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NSWCPD Applies AI and Machine Learning to Predictive Machinery Health
In a soundproof chamber at the Naval Surface Warfare Center, Philadelphia Division (NSWCPD), a steel-gray air compressor hums as engineers test an experimental machine learning model designed to detect early signs of failure through vibration analysis. The effort, part of NSWCPD’s growing portfolio of artificial intelligence (AI) and machine learning (ML) projects, shows early promise for potential future Navy use.
Here’s who’ll win the World Cup, according to science
Statisticians from across Europe teamed up to train a competition-predicting, machine learning algorithm. This is what they found.
‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field
‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field ‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field A field that once seemed like a direct path to stable, lucrative work is becoming less certain under AI, students and faculty told Spectator. By Ria Vasishtha and Arjun Menon May 3, 2026 Shanying Liu / Deputy Illustrations Editor Asia Genawi, SEAS ’29, first encountered artificial intelligence during her sophomore year at her high school in Indiana. Back in 2023, policies on AI varied widely—even within the same district—and some schools had no guidance at all. As Genawi began to see the effects of AI trickle into her classroom, she turned to guidance from her home state’s Department of Education, which she said she found “outdated and inaccessible.” Genawi spent the rest of her high school career dedicated to the issue, including writing a 15-page memo exploring varying AI policies across Indiana schools, which she sent to every Indiana state senator and representative whose contact information she was able to find. She later helped draft a bill that would have required public high schools to establish and share their own AI policies with students in an accessible format. But with so many other bills already awaiting referral, hers never made it to committee. When Genawi arrived at Columbia to study computer science, she found that the gaps she had spent years trying to address in Indiana had followed her there. Many students and professors were also grappling with what AI meant for the field. Across Columbia’s undergraduate schools, computer science has become the most popular major—accounting for around 11 percent of degrees awarded in Columbia College, 32 percent in the School of Engineering and Applied Science, and 12 percent in the School of General Studies in 2024. While the number of computer science majors in SEAS grew from 166 to 173 during the 2024-25 academic year, the total number across the three schools fell around 4.5 percent from 393 to 375, driven by declines at Columbia College and General Studies. At Barnard, it has grown to become the second most popular major as of 2025, up from third the year prior. The number of degrees awarded have grown fivefold since 2016. Faculty members and students in the department told Spectator that the surge in the major’s popularity has been driven in large part by its apparent promise of a secure, well-paid career path. However, they noted that the recent rise of generative AI has compromised the sense of stability that once defined the field, even as interest in technical skills among students persists. According to data from the Federal Reserve Bank of New York in 2024, out of 74 majors tracked, computer science had the fifth highest unemployment rate among majors at 7 percent, while computer engineering had the second highest at 7.8 percent. “Anecdotally, people are moving away from CS as a major,” Daniel Bauer, a senior lecturer in the computer science department, said. “They’re still taking some of our classes, right, because they think they need that, but they’re instead majoring in other things.” Bauer described a field that had shifted from what he called a “nerdy outlier discipline” to one that “everyone needs,” a sign that technical skills have become a baseline expectation across industries with the emergence of AI. “A lot of people thought, ‘Oh, it’s a guaranteed path to a stable income. You get a six-figure job right out of your undergrad,’” Bauer said. Now, companies may see AI as a way to reduce demand for entry-level programmers, especially in what he calls “code monkey jobs,” roles where a large number of employees are “just writing out code nine-to-five.” Chris Murphy, a senior lecturer in the computer science department, described the current moment as the convergence of two forces: the downturn of the job market and generative AI coming onto the scene. “Things kind of started to really pick up in 2014, 16,” he said, recalling the years after the weaker 2010 market. Murphy added that this was also a period where “students were getting more experience outside of class than they were inside of class” through internships, jobs, and side projects. Murphy suggested that the earlier momentum helped make computer science feel like both an academic program and a reliable pathway into work. That feeling is harder to sustain now as the industry reconfigures, which is a shift that students are seeing firsthand. Rebecca Yu, SEAS ’27, said that during a recent internship in Big Tech, she saw that “every single tech company was racing to integrate” large language models. She added that her manager said her intern class was the first to complete their assigned projects, which she attributed to the increased efficiency that comes with AI. Frank Liu, SEAS ’29, said that with AI lowering the barrier to coding, companies now expect everyone to write code. “A lot of companies outright say if you’re not using coding in your job, you might get fired for not being productive enough,” Liu said. Yu similarly expressed that students increasingly have to accept AI being a part of industry workflow, adding that many also “make use of it to maximize their productivity.” Richard Li, CC ’28, described a “franticness” among his peers as the pace of change accelerates. He explained how “some new tool” is released regularly, noting that tools rise and fall in prominence within weeks. He has come to believe that he doesn’t need to “match that speed all the time” and keep up with every development. Li has learned that AI tools allow him to “take a breath” and “get back into the loop much easier than before” when he needs to. While some companies specifically target Columbia when hiring, sending representatives to campus career fairs and recruiting large numbers from Columbia, a computer science degree cannot be treated as a static credential, Yu described. “You can’t just get your degree and stop learning after that. You have to always be learning,” Yu said. “That is something you basically have to expect for your job.” AI has not only affected students’ decisions to major in computer science, but also reshaped how the subject is taught. Across departments, faculty members no longer treat work done outside the classroom as a reliable measure of understanding, shifting instead toward in-person exams, quizzes, and attendance checks. While homework once made up as much as 60 to 70 percent of a student’s grade in computer science classes, Murphy said that breakdown has since flipped, with exams and quizzes now accounting for the majority. “The assumption—especially among pessimists—is that they’re using it to do their homework,” Murphy said. “They take the homework assignment. They put it into generative AI. Out comes the solution. Submit solution. The end.” Murphy said that faculty members ranged in their responses from fully prohibiting AI use to actively teaching students how to effectively work with AI. He said he shifted his approach this year after his teaching assistants called his belief that students weren’t using the technology “naïve,” and now permits students to use AI on assignments. His surveys of students in his introductory classes have told a more complicated story than the one so-called pessimists offer: Around 60 to 70 percent of students who reported using AI said they used it to have concepts explained to them, to clarify assignment instructions, to generate test cases, or to create practice questions. “Those are all ways that you might have had a tutor in the past to do that, and now you have this AI essentially as your tutor,” he said. Bauer has taken a different approach, teaching students how to use AI productively without sacrificing the core principles of the discipline. “People who are trained to actually use AI tools productively, while being in charge of designing the overall project, will be very much in demand in the next couple of years,” he said. Over the past year, a working group of computer science faculty has been redesigning the introductory and intermediate programming sequence to integrate generative AI as both a subject and a tool, representatives from SEAS wrote in a statement to Spectator. The effort is “coordinated” across the department, “not course-by-course,” with a systematic assessment of how AI integration is affecting student outcomes underway to shape the next round of changes. The goal, the department explained, is for students to be able to “read, verify, and reason about code, whether they or a model wrote it.” Tony Dear, senior lecturer in the computer science department, is piloting individualized AI feedback in a discrete mathematics course, while a machine learning course is also testing a Socratic AI dialogue system designed to “guide inquiry rather than supply answers,” the statement reads. Beyond individual courses, SEAS funded seven cross-departmental faculty projects in generative AI this past academic