissues, to perform their functions, rely on thousands of genes, expressed in many different cells, working together. Each gene, in turn, can control many aspects of a cell’s function. Testing these hundreds of genes in mice using current methods would be extremely slow and expensive — near impossible, in practice.” Allen says.

Revealing new biology through combined measurements

The team applied Perturb-Multi to study genetic controls of liver physiology and function. Their study led to discoveries in three important aspects of liver biology: fat accumulation in liver cells — a precursor to liver disease; stress responses; and hepatocyte zonation (how liver cells specialize, assuming different traits and functions, based on their location within the liver).

One striking finding emerged from studying genes that, when disrupted, cause fat accumulation in liver cells. The imaging data revealed that four different genes all led to similar fat droplet accumulation, but the sequencing data showed they did so through three completely different mechanisms.

“Without combining imaging and sequencing, we would have missed this complexity entirely,” Saunders says. “The imaging told us which genes affect fat accumulation, while the sequencing revealed whether this was due to increased fat production, cellular stress, or other pathways. This kind of mechanistic insight could be crucial for developing targeted therapies for fatty liver disease.”

The researchers also discovered new regulators of liver cell zonation. Unexpectedly, the newly discovered regulators include genes involved in modifying the extracellular matrix — the scaffolding between cells. “We found that cells can change their specialized functions without physically moving to a different zone,” Saunders says. “This suggests that liver cell identity is more flexible than previously thought.”

Technical innovation enables new science

Developing Perturb-Multi required solving several technical challenges. The team created new methods for preserving the content of interest in cells — RNA and proteins — during tissue processing, for collecting many types of imaging data and single-cell gene expression data from tissue samples that have been fixed with a preservative, and for integrating multiple types of data from the same cells.

“Overcoming the inherent complexity of biology in living animals required developing new tools that bridge multiple disciplines — including, in this case, genomics, imaging, and AI,” Allen says.

The two components of Perturb-Multi — the imaging and sequencing assays — together, applied to the same tissue, provide insights that are unattainable through either assay alone.

“Each component had to work perfectly while not interfering with the others,” says Weissman, who is also a professor of biology at MIT and an HHMI investigator. “The technical development took considerable effort, but the payoff is a system that can reveal biology we simply couldn’t see before.”

Expanding to new organs and other contexts

The researchers plan to expand Perturb-Multi to other organs, including the brain, and to study how genetic changes affect organ function under different conditions like disease states or dietary changes.

“We’re also excited about using the data we generate to train machine learning models,” adds Saunders. “With enough examples of how genetic changes affect cells, we could eventually predict the effects of mutations without having to test them experimentally — a ‘virtual cell’ that could accelerate both research and drug development.”

“Perturbation data are critical for training such AI models and the paucity of existing perturbation data represents a major hindrance in such ‘virtual cell’ efforts,” Zhuang says. “We hope Perturb-Multi will fill this gap by accelerating the collection of perturbation data.”

The approach is designed to be scalable, with the potential for genome-wide studies that test thousands of genes simultaneously. As sequencing and imaging technologies continue to improve, the researchers anticipate that Perturb-Multi will become even more powerful and accessible to the broader research community.

“Our goal is to keep scaling up. We plan to do genome-wide perturbations, study different physiological conditions, and look at different organs,” says Weissman. “That we can now collect so many types of data from so many cells, at speed, is going to be critical for building AI models like virtual cells, and I think it’s going to help us answer previously unsolvable questions about health and disease.”

As an electrical engineering student at Stanford University in the late 1970s, L. Rafael Reif was working on not only his PhD but also learning a new language.

“I didn’t speak English. And I saw that it was easy to ignore somebody who doesn’t speak English well,” Reif recalled. To him, that meant speaking with conviction.

“If you have tremendous technical skills, but you cannot communicate, if you cannot persuade others to embrace that, it’s not going to go anywhere. Without the combination, you cannot persuade the powers-that-be to embrace whatever ideas you have.”

