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Bird and Human Faces Share the Same Genetic Blueprint. Why Do They Look So Different?
Changes in non-coding DNA sequences make all the difference.
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.
Flag football is surging in popularity. How different is it from tackle football?
With flag football surging in popularity, here is a breakdown of the major differences from tackle football and how the novices learn it quickly.
Analysis: Fulham's European hopes peter out
After beating Aston Villa at the end of April, Fulham were firmly in the mix not only for European qualification but potentially for a Champions League spot. A loss at Arsenal was not unexpected, the manner of the defeat by Bournemouth was hugely frustrating but victory at the league's bottom side would have kept Marco Silva's side in contention going into the final day. A huge swing in goal difference and the most unlikely sequence of results would now be needed for Fulham to make the top eight.
Strike three? Not quite. St. Charles-Vandebilt title winner rallies after close pitches called balls
SULPHUR — One pitch made all the difference for St. Charles Catholic in the state championship game.
Computer Science Grad Helps Develop AI System to Track Bee Pollen
On the edge of the Arboretum and Botanical Garden at Cal State Fullerton, honeybees return to their hive carrying tiny packets of pollen, a protein source that can mean the difference between a thriving colony and a collapse. But the warning signs are easy to miss. Class of 2026 computer science grad Kshitij Pingle is […]
Andrew Abbott leaned into some science to get his season back on track
Andrew Abbott locked in on the stick-figure graphic of himself. The world of biomechanics is very advanced. But with Abbott going through the worst rut of his big league career, he opened up to some new ideas to get himself back on track. He asked for some help, and he leaned into some sports science. He said the stick figures made it clear, and he credits biomechanics for helping him get his season back on track. Over his last three starts, he has allowed two total runs in 17 ⅔ innings. “There’s a photo of (me) last year and the year before on a graph, and this year is in a different color,” Abbott said. “You see where you are in comparison to that. Where the hands are at this moment, where your feet are, where the knee is, where your head is at, where your shoulders are at, it breaks it all down if you want it.” When Abbott saw his mechanics in this objective way, he recognized exactly what had been going wrong. “When my tempo, my feet and my hands are aligned, then normally my mechanics are sound,” Abbott said. “And when my mechanics are sound, my stuff behaves normally. The fastball is jumpy. I’m up in velo. My extension is better.” Abbott said that he started diving more into his biomechanics in the middle of April. The Reds have a biomechanics team, which works closely with pitching coach Derek Johnson and hitting coach Chris Valaika. Leading the group is biomecahnics manager Franky Mulloy. “I laugh every day,” Francona said. “When Franky starts talking, I start laughing. I remind him to know his audience.” Francona isn’t a scientist. The Reds’ manager jokes about his own comprehension of the data. But it’s easy for him to recognize the value that the biomechanics team is providing. “That’s why we got them,” Francona said. Abbott said that he had his first big biomechanics-oriented meeting in the middle of April. Behind the scenes, there was already a lot of communication going on within the Reds’ staff. “Franky calculates all the things, builds it into these graphs, and DJ gets them,” Abbott said. “They have their internal communication. Then, we go to them and ask what about this or that. The main thing for me is arm angle.” The data shows Abbott how his arm angle evolves from start to start. He hasn’t meant to change it. But just like all of us, our posture might be different from one day to the next. Pitchers’ arm angles can make minor shifts without the pitcher or even coaches noticing. But the data can’t miss a change like that. Abbott recognized that his arm angle had been moving around without him noticing. Now, he checks in with the data to make sure that his mechanics are in sync. “They get into these cool looking graphs,” Abbott said. “I can’t really describe them. I don’t know what they are. But for me, it’s a photo of a stick figure of myself.” The stick figure of his current positioning is superimposed over previous versions of himself. With that format, Abbott can compare his mechanics on any given day to what they were when he was pitching at his very best. “It was very noticeable from the moment I saw this year as black compared to two years ago and last year, it was very, very different,” Abbott said. “I could see with my eyes without having to ask any questions about the difference in the photos.” For any pitcher, there are a lot of moving pieces in your delivery. For Abbott to be the best version of himself, he needs his head, his arm, his hand, his knee and his feet to be on the right “tempo.” “Tempo is a word I’ve used a lot,” Abbott said. “Getting everything in certain areas working together.” He has been much better at that recently. The data backs that up, and so does Abbott’s ERA. The biomechanics data has also given the Reds’ staff and Abbott some better ideas for some training that he can do between starts. He has added drills to shore up areas of his body that Abbott needed to make stronger. “They make a plan based off what they see,” Abbott said. “If things start changing, we know what to revert back to.” Overall, Abbott says that he’s not a very analytical guy. There’s so much information out there, and he says that it’s important for him to not get too analytical when he breaks down his specific pitches. He has learned the value of being “analytical about mechanics.” And the way that the Reds presented that information to him made it easier for him to make the right adjustments without being overloaded by information. “It was a very simple fix,” Abbott said. “It took a long time to finally get there, feel the results and see it. But for the most part, it was very simple to apply it.”
