SCROLL NEWS / DISCOVERY
Search headlines
Find the stories shaping the conversation.
Results for " machin " 834 found 🔀 AI-powered Shuffle
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.
Sunday Preview – Mighty war machines in the Age of Darkness
Tracked reinforcements arrive for the Legiones Astartes in next week’s pre-orders.
Scientists discover why some cancers survive chemotherapy
Scientists have uncovered a surprising new trick used by one of cancer’s most notorious proteins. MYC, already infamous for fueling runaway tumor growth, also appears to help cancer cells survive by repairing their damaged DNA — including damage caused by chemotherapy and radiation. Researchers found that MYC can rush directly to broken DNA and recruit repair machinery, effectively helping tumors recover from treatments meant to destroy them.
Prep Sports Report: Brossart scoring machine takes team title in Class 1A girls region track meet
By Terry Boehmker NKyTribune sports reporter The Brossart girls track and field team was a scoring machine that produced points in nearly event during the Class 1A, Region 4 meet on Friday at Brossart. In 11 of the 18 events, Brossart picked up 10 points or more and finished with a 166 team total to...
Kalihi machine shop closing after 80-plus years in business
Takeguchi owns and runs the family machine shop that his parents, Ted and Barbara Takeguchi, opened on Queen Emma Street in downtown Honolulu in 1944.
Inside the furor plaguing Democratic National Committee leader Ken Martin
Democrats keep winning at the ballot box. And yet Ken Martin, the man leading the Democratic National Committee, is facing a crisis of confidence among party officials who are increasingly concerned about the health of their political machine barely a year into his term.
Political Inequality Isn’t Responsible for Economic Inequality
Jennifer Gaudette finds that election officials can increase trust in elections by informing citizens of basic facts, such as that election machines are not connected to the Internet.
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.
Indiana Jones and the Great Circle Nintendo Switch 2 tech analysis, including frame rate and resolution
Digital Foundry has provided its technical analysis of Indiana Jones and the Great Circle on Nintendo Switch 2, which goes over the game’s frame rate, resolution, and more in detail. Overall, MachineGames has produced what looks to be a solid port. The game even retains ray tracing and other graphical features present in other versions. Here’s the full roundup of...
International Business Machines Corporation (IBM): Using Blockchain to Build Trust In AI
International Business Machines Corporation (NYSE:IBM) is one of the most undervalued blockchain stocks to buy now. On May 5, International Business Machines Corporation (NYSE:IBM) announced a broad expansion of its products and capabilities at its Think 2026 conference. In all this, there was a big focus on AI. For instance, the company unveiled expanded capabilities […]
Thinking Machines Lab sees key talent leave for Meta, OpenAI, xAI
Thinking Machines Lab, led by former OpenAI CTO Mira Murati, faces talent loss to rivals like Meta amid Silicon Valley's competitive AI sector.
Scientists discover a weak spot shared by polio and common cold viruses
Scientists at the University of Maryland, Baltimore County, have uncovered a crucial trick used by enteroviruses—the group behind diseases like polio, myocarditis, encephalitis, and even the common cold—to reproduce inside human cells. The team captured, in unprecedented detail, how viral RNA recruits both viral and human proteins to assemble the machinery needed for replication, acting almost like a molecular “on-off switch” that controls whether the virus copies itself or makes proteins.
Indiana Jones and the Great Circle's Nintendo Switch 2 port is finally here and it holds up pretty well
Bethesda and MachineGames' Nintendo Switch 2 port of Indiana Jones and the Great Circle is a looker with robust performance and a few neat tricks.
Thinking Machines wants to build an AI that actually listens while it talks
Right now, every AI model you've ever used works the same way. You talk, it listens. It responds, you listen. Thinking Machines is trying to change that by building a model that processes your input and generates a response at the same time, so it's more like a phone call than a text chain.
Chicago-area burglars target gaming machines in businesses: 'They're in and they're out'
Chicago police are searching for a group of suspects caught on camera smashing their way into a South Loop cannabis store early Monday.
Here’s what Mira Murati’s AI company is up to
Thinking Machines is showing off something called “interaction models.”
Valve Has a Plan to Stop Scalpers From Botting the Steam Machine
It's what Valve should have done with the Steam Controller from the start.