Now MIT president emeritus, Reif recently joined Anantha P. Chandrakasan, chief innovation and strategy officer and dean of the School of Engineering (SoE), for a fireside chat. Their focus: the importance of developing engineering leadership skills — such as persuasive communication — to solve the world’s most challenging problems.

SoE’s Technical Leadership and Communication Programs (TLC) sponsored the chat. TLC teaches engineering leadership, teamwork, and technical communication skills to students, from undergrads to postdocs, through its four programs: Undergraduate Practice Opportunities Program (UPOP), Gordon-MIT Engineering Leadership Program (GEL), Communication Lab (Comm Lab), and Riccio-MIT Graduate Engineering Leadership Program (GradEL).

About 175 students, faculty, and guests attended the fireside chat. Relaxed, engaging, and humorous — Reif shared anecdotes and insights about technical leadership from his decades in leadership roles at MIT.

Reif had a transformational impact on MIT. Beginning as an assistant professor of electrical engineering in 1980, he rose to head of the Department of Electrical Engineering and Computer Science (EECS), then served as provost from 2005 to 2012 and MIT president from 2012 to 2022.

He was instrumental in creating the MIT Schwarzman College of Computing in 2018, as well as establishing and growing MITx online open learning and MIT Microsystems Technology Laboratories.

With an ability to peer over the horizon and anticipate what’s coming, Reif used an array of leadership skills to develop and implement clear visions for those programs.

“One of the things that I learned from you is that as a leader, you have to envision the future and make bets,” said Chandrakasan. “And you don’t just wait around for that. You have to drive it.”

Turning new ideas into reality often meant overcoming resistance. When Reif first proposed the College of Computing to some fellow MIT leaders, “they looked at me and they said, no way. This is too hard. It’s not going to happen. It’s going to take too much money. It’s too complicated. OK, then starts the argument.”

Reif seems to have relished “the argument,” or art of persuasion, during his time at MIT. Though hearing different perspectives never hurt.

“All of us have blind spots. I always try to hear all points of view. Obviously, you can’t integrate all of it. You might say, ‘Anantha, I heard you, but I disagree with you because of this.’ So, you make the call knowing all the options. That is something non-technical that I used in my career.”

On the technical side, Reif’s background as an electrical engineer shaped his approach to leadership.

“What’s beautiful about a technical education is that you understand that you can solve anything if you start with first principles. There are first principles in just about anything that you do. If you start with those, you can solve any problem.”

Also, applying systems-level thinking is critical — understanding that organizations are really systems with interconnected parts.

“That was really useful to me. Some of you in the audience have studied this. In a system, when you start tinkering with something over here, something over there will be affected. And you have to understand that. At a place like MIT, that’s all the time!”

Reif was asked: If he were assembling a dream team to tackle the world’s biggest challenges, what skills or capabilities would he want them to have?

“I think we need people who can see things from different directions. I think we need people who are experts in different disciplines. And I think we need people who are experts in different cultures. Because to solve the big problems of the planet, we need to understand how different cultures address different things.”

Reif’s upbringing in Venezuela strongly influenced his leadership approach, particularly when it comes to empathy, a key trait he values.

“My parents were immigrants. They didn’t have an education, and they had to do whatever they could to support the family. And I remember as a little kid seeing how people humiliated them because they were doing menial jobs. And I remember how painful it was to me. It is part of my fabric to respect every individual, to notice them. I have a tremendous respect for every individual, and for the ability of every individual that didn’t have the same opportunity that all of us here have to be somebody.”

Reif’s advice to students who will be the next generation of engineering leaders is to keep learning because the challenges ahead are multidisciplinary. He also reminded them that they are the future.

“What are our assets? The people in this room. When it comes to the ecosystem of innovation in America, what we work on is to create new roadmaps, expand the roadmaps, create new industries. Without that, we have nothing. Companies do a great job of taking what you come up with and making wonderful things with it. But the ideas, whether it’s AI, whether it’s deep learning, it comes from places like this.” 