As a French mom raising my kid in Florida, I've had culture shock
I'm a French mom raising my daughter in Florida. I've noticed differences between the way I parent and how people in the US parent their kids.
Trump and Xi move towards business-first relationship after Beijing summit
The two leaders did not resolve differences on key issues like Iran and Taiwan, despite push to extend truce.
South Lansing Business Association honors local businesses at annual awards ceremony
Four businesses recognized for making a difference in the community
Forza Horizon 6 Comparison Shows Difference Between Xbox Series X And Series S
How the Xbox versions stack up
Trump’s gilded indifference ignores America’s growing economic pain
Americans are sending Donald Trump an urgent message: They’re hurting and they blame his policies.
Solar and wind —what a difference a border makes
If you ask most Americans where Colorado and Wyoming are on a map, they will likely be able to find us, somewhere out west. A few might be confused by our roughly similar shapes. Sure, Colorado is actually a 697-sided polygon (a hexahectaenneacontakaiheptagon) due to surveying errors during the 19th century. Wyoming, on the other hand, is a […]
Incredible Uncharted 4 Documentary Reveals Huge Gameplay & Story Differences in Amy Hennig Version
It's no secret that the original Amy Hennig version of Uncharted 4 was significantly reworked, but now we have great details on the changes.
What Are The Differences Between The Ford And GM 10-Speed Automatic Transmissions?
Ford and GM 10-speed automatic transmissions share the same core architecture, but they use different fluids, software, and even some internal parts.
Brain scans reveal a shocking difference between psychopaths and other people
Scientists have uncovered a striking brain difference linked to psychopathy: people with psychopathic traits were found to have a striatum — a brain region tied to reward, motivation, and decision-making — that was about 10% larger on average than those without such traits. Using MRI scans and psychological assessments on 120 participants, researchers connected this enlarged brain region to thrill-seeking, impulsive behavior, and a stronger drive for stimulation.
Nottingham Forest v Newcastle United: Key stats and talking points
Having had their dreams of European glory ruthlessly dashed by Aston Villa, Nottingham Forest must now pick themselves up and secure the points they need to secure their Premier League status for next season. Six points clear of West Ham with three games to go, four points would definitely see them stay up, but a win should also prove to be enough given their vastly superior goal difference over the Hammers. The improvement by Vitor Pereira's side in front of goal in recent weeks has been astonishing.
What is perimenopause? The overlooked stage prior to menopause
Understanding what perimenopause is can make a meaningful difference in helping individuals and families navigate it.
MADSEN: Understanding gendered communication: What science reveals
This article is the fifth in a series designed to help parents, family members, educators, and community members better understand gender differences so we can more effectively support the children, youth, and families in our lives. The first editorial examined how boys and girls see the world, the second explored how they hear, the third […]
MADSEN: Understanding gendered communication: What science reveals
This article is the fifth in a series designed to help parents, family members, educators, and community members better understand gender differences so we can more effectively support the children, youth, and families in our lives. The first editorial examined how boys and girls see the world, the second explored how they hear, the third […]
Diablo II: Resurrected Ladder Season 14 Coming Soon
Ladder Season 14 begins on May 22. Glean the differences coming with Patch 3.2 based off your feedback from our recent PTR.
