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Introducing Creator Assistant, Plus More Languages For AI Translations on Facebook
Creator assistant understands each creator's unique presence and helps them take action to grow.
Run Google's Gemini LLMs right on your Mac with the new AI Edge Gallery
Google has released its AI Edge Gallery app on the Mac for the first time, allowing AI fans to run its Gemini large language models (LLMs) locally, without the need for an internet connection.
Apple Announces Europe's First Developer Center
Apple today announced it will open Europe's first Apple Developer Center in Berlin later this year. The facility joins existing Developer Centers in Bengaluru, Cupertino, Shanghai, and Singapore. Apple said the Berlin center, located in Mitte district, will offer developers throughout Europe in-person sessions, workshops, and one-on-one appointments in multiple languages, with consultation areas and dedicated labs staffed by Apple experts.
Scientists are cracking the code of interspecies communication. Is that a good thing?
Bioacousticians are finding animal communication patterns once thought unique to humans — discoveries that challenge ideas about what makes human language special.
New book offers tips to translate climate science into political gains
At a time when climate politics in the United States and globally remain deeply polarized, Will Hackman, a climate advocate and political operative, argues that the climate movement needs a new language — one rooted less in doom, guilt and abstract planetary crisis, and more in people’s everyday lives, health, safety, costs and communities. In […]
New book offers tips to translate climate science into political gains
At a time when climate politics in the United States and globally remain deeply polarized, Will Hackman, a climate advocate and political operative, argues that the climate movement needs a new language — one rooted less in doom, guilt and abstract planetary crisis, and more in people’s everyday lives, health, safety, costs and communities. In […]
How Science Works
The 2nd Weather and Climate Livestream is now underway. It is organized by a group of early-career researchers concerned about declining funding for the field and eager to demonstrate how weather and climate research makes a difference in our lives. Last year’s event was a great success, drawing terrific press coverage including a piece in the NY Times. This year features a strong lineup of talks aimed at a general audience — I highly recommend tuning in. Share I gave a talk yesterday about How Science Works in Three Steps. You can see the short presentation here (I’ve appended a transcript below): And please tune into the livestream here. It runs through tomorrow at 6 pm ET. Thanks for reading The Climate Brink! Subscribe for free to receive new posts and support our work. Subscribe Cleaned up transcript: Thank you, and thanks to everyone tuning in. I want to talk about something I think a lot of people don’t really understand: how science actually works. Arguments over any scientific topic — climate change, vaccines, you name it — revolve around competing claims. For climate change, the claims are: Is the Earth warming? Are humans to blame? What will the impacts be? Over the last 150 years, the scientific community has developed a remarkably robust method for sorting out which claims are true and which aren’t. That method is what I want to walk through. So how does science generate knowledge? If you ask a high schooler, they’ll give you the version they learned in ninth grade: an individual scientist forms a hypothesis, runs an experiment, and draws a conclusion. That’s more or less what happens at the individual level, though in practice it’s more iterative — you design, test, and refine all at once, landing on a slightly different idea each time until you reach a conclusion about whatever hypothesis you’ve ended up with. But this isn’t science. It’s only step one. Any individual scientist can make mistakes — scientists are human. Maybe there’s a bug in the code, maybe there’s an error in the experiment. Just because someone ran an experiment and reached a conclusion doesn’t mean it’s right, and scientists don’t treat a single result as settled truth. Step two is peer review, the first layer of quality control. The scientist writes up what they did and submits it to a journal, which sends the paper to other experts in the field. Those experts judge whether the methods are sound, whether it properly references prior work, and whether the conclusions actually follow from the data. Ask any scientist and they’ll tell you peer review is miserable — the comments come back brutal, often harsh, and you have to revise and resubmit, sometimes multiple times, before an editor finally accepts the paper. Peer review catches an immense amount of bad science, but it can’t catch all of it. Reviewers get sloppy or overworked, they miss obvious mistakes, and they can’t catch hidden errors — a bug in the code, a misread instrument. So a peer-reviewed paper making it into a journal doesn’t mean the paper is right. Nobody treats a single paper as the final word. That’s where step three comes in: the crucible of science. Important results get retested by the broader community. If you publish something interesting, it gets dissected. Sometimes people try to reproduce your work directly but more often they test its implications. If someone claims X is true, and that implies that Y is also true, then scientists will go check Y. If a claim survives this gauntlet long enough, it becomes accepted. That’s how we generate knowledge. Let me give an example: the ozone hole. In 1985 — and I doubt many people watching were around then, but I remember it well — Joe Farman and his colleagues published observations from Antarctica showing that a huge chunk of ozone was disappearing every year. This was shocking because most atmospheric chemists had expected ozone loss to occur at mid-latitudes and high altitudes, not over the South Pole. When