Professors Xiao Wang and Rodrigo Verdi, both members of the 2023-25 Committed to Caring cohort, are aiding in the development of extraordinary researchers and contributing to a collaborative culture. 

“Professor Xiao Wang's caring efforts have a profound impact on the lives of her students,” one of her advisees commended.

“Rodrigo's dedication to mentoring and his unwavering support have positively impacted every student in our group,” another student praised.

For MIT graduate students, the Committed to Caring program recognizes those who go above and beyond.

Xiao Wang: Enriching, stimulating, and empowering students

Xiao Wang is a core institute member of the Broad Institute of MIT and Harvard and an associate professor in the Department of Chemistry at MIT. She started her lab in 2019 to develop and apply new chemical, biophysical, and genomic tools to better understand tissue function and dysfunction at the molecular level.

Wang goes above and beyond to create a nurturing environment that fosters growth and supports her students' personal and academic development. She makes it a priority to ensure an intellectually stimulating environment, taking the time to discuss research interests, academic goals, and personal aspirations on a weekly basis. 

In their nominations, her students emphasized that Wang understands the importance of mentorship, patiently explaining fundamental concepts, sharing insights from her own groundbreaking work, and providing her students with key scientific papers and resources to deepen their understanding of the field. 

“Professor Wang encouraged me to think critically, ask challenging questions, and explore innovative approaches to further my research,” one of her students commented.

Beyond the lab, Wang nurtures a sense of community among her research team. Her regular lab meetings are highly valued by her students, where “fellow researchers presented … findings, exchanged ideas, and received constructive feedback.”

These meetings foster collaboration, enhance communication skills, and create a supportive environment where all lab members feel empowered to share their discoveries and insights.

Wang is a dedicated and compassionate educator, and is known for her unwavering commitment to the well-being and success of her students. Her advisees not only excel academically but they also develop resilience, confidence, and a sense of belonging. 

A different student reflected that although they came from an organic chemistry background with few skills related to the chemical biology field, Wang recognized their enthusiasm and potential. She went out of her way to make sure they could have a smooth transition. “It is because of all her training and help that I came from knowing nothing about the field to being able to confidently call myself a chemical biologist,” the student acclaimed.

Her advisees communicate that Wang encourages them to present their work at conferences, workshops, and seminars. This helps boost the students’ confidence and establish connections within the scientific community.

“Her genuine care and dedication make her a cherished mentor and a source of inspiration for all who have the privilege to learn from her,” one of her mentees remarked.

Rodrigo Verdi: Committed and collaborative

Professor Rodrigo Verdi is the deputy dean of degree programs and teaching and learning at the MIT Sloan School of Management. Verdi’s research provides insights into the role of accounting information in corporate finance decisions and in capital markets behavior. 

Professor Verdi has been active in the majority of the Sloan students’ research journeys. He makes sure to assist students even if he does not directly guide them. One student states that “although Rodrigo is not my primary advisor, he still goes above and beyond to provide feedback and assistance.”

Verdi believes that “an appetite for experimentation, the ability to handle failure, and managing the stress along the way” is the kind of support necessary for especially innovative research.

Another student recounts that they “cannot think of a single recent graduate since … [they] started the PhD program that did not have Rodrigo on their committee.” This demonstrates how much students value his guidance, and how much he cares about their success.

Since his arrival at MIT, he has shown a strong commitment to mentoring students. Despite his many responsibilities as an associate dean, Rodrigo remains highly accessible to students and eagerly engages with them. 

Specifically, Verdi has interacted with more than 90 percent of recent graduates over the past 10 years, contributing significantly to the department’s strong track record in job placements. He has served on the dissertation committee for 18 students in the last 15 years, which represents nearly all of the students in the department.