Diablo II: Resurrected Ladder Season 14 Coming Soon
Ladder Season 14 begins on May 22. Glean the differences coming with Patch 3.2 based off your feedback from our recent PTR.
American living Spain shares biggest differences in social life, meals
As an American, the biggest life differences I've noticed after moving to and living in Spain for years focus on vacation, socializing, and meals.
The Science of Workplace Behavior
When performance issues look like people problems, they’re often really system problems—and this post shows how to spot the difference before you try to fix the wrong thing.
Trends in Female First Authorship in Anatomical Science International: A 24-Year Analysis (2002-2025)
In recent years, the promotion of gender equity and diversity in academic research has attracted increasing international attention. However, studies examining gender differences in authorship w...
Mozilla says AI helped squash 423 Firefox security bugs
Yet it remains unclear if Anthropic's uber model was effective, or if better model middleware is what makes the difference
New study finds increase in ‘political breakups’
UC Irvine psychologists report a third of Americans have lost relationships over political differences.
Following the call of purpose - UT Health San Antonio
As a nurse, Laquonda Finney, RN, wanted to make a difference on an even larger scale. “I didn’t want to stop at nursing,” Finney said. “I liked the idea of not being confined to a space, where I can go out within the community to see what those needs are and to try to […]
The National Academy of Sciences Elects Two Berkeley Lab Researchers
Two Lawrence Berkeley National Laboratory (Berkeley Lab) scientists have been elected to the National Academy of Sciences (NAS) in recognition of their distinguished and continuing achievements in original research. They are part of a cohort of 120 new members and 25 international members. The National Academy of Sciences is a private, nonprofit institution that was established under a congressional charter signed by President Abraham Lincoln in 1863. It recognizes achievement in science by election to membership, and — alongside the National Academy of Engineering and the National Academy of Medicine — provides science, engineering, and health policy advice to the federal government and other organizations. The 2026 electees bring the total number of active members to 2,705 with 557 international members. Gary Karpen is a biologist senior faculty scientist in Berkeley Lab’s Biological Systems and Engineering Division and a Professor of Cell and Developmental Physiology in the Department of Molecular and Cell Biology at UC Berkeley. Karpen studies how the structure, organization, and maintenance of genetic material impact inheritance and cell function. His lab focuses on chromatin — a packaged form of DNA strands wrapped around proteins that comprises our chromosomes — with a particular emphasis on centromeres, rarely expressed sequences called heterochromatin, DNA repair, and regulatory RNAs. His research reveals insights into fundamental aspects of nuclear organization and genome regulation that are shared across the tree of life, broadening our understanding of cancer and other diseases, birth defects, and aging. Kam-Biu Luk is a Professor Emeritus of physics at UC Berkeley and a retired senior faculty scientist in Berkeley Lab’s Physics Division. He is an experimental particle physicist whose research has focused on neutrinos: nearly massless, electrically neutral elementary particles that can pass through matter virtually undetected. Neutrinos are created during radioactive decay processes, including the fusion process occurring in stars and the fission process that powers nuclear reactors. Luk initiated and co-led the Daya Bay Reactor Neutrino Experiment, a project based in China with more than 200 international collaborators. In 2012, the Daya Bay experiment discovered a new form of a phenomenon called “neutrino oscillation” in which neutrinos change from one type to another. For this discovery, Luk and his collaborator Yifan Wang received the 2014 American Physical Society Division of Particles and Fields W.K.H. Panofsky Prize for outstanding work in experimental particle physics, and he was also the co-recipient of the 2016 Breakthrough Prize. Luk is currently participating in the Deep Underground Neutrino Experiment (DUNE) hosted by Fermi National Accelerator Laboratory, which will investigate any potential difference in behavior between neutrinos and antineutrinos that may shed light on the evolution of the universe, especially why antimatter is absent in the present universe. ### 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.