year, spanning biomedical engineering, chemical engineering, industrial engineering, and computer science, according to the statement. This phenomenon extends beyond the computer science department. SEAS’ new AI minor, which it announced in November 2025, reflects the University’s attempt to adapt. The minor is specifically designed for noncomputer science majors and aims to provide “essential AI knowledge” while emphasizing practical and responsible use across disciplines, according to the SEAS website. At the center of the minor is AI in Context, a course taught by a team of faculty from the departments of computer science, applied mathematics, philosophy, music, literature, and the Writing Center, the representatives told Spectator. Bauer described it as targeted at students who want to bring AI into fields like economics or biology, rather than as a path into software engineering itself. At the graduate level, SEAS announced a new Master of Science in artificial intelligence in February, with the inaugural cohort starting in the fall. While recognizing how AI has helped them navigate coursework, students also described its rise as isolating, even among members of the Application Development Initiative, one of Columbia’s largest computer science clubs. “As people start to ask AI for questions that they would normally ask students and members, it starts to isolate people” Li said. Alexa Kafka, BC ’27, echoed Li’s sentiment, pointing out that computer science has always been a collaborative discipline with code reviews and pull requests, even as AI makes independent work easier. Despite this, Kafka noted that she hasn’t been to office hours “in at least a year.” Bauer added that faculty are exploring long-term solutions to the assessment problem. “One of the things that we’re trying to do at the department or school level is to maybe move towards computerized testing facilities,” Bauer said, referring to ideas in the department to revitalize computer labs as an alternative to pen-and-paper exams. “We have to accept that this is the reality, for better or worse.” But more exams, students say, does not mean better learning, nor does it reflect how computer science is actually done. “Code is so variable and can be approached in so many directions, there’s never just going to be one correct answer. There can be multiple optimal solutions,” Kafka said. Students also expressed concern that the shift toward exam-heavy grading pulls focus away from the applied, project-based work that better reflects both discipline and industry expectations. ADI has tried to respond more quickly than the curriculum can. Through a vibe coding workshop this past semester, club members said they wanted to help students understand not just that AI exists, but how to use it deliberately. “Vibe coding” refers to the practice of prompting AI to write code for a task or project, and has become widespread enough that ADI felt the gap in formal instruction was worth addressing, according to Sophia Huang, BC ’29. The workshop introduced students to the concept of context engineering, which Shaina Sahu, SEAS ’27, described as not just writing a prompt, but optimizing “the rest of the relevant information that you would feed into the model.” Without that, students said that AI-generated code can fail at scale or introduce difficult-to-detect bugs. Genawi said the shift toward AI has changed what it means to be good at computer science. “I think the shift has kind of gone towards what can you create and what can you do, instead of, what do you know,” she said. Indeed, during ADI DevFest, its annual hackathon, Li said more teams are trying to complete their projects than in years past. While sign-ups have historically outpaced submissions, Li said this is beginning to change because teams “have a tool that completes the vision for them.” While many students see AI as an equalizer that lowers the barrier to entry for coding and technical work, Murphy said it also creates new equity concerns inside the classroom around who can afford stronger tools and who already knows how to use them. “The equity issues I worry about are students who have access to paid versions of things versus free versions of things,” he said. Although popular AI models like ChatGPT and Claude have free versions available, both have paid models that can reach over $100 per month, which output different qualities of code. Murphy also pointed to differences in prior exposure to AI literacy itself. “Students who go to high schools or K-12 education, where they’re taught AI literacy, prompt engineering, context engineering, they understand how to do this, as opposed to students who go to schools who don’t teach that,” he said. “Then they come in, and then they’re somehow expected to know how to use these tools.” For Genawi, those disparities begin long before students arrive on campus. “It’s very interesting to consider how lucky you were geographically,” she said. “To be born where you were is influencing your ability to get access to these spaces.” Students like Yu, though broadly apprehensive about the future of computer science, are not without hope. “I’m optimistic, because I think if you’re truly passionate about CS, there will definitely be a place in the field to do something you want to do,” she said. Senior Staff Writer Ria Vasishtha can be contacted at ria.vasishtha@columbiaspectator.com. Follow Spectator on X @ColumbiaSpec. Staff Writer Arjun Menon can be contacted at arjun.menon@columbiaspectator.com. Follow Spectator on X @ColumbiaSpec. Want to keep up with breaking news? Subscribe to our email newsletter and like Spectator on Facebook. Produced with Spectate by Emily Ta
PS6 Early Info on Cloud Streaming, Machine Learning, and New Horror Game Uncovered
PS6 advanced machine learning, cloud streaming upgrades, and a new horror game in development on Unreal Engine 5 uncovered.
MLOps transformation at BBVA: delivering business value faster
In a previous article, we explored how BBVA is scaling its Machine Learning capabilities with a new architecture based on AWS technologies.
OSF–ISU Data Science Competition Highlights Student Innovation and Healthcare Analytics Excellence
Illinois State University students showcased innovative machine learning models and predictive analytics solutions during the OSF–ISU Data Science Competition, addressing real-world healthcare challenges through collaboration with OSF HealthCare.
FLAT-WHITE: from target trial emulation to federated high-throughput causal machine learning using decentralized real-world data
npj Health Systems - FLAT-WHITE: from target trial emulation to federated high-throughput causal machine learning using decentralized real-world data
Team's post-op kidney injury risk model could aid prevention
The machine learning-derived model was 88% accurate in ranking preoperative patients according to risk for postoperative acute kidney injury.
Taylor Launches New Bachelor's Degree in AI and Machine Learning
Taylor University is launching a Bachelor of Science in Artificial Intelligence and Machine Learning, building on four decades of AI expertise.
Time for an AI checkup: Flaw found in machine learning for sepsis treatment
Shengpu Tang, Emory assistant professor of computer science, found a flaw in many peer-reviewed studies using the AI method known as reinforcement learning as a theoretical guide for the treatment of sepsis.
Guardant Health (GH) Will Present 20 Abstracts At WCLC 2026
Guardant Health (NasdaqGS:GH) plans to present 20 abstracts at the World Conference on Lung Cancer 2026. The research covers liquid biopsy, methylation based detection methods, AI driven analysis and real world clinical data in lung cancer. Several abstracts focus on machine learning models aimed at assessing metastasis risk and refining diagnostic workflows for clinicians. Guardant Health's WCLC 2026 abstract slate signals broader research momentum that our work places in a wider context...
A taste of discovery: Real-time X-ray data analysis with DONUT accelerates materials science at Argonne
Argonne’s DONUT, a machine learning tool, delivers real-time X-ray data analysis at the Advanced Photon Source, giving scientists faster insights into material structures and accelerating discovery in advanced materials research.
AI and brain-computer interface allow speechless ALS patient to work a full-time job
The hardware isn't new, but a UC Davis research team's machine learning-powered method of translating brain activity in an ALS patient into sentences with 92% accuracy is
KU Business announces 2026 Sutton Lecture with VML innovation leader Brian Yamada
The University of Kansas School of Business will host Brian Yamada, global chief innovation officer at VML, for the Walter S. Sutton Lecture at 5 p.m. April 8. His talk, “From Madison Avenue to Machine Learning: Business Ethics at the Speed of AI,” will explore the ethical questions emerging as artificial intelligence transforms marketing, technology and business.
Q&A with Brain Health Expert Dr. James Galvin
The Miller School neurologist describes a new Brain Health Index that uses machine learning to measure brain health.
UC San Diego Students Learn Spatial Data Science Through Real-World Applications
UC San Diego students are bridging GIS and machine learning to solve real-world challenges in public health and urban planning. In a new spatial analysis course, they analyze complex geospatial data to identify environmental risks and support smarter community decision-making.
Machine Learning for Life Scientists: A Practical Methods Guide
Learn practical machine learning methods for life science research, covering supervised learning, model evaluation, and how to choose the right approach.
