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University of New Haven Graduate Students Demonstrate Excellence at National Collegiate Sports Analytics Championship
Tasked with analyzing and interpreting a large amount of data in a short period of time, master’s candidates in sport management exceled presenting their findings to industry leaders and embracing the ultra-competitive, fast-paced environment simulating working in sports.
Homeland Security Task Force secures clinic owner’s conviction for $52M health care fraud, illegal narcotics distribution and kickback scheme
Tony Brown-Arkah, 78, of New York, was the owner of American Medical Centers, a medical clinic in Brooklyn that purported to provide substance abuse treatment.
A classic brain test exposed AI's biggest weakness
Researchers gave top AI models a classic attention test used in psychology and found a major flaw. While the models could correctly name colors in short lists, their performance deteriorated sharply as the task became longer and more complex. Some leading systems fell from over 90% accuracy to nearly complete failure.
Scientists uncover RNA's hidden role as protein chaperone
Proteins are how cells get work done. They carry out nearly every important cellular task, from ferrying messages to controlling which genes are turned on or off. And in order for proteins to perform ...
Opinion: Does science need autonomous AI?
As technology developers and researchers rush to develop autonomous AI research tools (i.e., systems that independently perform tasks by designing their workflows and utilizing available tools) an urgent but rarely
Gemini could soon get a lot better for multitaskers
Google seems to be working on making Gemini more useful for multitaskers by adding useful new features that could roll out soon.
Vance recommends DOJ criminal investigation into Tim Walz and Minnesota AG over state’s fraud scandal
Vice President JD Vance, who leads the White House anti-fraud task force, has referred allegations of complicity surrounding fraud in Minnesota, including against Democratic Gov. Tim Walz and Attorney General Keith Ellison, to the Department of Justice for criminal investigation.
We can create the future of science right now
Paul Litvak is the founder and Executive Director of the Robyn Dawes Institute and a Visiting Scholar at UC Berkeley. He has a PhD in Behavioral Decision Research from Carnegie Mellon where he studied emotions and the sunk cost bias. Over 15 years in industry, he solved a wide range of challenging technical problems. At Meta he created models optimizing the ad review process. At Google he ran experiments to measure social influence. At Airbnb, he was a product manager leading machine learning teams optimizing search ranking and price suggestions. He also co-founded and led product at Rhythmic Health, a venture-backed biosensing startup, creating an accurate low cost system using a color changing strip and a smart phone to measure salivary lactate. The bottleneck At this point, it is uncontroversial to say that science needs to stop using the PDF article as the unit of knowledge and currency. The unbearable slowness of scientific publishing, the profit motive and margins: I’m not saying anything new. The PDF also sucks because it’s hard to extract structured information, which makes it hard to do evidence synthesis. As a result, we do much less evidence synthesis than is needed. And evidence synthesis ultimately undergirds most policy and medical decision-making. I can see second-by-second real-time odds for any sporting or newsworthy event, but a school board can’t see the best evidence on whether their 8th graders should be taught algebra. As a society, we don’t treat this as an important problem. Again, not controversial. Not only is the problem well understood, but the solution has already been laid out. What we need is AI-assisted living evidence synthesis - (1) an open knowledge graph of atomic claims, (2) claims linked to evidence, (3) assessment and synthesis of each piece of evidence, and (4) continuous updating with new data. What few realize (yet) is that the technical capacity to build this vision for a significant portion of science already exists. Not only that, scientists and startup teams are already building many of these components. I know this because I’ve been surveying the space and talking to many of the builders. There are some missing pieces: for example, evaluations of how well some of the components work. But at this point, most of what’s missing is a fully end-to-end working integration of all of these parts. In the rest of this essay, I’m going to lay out all the parts of a working living evidence layer for science and who is working on them, and propose concrete next steps for building this system. What’s now possible The diagram above outlines the components of a living evidence synthesis platform, including some of the teams working on each component1. Scientific PDFs are processed into claims with associated evidence. The evidence is subjected to a forensic audit, methodological evaluation and robustness and reproducibility checks. Finally it’s given a weight in a continuously updating synthesis. What follows is a description and status of each component and a few of the teams working on them. Document understanding The first thing you need to be able to do is turn an article into structured data. Mostly that means parsing PDFs. There are often multicolumn layouts that confuse non-specialized PDF to text processing libraries. For scientific papers, there is the added complexity of parsing formulas and tables and figures. This is a really hot area - there are startups offering APIs, and it seems like a new open source package gets posted to Github every few weeks. What follows isn’t exhaustive. A package called GROBID was the state of the art for a while, they didn’t update their package for nearly two years until very recently. In the meantime reducto.ai released an AI powered PDF extraction API, PaddleOCR became popular, IBM released a model called Docling, and both Mistral and Gemini created models and libraries. I also know of at least one other well-funded psychology research group working on a paper parser. By contrast, there are few open evals in this space, with no extensive evals for complex table comprehension in particular. Nonetheless, I’m confident this will be a solved problem soon, given the combination of LLM advances and developer interest. Hypothesis level extraction There has been increasing interest in comprehending the extracted text of papers and linking information to evidence for each hypothesis. A lot of work has already been done. Trialstreamer (Marshall et al. 2020) and RobotReviewer LIVE (Marshall et al. 2023) demonstrated automated extraction of trial population, intervention, and outcome at scale on clinical RCTs. PaperQA2 (Skarlinski et al. 2024) and Ai2 ScholarQA (2024) extended this to retrieval-augmented question answering with citation grounding. Elicit, Consensus, and SciSpace operationalized claim-level extraction for end users. OpenEval (Booeshaghi et al. 2026) is the most recent and most ambitious: 1.96 million atomic claims extracted from 16,087 eLife manuscripts using Claude Sonnet 4.5, grouped into ~299,000 results, with LLM evaluations showing 81% agreement with human peer review on a 2,487-paper subset. None of these solutions link claims