New study finds increase in ‘political breakups'
UC Irvine psychologists report a third of Americans have lost relationships over political differences
New WAM Grants Support Research on Gut-Brain Interactions, Menopause and Sarcopenia
The Women’s Alzheimer’s Movement (WAM) at Cleveland Clinic has awarded grants for promising research in sex-based differences in the disease.
Enloe Health holds Nurses Making a Difference Award Ceremony during Nurses Week
Darrell Daugherty, a perioperative registered nurse at Enloe Health in Chico, has been named the second annual recipient of the health system’s Nurses Making a
IGN Entertainment releases internal data as public report, developed by Kantar and UC Berkeley
Generations in Play report highlights differences in game consumption habits, with Gen X as completionist Google users and Gen Z committed to social media
Jon Rahm on his contract with LIV Golf: 'I don’t see many ways out'
Rahm and the DP World Tour have settled their differences, setting up a Ryder Cup return for Rahm.
Tarik Skubal's elbow is now MLB's biggest mystery. His recovery time from surgery could be difference between a $250M and $450M deal
Citizen scientists can make a difference by observing the world
Citizen scientists volunteers attend an event or do observations through a specific research initiative on their own.
‘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
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Henry, Owner, Speedway Press, Mitchell Printing, Phoenix Press, Oswego “I’m going to answer this question by turning it around and giving my own advice that is, ‘Always treat every customer as if they were your only customer!’ We are celebrating our 50th anniversary in business this year. We started our business on March 25, 1976. My advice works.”” — Nick Chervinsky, President, Brewerton Pharmacy, Brewerton “’You need to toot your own horn, my grandfather, Howard D. Dice, told me.” — Beth Ann Dice, Women’s active aging and cancer recovery coach, Mission Movement Wellness Studio & Breast Cancer Recovery Studio, Fulton “In the micro enterprise class, they taught, ‘Get a good lawyer, accountant and bank.’ That was over 30 years ago and I still have those three today. I chose good people I could grow with.” — Tony Pauldine, Owner, Anthony M. Pauldine General Contractors Inc., Oswego “From my grandfather; ‘Listen to everyone! You never know where you will get the solution you need.’” — Kevin Dean, Executive director, Oswego County Opportunities, Fulton “‘Preparation is the mother of success.’ My dad always drilled that into my head growing up, and it has helped me avoid a lot of snags in both my personal and professional life. Thanks, Dad!” — Bekkah Frisch, Owner and president, iHeart Holdings, Inc., Fulton “If you don’t take a risk and try, you will never know.” — Justin Taber, Owner, Torbitt’s Service Center, Oswego “Be ready to take a loss.” — Cody Seymour, Manager, Damiano’s Eatery, Mexico “I’ve received so much good business advice over the years. When people are giving you advice, always be curious rather than judgmental. Follow your gut. Doing the right thing is never the wrong thing.” — Dana Decker, Principal owner/broker, Berkshire Hathaway Home Services, Oswego ’Do it and figure it out later.’ It hasn’t failed me yet. Too many people take time to think about doing this and this and this and then the opportunity is over with. If it’s somewhat reasonable — if it’s not something outrageous — do it and figure it out later.” — Jeremy Allen, Licensed real estate salesperson, Acropolis Realty Group, Syracuse “’Try to meet the needs of the customer. Treat them like you’d like to be treated.’ It’s like the Golden Rule says.” — Douglas Dickson, Physician and associate at CNY Foot Surgery and Podiatry Care, East Syracuse “The best piece of business advice I’ve ever received applies to life in general: always be honest. Never overstate your capabilities and always be willing to ask questions. The more specific you can be in any given topic will always lead to a better outcome.” — Tom Fernandez, President, Woodbine Hospitality, Syracuse “As a business owner, the advice would be ‘Don’t overextend yourself. Especially in agriculture, expect issues to come up.’ If something is going right, it’s not true agriculture. This last season, we scaled very hard to field another crew. We got more drones, crew and generators based on projections. This last season hit the ag community hard with a very wet spring. We did 15,000 to 20,000 acres for fungicide and the same for cover crop seeding in 2024. We scaled to 25,000 to 30,000 for 2025. We ended up doing 2,000 for fungicide and 3,000 for cover crop seeding. That’s why that advice was good.” — Rick Jordan, Operations manager, CNY Drove Services, Clinton “’Keep your head down, mind your own business and do the best for your clients that you can do.’ It has a lot to do with my success in working for Galloway Century 21.” — Ed Fayette, New York state licensed real estate agent, Century 21 Galloway, Oswego “’Expand in all ways, always.’ It just keeps you going and keeps you growing.” — Stephen Baier, Licensed real estate person, The Real Estate Agency & Bouck Commercial, Auburn “The best advice is, ‘Your biggest asset you won’t see on the books because it comes from your employees.’” — David Flickinger, Owner, Edible Arrangements, Syracuse “Don’t take things personally when you’re working with the public.” — Leeann Desimone, Manager, Annie’s Hallmark Shop, Auburn “‘Follow up.’ The customer knows that you care about what you are doing and that their opinion counts. Sometimes they might be on the fence about whether they want to use your company. If they’re asking your opinion on things, they feel you are concerned about what you’re doing. They like to be kept in the loop if they’re spending money with you.” — Dorothy Casper, Saslesperson, SweetWoods Memorials, Phoenix “The best business advice I received is ‘Never be the smartest person in the room. Surround yourself with people who are smarter than you.’ That’s how you learn.” — Debbie Bilello, Owner, Virtual Office Solutions, Central Square “’You need to grow your business but you can’t grow too big too fast.’ That’s what we’ve done at Pinnacle Maintenance. We’ve tried to grow but not too fast. Along those same lines, there’s another good piece of advice, ‘Don’t take on more than you can handle because it only leads to a bad name for your business and a bad experience for your customers.’ It goes along the same lines as growing your business slowly and carefully.” — David Wines, Owner, Pinnacle Maintenance, Central Square “Always be on time and do what you say you are going to do.’ I’ve done very well following that advice. Most of my work is referral and I don’t really do any advertising so I have a pretty good customer base. It’s mostly word-of-mouth from satisfied customers.” — Robert J. Fleming, Owner, Bob Fleming Windows & Doors, Brewerton “’Concentrate on your finances, always pay your taxes, and form a team you can depend on. Try to help the community you’re in as much as you can.’ We do all of these all the time here. We give and give and give to the community and the community gives back to us always.” — Susan Bonnet, Co-owner, Bonnet Sales and Service, Central Square “Keep smiling. It brings a positive attitude to the workplace.” — Karin Lindsley, Vice president, DTS Truck Service, Syracuse “‘Customer service above all.’ Following this advice has served us very well.’ — Guy Fox, Owner, Fox’s Discount Parts, West Monroe “The best thing to do is to make sure you’re taking in more money than you’re spending. I wish I could say I’m a good businessman but I have been in business since 1981. I’m not a bean counter but work well with my hands and its’s served me well all these years and I am still in business.” — Matt McGill, Owner, Matt McGill Collision, Brewerton
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“AI polls” are fake polls
A few weeks after Donald Trump’s second presidential win, I took the train up from London (where I was living at the time) to Oxford to attend a conference on polls and forecasts of the 2024 election. Most of the attendees were pollsters or academics, but I also watched presentations from Aaru and Electric Twin, two companies that do what is interchangeably called synthetic sampling, silicon sampling, or synthetic audiences. Stripped of startup jargon, that means they use large language models (LLMs) to simulate responses to public opinion polls by having AI agents take on the role of survey respondents. I had already heard of Aaru thanks to some articles with eye-catching headlines like “No people, no problem: AI chatbots predict elections better than humans” in the months leading up to Election Day. The founders were making some big, some might even say far-fetched claims, such as: “within two years, we will simulate the entire globe — from the way crops are grown in Ukraine to how that impacts production of oil in Iraq, trade through the strait of Malacca, and elections for the mayor of Baltimore.” When Semafor asked Aaru’s cofounders — Cameron Fink and Ned Koh — about my boss, they said “we respect all those who came before us.” Nate (as he so often does) shared his thoughts on Twitter: Fink and Koh were relatively good-natured about this back-and-forth when we spoke at Oxford. They even offered to mail me one of the t-shirts featuring Nate’s quote they apparently had