Five Berkeley Lab Scientists Receive DOE Early Career Research Awards
Five scientists from Lawrence Berkeley National Laboratory (Berkeley Lab) have received Early Career Research Program (ECRP) awards from the Department of Energy. The ECRP program, now in its 16th year, was established to bolster the nation’s scientific workforce by supporting exceptional researchers at the outset of their careers, when many scientists do their most formative work. Recipients use the funding to pursue projects that have the potential to solve scientific challenges and advance the country’s STEM capabilities. To be eligible for the program, a researcher must be an untenured, tenure-track assistant or associate professor at a U.S. academic institution, or a full-time employee at a DOE national laboratory or Office of Science user facility, who is within 10 years of having earned their doctorate. Awards to an institution of higher education will be approximately $875,000 over five years, and awards to a DOE national laboratory or Office of Science user facility will be approximately $2,750,000 over five years. This year’s Berkeley Lab awardees and their projects are listed below: Timon Heim: Better detectors for more powerful particle colliders Timon Heim is a staff scientist in the Physics Division. His ECRP project will develop a new type of detector that can track the particles generated by collisions in particle colliders with high spatial and temporal resolution. Current detectors in use at high-energy colliders, like the ATLAS detector at the Large Hadron Collider (LHC) at CERN, can track the trajectory and measure momentum of particles, but they can’t track a particle’s time of arrival with sufficient precision to discern from which of the many particle interactions they originate. Heim’s proposed technology — called a 4D tracking detector, because it captures data about particles’ positions in space and time — aims to solve this challenge, allowing researchers to glean more precise data from collision experiments to uncover new insights into fundamental forces and potentially discover new particles. The enhanced capabilities of 4D detectors will be a necessity for experiments at future colliders, such as the Muon Collider or Future Circular Collider. The project will develop custom microelectronics that process the signal of a pixelated silicon sensor that can register charged particles passing through it in an extreme radiation environment that these colliders produce during operation. The goal is to measure individual particles’ position with 50-micro-meter spatial and 50 pico-second (one trillionth of a second) temporal precision, for an incoming flux of around 1 billion particles per second per square centimeter. Harrison Lisabeth: Studying underground processes to unleash abundant energy Harrison Lisabeth is a research scientist in the Energy Geoscience Division focused on combining real-world experiments and AI to guide development of geothermal energy systems. His project will explore how deep underground rock formations – where geothermal wells are dug – are stressed by chemical reactions of the minerals and mechanical strain from movements in the Earth. A better understanding of subsurface stresses is essential to engineer safe and reliable geothermal wells that access the heat of deep rocks to produce electricity. It is estimated that the U.S. could generate ten times the amount of energy needed to power the grid using geothermal sources. Lisabeth plans to simulate how minerals grow, dissolve, and influence the behavior of surrounding rock in geothermal reservoirs by tracking chemical changes in rock samples under pressure in the lab. The project will also develop machine learning models to predict geologic strain based on field measurements and refine existing models that describe how common rock types deform under stress. Combining these approaches, Lisabeth hopes to develop a framework to predict stress to improve stability and longevity of geothermal wells. Daniel Carney: Pushing the quantum boundaries of particle measurement Daniel Carney is a staff scientist in the Physics Division who uses quantum information science to study particle physics and gravity. At very small scales, “quantum noise” — random fluctuations from the act of measurement — can drown out faint signals from rare processes. Carney’s ECRP project, “Fundamental Physics at the Quantum Limits of Measurement,” aims to build a new mechanical quantum sensor that can measure extremely small particle interactions below the limits of that quantum noise. The approach uses lasers to hold a tiny bead containing radioactive atoms; when one decays, the system measures the bead’s motion and the energy of any particles emitted. The experiment’s initial goal is to search for heavy sterile neutrinos, hypothetical particles predicted by some extensions of the Standard Model. The system could also help measure the mass of the neutrino, study the way particles interact through the electromagnetic and weak forces, and hunt for low-mass dark matter — all unsolved mysteries in particle physics. Callum Wilkinson: Using machine learning to demystify neutrinos Callum Wilkinson is a staff scientist in the Physics Division and part of the international Deep Underground Neutrino Experiment (DUNE) hosted by Fermi National Accelerator Laboratory. DUNE will create an intense beam of neutrinos that interact in detectors in Illinois and, after an 800-mile journey through the earth, in massive detectors in South Dakota. By precisely measuring neutrinos and how they change, researchers will get a better understanding of their fundamental properties and whether they are responsible for why there is so much more matter than antimatter in our universe. Wilkinson’s ECRP project will apply two machine learning techniques to improve the analysis of DUNE’s data and enhance the experiment’s precision. One approach will look for hidden patterns in how neutrinos interact and produce low-energy particles, while the other will help correct for distortions in what the detectors record. The methods will be tested on one of DUNE’s prototype detectors and could be used in other neutrino experiments, improving their measurements and helping scientists build better models. Aditi Krishnapriyan: Machine learning methods that scale as datasets grow Aditi Krishnapriyan is a faculty scientist in Berkeley Lab’s Applied Mathematics and Computational Research Division (AMCR) and assistant professor at UC Berkeley. Her project, awarded through UC Berkeley, will develop machine learning methods that scale with dataset size for modeling complex, multi-scale phenomena. The resulting tools can be applied in a wide range of research, including the study of new electrolytes for batteries or the examination of molecular interactions. ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.
Solving real-world challenges with data, AI and machine learning
UT San Antonio graduate students partner with industry and community organizations to develop practical solutions.
Jesse Thaler named director of the Laboratory for Nuclear Science
Jesse Thaler has been named director of the MIT Laboratory for Nuclear Science. Thaler is a theoretical particle physicist who combines techniques from quantum field theory and machine learning to address outstanding questions in fundamental physics.
Startups to watch: Baton Rouge’s entrepreneurial surge
Put researchers, incubators and investors in the same ecosystem, and you get a region where innovation can thrive. That momentum is showing up in the number of Capital Region startups now drawing attention far beyond Louisiana. CodeGig CodeGig is a Baton Rouge software firm that leverages artificial intelligence and machine learning to help industrial companies […]
UTC computer science team’s AI research paper accepted to NeurIPS 2026
A research team in the University of Tennessee at Chattanooga Department of Computer Science and Engineering has had a paper accepted to the main track of NeurIPS 2026, one of the world’s leading conferences in artificial intelligence and machine learning.
Scientists Finally Figured Out How Much Groundwater There Is Under the US and It Is Enough to Fill the Great Lakes 13 Times Over
Researchers used 1 million data points and a machine learning algorithm to estimate groundwater stores with higher resolution than ever before.
PlayStation Acquires Machine Learning Specialist to Achieve 'New Levels' of Game Visuals
Cinemersive Labs
AI discovers the hidden signal of liquid-like ion flow in solid-state batteries
Solid-state batteries could be safer and more energy-dense than today’s lithium-ion technology, but finding materials that allow ions to move quickly through solid electrolytes has been difficult. Researchers developed a machine learning pipeline that predicts Raman spectra and identifies a distinctive low-frequency signal linked to liquid-like ion motion inside crystals. This signal appears when rapid ion movement temporarily disrupts a crystal’s symmetry. The approach could dramatically speed up the discovery of superionic materials for advanced batteries.
Four researchers win data harmonization competition
The U.S. National Science Foundation National Synthesis Center for Emergence in the Molecular and Cellular Sciences (NCEMS) at Penn State has awarded four researchers from across the world for their efforts in a competition to build machine learning-based approaches for streamlining publicly available datasets for reuse.
Teradata Brings Elastic Python Compute to AI Studio, Unifying Data, Governance, and AI
New distributed Python capability accelerates large-scale AI operations while keeping data and compute within the Teradata platform SAN DIEGO, Oct. 14, 2026 — Teradata today announced the general availability of Elastic Python Compute within AI Studio. The capability gives enterprise data science teams a managed, distributed Python environment for scaling AI compute and machine learning […]
New evidence suggests spinosaurus spent most of its life underwater
Millions of years after animals first conquered land, some took an extraordinary evolutionary U-turn and returned to the water. Using machine learning and thousands of fossil measurements, Yale scientists have uncovered surprising clues about which prehistoric creatures truly became aquatic. Their findings suggest that Spinosaurus spent most of its life underwater, while some ancient marine reptiles previously thought to be fully aquatic actually spent considerable time on land.
Master’s degree in artificial intelligence gets underway
A new master’s degree in artificial intelligence being offered this fall at WSU will provide students with high-demand skills in AI and machine learning to meet industry needs around the state and region.
How BMO uses AI to get more of business clients' business
The Canadian bank is using machine learning to analyze customers' payments and provide recommendations to salespeople.
I overcame my fear of learning about AI and landed a Big Tech AI job
A product designer share how embracing her inner "mad scientist" and experimenting with AI helped her land a job at Adobe, with no formal background in machine learning.
Team's prediction task compares GPT-4o with classic machine learning
Large language models have been functionally opaque. Seeking transparency, a team undertook a comparison with traditional machine learning.
Where Forensic Science Meets AI: eSleuth, Inc. Names Former ATF Director Ken Melson to Its Advisory Board
eSleuth, Inc., the AI-driven investigative platform putting the power of advanced data analysis and machine learning directly in the hands of investigators, today announced the appointment of Kenneth E. Melson, former Acting Director of the Bureau of Alcohol, Tobacco, Firearms and Explosives, to its Advisory Board. Melson brings a rare depth of expertise spanning federal prosecution, agency leadership, and forensic science policy that directly strengthens eSleuth AI's mission to transform how la
68 quadrillion underground miles of fungi
With machine learning and a high-resolution imaging robot, scientists measured and mapped the extent of Earth’s carbon circulatory system.
Advancing AI for science: extracting and fusing cross-disciplinary expert knowledge with data to accelerate alloy discovery
High-entropy alloys are promising advanced materials for demanding applications, but discovering useful compositions is difficult and expensive due to the vast number of possible element combinations. Now, researchers have developed a novel AI-driven framework that integrates experimental data, computational modeling, and cross-disciplinary expert knowledge extracted from scientific literature. By combining these sources in a way that accounts for uncertainty, their approach can make reliable predictions even for poorly studied alloy compositions, outperforming conventional data-driven machine learning methods that rely on training data alone.
Blending Fusion Science, AI to Predict DIII-D Tokamak Behavior
Data scientists from the U.S. Department of Energy’s Thomas Jefferson National Accelerator Facility and partner institutions have developed a deployable machine learning (ML) framework that can adaptively predict changes in a magnetic confinement fusion research machine’s hardware. The innovative method, recently published in the journal Machine Learning with Applications, promises to help make the operation of fusion devices more stable and accelerate humanity’s pursuit of a superabundant
Latest News In AI Chips - AI-Powered Shift: Transforming Data Into Business Intelligence
Treasure Global Inc. has secured a US$15 million contract to develop an AI-powered Social Listening Platform for Creative World Industries, emphasizing the growing demand for AI systems that convert digital data into actionable insights. This platform leverages machine learning and NVIDIA-powered infrastructure to provide real-time sentiment and trend analytics across various sectors. The deployment underscores a trend in the AI chips industry towards enhancing real-time decision intelligence...