scientists see something they don’t understand, they rush to explain it, and several competing hypotheses quickly appeared in the peer-reviewed literature. One pointed to chlorine chemistry from chlorofluorocarbons. A second blamed nitrogen chemistry, possibly driven by solar proton events — meaning the sun was responsible. A third argued it wasn’t chemistry at all but dynamics: winds were carrying naturally low-ozone air from the lower atmosphere up into the stratosphere. Then the community went to work testing which theory could survive the crucible. Aircraft flew into the ozone hole and found no nitrogen, ruling out the nitrogen theory. Measurements showed the air in the polar vortex was descending, not rising — which would bring high-ozone air down, eliminating the dynamics theory. Meanwhile, those same flights measured enormous amounts of chlorine, strongly correlated with exactly the regions where ozone was being destroyed. That became known as the “smoking gun” paper, and it established that chlorine chemistry was the cause. Through this process, a consensus emerged. And it’s worth being clear that this consensus is organic. There’s no vote, no poll, no meeting where it gets decided. Scientists go to conferences, talk to one another, and independently arrived at the same answer. It’s the ultimate free market of ideas. After the consensus was established, the peer-reviewed literature doesn’t even mention the rival theories — it simply takes chlorine chemistry as given. That’s what a consensus looks like. It doesn’t mean every single scientist agrees, but it does mean there’s no longer any legitimate debate. So where can you see what scientists actually think? It’s all in the peer-reviewed literature, which contains the entire historical record of the argument: the discovery, the competing theories, the debate, the testing, and finally the community coalescing around one answer. But there’s a lot of room for misrepresentation. You can dig up an old paper claiming the ozone hole was caused by dynamics and wave it around as if there still was a debate about the cause of the ozone hole — without mentioning that the idea was disproven long ago and that even its own authors no longer believe it. There’s a bigger problem, though: the peer-reviewed literature is written in a language I call “nerd.” It’s extremely hard to read — honestly, some of it is hard for me to follow. It takes graduate students years to work through the literature in a single field, so we can’t expect policymakers or ordinary people to do it. This is where scientific assessments come in. The most famous is the IPCC — the Intergovernmental Panel on Climate Change. The job of an assessment is to read and summarize the peer-reviewed literature, synthesizing what’s settled, what’s been disproven, and what remains genuinely debated. To ensure the assessments accurately summarize the peer-reviewed literature, assessments are written by large teams, which are far less prone to bias than individuals and which dilute the influence of any one scientist with an axe to grind. The teams are deliberately assembled to avoid stacking the deck, and the reports themselves go through multiple rounds of peer review. That rigorous process is exactly why you can trust them and why people consider the IPCC to be the gold standard of what we know about the climate system. An assessment produced through a different process may well not be credible. You can even watch the consensus evolve across assessments. Take the attribution of warming. The 1990 IPCC report said the observed warming was broadly consistent with model predictions but also comparable to natural variability — in other words, we don’t know whether humans are responsible. By 1995, it concluded that the balance of evidence suggested a discernible human influence — a sentence only a committee could write, and one that barely commits to anything. By 2001, most of the warming over the previous 50 years was judged “likely” due to greenhouse gases; by 2007, “very likely”; by 2013, “extremely likely.” The 2023 report says essentially the same as 2013 — there’s not much stronger language available. So, to wrap up: science is our ultimate tool for deciphering the universe — the most successful method we’ve ever developed for determining what’s true and what isn’t. Our understanding is written into the peer-reviewed literature, so if you want to know what scientists think, that’s where to look. And for those of us who don’t speak nerd, scientific assessments are the Rosetta Stone — they translate nerd into English. Let me open the floor to questions. Thank you.
St. John’s Hosts National Science Foundation-Supported Conference on Bilingual Research
The impact of age, language background, cognitive ability, and other ingredients on bilingualism was the subject of a US National Science Foundation (NSF)-support
Turbo-charging battery research with AI: An ambitious vision
Argonne battery researchers and data scientists have come together to review emerging large language model applications in the battery field, outlining an ambitious technical roadmap to accelerate battery breakthroughs.
AI Boosting Business Formation
The surge in new US business formation is being fueled by AI and large language models, which are dramatically reducing the cost and complexity of launching a company, see chart below. Subscribe for daily updates.
The Politics of Visibility - Robert & Ethel Kennedy Human Rights Center
To say that Michigan is facing a housing crisis often comes with the language of urgency, as if something sudden has happened, as if instability has only recently emerged. But that framing depends on the assumption that there was ever a moment of stability equally accessible to everyone. The reality is less abrupt and more patterned as housing insecurity in Michigan is not a new condition.
The Return of Blaming and Shaming in Public Health
For years, medicine has tried to eliminate stigma. Now, Robert F. Kennedy Jr. is bringing back the language personal responsibility.