made. I never took them up on the offer, which I now somewhat regret. These synthetic sampling companies fell off my radar for a while, but they do still exist. In fact, Aaru recently received a $1 billion valuation. Is what they’re doing anywhere close to the most important frontier in AI development? Not by a longshot, especially when Anthropic just developed a model so adept at exploiting software vulnerabilities that it’s only being released to 40 companies. Still, silicon sampling is increasingly finding its way into public polling. Axios reported in March that “a majority of people trust their own doctors and nurses” based on findings from Aaru — without mentioning that the “people” in that sentence were actually LLMs. Around the same time, the Public Sentiment Institute “boosted” their online sample of 373 real survey respondents with 114 AI agents.1 (Spoiler alert: even the co-founder of Electric Twin doesn’t think that’s a particularly defensible approach.) Polling companies like Qualtrics and Ipsos are also developing synthetic data panels. So, what should we make of these … “polls”? Let’s get one thing out of the way: whatever they are, they’re not polls in the way that term is usually defined. Subscribe You can’t replace polls with AI On one hand, using LLMs to essentially make up fake survey respondents sort of sounds like the dumbest idea ever, one that will at best imperfectly replicate real polls while introducing all sorts of biases. On the other hand, with LLMs improving at a remarkable, perhaps even alarming rate, maybe that means I’m a dinosaur at the ripe old age of 24 because I still want to rely on polls that talk to actual people. I’m not going to argue that synthetic samples are completely useless. In fact, as I’ll return to later, there is evidence that some techniques can replicate topline survey results quickly and cheaply. But the marketing from certain companies can be slightly optimistic. “No traditional poll will exist by the time the next general election occurs,” said Fink in 2024. We’re just 206 days away from the midterms, and based on the fact that I still have to collect a bunch of polls every day, I’d say he should have run that prediction by a sample of AI agents before the interview.2 To see why synthetic samples can’t replace polls, here’s a quick primer on how they work. The simplest version of these models involves taking a LLM (like ChatGPT or Claude), giving it a demographic profile (e.g., a white, college-educated woman who lives in Utah and makes $70k a year), and then asking it to respond to a survey question. You repeat that process a few thousand times using different demographic profiles and end up with a sample of synthetic survey responses. The actual models used by private companies are more sophisticated than this, usually because they incorporate more hypothesized demographic characteristics for each agent and provide them with extra information. Aaru, for example, feeds agents a diet of news and information they’d be likely to consume, while Electric Twin incorporates their customers’ proprietary data about the audience they’re trying to replicate. The way Ben Warner, the co-founder of Electric Twin, explained it to me was “we have a large amount of data on […] for instance, 5,000 people. Can we make an accurate prediction of how they would respond to another question?” Still, it should be obvious why synthetic samples can’t replace polls. Polling is fundamentally a data collection process. We might use surveys to make predictions by feeding them into election forecasts, but the main purpose of a poll isn’t prediction, it’s gathering new data about what people think and how they feel. Silicon sampling, on the other hand, produces no new data. It’s simply a model: you input LLM training data, demographic prompts, and a bunch of other information, and it spits out a prediction for what a poll would say. We love models here, but models aren’t polls. That difference is an important philosophical sticking point for most pollsters I talk to. “I think politics should stay away from [synthetic sampling], because we’re trying to […] represent the voice of the people,” said Natalie Jackson, a vice president at GQR Insights. Democratic pollster John Hagner told me: “I think I’m just incredibly skeptical of this idea. I don’t think it’s research. At that point, you’re asking the machine to tell you what you already believe.” Hagner has seen some presentations of early synthetic sampling experiments, but so far, “if it’s being used in a campaign, people are keeping it incredibly quiet.”3 But Eli, I hear you saying, aren’t polls themselves increasingly governed by modeling decisions? Indeed they are: pollsters’ choices on