to test-statistics, as you would need to evaluate randomized controlled trials. This is why I built the evidence.guide API - to extract hypotheses and associated test statistics from behavioral science papers. The best public eval of this kind of extraction I’m aware of comes from the recent SCORE project - they had humans code thousands of psychology papers to extract their claims by hand. It would be extremely helpful to the world if all scientific PDFs were available as structured open data. I’ve been working to make this happen, both directly at Berkeley and through coordination with large entities I can’t yet speak of; as hard as it is to do, I think it’s possible2. Forensic audit A lot of work has been done on forensic audit, but some gaps remain. Of course, for biology papers that rely on images for evidence, there are a variety of tools (notably Proofig and ImageTwin) to spot anomalies. These are still well short of what sleuths like Elizabeth Bik can do on her own, but these tools are constant companions among fraud analysts. There’s someone working on auditing Excel files for anomalies, and a number of teams are automating numerical checks like GRIM and SPRITE, including Scrutiny project, the INSPECT-SR team as well as statcheck. The regcheck team is building a way to use AI to compare preregistrations to analyses in papers, to ensure there aren’t significant deviations. Nonetheless, there are many other kinds of anomalies to screen for, both public and less publicly known. And there are no formal evals for anomaly detection that I’m aware of. Still, there’s a lot to draw from in this space and I’m pretty certain we will be able to scan papers for most kinds of obvious anomalies in the near future. Methodological review This area has been white hot, though I fear for many of the startups in this space, because this capability may become commoditized. There are at least six different AI peer review companies, including Refine.ink, Reviewer3, ReviewerZero.ai, Q.E.D. Science, Paper Wizard, and Isitcredible. Coarse (a pun on refine) was also recently created as an open source alternative. These systems provide qualitative feedback on the content of papers, spotting methodological weaknesses and mathematical errors. They seem to work pretty well, and many academics report bitterly that they exceed the average quality of typical peer reviewers. But there are few evals here either. What evals exist so far involve using LLM-as-judge (circularity problems abound) or comparing against human reviews of questionable quality. What you’d ideally want is an eval that measures capturing known errors in papers3. Reproducibility and robustness Another active area has been using AI agents to automate computational reproducibility4 and robustness5 checks in papers that report numerical results. For more recent papers where data and code are available, AI agents can see whether they can re-run the analyses and produce the numbers reported in the published paper. In addition to a handful of individual academics who have been experimenting using Claude Code for this, the Institute for Replication is a leading group working on building an end to end system. The evals related to this problem are the most mature, with CORE-Bench (Siegel, Kapoor, Narayanan 2024) and PaperBench (OpenAI 2025) available to benchmark agents on this task. There is also work on getting AI agents to test alternative ways of analyzing the data to ensure the results are robust to small analytic design choices. Synthesis This is the most underdeveloped area where significant investment is required. Although some automated evidence synthesis systems exist — for example, otto-sr is building an AI agent to write systematic reviews — none of these incorporate the full range of paper level signals to weight evidence appropriately. Nor is there anything like an eval or a gold standard for a good systematic review. Arguably Cochrane reviews are the closest we have to gold standard human systematic reviews, though I’ve heard academics in the know complain about their uneven quality. A key question for a synthesis platform is how to weight anomalies and methodological issues in assessing the quality of a piece of evidence. This is an unsolved problem and one I’m very keen to work on. Continuous updating There are many pieces of basic infrastructure available for monitoring for new research and initiating updates. OpenAlex is the current open citation graph. Retraction Watch integrated into Crossref in October 2023. Scite tracks how citations support, contrast, or mention prior claims. The Living Evidence Network demonstrated continuous-update workflows in clinical guidelines. Engineering this is a relatively straightforward task. When you look over this technical architecture and all the progress being made, it’s hard not to be optimistic that a living guide to scientific evidence will be built. The stakeholders are ready The social infrastructure for this is starting to coalesce — it’s not just a pie in the sky academic exercise to imagine this coming into existence. Institutions like the Center for Open Science, the Institute for Replication, the INSPECT-SR, the Living Evidence Network and more are all working on scaling work to improve research quality. Funders are also aligned. The Sloan Foundation has funded living evidence work through COS. Coefficient Giving supports the Institute for Replication and COS. The Astera Institute and the Institute for Progress have shown interest in this space. NIH has established an Office for Replication and Reproducibility. Although there are (very unfortunately) serious headwinds in science funding generally, there is an active group of funders interested in metascience. A brief word about what I’ve been doing at RDI. First, as a Visiting Scholar at Berkeley I’ve been actively figuring out how a non-profit and a public university can conduct and make public the results of large-scale academic article data mining. With some of the money I raised from donors, I commissioned a legal analysis of recent case law and publisher text data mining (TDM) agreements in order to understand whether a massive open data mining of academic articles is possible (with caveats, it is). I’ve also been working to bring together stakeholders in this space, and identify gaps. I’ve also been doing some software development in this space, with more to come. A pilot proposal The assumption undergirding all of this is that an AI, given all this information, would make the right judgment about a scientific claim with lots of conflicting evidence, weighing all the factors appropriately. That’s the hypothesis we need to test. Randomized control trial research is the best place to focus on first. RCTs are used to make many of the important decisions in society - from medical trials to public policy changes. And they use a relatively uniform set of inferential statistics with lots of known and available diagnostics. Behavioral science experiments, within the broader realm of RCTs, should be first used as a testbed whose results can be generalized. Because behavioral science is at the vanguard of open science practices, replications abound (there are thousands of them) to serve as ground truth training data. Key Hypothesis Therefore the pilot would test, in behavioral RCTs that have been replicated, whether the quality of evidence for a claim can be used to