New MatterChat Model Helps AI to ‘See’ the Language of Science
From writing emails to generating computer code, much of the artificial intelligence prevalent in our daily lives has succeeded by mastering one domain: text. However, this leaves a major blind spot in the physical sciences, where models depend on the high-resolution, three-dimensional data of the physical world, like the intricate lattice of atoms in a crystal. Delivering on the promise of using AI for science requires teaching these data-driven text models to seamlessly “talk to” physics-based models. Now, a new AI framework from Lawrence Berkeley National Laboratory (Berkeley Lab), called MatterChat, solves this problem by creating a specialized “bridge.” It connects the conversational power of a Large Language Model (LLM) with a physics-based AI that models “interatomic potentials”: the complex physical forces between atoms. The resulting system already significantly outperforms general-purpose AI tools like GPT-4 at predicting material properties, and the team hopes it can accelerate scientific discovery by serving as a robust research partner that provides grounded insights and generates step-by-step instructions for synthesizing novel materials. A paper describing this work was recently published in Nature Machine Intelligence. “Traditional simulations can provide the physical rigor required for materials science, yet their computational cost remains prohibitive for high-throughput screening. Conversely, while LLMs excel at rapid knowledge synthesis, they inherently lack the ‘structural vision’ to interpret materials directly from their underlying atomic coordinates,” said Yingheng Tang, a postdoctoral researcher in Berkeley Lab’s Applied Math and Computational Research Division (AMCR) and lead author on the paper. “MatterChat was built to solve this dilemma, empowering LLMs with a structural ‘vision’ that allows researchers to leverage their full potential for solving complex, real-world materials challenges.” Empowering language models to solve complex challenges in materials science To build MatterChat, the Berkeley Lab team drew inspiration from technologies like Vision Question Answering (VQA) and Text-to-Image (T2I) generation. In these tasks, AI must translate high-level text concepts into visual images, or vice versa. Doing so requires developers to build tools that “bridge” two fundamentally different forms of data. The researchers adapted this concept to the physical sciences. With MatterChat, they created a “bridge model” that successfully connects an LLM’s general knowledge with the deep understanding of the atomic-scale world encoded in scientific interatomic potentials. Until now, researchers using LLMs to solve materials problems usually had to feed them raw data files as if they were just strings of text. It’s like asking an AI to understand a complex 3D engine based only on a parts list: the LLM can read the names, but it can’t “see” how the atoms fit together in space. MatterChat solves this by training a specialized AI bridge model, pre-trained on millions of crystal structures and an LLM, to align the LLM’s representation of the world with the interatomic potential’s representation of the world. The bridge model can translate physical insights into a format the LLM can actually understand. By giving the LLM these “scientific eyes”— a scientific “inductive bias” in the terminology of AI — the Berkeley Lab team has transformed it into a robust research tool capable of providing grounded scientific insights into complex materials challenges, such as predicting thermal stability or analyzing electronic band gaps. “We think of atoms as living in a physical space, but from a machine learning perspective, they are just vectors living in some very non-trivially structured manifold in a high-dimensional Euclidean space; and, of course, the same is true for the sentences and paragraphs that we use to express our ideas about those atoms,” said co-author Michael Mahoney, Berkeley Lab’s Scientific Data Division (SDD) AI Initiative Research Lead. “The bridge model basically gets those two structures to ‘talk with’ each other.” As a proof-of-concept of this general approach, the team trained their bridge model on a dataset curated by pairing nearly 143,000 stable atomic structures from the Materials Project with their corresponding physical properties. This training data was automatically assembled using the Materials Project’s API and deliberately enriched with properties fundamental to microelectronics design — like formation energy and bandgap — allowing MatterChat to learn the complex patterns connecting a material’s atomic blueprint to its functional performance. To validate their model, the researchers benchmarked MatterChat against a suite of other AI systems, from general-purpose LLMs to other specialized scientific AI methods. The results show that MatterChat consistently outperformed its competitors across a range of tasks. The model was more accurate in classifying material types and demonstrated superior precision in predicting numerical properties. For example, it excelled at predicting a material’s bandgap, a property critical for designing new electronics from high-capacity energy storage to next-generation computer chips. “Our design is significantly more efficient because we don’t have to build a massive AI model from the ground up,” said co-author Zhi (Jackie) Yao, a research scientist in Berkeley Lab’s AMCR. “Instead, we take two powerful, pre-trained models — a structural encoder for materials physics and an open-source LLM — and use them off-the-shelf. The only component we actually train is the lightweight ‘bridge model’ that translates between them. It’s the difference between building an entire car factory and simply designing a smart adapter that connects a world-class engine to a world-class navigation system. This approach is not only computationally efficient, but also makes the system modular, so we can easily upgrade components or adapt the bridge for other scientific domains in the future.” Crucially, this modular design highlights exactly how institutions like Berkeley Lab and the Department of Energy are carving out a highly valuable niche in the booming AI landscape. Rather than competing with Silicon Valley tech giants to build ever-larger language models from scratch, the lab is focusing on the specialized connective tissue that makes commercial AI useful for hardcore science. Because the bridge model approach underlying MatterChat is forward-compatible, it is perfectly positioned to leverage these parallel tracks of innovation. As Mahoney pointed out, “We expect that industry will continue to develop improved LLMs, and we expect domain scientists and facilities will continue to generate new data. An important part of scientific machine learning is not simply to solve problems on today’s data, but instead to develop general methods that will be forward-compatible with orders of magnitude more data, whether from scientific domains or from LLMs.” According to Yao, the MatterChat project, which was initially developed and enhanced with funding from a Berkeley Lab Laboratory Directed Research & Development (LDRD) Program, will now expand its capabilities. In a collaboration with Fermilab, MatterChat is already contributing to a U.S. Department of Energy Genesis Mission project — called Accelerating eXtreme Environment Specs-to-Silicon (AXESS) — that aims to speed up the development of next-generation, high-speed, radiation-hardened detectors for challenging particle physics experiments by using advanced 3D integrated circuits (chiplets) and AI-driven data analysis. In addition to the LDRD support, the team also credits supercomputing resources at the National Energy Research Scientific Computing Center (NERSC), located at Berkeley Lab, with MatterChat’s success. “We are incredibly grateful to NERSC; this research simply would not have happened without access to the Perlmutter supercomputer through their AI for Science program,” said Tang. Wenbin Xu, a NERSC postdoctoral fellow at the time, was also a major co-author of the work, as was Benjamin Erichson, a research scientist in Berkeley Lab’s SDD, highlighting the benefits of AMCR-SDD collaboration on AI for science. ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.
Global Health & WASH: May 2026 Funding Opportunities (14 new opportunities!)