The Return of Blaming and Shaming in Public Health
For years, medicine has tried to eliminate stigma. Now, Robert F. Kennedy Jr. is bringing back the language personal responsibility.
IPL 2026: Behind the scenes at JioStar — ‘The future of sports broadcasting’
This year's IPL has surpassed 1.1bn viewers across JioStar's television and digital platforms, with feeds pumped out in 12 languages
Building public trust through interaction with scientists, health workers
As trust in science falls in the U.S., Emma Courtney, a PhD student at Cold Spring Harbor Laboratory, is doing her part to help reverse that course. Courtney will be speaking at APHA’s Policy Action Institute June 9-11.
"They will ruin my life": Microsoft threatens to wield 'Digital Crimes Unit' over zero-day exploit disclosures — causing uproar in the cybersec community
Microsoft's approach to working with security researchers has long been controversial, but some of its recent language has alarmed some commentators.
When used in the ‘wild’ conditions of the real world, large language AI stumbles
Asking medical questions of AI with language spoken “in the wild”—meaning with consumer-level LLM prompts—brings back answers with decidedly mediocre accuracy—76%—a new study shows.
Scientists Challenge a 70-Year-Old Theory of Language With a Surprising Discovery
Researchers have identified a previously overlooked structure underlying human language, revealing patterns that extend beyond traditional measures of emotion.
Jayne M. Brandel, Ph.D., CCC-SLP, Named Dean of School of Health and M
Jayne M. Brandel, Ph.D., CCC-SLP, a prominent scholar in the field of speech-language pathology, has been named Dean of the School of Health and Medical Sciences, effective July 1, 2026.
A Not So Happy Anniversary: A Year of Deceptive Science “Standards”
Saturday, May 23rd marked a year since President Trump signed an executive order (EO) to “restore” so-called “gold standard science.” On the surface, the language and title of the order sounded promising. But to science policy nerds like me, the EO—and the actions the administration has taken to imp
Semiconductor decoupling. Powell's politics. Industrializing Ogaden. Free trade and the Magna Carta.
Thank you for opening your Chartbook email. Feliks Topolski, 1907-1989 8th Army’s Front, Italy 1944 Semiconductor decoupling Source: FT One thing that Jerome Powell deeply understood is that chairing the Fed under current conditions is a deeply political job. “Powell has successes and mistakes to his name. But he stands out as having thought more carefully about the Fed’s democratic legitimacy than any chair since Paul Volcker,” said Paul Tucker, a former deputy governor at the Bank of England, referring to the Fed chair who successfully fought the great inflation of the 1970s and early 1980s. … While Powell was at the Treasury in the early 1990s, he watched Alan Greenspan build relationships within the government. That convinced Powell he needed to follow the former Fed chair’s lead and spend more time in Congress than his predecessors Yellen and Bernanke. “The accountability for the Fed runs through Congress,” said Claudia Sahm, a former Fed official who is now chief economist at New Century Advisors. “That’s not just about having people on speed dial — it’s answering questions, explaining policy and getting feedback.” Source: FT Ethiopia’s PM Abiy and Nigerian business tycoon, with Chinese engineering, are driving industrialization in Ogaden/Somali region of Ethiopia. - Michael Masri’s reporting for Africa Report if fascinating. Ethiopia has launched a cluster of large industrial schemes in the Somali region, laying foundation stones for a urea plant and an oil refinery in Gode while opening the first two phases of the Ogaden LNG project. Abiy Ahmed framed the push as part of a broader strategy to link energy, agriculture and industry. “These projects embody more than industrial growth,” he said. “They represent our shared responsibility to harness opportunities, strengthen co-operation and promote peace.” The Gode urea plant, backed by Dangote Group and Ethiopian Investment Holdings (EIH), is designed for 3m tonnes a year. Dangote owns 60%, while EIH holds 40%. Officials say Dangote’s equity totals about $2.5bn, while the state provides land and implementation oversight. Gas from the Calub fields will be piped 108km to the site. Construction is slated for 40 months, with operations targeted for 2028. Ethiopia imports most of its fertiliser, spending more than $1.2bn a year for 1.5m–2m tonnes. Usage was roughly 1.7m tonnes in 2023, almost half of it urea, and demand for 2024–25 is put above 2.4m tonnes. If delivered on time, the Gode plant could cover domestic needs and allow exports to neighbours. Brook Taye, chief executive of EIH, called the venture “a significant milestone in the journey toward industrial self-sufficiency”. Dangote told the groundbreaking crowd that the complex would be integrated with gas supply and a polypropylene unit to support local inputs. He pointed to Ethiopia’s Grand Ethiopian Renaissance Dam (GERD), with a capacity of more than 5,000MW, and noted his group’s cement operations and expansion plans. The Gode refinery is being developed by Golden Concord Group of China. It is designed to process 3.5m tonnes a year of crude and condensate from the Hilala field, with