accurately predict whether that claim will replicate. Secondary Hypotheses Compared to claims that replicated, non-replicated claims demonstrate a greater share of forensic anomalies in their source literatures. Hypothesis level claim and statistic extraction is accurate enough to scale living evidence without onerous human review costs. Replication prediction is more accurate than prediction markets6 or journal prestige. If all the different quality signals we gather do accurately predict which studies will replicate, then we can use that model to score evidence to power the living evidence layer. Why this is informative regardless of outcome If the pilot succeeds, the architecture extends to medical RCTs (where Living Evidence already operates and integration is mostly about claim representation), then to slices of basic biology with stable replication structure. If it fails, the field learns which quality signals are load-bearing and which ones metascience has oversold. Either result is a contribution to knowing what the literature supports. Implications for funders Because this burgeoning ecosystem of builders already exists, a well-informed philanthropic or government funder could play a crucial catalyzing role in bringing this future about. They could play at least three roles: creating open structured datasets, publishing open benchmarks and incentivizing the solving of key technical challenges. First, open archives of papers that the government maintains, like PubMed, could be turned into structured data amenable to large scale metascience and claim aggregation. I know the US government already has an interest in doing this, though some key open questions remain unanswered. How do you determine the best models and systems for accurately extracting information from papers? How can you establish a robust way to allow researchers to flag errors and correct them? And finally, how do you create a legal regime in working with publishers to maximize the scope of available papers? For the latter, a university or a private philanthropy may be better positioned to make structured data publicly available under journal subscription terms or fair use. Philanthropists or government funders could also coordinate to create or commission benchmarks that evaluate whether important problems have been solved. For example, an open benchmark for claim extraction from a range of different scientific article types would be extremely helpful. Ensuring the underlying data are accurate is vital for creating these evaluations. I’ve discussed opportunistically using various human-created datasets for this purpose, but a consistent problem is that errors in human data make it difficult for them to serve as a gold standard. Finally, with structured data and benchmarks available, the government or private philanthropy could use them to incentivize groups to develop machine learning systems that meet these benchmarks. Prizes are one potentially valuable tool for this. For example, you could establish a prize for a system that accurately updates a living meta-analysis for a small set of claims. Prizes are particularly useful as signals of problem importance, and can help create vibrant ecosystems of public and private research—see, for example the role the government played in kickstarting the current work on nuclear fusion. Conclusion The drawbacks of the current scientific publishing system are known. Scientists agree, metascientists agree, philanthropists agree: the published PDF plus citation graph isn’t the right substrate for maintaining a representation of the evidence base in science. The pieces needed to build the alternative either already exist or are rapidly taking shape. The community is forming around exactly this problem, with concrete partnerships and shared infrastructure. A pilot should start on behavioral science RCTs because that’s the slice of empirical science most amenable to legibility, where replication ground truth is richest, and where the failure modes are best documented. What’s been missing is the galvanizing mission to assemble these pieces into something that works. That’s what I’m proposing to build. *** 1 I have a broader field map that I’ll release publicly soon. This is me, building in public! 2 If this is something that you are excited about, please reach out and talk to me. 3 More on this very soon too! 4 This tests whether, given the code and the data, you can get the same statistics as reported in the published paper. 5 This tests whether the results are the same as a paper’s given alternative analytical decisions in conducting the analysis (like outlier omission). Closely related is the idea of a “multiverse” where you come up with many different ways of answering the same underlying research question with the same data, and test whether the results hold in all those alternative methods. There’s been work on the latter as well. 6 Some of the replications, e.g. those from the SCORE project, had paired the experiments with forecasts from prediction markets. So we get to look at this for free.
Commentary: Does science need autonomous AI?
As technology developers and researchers rush to develop autonomous AI research tools (i.e., systems that independently perform tasks by designing their workflows and utilizing available tools) an urgent but rarely […]
Scientists Reveal the Terrifying Truth Behind Earth’s Most Powerful Eruption - Futura-Sciences
Scientists Raise the Alarm This time, the University of Oxford has taken on the task, announcing that super volcanic eruptions—far more frequent than previously believed—could threaten our entire civilisation. Let’s not beat around the bush, because this is just about the worst natural disaster imaginable. An explosive display of such...
Robot.com CEO wants to automate the work that makes people quit
Robot.com CEO Felipe Chavez said he wants to build an ecosystem of robots that will handle boring, repetitive tasks.
WHO urges targeted action to strengthen emergency workforce capacities for shared health risks across the Asia-Pacific region
Kuala Lumpur, Malaysia, 11 June 2026 – At a time when the world is more interconnected than ever, the World Health Organization (WHO) is disseminating a comprehensive regional analysis of shared health risks across Asia and the Pacific, urging Member States to invest in enhancing workforce capacities regionwide to mitigate these risks and ensure health systems are better prepared to anticipate, tackle and withstand a range of crises and shocks. Over 100 delegates from 49 countries and areas in the Asia-Pacific region reviewed results from the regional risk analysis during the WHO-led Asia Pacific Health Security Action Framework (APSHAF) Stakeholders Meeting in Malaysia. Synthesizing insights from 21 strategic risk assessments and more than 800 multisectoral experts, the regional analysis highlighted an urgent imperative: countries must proactively invest in emergency workforce preparedness to navigate a landscape where climate hazards, disease outbreaks and geophysical events increasingly overlap. These complex converging crises create devastating, compounding impacts on health systems and communities - reiterating the need for investing in a fit-for-purpose regional health workforce and multisectoral action. Key findings: an evolving risk landscape More than half of the participating countries ranked flooding, cyclones, dengue