The May update for Global Health & WASH brings new calls that cluster around three distinct shifts: LMIC research leadership moving from funder encouragement into a hard eligibility constraint, pharmaceutical and corporate-linked funders running structurally independent education and use-inspired research portfolios at substantial scale, and AI adoption shifting decisively from speculative tool to operational capability across health and WASH systems alike. In LMIC Research Leadership as Eligibility, Not Encouragement, Wellcome anchors the dominant signal of the month with three separate major calls that all require or strongly center LMIC leadership in their basic eligibility architecture. Wellcome’s ESIC Hubs (£1.5-£1.9M per hub over 3-5 years) explicitly requires an LMIC-based lead applicant, treating Global South leadership as a design constraint rather than a participation goal. Wellcome’s Infectious Disease Clinical Trial Development Award funds transdisciplinary teams whose lead must be a mid-career or established researcher based at an organization in Africa, South Asia, or Southeast Asia. Wellcome’s larger Infectious Disease Clinical Trial Award (£1M-£8M for optimizing licensed interventions) requires the administering organization to be in eligible LMIC regions and at least 50% of applicants based there. Around Wellcome, the same pattern shows up at smaller scale and across funders: CHINNOVA channels $1M into West and Central African research institutions for climate-health work; IBRO’s Neuroscience Training Grants explicitly tier ceilings by region of residence with Africa receiving the highest amount ($5,000); IHME’s GBD Emerging Researcher Award reserves at least one of its two annual awards for an LMIC researcher; Sidaction’s HIV Cure call funds research teams across France, the Netherlands, and eligible African countries with a minimum two-country collaboration requirement. Taken together, these calls signal a meaningful shift in how research equity is being operationalized — not as encouragement language at the bottom of a call, but as a structural feature of who can lead, where the work must be administered, and what proportion of the team must be regionally rooted. In Pharma-Funded Independent Medical Education and Use-Inspired Research, the lineup this month is unusually deep. Pfizer alone runs four parallel calls under independent education and quality improvement frameworks: a Migraine and Women’s Health RFP (200K x 2-year, $500K pool), Migraine IME ($200K, $1M pool), JAK Inhibitor evidence-based education ($100K), and Maternal Vaccination HCP Education in Saudi Arabia ($75K), plus a separate Pediatric Pneumococcal Surveillance research call in Saudi Arabia ($400K per project). Novo Nordisk Foundation runs four parallel calls of its own at substantial scale: Infectious Diseases Catalyst Grants (DKK 60M pool, up to DKK 7M collaborative), Non-Diabetic Endocrinology Collaborative Grants (DKK 53M pool, DKK 5-10M per project), Pioneer Innovator Grant Health (DKK 1.1M), and Distinguished Innovator Grant Health (DKK 6.8M). LEO Foundation deploys DKK 2-4M per project for dermatology research excellence. Pfizer’s framing across calls deliberately separates the funding from product promotion, emphasizing measurable practice-relevant change and structural distance between scientific outputs and commercial activity. Novo Nordisk’s “use-inspired” hard filter pushes researchers toward a credible translation pathway from mechanism to deployable tool. The pattern: industry-aligned funders are running structurally independent portfolios at meaningful $ that fund implementation evidence, not promotion. In AI as Operational Capability, Not Speculation, several calls this month explicitly treat AI adoption as a near-term operational question with real evidence and governance requirements, rather than a speculative trend to be watched. Wellcome’s ESIC Hubs frames AI as “the accelerator” for evidence synthesis, asking hubs to translate technical innovation into adoption-ready workflows that can match real-world policy timelines. UNICEF Venture Fund’s new Climate Tech for Children’s Health call funds for-profit startups deploying AI, machine learning, and blockchain in low-resource environments, with a strict requirement that solutions must already have a working prototype with promising pilot results. The Water Research Foundation has launched two simultaneous AI calls treating reproducibility, cybersecurity guardrails, and human-in-the-loop deployments as core design constraints rather than nice-to-haves: one on GenAI and Agentic AI in water utilities ($200K), one on alternatives to water shutoffs that explicitly screens for implementation economics evidence. Nordic Innovation’s quantum technology call (NOK 4.5M) extends the same logic into life science and healthcare, demanding consortium-stage projects mature enough to test with real users. TEF-Health offers €300K in subsidized testing infrastructure for European healthcare AI and robotics SMEs. The pattern: funders are converting AI pilots into playbooks, requiring transfer evidence and risk controls before AI can move from experimentation to sector-wide operational practice. For LMIC-based researchers in infectious disease or evidence synthesis, the three Wellcome calls together represent the deepest concentration of LMIC-led research capital in recent memory — pair the Infectious Disease Clinical Trial Development Award (£200K, deadline May 19) as a strategic pipeline feeder into the larger Infectious Disease Clinical Trial Award (£1-£8M, deadline June 2), since the development award functions explicitly as a track into the larger trial scheme. For Nordic researchers, the four Novo Nordisk Foundation calls plus LEO Foundation and Nordic Innovation Quantum represent ~$30M in regional research capital this cycle, all with deadlines clustered in May-August. For WASH practitioners, the three Water Research Foundation calls together (each up to $200K) are a cluster worth reading across — one on AI deployment, one on shutoff alternatives, one rewarding applied innovation — and the strongest applicants will articulate how their work bridges utility operations to policy-relevant evidence. For US-based clinical researchers and educators, the four Pfizer calls plus the APF Portfolio’s 80+ programs form an unusually accessible recurring pipeline; APF’s prohibition on indirect costs is a meaningful structural feature for early-career applicants whose institutions might otherwise absorb significant overhead. Snapshot of New Opportunities Total Estimated Funding Pool: $100 Million+ USD The grants are organized into three categories: Open Calls: Current grant and opportunities with a deadline. Grants are listed by closing date. 39 open opportunities- 14 new! Rolling Applications: current grant and opportunities with rolling applications (but it’s still best to submit as early as possible). 14 rolling opportunities- 1 new opportunity! Long term planning: Grants that have closed their current rounds, but are expected to open new windows. 4 long term opportunities! A quick tip for returning readers: if you want to jump straight to the newest additions, use CTRL F to search for “New!” and navigate quickly to the latest funding opportunities This post is for paid subscribers. This helps support the time and effort it takes to curate and organize these opportunities. Subscribe To keep this accessible to everyone who needs it, we’re happy to offer pay what you can rates. You can find more details here. Open Calls: Migraine Competitive Grant Program: Migraine and Women’s Health (Quality Improvement and/or Research RFP), Pfizer.*Closing soon!* Pfizer, in collaboration with the American Headache Society, is seeking independent quality improvement and research projects that close persistent gaps in migraine care for women across the United States. The funding logic centers on measurable, practice-relevant change: proposals should generate actionable evidence, tools, or system improvements that can be shared and used widely, not just within a single site. Priority areas span menstrual migraine and screening in women’s health settings, the role of hormonal fluctuations across the lifespan, sex-specific risk factors, comorbidities and quality-of-life burden, and disparities in outcomes by race, ethnicity, and socioeconomic status. The call is structured as a two-step competitive process with an initial Letter of Intent, with the strongest concepts expected to translate scientific insight into implementable improvements in diagnosis, management, and equity. Geographies: United States. Who can apply: U.S.-based organizations (not individuals), including professional schools, healthcare institutions, professional organizations, and other entities focused on healthcare improvement. Funding amount: Up to USD $200,000 total over 2 years; total pool USD $500,000. Targeted Sectors / SDGs: Health; Focus areas: migraine, women’s health, quality improvement, clinical research, menstrual migraine, health disparities. Deadline: LOI: May 5, 2026; Full proposal (by invitation): August 28, 2026. Learn more and apply here. This RFP rewards “shareable impact”: the most competitive submissions will pair rigorous methods with a clear plan for system uptake and dissemination beyond the originating institution. ReSSARC: Strengthening the Health Sector and Food Security in the Central African Republic (AID 013381/01/0) Call for Proposals, Italian Agency for Development Cooperation (AICS). *Closing soon!* AICS is seeking a limited set of high-capacity humanitarian partners to deliver an integrated package that stabilizes essential health services while reducing acute malnutrition and strengthening food security resilience in priority areas of the Central African Republic. The funder’s logic is explicitly nexus-based: proposals should meet urgent, life-saving needs while reinforcing local systems, coordination mechanisms, and community-level capacity so results persist beyond the emergency window. AICS emphasizes inclusion and protection outcomes, expecting gender-sensitive design and explicit measures for groups facing compounded vulnerability, including women, children under five, internally displaced people, and people with disabilities. The call also signals a localization pathway, encouraging stronger collaboration with national NGOs and balanced partnerships, alongside clear coordination with existing cluster and humanitarian actors to minimize duplication and improve coverage. Geographies: Central African Republic (Bangui, Ombella-Mpoko, Ouham, Ouham-Pende, Lim-Pende, Lobaye). Who can apply: Non-profit organizations registered with AICS, plus eligible non-profits without an office in Italy that have a pre-existing collaboration agreement with an AICS-listed organization. Funding amount: Total pool EUR €1,800,000; max EUR €800,000 (single applicant) or EUR €1,000,000 (ATS). Targeted Sectors / SDGs: Health; Focus areas: malnutrition prevention and treatment, maternal and child health, community health services, food security and resilience, localization partnerships Deadline: May 5, 2026. Learn more and apply here. This call is structured to reward partners who can link frontline service delivery to system durability and localization, not just short-term coverage gains. Contracts for Innovation in drug and alcohol addiction healthcare, Innovate UK. *Closing soon!* Innovate UK, on behalf of the Office for Life Sciences Addiction Healthcare Goals program, is procuring R&D to accelerate innovations that can improve treatment outcomes, strengthen recovery, and reduce harm and deaths linked to drug and alcohol addiction. The competition is positioned as a market-facing readiness push: selected projects are expected to advance solutions toward later-stage validation, generate evidence of user acceptability and UK market fit, and map credible routes through regulatory and certification requirements. Innovate UK signals a preference for innovations that can be field-tested in relevant UK settings and progressed to TRL 6 or 7, with practical plans for commercialization after contract completion. This structure favors applicants that can execute most work in-house, co-develop with service providers and people with lived experience, and translate technical progress into deployable tools for addiction healthcare delivery. Geographies: United Kingdom. Who can apply: Single organizations of any size (including EU, EEA, or international) leading delivery, with most work and key deliverables carried out in the UK; subcontractors for specialist skills only. Funding amount: GBP £200,000–1.5 million per project (inclusive of VAT); Total pool: GBP £20 million (across two strands). Targeted Sectors / SDGs: Health; Focus areas: Mental Health, Health Systems Strengthening, Research & Development, Technology Access. Deadline: May 6, 2026 (11:00am). Learn more and apply here. This call uses procurement to pull near-market addiction innovations toward operational proof and adoption pathways in UK services.
Q&A with chemist Lily Robertson: Pioneering autonomous discovery to accelerate science at Argonne
Argonne assistant chemist Lily Robertson explains how her team is using robotics and machine learning in the Robotic Autonomous Platforms for Innovative Discovery-200 lab to accelerate materials discovery.
Scientists identify 10,000 'impossible' exoplanet candidates, potentially tripling the number of known alien worlds
A new study has identified a potentially record-breaking haul of transiting exoplanets, thanks to a machine learning algorithm that analyzed the light curves of more than 80 million previously overlooked stars.