which sampling method to use, how to define their likely voter models, and how to weight their samples can and do lead to dramatic differences in the results they publish. Aaru even referenced these limitations in the methodology statement included with that maternal mortality “poll” — although I’m using the term “methodology statement” loosely here, because it doesn’t really explain how the model works at all. We can ignore the (frankly preposterous) implication that synthetic sampling isn’t subject to a separate set of biases. The important point is that there’s still a meaningful difference between using weighting and other statistical techniques on actual polling data and using a model to predict what a poll would say. The latter is far closer to election forecasts or techniques like MRP — potentially useful models, but not a replacement for polls.4 To be fair, other synthetic sampling companies are perfectly happy with the distinction between polls and models. Warner compared polling and synthetic sampling to different tools in a toolbox. “The mistake I think we make is we think that these new tools should either work in exactly the same way or somehow replace these old tools,” he said. “Rather than thinking of it as, okay, so we’ve always had the hammer, we’ve always had the screwdriver, now we’ve got a saw. But don’t use a saw to try [to] do the job of a hammer.” A quick comment from Nate Eli didn’t ask me for a comment — rather rude of him, don’t you think? But since I’m editing this story, I figured I’d add a few quick thoughts rather than putting words in his mouth. Beyond the frequently misleading marketing, what bothers me about the AI “poll” hype is that as AI tools make statistical inference cheaper and/or better (note that these are not synonyms) that actually increases the comparative value of collecting original data. You might be able to train a model to make a reasonable estimate of what some hard-to-reach poll respondent would say — say, a young Black man who voted for Trump. (Such a person checks a number of boxes for a voter who is usually hard to reach in surveys.) Indeed, this is closely related to what models like the Silver Bulletin forecast already do. They essentially smooth out the kinks in noisy survey data by making inferences based on past voting patterns or national polls or surveys of other states. But you don’t actually know what these voters think unless you’re reaching them directly. If there’s a shift in opinion among this subgroup, you’re not going to detect it. So if I were running a campaign, I’d invest more in going the extra mile to find a representative sample of those voters. And then I’d hire some smart quants — or Claude? — to figure out the implications for campaign strategy based on proprietary data that my competitors didn’t have access to. -Nate Silver Are these models any good? If synthetic surveys are just a new type of model, the next obvious question is whether the models are at least accurate. The answer very much depends on who you ask. On one end of the spectrum, you have the maximalist argument that synthetic sampling is more accurate than actual polls. “It’s an incredibly challenging problem to go to someone and say ‘hey, we’re going to be more accurate at predicting human behavior than you, even when you talk to your customers directly’,” Koh recently told CNBC. In his view, synthetic sampling isn’t a saw to polling’s hammer, it’s “magic.” There’s certainly evidence that synthetic samples can replicate certain survey toplines. But if Aaru does have any examples of their approach outperforming the polls, they’re keeping those to themselves.5 Aaru’s 2024 election model, for example, had Kamala Harris leading in Michigan, Nevada, Pennsylvania, and Wisconsin on November 4th. And although they’ve since taken down their forecast page, they gave Harris a 50.5 percent chance of winning the race on November 2nd.6 After the election, Fink told Semafor he was happy enough with those results because they were “within margin of error,” a term that is completely meaningless when applied to a “sample” of AI agents. And of course, Aaru says their models have improved since 2024, so supposedly now they’d be more accurate than the polls? Still, their stronger argument is on cost: “We are significantly faster and cheaper than traditional polling, and still more accurate,” said Fink. The first two claims are undeniably true, but the third brings us to the opposite end of the spectrum. Both Jackson and Hagner are skeptical that these models are reliable for anything beyond replicating common survey toplines. “I just […] don’t think the machines are what we want when we’re looking for nuanced views. My example on this is people