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.
Update Hints Steam Machine Will Arrive in 512GB and 2TB Versions, Despite AI-led SSD Pricing Surge
How much would you pay?
The skincare guru Hollywood stars trust for red-carpet skin
High-tech machines and $900 treatments: How Keren Bartov built a skincare empire and global celebrity following.
Steam Machine Buyers Will Face a Queue System For Its 4 Models, as Valve Moves to Prevent Scalper Markups Early
Valve leaked code reveals a reservation queue and four models for the Steam Machine to stop scalpers and prevent another launch fiasco
Crimson Desert Adds Surprise Claw Machine Mini-Game and Lets Pet Dogs Attack Enemies as Part of Update 1.06.00
Crimson Desert developer Pearl Abyss has released this week’s update as promised, and it adds what it said it would add. But it also adds a whole lot more, including a surprise claw machine mini-game that gives unique rewards.
Cricut’s $99 craft cutting machine helped me feel creative again
You’ll make things quickly, eventually.
Machining A Two-Stroke Engine Out Of Aluminium
Recently [Camden Bowen] took a swing at machining a two-stroke engine out of billet aluminium, following adventures in 3D printing such an engine, as well as building one out of parts largely sourc…
AI may be making science faster but less original
While AI boosts speed and productivity, it may also reduce intellectual diversity and shift science toward machine-friendly questions.
Accel Entertainment (ACEL) Stock Trades Down, Here Is Why
What Happened? Shares of slot machine and terminal operator Accel Entertainment (NYSE:ACEL) fell 9.1% in the afternoon session after the company reported fir...
Accel Entertainment (NYSE:ACEL) Exceeds Q1 CY2026 Expectations
Slot machine and terminal operator Accel Entertainment (NYSE:ACEL) announced better-than-expected revenue in Q1 CY2026, with sales up 8.5% year on year to $3...
TPCi's TCG Vending Machines Grow 27% in Second-Biggest Year, But 1...
After manually going through Pokemon.com's locator, we can confirm TPCi now operates 1,871 Pokemon TCG vending machines across 28 states. That's a 27%
Valve Addresses Steam Machine Delays: "Obviously, We're Bummed"
Even though it looks as though Valve may be preparing for some upcoming large-scale hardware shipments, and it has officially launched the Steam Controller that was slated to launch with the Steam Machine, the gaming giant has yet to even announce a price for the Steam Machine. While we know from a ...
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.
AMD is adding HDMI 2.1 support for Linux. That's good news for the Steam Machine.
Fixed Rate Link being added now; Display Stream Compression coming soon.
The ravioli king of Brooklyn making nearly $2 million a year
George Joseph Switzer III runs Queen Ann Ravioli, a Brooklyn pasta shop founded in 1972. Inside, vintage machines make thousands of pounds of pasta.
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.
Advancing Athlete Recovery: The Role of Biodex Isokinetic Testing and Force Plates
Cleveland Clinic has been exploring ways to expand the usage of Biodex testing machines and force plates beyond lower-limb injuries.
Accel Entertainment (ACEL) To Report Earnings Tomorrow: Here Is What To Expect
Slot machine and terminal operator Accel Entertainment (NYSE:ACEL) will be announcing earnings results this Tuesday after market hours. Here’s what to expect...
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.
‘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
The hidden costs of the youth sports arms race
In the shadow of America's new youth sports machine, families weigh the cost of athletic dreams against the fear of not doing enough.
Steam Machine may get full HDMI 2.1 support, AMD confirms plans for full AMDGPU implementation
AMDGPU full HDMI 2.1 implementation under way AMD's first public patch series adds HDMI FRL, while a full implementation is expected once the remaining
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.
Upstart vending business is more than a side hustle
A vending machine business launched in the Grand Valley in November currently operates just two machines, both located at the Grand Junction Police Station.
"It Is Just One-To-One" - MachineGames Talks Preserving 'Indiana Jones And The Great Circle' On Switch 2
"We haven't scaled back or cut content"
The Whistleblower Who Uncovered the NSA’s ‘Big Brother Machine’
An unexpected visitor gave my team the evidence we needed to prove that the government was secretly wiretapping Americans.