ISU-OSF Data Science Competition returns to tackle missed medical appointments
The ISU–OSF Data Science Competition invites students to develop machine learning models that predict missed medical appointments and help improve healthcare delivery.
NASA Uses Machine Learning to Enhance Flash Flood Warnings
The Transient Artifact and Continuous Learning System (TACLS) leverages data from continuously operating satellite networks coupled with machine learning models to help meteorologists at the National Weather Service forecast flash floods more efficiently.
SWFL nonprofit donor consulting firm surpasses $120M in revenue
VeraData, founded by Michael Peterman, has tapped into machine learning and AI to innovate — and influence — the way nonprofits find and connect with donors.
Assistant Professor of Climate Data Science | The Journal of Blacks in Higher Education
Boston University invites applications for a faculty position at the rank of Assistant Professor in the Department of Earth Environment. This tenure-track position in Climate Data Science , beginning on July 1, 2027, is part of the ongoing cluster hire in Environmental Data Science and complements faculty searches within the Faculty of Computing and Data Science.rnrnWe particularly welcome candidates whose research focuses on the physical basis of climate, climate variability, and climate change, and who employ data science methodologies to deepen our understanding of how the climate system is changing and how these changes affect the global environment. Areas of interest include—but are not limited to—the changing frequency and intensity of extreme weather events; interactions between the atmosphere and terrestrial, oceanic, biospheric, and cryospheric systems; and improved understanding of Earth system predictability at seasonal, decadal, and longer time scales. We envision this position playing a central role in connecting and enhancing existing departmental strengths in climate-related research and teaching, including terrestrial and marine biogeosciences, land surface and coastal processes, paleoclimatology, energy and economic policy, and environmental sustainability. This faculty member will also contribute to university-wide interdisciplinary initiatives — including the Urban Biogeoscience and Environmental Health (URBAN) program, the Institute for Global Sustainability, and the Center for Climate and Health at the School of Public Health — and is expected to develop collaborative ties with the Faculty of Computing and Data Sciences, where shared interests in machine learning, large-scale data analytics, and Earth system modeling provide natural ground for joint advising, co-teaching, and grant development. This hire reflects our commitment to advancing interdisciplinary scholarship at the intersection of climate and data science. This position contributes to the University convergence themes of Global Sustainability as well as Artificial Intelligence, Data Science, Computing that are core pillars of BU's cross-disciplinary research priorities.rnrn Position Qualifications:rn Candidates should have a PhD or equivalent degree, demonstrated ability to obtain external funding to support their program of research and scholarship, and a commitment to excellence and innovation in teaching, education, and mentorship of students. The successful applicant will be expected to supervise graduate research in Ph.D. programs, teach at all levels in the Earth Environment curriculum, and maintain an externally funded research program. We seek applicants whose research complement strengths in the Department and around the University. For more information about the Department, see
I overcame my fear of learning about AI and landed a Big Tech AI job
A product designer share how embracing her inner "mad scientist" and experimenting with AI helped her land a job at Adobe, with no formal background in machine learning.
Class of 2026: Data Science Student Ready to Support Small Businesses and Underserved Communities Through Cybersecurity and AI
By Kelsey Kendall For Makendra Crosby (B.S. ’24), data science and cybersecurity are not just about numbers and computers, they are about people. As she prepares to graduate with her master’s in data science and analytics with a concentration in artificial intelligence and machine learning, she is focused on using her skills to serve others and make technology more accessible.
Inaugural ASU–Science prize recognizes research that serves farmers from the ground up | American Association for the Advancement of Science (AAAS)
Meha Jain uses satellite imagery and machine learning to help smallholder farmers increase their food production sustainably.
Loyola’s Data Science in Everyday Life
Loyola University Maryland will hold “Data Science in Everyday Life: Applicability, AI, and Other Trends,” a networking and panel event exploring data science from a range of perspectives across industry and government.A keynote address delivered by data science industry expert David Norton, MBA ’95, will begin the event. Norton is the chairman and chief marketing officer of GALE Partners, a digital and analytics-based branding agency. He was named Chief Marketing Officer of the Year in 2010 and wrote “The High Roller Experience,” a book about data-driven marketing.Norton will moderate the panel discussion, which includes:• Elise Beisecker, senior data scientist in the U.S. Government Accountability Office• Amanda Muller, Ph.D., chief of responsible technology at Northrop Grumman• Franklin Hernandez, MBA ’13, M.S. ’20, senior lead machine learning engineer at Booz Allen Hamilton• Joseph Girsch, M.S. ’21, machine learning analyst at The Johns Hopkins University Applied Physics Laboratory• Mark Wicks, retired vice president of data science, data scientist, machine learning engineer and software developerHernandez and Girsch each earned an M.S. in Data Science from Loyola.The event is free and open to the public. Networking and appetizers will begin at 5:30 p.m. followed by the keynote address and panel discussion at 6:30 p.m. and audience questions and closing remarks at 7:30 p.m. To learn more and register to attend or stream the event online, visit the event webpage.
Cultivating Data-Driven Approaches with Plant Science
Launched this winter, the Data-Driven Plant Science course bridges experimental biology with embedded sensing, bioinformatics, and machine learning.
A simple oxygen hack creates 7 new ceramic materials
Penn State researchers created seven new high-entropy oxides by removing oxygen during synthesis, enabling metals that normally destabilize to form rock-salt ceramics. Machine learning helped identify promising compositions, and advanced imaging confirmed their stability. The method offers a flexible framework for creating materials once thought impossible to synthesize.
New Android malware uses AI to click on hidden browser ads
A new family of Android click-fraud trojans leverages TensorFlow machine learning models to automatically detect and interact with specific advertisement elements.
Is an Online Master’s Degree in AI a Good Idea?
A look at the real-world value of online graduate AI programs, combining hard data with firsthand experience of a big tech machine learning engineer
NYU Courant inducts Indian American computer science scholars
Gautam Kamath, Pratyusha Sharma bring expertise from MIT, and IIT Delhi to advance reliable machine learning.
Causal Inference Is Eating Machine Learning
Your ML model predicts perfectly but recommends wrong actions. Learn the 5-question diagnostic, method comparison matrix, and Python workflow to fix it with causal inference.
Machine learning analysis of CT scans
An AI-powered tool can interpret 3D images from CT scans and diagnose certain disorders.
Some assembly required, with machine learning
WashU researchers are working on ways to use machine learning to not just make predictions about potential chemical compounds but also to model out the synthesis of those materials.
Taylor Launches New Bachelor's Degree in AI & Machine Learning
Taylor University is launching a Bachelor of Science in Artificial Intelligence and Machine Learning, building on four decades of AI expertise.
Giving quantum computers a way to pick up where they left off
FIU is researching how to restart quantum workflows after interruptions to preserve computational progress. It lays the groundwork for quantum machine learning for drug discovery and climate modeling.
High schoolers are using AI to tackle cancer and build robotic surgeons
Finalists in the Society for Science and Regeneron $1.8 million science competition are increasingly turning to machine learning, and organizers are balancing innovation with misuse.
How an interdisciplinary approach is enabling faster health breakthroughs
Three ways that different disciplines, from astronomy to machine learning, are advancing medicine.
A machine learning system that identifies cancer cells based on how they scatter light
Cytology tests are widely used for cancer screening, but diagnosis can be challenging when tumor and non-tumor cells look very similar. Researchers from Japan developed a machine learning system that identifies tumor cells based on their white-light scattering spectra. The approach achieved high diagnostic accuracy for mesothelioma and showed potential for distinguishing several other tumor types, offering a promising approach for enhancing cytological diagnosis.
Ollama Now Runs Faster on Macs Thanks to Apple's MLX Framework
Ollama, the popular app for running AI models locally on a computer, has released an update that takes advantage of Apple's own machine learning framework, MLX. The result is a hefty speed boost on Macs with Apple silicon. According to Ollama, the new version processes prompts around 1.6 times faster (prefill speed) and nearly doubles the speed at which it generates responses (decode speed).
Human health risk from manganese in groundwater
Nature Water - Machine learning is the key to uncovering where populations across the globe are at risk from manganese in their groundwater-sourced drinking water.