and pandemic-potential respiratory pathogens as being of high or very high risk, while antimicrobial resistance, landslides and gastroenteritis remain widespread concerns as well. “Given that 51% - over half - of these assessed hazards are deeply interconnected, isolated health interventions are no longer sufficient,” said Dr Saia Ma’u Piukala, WHO Regional Director for the Western Pacific. “Extreme weather events like cyclones and floods directly trigger subsequent disease outbreaks, while environmental degradation - driven by sea-level rise, coastal erosion and saltwater intrusion - fuels health risks across the Pacific. Mitigating these cascading risks requires unified, cross-sectoral action linking environmental, agricultural and public health policies to build true regional resilience.” Priority recommendations for action To address critical vulnerabilities, WHO calls on Member States in the Asia-Pacific region to urgently prioritize the following strategic actions: Bridge workforce capacity gaps. Embed and standardize emergency preparedness within academia to promote regional workforce interoperability. Foster multisectoral coordination. Address ad-hoc taskforce responses and fragmented surge support through formalized contingency plans and standard operating procedures that are frequently updated and tested. Secure sustainable financing for emergencies. Overcome systemic barriers to access dedicated funding for both immediate anticipatory actions and long-term emergency preparedness initiatives. Enable emergency-ready primary health care. Embed emergency management into public health workforce readiness planning. Ensure that emergency planning is inclusive. Establish clear pathways for inclusive emergency planning that actively involves communities most at risk and other vulnerable groups. Optimize communication. Update risk communication and community engagement strategies, aligning them with seasonal risk calendars. Elevating workforce readiness to meet regional health risks While the gaps identified are significant, the Asia-Pacific region possesses solid foundational pillars, including dedicated clinicians, laboratory staff, rapid response teams, emergency medical teams and extensive community networks. However, to adequately address the compounding nature of modern crises, the various components of the emergency workforce must collaborate cohesively. True resilience depends entirely on transforming this currently fragmented workforce into a highly trained, interconnected and rapidly deployable asset. To achieve this, WHO is actively advancing regional workforce capacities through the Global Health Emergency Corps initiative. This initiative - supported by the Gates Foundation and Institute of Philanthropy - seeks to build a seamless network of emergency workforce across borders and sectors, ensuring that when a crisis strikes, a standardized, well-coordinated and trained workforce is immediately ready to respond. “Scaling up emergency workforce readiness is no longer optional,” said Dr Gina Samaan, Regional Emergency Director for WHO in the Western Pacific. “It is the definitive factor that will determine how effectively the Asia-Pacific region safeguards health and protects lives in an increasingly unpredictable future.” For more information, or to arrange media interviews, contact: Ara Johannes, WHO Western Pacific Health Emergencies Division: johannesa@who.int WHO Western Pacific Communications: wprocom@who.int
Stupid hot: Heat waves cause cognitive changes in animals, making them more aggressive and unable to complete basic tasks
As temperatures rise, some creatures pick fights while others struggle to learn. The consequences of these behavioral changes may ripple through ecosystems.
What to Expect from Apple's Massive iOS 27 Update
Explore the expected features of iOS 27, including Siri 2.0, split-screen multitasking, and major stability improvements for your iPhone.
Scientists are teaching AI-powered robots to run lab experiments
Scientists are building autonomous robotic labs powered by artificial intelligence. The goal, they say, is for these robots to take over human researchers' most laborious, time-consuming tasks.
Scientists have long said we can’t multitask. A new study says we can
‘This is unlocking a whole new set of questions,’ one researcher said
Science reveals people are capable of multitasking
New research reveals a mental workaround that is activated through repetition and experience.
Shedeur Sanders completely outclasses Deshaun Watson
The Cleveland Browns are entering the franchise’s first season under new head coach Todd Monken after letting Kevin Stefanski go from the same role shortly into the offseason. Now, Monken is tasked with determining who Cleveland’s starting quarterback will be heading into the season. The offseason battle for the job is widely viewed by fans,…
Dodgers find their offense, then hold on to beat Diamondbacks
Bullpen, tasked with longest outing in 2 weeks, allowed 11 to reach base, but had just enough cushion to hold the lead
Meet Microsoft Scout, Your AI Coworker That Never Logs Off
Microsoft’s OpenClaw-style agent appears in Teams, just like a human colleague, and automates your dull office tasks.
Apple’s foldable iPhone may offer monstrous performance thanks to this rumored feature
The foldable iPhone Ultra could be the best foldable for mobile gaming and other heavy tasks.
Everyday task may help detect early dementia signs before diagnosis, study finds
Scientists have discovered that a simple writing test could detect cognitive impairment in older adults before more serious symptoms develop.
New AI Inference Service Now Ready for Science at Argonne
AI can help accelerate scientific discovery, but setting up and running a foundation model is not a simple task. Thanks to the work of the Argonne Leadership Computing Facility, scientists affiliated with DOE National Labs and the Genesis Mission can now tap into a new AI inference service running on ALCF supercomputers. Dubbed the ALCF […]
MSI’s New MEG Vision X2 AI+ Desktop Ships With A Talking Holographic Pet That Tweaks Your RGB And Performance On Command
MSI is taking the AI experience to the next level by introducing a live AI agent that helps users in real-time. MSI Unveils MEG Vision X2 AI+ Desktop, Coupled With LuckyClaw That Becomes Personal AI Companion for Making Tasks Easier Artificial Intelligence is no longer limited to chats and image generation. It's now assisting users in real-time, and the new approach just took MSI's newly launched desktop to a new level that might become a common theme in future PCs. MSI has launched MEG Vision X2 AI+, which it calls the "next evolution" in intelligent gaming desktops. Alongside the MEG […]
I put Google's 24/7 AI assistant Gemini Spark to work, and it's actually pretty useful
Gemini Spark helps automate everyday tasks, from inbox summaries to local event planning, but it’s unclear why Google made it a separate product.
Google’s Gemini Spark is ready to run your digital errands while your phone is off
You can now give Gemini Spark a task, turn off everything, and it still gets the job done.
Business leaders join calls for change in how university trustees are picked
A proposed constitutional amendment would task the governor with appointing members of the governing boards for the University of Michigan, Michigan State University and Wayne State University.