A student remarked that “Rodrigo has been an exceptional advisor during my job market period, which is known for its high levels of stress.” He offered continuous encouragement and support, making himself available for discussions whenever the student faced challenges. 

After each job market interview, Verdi and the student would debrief and discuss areas for improvement. His insights into the academic system, the significance of social skills and networking, and his valuable advice helped the student successfully get a faculty position.

Rodrigo’s mantra is, “people won't care how much you know until they know how much you care,” and his relationships with his students support this maxim.

Verdi has made a lasting impact on the culture of the accounting specialty and is an important piece of the puzzle with regard to interactions found in the Sloan school. One of his students praised, “the collaborative culture is impressive: I’d call it a family, where faculty and students are very close to each other.” They described that they “share the same office space, have lunches together, and whenever students want feedback, the faculty is willing to help.” 

Verdi has sharp research insights, and always wants to help, even when he is swamped with administrative affairs. He makes himself accessible to students, often staying after hours with his door open. 

Another mentee said that “he has been organizing weekly PhD lunch seminars for years, online brown-bags among current and previous MIT accounting members during the pandemic, and more recently the annual MIT accounting alumni conference.” Verdi also takes students out for dinner or coffee, caring about how they are doing outside of academics. The student commended, “I feel lucky that Rodrigo is here.”

Several researchers have taken a broad view of scientific progress over the last 50 years and come to the same troubling conclusion: Scientific productivity is declining. It’s taking more time, more funding, and larger teams to make discoveries that once came faster and cheaper. Although a variety of explanations have been offered for the slowdown, one is that, as research becomes more complex and specialized, scientists must spend more time reviewing publications, designing sophisticated experiments, and analyzing data.

Now, the philanthropically funded research lab FutureHouse is seeking to accelerate scientific research with an AI platform designed to automate many of the critical steps on the path toward scientific progress. The platform is made up of a series of AI agents specialized for tasks including information retrieval, information synthesis, chemical synthesis design, and data analysis.

FutureHouse founders Sam Rodriques PhD ’19 and Andrew White believe that by giving every scientist access to their AI agents, they can break through the biggest bottlenecks in science and help solve some of humanity’s most pressing problems.

“Natural language is the real language of science,” Rodriques says. “Other people are building foundation models for biology, where machine learning models speak the language of DNA or proteins, and that’s powerful. But discoveries aren’t represented in DNA or proteins. The only way we know how to represent discoveries, hypothesize, and reason is with natural language.”

Finding big problems

For his PhD research at MIT, Rodriques sought to understand the inner workings of the brain in the lab of Professor Ed Boyden.

“The entire idea behind FutureHouse was inspired by this impression I got during my PhD at MIT that even if we had all the information we needed to know about how the brain works, we wouldn’t know it because nobody has time to read all the literature,” Rodriques explains. “Even if they could read it all, they wouldn’t be able to assemble it into a comprehensive theory. That was a foundational piece of the FutureHouse puzzle.”

Rodriques wrote about the need for new kinds of large research collaborations as the last chapter of his PhD thesis in 2019, and though he spent some time running a lab at the Francis Crick Institute in London after graduation, he found himself gravitating toward broad problems in science that no single lab could take on.

“I was interested in how to automate or scale up science and what kinds of new organizational structures or technologies would unlock higher scientific productivity,” Rodriques says.

When Chat-GPT 3.5 was released in November 2022, Rodriques saw a path toward more powerful models that could generate scientific insights on their own. Around that time, he also met Andrew White, a computational chemist at the University of Rochester who had been granted early access to Chat-GPT 4. White had built the first large language agent for science, and the researchers joined forces to start FutureHouse.

The founders started out wanting to create distinct AI tools for tasks like literature searches, data analysis, and hypothesis generation. They began with data collection, eventually releasing PaperQA in September 2024, which Rodriques calls the best AI agent in the world for retrieving and summarizing information in scientific literature. Around the same time, they released Has Anyone, a tool that lets scientists determine if anyone has conducted specific experiments or explored specific hypotheses.