South Loop business hit by burglary crew targeting ATM and slot machines for third time
How Eric Trump Got Rich From Bitcoin While Losing Investors A Fortune
The president’s second son pitches his bitcoin company as a money-printing machine. It’s actually an arbitrage vehicle that preys on MAGA-minded investors.
Steam Frame isn't Launching Alongside Steam Controller on May 4th Due to RAM Shortage
Steam Controller, Valve’s next-gen gamepad, is slated to launch on May 4th for $100, although you shouldn’t expect to see a big ‘buy’ button next to Steam Frame or Steam Machine. Speaking to Polygon, Valve revealed that it’s only releasing Steam Controller next month for a pretty important (and slightly obvious) reason: Steam Controller “doesn’t […]
Valve Says It's 'Hard at Work' on Steam Deck 2, but There's Still No Release Window
Valve has said it’s “hard at work” on Steam Deck 2, while continuing to work on getting the Steam Machine, Steam Controller, and Steam Frame out the door.
Valve launches the Steam Controller without the Steam Machine
The Steam Controller is officially coming next week.
Enter The Gungeon Creators Say Roguelikes Have Become 'Cash-Grabby'
Dodge Roll's co-founder believes the 'slot machine' aspect of Balatro and Vampire Survivors has led to a shift in the design of roguelikes
Watchdog report exposes teachers' union 'political machine' funneling more than $1 billion to liberal causes
New reports reveal teachers unions poured over $1 billion into left-wing political causes in the past decade, according to Defending Education.
‘Boy Meets World’ Star Maitland Ward Says Child Actors Were Treated Like ‘Product’ in ‘Hollywood Machine’
The 'Bold and the Beautiful' actress will be featured in 'Hollywood Demons: Child Stars Gone Wild.'
New Valve Hardware Given Pricing Update, Steam Machine Hopes Crushed
The Steam Machine may not be as "competitively priced" as we hoped.
Valve reportedly close to announcing Steam Machine price and release date
Steam Machine closer, claims Insider Gaming Valve has not announced pricing for its new Steam hardware, but one figure may have slipped early. Yesterday
Valve Steam Machine release date and price announcement reportedly coming soon
An insider believes that Valve will soon make a final decision on the Steam Machine price. That could mean an abrupt end to the release date uncertainty caused by the memory shortage. The Steam Controller MSRP also leaked, indicating that news on the other new hardware is near.
Steam Controller review leaks, but price of Steam Machine accessory may disappoint
After reports that Valve had received shipments of the accessory, a Steam Controller review has appeared. Deleted ahead of a likely release date update, a YouTuber praised its responsive dual trackpads. Even so, the price may make it more difficult to discount the Steam Machine.
Big Machine Records Names Allison Jones EVP of A&R
Big Machine Records/Nashville Harbor has named Allison Jones EVP of A&R. She has helped sign Thomas Rhett, Florida Georgia Line, Riley Green and more.
BREAKING: Big Machine Records/Nashville Harbor Records & Entertainment Taps Allison Jones As EVP Of A&R
Big Machine Records/Nashville Harbor Records & Entertainment has tapped Allison Jones as EVP of [...]
Base Mac Mini Sold Out From Apple Online Store
Apple appears to be sold out of the base Mac mini, and the machine is listed as “Currently Unavailable” from the Apple Online Store. The base Mac mini is the model with an M4 chip, 256GB of storage, and 16GB RAM. M4 Mac mini models with upgraded storage are still in stock, as are Mac mini models that are equipped with the M4 Pro chip. Configurations with 24GB RAM are also still available, but some models that use 32GB RAM or higher are out of stock.
A first for the Tack Lecture: Business, AI and the liberal arts
Rachel Chung will present a lecture on “Wisdom & Machines: How Liberal Arts & AI Learn from Each Other.”
Possessor(s) coming to Switch 2 on April 29
Publisher Devolver Digital and developer Heart Machine will release side-scrolling action adventure game Possessor(s) for Switch 2 on April 29, the companies announced.
Volume 25 Science Of Speed
Our annual racing deep dive goes full engineering — from chassis and powertrains to tires, batteries, aero, and driver fitness, featuring machines like the new Corvette ZR1 and McLaren Solus GT.