Advancing high-threat pathogen preparedness in WHO South-East Asia Region: 2025 in review
Effective preparedness for high-threat pathogens demands more than technical tools — it requires a coherent strategy, sustained country engagement, and the institutional infrastructure to translate evidence into action. In 2025, the Pandemic and Epidemic Management (PEM) Unit advanced all three, working across the full spectrum of high threat pathogen (HTP) preparedness: from methodology development and evidence generation to country-level technical support, emergency response, and regional workforce strengthening.At the core of the PEM unit's work is building the regional evidence and policy architecture needed to prioritize and respond to HTPs systematically. A major milestone was the development of the HTP Prioritization Methodology — a structured, multi-criteria approach to ranking pathogens by epidemic potential, severity, and national context — and its national pilot in Bangladesh, where SARS-CoV-2, Nipah, dengue, and cholera emerged as the highest-priority threats, informing the PEM unit's contribution to 2026–27 biennium prioritization and workplan development (Access link here).Underpinning this work, the PEM unit advanced the peer-reviewed evidence base for HTP preparedness in the South-East Asia Region (SEAR), with three publications in international journals in 2025: Development of a zoonotic influenza distribution assessment and ranking system (ZIDAR): Technical application in Nepal to support cross-sectoral risk-based surveillance in One Health; Implications for influenza A virus surveillance in Southeast Asian Region countries: a scoping review of approaches for the surveillance of swine influenza viruses at human-swine interfaces in BMJ Public Health; and Operationalizing multisector partnerships: a Theory of Action and Reflection tool for zoonotic influenzas in Health Policy and Planning. Together, these contributions move the Region’s HTP preparedness agenda from practice to science — generating the transferable methodological foundations that will inform evidence-based capacity strengthening in Member States.Country-level engagement in 2025 reflected the breadth of contexts in which HTP preparedness must function. Technical support was provided for Mpox and cholera events across the Region, including coordination of cholera and acute watery diarrhea prevention and control in Myanmar in the wake of the March 2025 earthquake. In Myanmar, the PEM unit contributed technical leadership to the earthquake emergency response — one of the most complex humanitarian emergencies in the SEAR — as Health Operations and Technical Support lead within the Incident Management Support Team. This included strengthening emergency response planning, supporting operational and after-action reviews, and providing monitoring and evaluation support for both acute and protracted crisis response. The breadth of HTP support provided to emergency operations across the SEAR was documented in the PEM unit’s contribution to the regional report for the Health Security Interface Technical Advisory Group.In Sri Lanka, technical collaboration with the WHO Country Office and the Ministry of Health laid the groundwork for the National Steering Committee's 2026 annual meeting, development of Standard Operating Procedures for specialized emergency response, and a national training programme — all structured around evidence-based risk assessment for specialized hazards including HTPs.Across the SEAR, the unit contributed to strengthening the workforce architecture that underpins HTP response. Technical inputs shaped the WHO South-East Asia Regional Health Emergency Workforce Strategy 2025–2030, and the PEM unit supported conceptual development of the health emergency workforce framework for the Pandemic Fund Round 3 proposal.Alongside this, the unit's sustained engagement on the meningitis agenda culminated in the launch of the Regional Implementation Framework to Defeat Meningitis in South-East Asia by 2030 at the Seventy-eighth Session of the Regional Committee, ensuring the Region's priorities were reflected in the Global Technical Task Force and expanding the unit's HTP engagement beyond outbreak-prone zoonotic pathogens (Regional Implementation Framework to Defeat Meningitis in South-East Asia by 2030).Taken together, 2025 demonstrated that meaningful HTP preparedness is built incrementally — through methodology, engagement, and the steady accumulation of institutional capacity across Member States.Bangladesh piloted the HTP Prioritization Methodology and identified the highest-priority pathogens in the country context (Photo credit: WHO Country Office, Bangladesh).
Fujitsu develops self-evolving multi-AI agent technology that learns and adapts to business operations
Fujitsu Limited today announced the development of a self-evolving multi-AI agent technology that enables multiple AI agents to perform tasks as a team, continuously and safely learning from daily execution results, human feedback, policy revisions, and specification changes.
Bees get distracted just like us, hinting at their own awareness
Even tiny insects need to focus. In a recent study, honey bees—usually quick to learn which scent means sugar—completely flubbed the task when a flashing light joined the party. This surprisingly human-like ...
Opinion | Firing Prevention Experts Will Not Make Us Healthy Again
The future of health advice is uncertain.
Trump’s Pursuit of a Partnership With China Raises Concerns in India
Secretary of State Marco Rubio has a “gargantuan task” during his visit to Delhi: defuse tensions over President Trump’s anti-India aggression and overtures to China.
Inside Meta's effort to draft 7,000 workers into its AI task force
Meta reassigned thousands of employees to a new AI task force during this week's layoffs. Some are anxious and confused about what comes next.
Replacing RAG with bash cut AI retrieval costs 30%
DCI lets AI agents search raw files with grep and bash instead of embeddings — boosting accuracy 11 points and cutting retrieval costs 30% on complex tasks.
Former Viking Tasked with Sack Duty in Week 1
The Minnesota Vikings are set to face the Green Bay Packers at U.S. Bank Stadium in Week 1 of the […]
How A Microtask Scam Used Our Website—And How To Spot Ones Like It
Inside the rise of fake online micro-gig scams.
RFK Jr. terminates heads of preventive services task force amid overhaul
US Health and Human Services Secretary Robert F. Kennedy Jr. quietly removed the two top leaders of a key preventive services panel last week as he seeks to overhaul its membership.
RFK Jr. fires leaders in charge of your health screenings
The U.S. Preventive Services Task Force, or USPSTF, has a major role in choosing what preventive health services are covered by insurance.
US Health Secretary Kennedy fires heads of key preventive health panel
US Health Secretary Robert F. Kennedy Jr. earlier this month fired the chair and vice chair of the influential task force that decides which preventive medical care is provided at no cost to patients.
US Health Secretary Kennedy Fires Heads of Key Preventive Health Panel
NEW YORK, May 20 (Reuters) - US Health Secretary Robert F. Kennedy Jr. earlier this month fired the chair and vice chair of the influential task force that decides which preventive medical care is provided at no cost to patients. The Preventive Services Task Force, which typically has 16 ...
RFK Jr. Fires Leaders of Key Health Task Force
The health secretary has undermined the work of the U.S. Preventive Services Task Force over the past year.