in Arizona and Nevada in 2024 who voted for Trump and voted for expanding abortion in their states on ballot initiatives,” said Jackson. Hagner identified another issue. Maybe the synthetic respondents, like sycophantic LLMs, are inhuman in one important way: they’re too nice. “The reports that have come through at the meetings that I’ve been at are that the early experiments on this, they cannot get respondents to be as racist or sexist or, frankly, as negative as human respondents,” he said. Academic research mostly agrees on this point. While there are some papers that show promising results when using LLMs to replicate polling data, most show that LLMs suffer from various quirks like producing too few “don’t know” responses and can seriously overpredict the favorability of politicians like Donald Trump and Kamala Harris. They also seem to struggle with too little variation between demographic subgroups, so the difference in predicted opinion between Democrats and Republicans, for example, is too small. When I asked Warner about these studies, his response to these papers was that just because academics can’t get synthetic sampling to work doesn’t mean that the technique doesn’t work in general. “Actually, the argument is, okay, yours does not [work]. That does not mean […] for this complex set of machinery, which uses a lot of investment, a lot of time, a lot of money, you can’t get it to work.” Cards on the table, I’m somewhat sympathetic to this argument because academics aren’t exactly great at making election forecasts. Usually, the people with skin in the game are the most accurate. Warner’s argument is that the approach Electric Twin takes — which includes, for example, making multiple predictions for each synthetic respondent using different models and prompts and subsequently averaging those to get a final prediction in a sort of ensemble forecast — produces better results than the simpler academic models. Warner shared a comparison between his method and the method from a recent academic paper with me, and Electric Twin was indeed able to get more accurate replication. But even still, he acknowledged that synthetic sampling “is not a crystal ball.” “If you asked me, do I think using other data sources will be more accurate than asking somebody who they will vote for, I would probably say no. But if you asked me ‘would your system be useful for our turnout modeling today?’ I would say yes.” For better or worse, it looks like the method is already getting more popular in the market research world. Most of the clients Aaru touts these days are businesses like EY and McDonald’s. And AI will probably begin to pop up in other parts of the political polling process. Pollsters are already using it to code open-ended survey responses, and some firms, like YouGov, are testing using LLMs to ask survey respondents questions. More worryingly, one danger to actual polls is that AI agents can be used to infiltrate online surveys. Most online polls use various checks to prevent that from happening, but there’s conflicting evidence on how effective those filters are and how prevalent AI agents currently are in online panels. If those agents ever become impossible to detect, it might spell the end of online polling, but the solution isn’t to replace all of your respondents with ChatGPT. Silver Bulletin is a reader-supported publication. To receive new posts and support our work, consider becoming a subscriber. Subscribe 1 That particular poll obviously doesn’t meet Silver Bulletin standards for aggregation. But we exclude all Public Sentiment Institute polls from our averages because we classify them as an amateur polling firm. 2 You could argue that Fink meant the next presidential election, but (a) I’m also confident we’ll still have real polls in 2028 and (b) in that case he should have asked an LLM to define “general election.” 3 Quick caveat: that’s reporting from a Democratic pollster. It’s possible that Republicans are more willing to use AI in political campaigns. 4 Indeed, Silver Bulletin does not include “polls” produced by MRP in our forecasts or averages, and we think it’s extremely misleading when their practitioners describe them in a way that suggests original data had been collected among a large number of states or Congressional districts. 5 A recent report from Aaru and EY did show two examples of a synthetic estimate being closer than a survey to a real-world benchmark — but I’d take those findings with a grain of salt because the report reads more like an ad and didn’t involve any sort of prediction being made ahead of time. 6 For comparison, our odds for Harris on the same day were 48.2 percent.
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