Volume 25 Science Of Speed
Our annual racing deep dive goes full engineering — from chassis and powertrains to tires, batteries, aero, and driver fitness, featuring machines like the new Corvette ZR1 and McLaren Solus GT.
The Closer: Computer Science Grad Takes Every Project to the Finish Line
Computer science student Ashley Baird is the kind of person who thrives on completing what she starts. Baird's drive to see things through resulted in a successful machine learning project for the U.S. Department of Defense and an internship at Nissan working on the team that developed an industry-first personalized sound app.
Xbox Exec Chimes In As Rumours Suggest Project Helix Chip Will Be Used For 'Other Machines'
Jason Ronald makes it clear we're getting a first-party console
Latest Leak Reveals Xbox Helix Will Effectively Just Be a PC With No Custom APU
When Microsoft announced the next-gen Xbox Project Helix, it confirmed what had been long rumored—that Helix would be a console-PC hybrid, just like Valve's upcoming Steam Machine, able to play both PC and Xbox games. Although it has not yet been confirmed what shape this hybrid experience will take...
Meta attracts more Thinking Machines Lab talent in AI shakeup
Mira Murati's Thinking Machines Lab faces an AI talent drain to Meta, with founders and a researcher among recent departures from the startup.
Free 'Bluebox' health vending machine opens in Jackson County
Jackson County health leaders have created a new way to promote health and wellness in the community.
Dipika Kakar Breaks Down During MRI Scan, Shoaib Ibrahim Shares Health Update: "Chote-Chote Dots Nikle Hain"
Dipika Kakar said, "Ghabra rahi thi main... Main royi bhi; machine mein jaane se pehle bhi royi, aur machine mein bhi royi"
Scientists stunned as bacteria rewire DNA machinery to shape cells
Cyanobacteria—ancient microbes that oxygenated Earth and made complex life possible—are still revealing surprises billions of years later. Scientists have now discovered that a molecular system once used to separate DNA has been repurposed into something entirely different: a structure that shapes the cell itself.
Playnix launches Steam Machine competitor with Radeon RX 9060 XT 16GB, now costs €1140
Playnix gaming PC pairs a 65W Ryzen 5 with a 150W Radeon RX 9060 XT Playnix is now on sale through the company’s official store as a console-style Linux
Two arrested after 29 illegal gambling machines seized at Tallahassee business
Two people are behind bars, and a gambling house is shut down after law enforcement seized over two dozen illegal gambling machines from a Tallahassee business.
Two arrested after 29 illegal gambling machines seized at Tallahassee business
Two people are behind bars, and a gambling house is shut down after law enforcement seized over two dozen illegal gambling machines from a Tallahassee business.
Two arrested after 29 illegal gambling machines seized at Tallahassee business
Two people are behind bars, and a gambling house is shut down after law enforcement seized over two dozen illegal gambling machines from a Tallahassee business.
Two arrested after 29 illegal gambling machines seized at Tallahassee business
Two people are behind bars, and a gambling house is shut down after law enforcement seized over two dozen illegal gambling machines from a Tallahassee business.
Jackson County installs free health vending machine to combat overdose deaths
Jackson County Public Health installed its first free health vending machine in downtown Lee’s Summit, offering naloxone and other medical supplies around the clock.
Two arrested after 29 illegal gambling machines seized at Tallahassee business
Two people are behind bars after law enforcement seized over two dozen illegal gambling machines from a Tallahassee business.
5+ Things to Know About the Next Mac Studio
Apple is working on an updated version of the Mac Studio that's expected at some point in 2026, and with supplies of existing machines running low, we thought we'd highlight what's next for Apple's most powerful desktop machine. Design We're not expecting Apple to redesign the Mac Studio, and there haven't been rumors of a design update. The Mac Studio will continue to have an Apple TV or Mac mini-like squircle design with rounded corners.
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.
Bournemouth 'lucky' to have teenage goal machine Kroupi
Former Bournemouth midfielder Joe Partington heaped praise on Eli Junior Kroupi after his goal against Arsenal on Saturday and described the 19-year-old as one of "the world's best teenage goalscorers". Kroupi's opener in the 2-1 win made him the first teenager to score 10 goals in his maiden Premier League season since Robbie Keane for Coventry in 1999-2000.