Neural representation of action symbols in primate frontal cortex
Nature - A drawing-like task designed to study compositional generalization identifies a specific neural population in the ventral premotor cortex in primates that encodes action symbols.
Android 17’s ‘Continue On’ will let you move tasks between devices, like Apple’s Handoff
Google is setting the stage for Android’s version of Apple Handoff, with the new “Continue On” feature in Android 17...
Google’s AI future demands trust — and your personal data
Gemini Spark is giving Google a whole new level of access to your data.
Two AI-based science assistants succeed with drug-retargeting tasks
Both tools generate hypotheses; one goes on to analyze some of the data.
With Gemini 3.5 Flash, Google bets its next AI wave on agents, not chatbots
Google launched Gemini 3.5 Flash, its most powerful coding and agentic AI model yet, at the company's annual developer conference. It is capable of autonomously executing complex tasks and building software from scratch.
Google Unveils New Gemini AI Agent for Personal Tasks
The tech firm highlights both Spark and Omni, a video-creation tool, at its annual developer conference.
Blanche’s Quest for Knowledge
From Tuesday, May 26, at 10:00 a.m. to Monday, June 1, 2026, at 8:00 p.m. local time. Koin Blanche for a research-focused adventure featuring Field Research tasks and Incense encounters. The following Pokémon will appear more frequently in the wild: Some Trainers might even encounter the following: The following Pokémon will be attracted to Incense: […]
Five years later, Windows 11 brings back much-missed taskbar options (and more)
Microsoft is also testing a smaller taskbar and more customizable Start menu.
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.
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Now What? With Carbon Pricing Eroded and Pipeline Politics Advancing, Here Are the Pathways for Climate Success
At the end of a whirlwind week of news on carbon pricing and electricity strategy, capping a madcap year of pretty much non-stop, dramatic change, the word across much of Canada’s climate and energy transition community is that climate policy in this country has been shredded. Eviscerated. Hit with a sledgehammer by policy rollbacks that have pushed the country’s 2050 net zero target “well out of reach”. That means it’s never been more important to look for the rays of hope and glimmers of possibility. To glimpse the seeds of the next set of strategies to get Canada’s climate pollution under control and the country into the accelerating global dash away from fossil fuels. And then, once we’ve spotted those possibilities, get on with the hard, day-to-day slog of trying to make them a reality. Nothing takes away from the frustration and the deep disappointment so many climate and energy hawks are expressing, the sense of betrayal by the author of Value(s) and the former UN special envoy for climate action and finance. But we knew two things as the week drew to a close. We’re looking at the world as it most definitely is. And we don’t get to give up finding the pathways to confront climate change and deliver faster, deeper carbon cuts that leave no one else behind. So really, the first question we all have to ask ourselves is: What’s next? Subscribe All the Oxygen in the Room The outpouring of anger and grief over the carbon pricing deal between Prime Minister Mark Carney and Alberta Premier Danielle Smith has been absolutely understandable. So much of the climate community poured years, more than a decade, into dragging the current federal pricing regime across the finish line, then defending it from a deluge of three-syllable rhymes after the Trudeau government proved utterly incapable of delivering its own message. The policy foisted on us by neo-classical economists took up all the oxygen in the room, burned through years of climate response time that we won’t get back, while cornering climate hawks into the position that the best response to the crisis of our lifetimes is a new tax…in an era of historic anti-tax sentiment. I mean, really—what could possibly have gone wrong? Against that history, let’s appreciate columnist Max Fawcett’s point that even a modest carbon pricing agreement with a government as off-the-wall libertarian/conspiracist as Danielle Smith’s—those adjectives are mine, not Max’s—is a win worth savouring (as long as Smith keeps her promises for a change). But part of living in the world as it is (no, I’m not letting that go) is to be clear-headed about what we can and can’t control. And one thing we get to decide, after seeing this week’s carbon price festivities take up all the oxygen yet again, is whether we want to keep letting that happen. Particularly when success down that road depends on a factor that no one with a climate agenda can control or influence—a provincial carbon pricing mechanism that has been manipulated and eroded beyond its lowest possible denominator. Thankfully, that’s not the way it has to go. Not when we have so many other powerful, practical tools at our disposal, including the ones still available to us through this abominable Memorandum of Understanding (MOU) between Canada and Alberta. Tools In Our Toolbox When I started roughing out this list, I didn’t expect it to grow to nearly a dozen bullet points. It’s probably still incomplete. Some of the greatest hits in today’s climate solutions toolbox include: • No Carbon Capture, No Pipeline: On Friday, Prime Minister Mark Carney explicitly stated that there’s been no change to the MOU provision that there will be no West Coast pipeline without an industry commitment to build its massive, $16.5-billion carbon capture and storage (CCUS) hub in Northern Alberta, and vice versa. The two megaprojects were joined at the hip when the MOU was signed Nov. 27, and the implementation agreement this week affirmed that they still are. A parade of independent analysts have spent years pointing to the economic and technical flaws in CCUS, while the industry kept postponing its investment decision until it could arm-twist the government for more “clarity” (by which it meant even more lavish taxpayer subsidies) on the project. Now the industry itself is running away from the CCUS hub and insisting the pipeline should proceed without it, but Carney doesn’t seem to be budging from a central tenet of the MOU to which Smith has signed on. • No Subsidies, No Investors: Climate analysts and advocates have quite rightly been hammering away at Carney and his team, insisting that any new capacity the industry wants to build must proceed without new subsidies. The government has responded with a growing collection of funding and financing mechanisms for the very wide menu of industrial development and nation-building projects they say they want to take on, and that menu always includes fossil fuel infrastructure. But it’s hard to see how even the combined fiscal clout of the federal and supportive provincial governments will deliver the financial backing private investors would need when the business case for new fossil fuel development is evaporating before their eyes. Particularly when… • No Market, No Demand, No Investors: As our friend Markham Hislop at Energi Media (our apparently sleepless friend, given the massive volume of great material he’s been churning out) points out this