Companies pledge free software and computing for AI in science
The European Commission has announced that a group of companies and foundations will offer free services, such as software or access to supercomputers, to European scientists using AI in their research.Unveiled on October 15 at a conference in Dublin, the initiative is the latest step by the Commission to try to help researchers use the technology. Scientists have used forms of AI, such as machine learning, for decades. But advances in deep learning in the past decade, and the wider interest in large language models since the release of ChatGPT in 2022, have spurred governments to invest in new AI tools that could help the scientific process.In July, the US announced it is spending $5 billion on the Genesis Mission, which it describes as a “national effort to harness AI for science,” funding 278 projects across areas such as autonomous labs or new AI tools in astronomy. The Commission has also identified AI in science as a priority, but the €107 million it pledged from Horizon Europe in 2025 does not come close to US spending levels. That said, the vast majority of public research spending in the EU comes from national funds. Today’s announcement at the European AI in Science Summit doesn’t commit any new public money. Instead, it launches the AI in Science Enablers Initiative, a set of voluntary contributions from companies and foundations. The idea is that the companies also benefit by getting access to scientific ecosystems, and feedback on how their tools work. Google, for example, has put up €3 million of cloud computing credits to European researchers. The Novo Nordisk Foundation intends to make its supercomputer, Gefion, available to European researchers, according to the Commission statement. The chipmaker Nvidia said it would provide access to tools, compute, training and collaborative projects.The publisher Elsevier also offered complementary licenses to some of its AI tools, plus AI training for PhD, postdoctoral and master’s students. The French AI firm Mistral will provide the European research community with access to its open-weight models, Vibe seats and application programming interface credits, according to the Commission’s statement. Vibe is one of the firm’s chatbot models. The idea is that more firms and foundations will come on board in the future as the initiative grows.
Scientists asked machine learning what a “youthful” diet looks like — the answer comes with an important catch
Nutrition research has spent decades asking what people who live longer tend to eat. A new study tried a more computational approach: let machine learning sift through the diets of nearly 192,000 people and identify the food pattern most strongly associated with healthier aging. The researchers called the result the Machine-learning YouTHful, or MYTH, Diet. People with higher scores had lower aging-related mortality and more favorable patterns on several aging measures. It is a clever use of a h
The weather and climate science AI revolution isn’t revolutionary
Machine learning has its limits—how is it being used?
Accelerating AI-Powered Chemistry and Materials Science Simulations with NVIDIA ALCHEMI Toolkit-Ops
Machine learning interatomic potentials (MLIPs) are transforming the landscape of computational chemistry and materials science. MLIPs enable atomistic simulations that combine the fidelity of…
AI just supercharged the race to find room temperature superconductors
Scientists have combined machine learning with quantum physics to discover two new superconductors and create a much faster way to search for many more. The technique could bring researchers significantly closer to the long-sought goal of a room-temperature superconductor.
Researchers gather at Cornell Tech to explore AI for molecular science
Researchers from around the world gathered at Cornell Tech for a workshop exploring how physics-aware artificial intelligence can accelerate the discovery of molecules and materials while improving the reliability of machine learning models.
Gut microbiome-mediated transformation of dietary phytonutrients is associated with health outcomes
Nature Microbiology - Systematic analysis and machine learning coupled with in vitro and in vivo validation link gut microbiome-mediated transformation of dietary phytonutrients to health outcomes.
SDSC to Host Fast Machine Learning for Science Conference
July 17, 2026 — The Fast Machine Learning (FastML) Foundation will hold the Fast Machine Learning for Science Conference 2026 through the University of California San Diego from Aug. 31 […]
Scientists Measure Earth’s Vast Underground Fungal Webs
With machine learning and a high-resolution imaging robot, scientists measured and mapped the extent of Earth’s carbon circulatory system.
Business Rules Management System Market to Reach $4.20 Billion by 2035 as AI-Driven Decision Automation Accelerates
The U.S. Business Rules Management System Market is Projected to Grow from $0.47 Billion in 2025 to $1.09 Billion by 2035, While Europe is Expected to Expand from $0.54 Billion to $1.36 Billion, Driven by Real-Time Decision-Making, Regulatory Compliance, Enterprise Automation, and Rising Adoption of AI- and Machine Learning-Enabled Business Rules PlatformsAustin, United States, Sept. 07, 2026 (GLOBE NEWSWIRE) -- The Business Rules Management System Market was valued at USD 1.79 Billion in 2025 a
Sony Interactive Entertainment Acquires Cinemersive Labs
With deep expertise in computer vision and machine learning, Cinemersive Labs will contribute to Sony Interactive’s efforts to enhance gameplay
From Nanochemistry to Smart Roads: The Multi-Scale Science Transforming Sustainable Pavements
A new Frontiers in Materials editorial maps how functional additives, multi-scale analysis, and machine learning are turning road construction into a quantifiably sustainable, data-driven discipline.
Machine learning models predict common cancer risks using routine health data
Machine learning models using routinely collected health information, such as blood tests, medical history and lifestyle factors, could help identify people at higher risk of several common cancers, according to an international study reported at the ESMO Congress 2026.
We can create the future of science right now
Paul Litvak is the founder and Executive Director of the Robyn Dawes Institute and a Visiting Scholar at UC Berkeley. He has a PhD in Behavioral Decision Research from Carnegie Mellon where he studied emotions and the sunk cost bias. Over 15 years in industry, he solved a wide range of challenging technical problems. At Meta he created models optimizing the ad review process. At Google he ran experiments to measure social influence. At Airbnb, he was a product manager leading machine learning teams optimizing search ranking and price suggestions. He also co-founded and led product at Rhythmic Health, a venture-backed biosensing startup, creating an accurate low cost system using a color changing strip and a smart phone to measure salivary lactate. The bottleneck At this point, it is uncontroversial to say that science needs to stop using the PDF article as the unit of knowledge and currency. The unbearable slowness of scientific publishing, the profit motive and margins: I’m not saying anything new. The PDF also sucks because it’s hard to extract structured information, which makes it hard to do evidence synthesis. As a result, we do much less evidence synthesis than is needed. And evidence synthesis ultimately undergirds most policy and medical decision-making. I can see second-by-second real-time odds for any sporting or newsworthy event, but a school board can’t see the best evidence on whether their 8th graders should be taught algebra. As a society, we don’t treat this as an important problem. Again, not controversial. Not only is the problem well understood, but the solution has already been laid out. What we need is AI-assisted living evidence synthesis - (1) an open knowledge graph of atomic claims, (2) claims linked to evidence, (3) assessment and synthesis of each piece of evidence, and (4) continuous updating with new data. What few realize (yet) is that the technical capacity to build this vision for a significant portion of science already exists. Not only that, scientists and startup teams are already building many of these components. I know this because I’ve been surveying the space and talking to many of the builders. There are some missing pieces: for example, evaluations of how well some of the components work. But at this point, most of what’s missing is a fully end-to-end working integration of all of these parts. In the rest of this essay, I’m going to lay out all the parts of a working living evidence layer for science and who is working on them, and propose concrete next steps for building this system. What’s now possible The diagram above outlines the components of a living evidence synthesis platform, including some of the teams working on each component1. Scientific PDFs are processed into claims with associated evidence. The evidence is subjected to a forensic audit, methodological evaluation and robustness and reproducibility checks. Finally it’s given a weight in a continuously updating synthesis. What follows is a description and status of each component and a few of the teams working on them. Document understanding The first thing you need to be able to do is turn an article into structured data. Mostly that means parsing PDFs. There are often multicolumn layouts that confuse non-specialized PDF to text processing libraries. For scientific papers, there is the added complexity of parsing formulas and tables and figures. This is a really hot area - there are startups offering APIs, and it seems like a new open source package gets posted to Github every few weeks. What follows isn’t exhaustive. A package called GROBID was the state of the art for a while, they didn’t update their package for nearly two years until very recently. In the meantime reducto.ai released an AI powered PDF extraction API, PaddleOCR became popular, IBM released a model called Docling, and both Mistral and Gemini created models and libraries. I also know of at least one other well-funded psychology research group working on a paper parser. By contrast, there are few open evals in this space, with no extensive evals for complex table comprehension in particular. Nonetheless, I’m confident this will be a solved problem soon, given the combination of LLM advances and developer interest. Hypothesis level extraction There has been increasing interest in comprehending the extracted text of papers and linking information to evidence for each hypothesis. A lot of work has already been done. Trialstreamer (Marshall et al. 2020) and RobotReviewer LIVE (Marshall et al. 2023) demonstrated automated extraction of trial population, intervention, and outcome at scale on clinical RCTs. PaperQA2 (Skarlinski et al. 2024) and Ai2 ScholarQA (2024) extended this to retrieval-augmented question answering with citation grounding. Elicit, Consensus, and SciSpace operationalized claim-level extraction for end users. OpenEval (Booeshaghi et al. 2026) is the most recent and most ambitious: 1.96 million atomic claims extracted from 16,087 eLife manuscripts using Claude Sonnet 4.5, grouped into ~299,000 results, with LLM evaluations showing 81% agreement with human peer review on a 2,487-paper subset. None of these solutions link claims to test-statistics, as you would need to evaluate randomized controlled trials. This is why I built the evidence.guide API - to extract hypotheses and associated test statistics from behavioral science papers. The best public eval of this kind of extraction I’m aware of comes from the recent SCORE project - they had humans code thousands of psychology papers to extract their claims by hand. It would be extremely helpful to the world if all scientific PDFs were available as structured open data. I’ve been working to make this