“We were just sitting around asking, ‘What are the kinds of questions that we as scientists ask all the time?’” Rodriques recalls.

When FutureHouse officially launched its platform on May 1 of this year, it rebranded some of its tools. Paper QA is now Crow, and Has Anyone is now called Owl. Falcon is an agent capable of compiling and reviewing more sources than Crow. Another new agent, Phoenix, can use specialized tools to help researchers plan chemistry experiments. And Finch is an agent designed to automate data driven discovery in biology.

On May 20, the company demonstrated a multi-agent scientific discovery workflow to automate key steps of the scientific process and identify a new therapeutic candidate for dry age-related macular degeneration (dAMD), a leading cause of irreversible blindness worldwide. In June, FutureHouse released ether0, a 24B open-weights reasoning model for chemistry.

“You really have to think of these agents as part of a larger system,” Rodriques says. “Soon, the literature search agents will be integrated with the data analysis agent, the hypothesis generation agent, an experiment planning agent, and they will all be engineered to work together seamlessly.”

Agents for everyone

Today anyone can access FutureHouse’s agents at platform.futurehouse.org. The company’s platform launch generated excitement in the industry, and stories have started to come in about scientists using the agents to accelerate research.

One of FutureHouse’s scientists used the agents to identify a gene that could be associated with polycystic ovary syndrome and come up with a new treatment hypothesis for the disease. Another researcher at the Lawrence Berkeley National Laboratory used Crow to create an AI assistant capable of searching the PubMed research database for information related to Alzheimer’s disease.

Scientists at another research institution have used the agents to conduct systematic reviews of genes relevant to Parkinson’s disease, finding FutureHouse’s agents performed better than general agents.

Rodriques says scientists who think of the agents less like Google Scholar and more like a smart assistant scientist get the most out of the platform.

“People who are looking for speculation tend to get more mileage out of Chat-GPT o3 deep research, while people who are looking for really faithful literature reviews tend to get more out of our agents,” Rodriques explains.

Rodriques also thinks FutureHouse will soon get to a point where its agents can use the raw data from research papers to test the reproducibility of its results and verify conclusions.

In the longer run, to keep scientific progress marching forward, Rodriques says FutureHouse is working on embedding its agents with tacit knowledge to be able to perform more sophisticated analyses while also giving the agents the ability to use computational tools to explore hypotheses.

“There have been so many advances around foundation models for science and around language models for proteins and DNA, that we now need to give our agents access to those models and all of the other tools people commonly use to do science,” Rodriques says. “Building the infrastructure to allow agents to use more specialized tools for science is going to be critical.”

American democracy runs on trust, and that trust is cracking.

Nearly half of Americans, both Democrats and Republicans, question whether elections are conducted fairly. Some voters accept election results only when their side wins. The problem isn’t just political polarization—it’s a creeping erosion of trust in the machinery of democracy itself.

Commentators blame ideological tribalism, misinformation campaigns and partisan echo chambers for this crisis of trust. But these explanations miss a critical piece of the puzzle: a growing unease with the digital infrastructure that now underpins nearly every aspect of how Americans vote...

Business interests have shifted strategies now that President Donald Trump is back in office.

The state tried to link building codes to hazardous areas, provoking a yearslong backlash from rural communities.

S&P Global Ratings says shifting disaster costs to state and local governments would weaken them fiscally and raise borrowing costs.

The state is speeding up its timeline to cut the carbon intensity of fuels, which could increase gasoline prices by between 6 and 15 cents per gallon.

Congressional leaders are focused on raising new revenue to shore up the Highway Trust Fund.

Countries jostle to fill the vacuum left by Washington at midyear negotiations.

Copenhagen's bold climate credentials face the hard grind of EU politics — and a bloc more interested in competitiveness than carbon cuts.