morning, the biggest vulnerability in any pipeline deal is the assumption that Asian buyers will want the oil or liquefied natural ga that our fossil industry is so keen to send them. Hislop writes in part: Ottawa and Alberta are effectively betting that countries like China and India will continue increasing oil imports for decades as their economies grow and energy consumption rises. That assumption underpins the entire economic rationale for expanding oil sands production and building billions of dollars in new export infrastructure. But Asian energy systems are changing rapidly… China, India, South Korea, and other major hydrocarbon importers are now investing hundreds of billions of dollars into electricity systems built around wind, solar, batteries, nuclear, hydro, and expanded transmission infrastructure. Domestic “energy sovereignty” is increasingly replacing the old energy-security model based on stable oil and gas imports. Those investments are expected to sharply slow oil demand growth and eventually reverse it, even as overall energy consumption continues rising. There are also refining constraints. Many Asian refineries and petrochemical complexes are already optimized to process discounted heavy crude from the Middle East and Latin America. That raises serious questions about how much additional Canadian bitumen Asian markets can absorb at profitable prices. Small wonder that there’s still no private sector investor for the project, despite Smith’s government desperately beating the bushes to put together a deal. For now, Alberta is acting as proponent and covering early start-up costs for a pitch to the federal Major Projects Office, which could actually turn into the next tough hurdle for Smith and her pipeline to jump. • Canada Day Looms: How ironic that Canada’s birthday this year could become a major chokepoint for the provincial premier who’s been working so diligently to tear the country apart! The July 1 target date for Alberta’s pipeline proposal is baked into the MOU, was reaffirmed by the implementation plan, but it hadn’t dawned on me that it cuts both ways. As one very smart colleague wrote in an email yesterday, “they’ve thrown the ball into Alberta’s court to get the project proposal on the table really quickly and to have to do a whole pile of work to put together a massive, complicated project that would typically take years to assemble, and they’ve given them weeks to do it.” That makes July 1 a looming deadline that Smith won’t be able to run away from politically, especially with the separatists in her party and her caucus breathing down her neck. (See previous ref to off-the-wall.) But if Alberta comes in with a sloppy, incomplete submission, even after Carney bent over backwards to simplify the application process and the rules that govern it, it may be harder for the province or the industry to blame Ottawa and insist on the faster process they claim their investors are demanding. (Oh, wait—which investors would those be??) • We’ll See You In Court (Then We’ll See You Again): If Carney did cave, that would be just one more reason for Indigenous and other affected communities to take the government and the process to court—Just as Alberta First Nations did, successfully, in response to Smith’s half-baked separation referendum. Veterans of the Harper era have been warning that a shoddy process around fossil fuel infrastructure or other major projects will be a recipe for the very delays that process seeks to avoid. And now more than ever, with renewable energy and energy storage eating fossil fuels’ lunch in import markets around the world, a delayed pipeline or LNG terminal may be as good as a cancelled one. • Charged Up, Ready to Go: With its goal of doubling Canada’s electricity supply to meet a doubling of demand by 2050, the national electricity strategy that Carney unveiled Thursday has a lot to like. With its explicit language about speeding the shift from fuels to more efficient and affordable electricity, global investment and deployment patterns that already favour that shift, an emerging bidirectional power grid, the role of front-line options like rooftop solar and heat pumps, the emergence of electric vehicles as behind-the-meter energy storage, and the domestic jobs and manufacturing to make it all happen, the document doubles down on the essential cornerstone of a faster shift off fossil fuels. The strategy envisions a more prominent role for gas and nuclear power. But some of those provisions were already in the Clean Electricity Regulations that climate hawks are now fighting so hard to defend, inserted as a compromise in a futile attempt to mollify the industry and its political representatives in Alberta and Saskatchewan. But once again, gas will be a tougher sell when it’s more expensive and now so much more unpredictable than the clean alternatives. The new nuclear technologies are still speculative and deeply vulnerable to cost overruns. Increasingly, utilities, developers, and investors know it. Carney declared Thursday that “it doesn’t do us good to be sitting in court all the time with provinces. It doesn’t do us good to be talking past each other. What does do us good is to come together with specific projects.” There are no guarantees, but so much of the language and narrative-building in the electricity strategy suggests the lion’s share of those projects will be renewable. • The Party Has Already Started: The electricity strategy only rarely digs down to specific projects, but provinces representing three-quarters of Canada’s population are already embracing a faster transition. Hydro-Québec is working on a 10-year, $185-billion renewable energy and grid buildout by 2035. Ontario recently brought 10 provinces and territories together in a National Energy Corridor Agreement to boost interprovincial and -territorial transmission infrastructure. And the MOU implementation agreement commits Alberta to facilitate investment in renewable energy projects, a big step back from its continuing, punitive restrictions on renewables development. The electricity strategy envisions much of the financing and infrastructure that will be needed to pull those advances together into an integrated whole. • Delivering on Affordability: The MOU implementation plan has little or nothing to say about affordability (are you surprised?), but the electricity strategy does, with a top-line pledge to reduce home energy costs for seven out of 10 Canadian households by 2050, a total saving of $15 billion across the board. It’s just a start, but it puts affordability on the government’s narrative map. If they’re serious and deliberate about patching together a least-cost energy strategy, it’ll have little if any room for oil, gas, or nuclear—not with powerful evidence, for example, that electricity prices are higher when those prices are set by gas. • Smith Proves that Canada Works: By signing a deal that makes it harder to claim the federal government is blocking a new pipeline, Smith has helped prove Carney’s constant anti-separatist refrain that “Canada works”—incurring the rage, no doubt, of her own political base. Canada also needs to work for anyone who depends on a clean environment, healthy and thriving ecosystems, and safe, stable climate. But with Smith and her separatist constituents setting up as a fifth column for annexation