happen, both directly at Berkeley and through coordination with large entities I can’t yet speak of; as hard as it is to do, I think it’s possible2. Forensic audit A lot of work has been done on forensic audit, but some gaps remain. Of course, for biology papers that rely on images for evidence, there are a variety of tools (notably Proofig and ImageTwin) to spot anomalies. These are still well short of what sleuths like Elizabeth Bik can do on her own, but these tools are constant companions among fraud analysts. There’s someone working on auditing Excel files for anomalies, and a number of teams are automating numerical checks like GRIM and SPRITE, including Scrutiny project, the INSPECT-SR team as well as statcheck. The regcheck team is building a way to use AI to compare preregistrations to analyses in papers, to ensure there aren’t significant deviations. Nonetheless, there are many other kinds of anomalies to screen for, both public and less publicly known. And there are no formal evals for anomaly detection that I’m aware of. Still, there’s a lot to draw from in this space and I’m pretty certain we will be able to scan papers for most kinds of obvious anomalies in the near future. Methodological review This area has been white hot, though I fear for many of the startups in this space, because this capability may become commoditized. There are at least six different AI peer review companies, including Refine.ink, Reviewer3, ReviewerZero.ai, Q.E.D. Science, Paper Wizard, and Isitcredible. Coarse (a pun on refine) was also recently created as an open source alternative. These systems provide qualitative feedback on the content of papers, spotting methodological weaknesses and mathematical errors. They seem to work pretty well, and many academics report bitterly that they exceed the average quality of typical peer reviewers. But there are few evals here either. What evals exist so far involve using LLM-as-judge (circularity problems abound) or comparing against human reviews of questionable quality. What you’d ideally want is an eval that measures capturing known errors in papers3. Reproducibility and robustness Another active area has been using AI agents to automate computational reproducibility4 and robustness5 checks in papers that report numerical results. For more recent papers where data and code are available, AI agents can see whether they can re-run the analyses and produce the numbers reported in the published paper. In addition to a handful of individual academics who have been experimenting using Claude Code for this, the Institute for Replication is a leading group working on building an end to end system. The evals related to this problem are the most mature, with CORE-Bench (Siegel, Kapoor, Narayanan 2024) and PaperBench (OpenAI 2025) available to benchmark agents on this task. There is also work on getting AI agents to test alternative ways of analyzing the data to ensure the results are robust to small analytic design choices. Synthesis This is the most underdeveloped area where significant investment is required. Although some automated evidence synthesis systems exist — for example, otto-sr is building an AI agent to write systematic reviews — none of these incorporate the full range of paper level signals to weight evidence appropriately. Nor is there anything like an eval or a gold standard for a good systematic review. Arguably Cochrane reviews are the closest we have to gold standard human systematic reviews, though I’ve heard academics in the know complain about their uneven quality. A key question for a synthesis platform is how to weight anomalies and methodological issues in assessing the quality of a piece of evidence. This is an unsolved problem and one I’m very keen to work on. Continuous updating There are many pieces of basic infrastructure available for monitoring for new research and initiating updates. OpenAlex is the current open citation graph. Retraction Watch integrated into Crossref in October 2023. Scite tracks how citations support, contrast, or mention prior claims. The Living Evidence Network demonstrated continuous-update workflows in clinical guidelines. Engineering this is a relatively straightforward task. When you look over this technical architecture and all the progress being made, it’s hard not to be optimistic that a living guide to scientific evidence will be built. The stakeholders are ready The social infrastructure for this is starting to coalesce — it’s not just a pie in the sky academic exercise to imagine this coming into existence. Institutions like the Center for Open Science, the Institute for Replication, the INSPECT-SR, the Living Evidence Network and more are all working on scaling work to improve research quality. Funders are also aligned. The Sloan Foundation has funded living evidence work through COS. Coefficient Giving supports the Institute for Replication and COS. The Astera Institute and the Institute for Progress have shown interest in this space. NIH has established an Office for Replication and Reproducibility. Although there are (very unfortunately) serious headwinds in science funding generally, there is an active group of funders interested in metascience. A brief word about what I’ve been doing at RDI. First, as a Visiting Scholar at Berkeley I’ve been actively figuring out how a non-profit and a public university can conduct and make public the results of large-scale academic article data mining. With some of the money I raised from donors, I commissioned a legal analysis of recent case law and publisher text data mining (TDM) agreements in order to understand whether a massive open data mining of academic articles is possible (with caveats, it is). I’ve also been working to bring together stakeholders in this space, and identify gaps. I’ve also been doing some software development in this space, with more to come. A pilot proposal The assumption undergirding all of this is that an AI, given all this information, would make the right judgment about a scientific claim with lots of conflicting evidence, weighing all the factors appropriately. That’s the hypothesis we need to test. Randomized control trial research is the best place to focus on first. RCTs are used to make many of the important decisions in society - from medical trials to public policy changes. And they use a relatively uniform set of inferential statistics with lots of known and available diagnostics. Behavioral science experiments, within the broader realm of RCTs, should be first used as a testbed whose results can be generalized. Because behavioral science is at the vanguard of open science practices, replications abound (there are thousands of them) to serve as ground truth training data. Key Hypothesis Therefore the pilot would test, in behavioral RCTs that have been replicated, whether the quality of evidence for a claim can be used to accurately predict whether that claim will replicate. Secondary Hypotheses Compared to claims that replicated, non-replicated claims demonstrate a greater share of forensic anomalies in their source literatures. Hypothesis level claim and statistic extraction is accurate enough to scale living evidence without onerous human review costs. Replication prediction is more accurate than prediction markets6 or journal prestige. If all the different quality signals we gather do accurately predict which studies will replicate, then we can use that model to score evidence to power the living evidence layer. Why this is informative regardless of outcome If the pilot succeeds, the architecture extends to medical RCTs (where Living Evidence already operates and integration is mostly about claim representation), then to slices of basic biology with stable replication structure. If it fails, the field learns which quality signals are load-bearing and which ones metascience has oversold. Either result is a contribution to knowing what the literature supports. Implications for funders Because this burgeoning ecosystem of builders already exists, a well-informed philanthropic or government funder could play a crucial catalyzing role in bringing this future about. They could play at least three roles: creating open structured datasets, publishing open benchmarks and incentivizing the solving of key technical challenges. First, open archives of papers that the government maintains, like PubMed, could be turned into structured data amenable to large scale metascience and claim aggregation. I know the US government already has an interest in doing this, though some key open questions remain unanswered. How do you determine the best models and systems for accurately extracting information from papers? How can you establish a robust way to allow researchers to flag errors and correct them? And finally, how do you create a legal regime in working with publishers to maximize the scope of available papers? For the latter, a university or a private philanthropy may be better positioned to make structured data publicly available under journal subscription terms or fair use. Philanthropists or government funders could also coordinate to create or commission benchmarks that evaluate whether important problems have been solved. For example, an open benchmark for claim extraction from a range of different scientific article types would be extremely helpful. Ensuring the underlying data are accurate is vital for creating these evaluations. I’ve discussed opportunistically using various human-created datasets for this purpose, but a consistent problem is that errors in human data make it difficult for them to serve as a gold standard. Finally, with structured data and benchmarks available, the government or private philanthropy could use them to incentivize groups to develop machine learning systems that meet these benchmarks. Prizes are one potentially valuable tool for this. For example, you could establish a prize for a system that accurately updates a living meta-analysis for a small set of claims. Prizes are particularly useful as signals of problem importance, and can help create vibrant ecosystems of public and private research—see, for example the role the government played in kickstarting the current work on nuclear fusion. Conclusion The drawbacks of the current scientific publishing system are known. Scientists agree, metascientists agree, philanthropists agree: the published PDF plus citation graph isn’t the right substrate for maintaining a representation of the evidence base in science. The pieces needed to build the alternative either already exist or are rapidly taking shape. The community is forming around exactly this problem, with concrete partnerships and shared infrastructure. A pilot should start on behavioral science RCTs because that’s the slice of empirical science most amenable to legibility, where replication ground truth is richest, and where the failure modes are best documented. What’s been missing is the galvanizing mission to assemble these pieces into something that works. That’s what I’m proposing to build. *** 1 I have a broader field map that I’ll release publicly soon. This is me, building in public! 2 If this is something that you are excited about, please reach out and talk to me. 3 More on this very soon too! 4 This tests whether, given the code and the data, you can get the same statistics as reported in the published paper. 5 This tests whether the results are the same as a paper’s given alternative analytical decisions in conducting the analysis (like outlier omission). Closely related is the idea of a “multiverse” where you come up with many different ways of answering the same underlying research question with the same data, and test whether the results hold in all those alternative methods. There’s been work on the latter as well. 6 Some of the replications, e.g. those from the SCORE project, had paired the experiments with forecasts from prediction markets. So we get to look at this for free.
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