🏆Shocks and LOTS of goals: Thursday's Sudamericana highlights
That’s a wrap on all of Thursday’s Copa Sudamericana matches, and here we bring you the star, the surprise, and the disappointment of the day.Álvarez’s machine got goingAfter the draw in Paragu...
NYC Health + Hospitals/Gotham Health, Sydenham Announces New State-Of-The-Art 3D Mammogram Machine - NYC Health + Hospitals
NYC Health + Hospitals/Gotham Health announced that a state-of-the-art 3D mammogram machine, which provides cutting-edge breast cancer detection and a
Thinking Machines Lab loses another founding member to Meta
Meta has hired another founding engineer from Thinking Machines Lab as the AI talent wars intensify. The startup is still rapidly expanding.
Thinking Machines Lab loses another founding member to Meta
Meta has hired another founding engineer from Thinking Machines Lab as the AI talent wars intensify. The startup is still rapidly expanding.
Beyond the Dialysis Machine: Shaping the Future of Kidney and Cardiovascular Health
At Yale School of Medicine, Christopher El Mouhayyar, MD, works at the forefront of managing dialysis treatment in patients while addressing underlying disease.
Nothing makes it easy to share files between any Android phone and a Mac
The Warp app quickly sends files between Android and a Mac, Windows, or Linux machine.
Machine Learning As A Transformative Tool for (Exo-)Planetary Science
The exploration of planetary bodies in our Solar system and beyond relies on the processing
AIFF to offer 40 film programs, four days of entertainment
Ashland-born filmmaker to screen debut feature film ‘A Simple Machine’ Celebrating its 25th anniversary, the Ashland Independent Film Festival is set to return in late April, bringing in acclaimed filmmakers […]
UConn Health Offers New Level of Precision in Cancer Care
High-tech radiation therapy machine enhances accuracy, safety
“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.
Searching for Truth Inside Viktor Orban’s Media Machine
In Hungary, politics often splits along generational lines. The election result may hinge on how many voters still trust what they see on state television.
Databricks co-founder wins prestigious ACM award, says 'AGI is here already'
Matei Zaharia has won the top honor from the Association for Computing Machinery. Now he's working on AI for research and says AGI is simply misunderstood.
Computer Science Students Compete in Software Hackathon - University of the Ozarks
A group of University of the Ozarks computer science students took part this past weekend in HogHacks, a 24-hour collegiate hackathon held at the University of Arkansas. Hosted by the Association for Computing Machinery (ACM) at the U of A, the event is an overnight software engineering competition among area college students that takes place […]
Earthquake science unites threatened scholar with Cornell researchers
A team of geophysicists from Cornell, Cameroon and South Africa is using machine learning tools to unearth new information from earthquake data collected by Cornell 15 years ago – providing a lifeline for a scholar whose career was upended by conflict.
When Algorithms Decide: Are Business Schools Preparing Leaders Or Training Managers To Obey Machines?
B-schools are racing to teach AI skills – but they risk producing grads who defer to algorithms instead of leading through them.
Health Law Navigator: The Compliance Department vs. The Machines: A Playbook for Using AI
In 2026, health care facilities are under relentless pressure to use AI. From diagnostic algorithms that outperform radiologists to predictive
Rome unleashed an ancient ‘machine gun’ on Pompeii
The polybolos was centuries, if not millennia, before its time.
Natural Gas Reliability as a Public Health Essential
Whether it’s a sprained wrist from trying to relive a childhood outdoor game, the sniffles from an unexpected rain shower on an evening walk, or questionable leftovers you knew may not be good; hospital trips happen every day. When anyone walks into a hospital, they expect machines to be working properly, temperatures to be comfortable, and equipment to be clean. The way patients rely on hospitals for care would not be […]
8 Gadgets That Can Help Reduce Stress
Here are 8 highly rated gadgets that are designed to help you feel more relaxed, from fitness trackers and eye massagers to noise machines and grounding mats.
Devils on the Moon brings the score-chasing of pinball to the Playdate
Devils on the Moon turns the Playdate into a surprisingly effective pinball machine.