by Donald Trump’s United States, let’s set aside any notion that 51st state status for Canada is a recipe for anything other than climate disaster (and so much else). Resisting and withstanding Trump (just 170 more sleeps until mid-term elections, which is a damn sight better than the 730 we started with) doesn’t mean a free pass for a pipeline, but we can still appreciate it that keeping Canada whole and sovereign is still Carney’s Job One. • The Words Not Spoken: Apart from a commitment to export a million barrels of “low-emission” Alberta bitumen per day (memo to Smith and Carney: You realize that that’s less than the industry is already exporting??), the MOU implementation plan says nothing about the international trade and economic sovereignty deals that Carney has been travelling the world to negotiate. It didn’t have to. Carney doesn’t need permission from Danielle Smith, her American enablers, or her separatist allies to continue building the alliances that will ratchet down our economic dependency on our unstable, unreliable neighbour to the south. But every connection he builds with countries outside North America helps align our economy and our future with a faster shift off fossil fuels. Little if any of this was on our bingo cards a year or two ago. But it’s the pathway that has opened up to us. And if it gives Canada any climate or energy gains at all in an era still dominated by Trump and his increasingly ridiculous tariffs, his loutish trade negotiations and his continuing annexation threats, we have to take the wins. Not least because they’ll be the point of departure for what we can achieve in 2027, 2029, and beyond, as Trump’s grip and this era begin to loosen and then fade. What is Carney Doing? If this line of thought is right—and it’s a possibility, not a prediction—it means that Carney’s actions and decisions are largely about buying time. Time for Trump’s power to wane. Time for Alberta’s separatist/conspiracist virus to spread, mutate, die out, or at least die back, with help from a vaccine of federal steadiness and calm (appropriate enough, for a movement populated largely by vaccine deniers) that no conspiracy theory can dismiss. And time for the government’s trade and investment agenda to deliver results—even knowing that some of those results will bring impacts that front-line communities will not and should not accept. In the immediate moment, none of this changes the way the wider climate community has to respond, from campaigners to investors to front-line practitioners. All of our voices are still essential. But those voices will be stronger and more confident if we have a clearer sense of the brutal game we’ve been pulled into, and a plausible path (or two it three) for playing it to the win we need. This conversation also underscores our biggest task for the remaining years of what was supposed to be the “decisive” decade for climate response: if we can shift the energy narrative in Canada, make it a mainstream view that the country’s preoccupation with oil and gas has us falling farther behind a changing world, we’ll see far better results in the 2030s and beyond. For many veteran climate advocates, taking on that part of the fight will begin with recognizing that most of our friends and family, neighbours and colleagues already understand that climate change is real and right in front of us: they’re looking away and staying silent because they don’t have what climate communicator Katharine Hayhoe calls “efficacy”. As Hayhoe recently pointed out in a feature interview with The Energy Mix, it amounts to science denial to keep hammering away at the dire news about global warming without also helping audiences understand what they can do about it, with multiple studies telling us why that won’t work. Far better to open those conversations with the dominant crises like energy security, food security, emergency preparedness and business continuity, affordability, and financial security that have knocked climate change far lower on the list of public priorities, and will likely keep it there. We Knew This Would Happen Climate hawks have always known this day would come. For decades, we warned that everyone had to pay attention to climate change right now, because it would be much tougher to respond once the crisis itself started affecting our daily lives. We were right, and here we are. But now, thanks to decades of hard work, communities and countries have an alternative to doubling down on fossil fuels. “In 10 years everyone will have ‘always known’ that solar and batteries were going to win,” Morgan Solar Executive Chair Mike Andrade writes on LinkedIn. With the disruption in the Strait of Hormuz roiling energy priorities around the world, Canada and the United States are among the shrinking number of countries that are still very keen on fossil fuel development. Understanding that gap is a first, essential step in spotting the opportunities ahead, and averting the deep economic as well as environmental and social risk we face if we don’t seize them. Thanks for reading The Energy Mix Weekender! Subscribe for free to receive new posts and support our work. Subscribe Mitchell Beer traces his background in renewable energy and energy efficiency back to 1977, in climate change to 1997. Now he and the rest of the Energy Mix team scan 1,200 news headlines a week to pull together The Energy Mix and The Energy Mix Weekender. Chart of the Week BYD Eyes 20 Dealership Locations After Canada Greenlights Chinese EV Imports ‘Sledgehammer’ Carbon Price Deal Boosts Emissions by 230Mt, Aims for Fall 2027 Pipeline Approval ‘Worse Than Harper’: Regulatory Rollback Would Stop Canada from Assessing Project Impacts, Critics Warn Electricity Strategy Aims for Doubling by 2050, Allows ‘Flexibility’ for Gas Power Plants Opposition Mounts as O’Leary’s Data Centre Approved in Drought-Stricken Utah Diluted Carbon Price Misses ‘Reality of the World We’re In’, Could Impede Diversified Trade: Climate Institute $200B in Clean Energy Investment Will Need On-Time Delivery, Reliable Policies, Trade Association Says ‘Sovereign Pipeline’: CEO Pitches to Keep Trans Mountain in Government Hands What a Supercharged El Niño Could Mean for Canada Canada’s future is electric, and here’s how a power grid superhighway would get us there (Toronto Star) The Perilous Waters of Hecate Strait (Globe and Mail) Saudi Aramco profits jump despite conflict in Middle East (The Guardian) Farmers confront rising cost of fertilizer and fuel as spring seeding under way (Canadian Broadcasting Corporation) Clarkson forecasts 24% decline in offshore oil, gas capital expenditures (Oil & Gas Journal) Pipe Dreams: How Oil and Gas Fail to Deliver Economic Development in Africa (Oil Change International) Developing countries must hold the pen to script the fossil fuel transition (Climate Home News) Iran war threatens the dream of a post-oil economy in the Gulf (Washington Post) U.S. Oil, Gas Work Force Hits Lowest Level Since 2022 (Rigzone) Why Polestar Is Doubling Down on Net Zero as Rivals Pull Back on EVs (Bloomberg) Why this Michigan utility plans to sell its hydro dams for $13 (E&E News/Politico) Levees can no longer save New Orleans (Vox)
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‘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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