associated storage. Backers say it will ease foreign-exchange pressure from fuel imports and anchor further industrial activity, including a proposed steel smelting site in Dire Dawa. GCL’s chair, Zhu Gongshan, cast the project as “a key step in Ethiopia’s path to energy sovereignty”, underscoring the Beijing–Addis ties. Financing details are still being finalised, but the refinery is central to the government’s energy-security narrative. Source: The Africa Report Dangote’s Ethiopian vision: Dangote began with cement. His crown jewel, Dangote Cement, dominates Nigeria’s market and has 10 operations in other nations in Africa. The formula has been consistent: identify sectors with chronic shortages – cement, fertiliser, sugar – commit massive local investments, and scale up to serve regional demand. Ethiopia was a natural extension of that model. In 2015, Dangote launched the Mugher Cement Plant outside Addis Ababa at a cost of $600m, with an annual capacity of 2.5 million tonnes. The investment gave him a firm foothold in one of Africa’s fastest-growing construction markets. But success came with risks. In May 2018, violent unrest led to the killing of Dangote’s country manager and temporary shutdowns at Mugher. While other investors might have retreated, Dangote doubled down. Today, he is preparing to spend $400m to expand Mugher’s capacity to 5 million tonnes, alongside a new grinding unit near Addis Ababa. Cement is just one market Dangote is targeting. Ethiopia’s enormous state-controlled sugar industry, long plagued by inefficiency and debt, has struggled for years to reform. Its Omo Kuraz Sugar Development Project, touted as one of the continent’s largest initiatives, continues to face delays and mismanagement. … The most ambitious venture of all is the planned fertiliser production complex in Gode, Somali region. Publicised on 28 August 2025, the Dangote Group and Ethiopian Investment Holdings (EIH) deal plans one of the world’s largest single-site urea manufacturing plants. The project costs an estimated $2.5bn and has an estimated capacity of 3 million metric tonnes annually, a rate that would not only render Ethiopia independent, but even a net exporter of fertiliser. EIH, the state wealth fund of Ethiopia, will hold 40%, while 60% is held by Dangote Group. … For Dangote, Ethiopia is a trifecta: Cement demand is projected to grow by 10% annually, its sugar consumption is growing with rapid urbanisation, and fertiliser demand is all but insatiable, given that farming supports the livelihood of 70% of Ethiopians. Source: The Africa Report The stakes for Abiy are high: For Addis Ababa, the $4bn fertiliser project in Ethiopia’s Somali region is no longer simply a manufacturing investment. It is increasingly being framed as a strategic state project tied to food sovereignty, import substitution and the government’s broader attempt to build economic resilience under mounting fiscal pressure. “Becoming self-sufficient in food is our main agenda,” Abiy says during the visit. “Land is needed; we have it. Water is needed; we have it. Human resources are needed; we have it.” The scale is enormous even by African industrial standards. Once operational, the plant is expected to produce three million metric tonnes of urea annually, potentially making it one of the continent’s largest fertiliser complexes. Ethiopian officials say the project now includes a 110km gas pipeline, a 120MW power plant, a polypropylene packaging facility and a two-million-tonne NPK blending plant. For a government grappling with chronic foreign exchange shortages, ballooning import bills and pressure on agricultural productivity, the numbers matter politically as much as they do economically. The decision to anchor the project in Gode is deliberate. Long viewed as peripheral to Ethiopia’s industrial economy, the Somali region is now being repositioned by the federal government as a future irrigation and agro-industrial corridor. Officials argue that the area’s vast land reserves and access to water could eventually support large-scale commercial farming capable of reshaping Ethiopia’s food economy. The government is simultaneously building housing infrastructure for about 5,000 residents near the plant, while officials project broader industrial expansion along the corridor. Abiy goes further, comparing the long-term revenue potential of the Gode development zone with Ethiopia’s flagship state enterprise. “The investments we are making in this Gode area, combined, will generate high revenue for Ethiopia, at par with Ethiopian Airlines,” he says. Source: The Africa Report Feliks Topolski 1907-1982 Topolski’s Chronicle, ‘Nazi Revival’, Vol. 8, Is. 1-2, 1960 HEY READERS, THANK YOU for opening the Chartbook email. I hope it brightens your day. I enjoy putting out the newsletter, but tbh, what keeps this flow going is the generosity of those readers who clicked the subscription button. Photo by Jakub Dziubak on Unsplash If you are persuaded to click, please consider the annual subscription of $50. It is both better value for you and a much better deal for me, as it involves only one credit card charge. Why feed the payments companies if we don’t have to. Subscribe Grim vision of drone warfare by Sylvie Kaufmann For contributing subscribers only. Subscribe The state of the art in Colbert-studies. Jean-Baptiste Colbert was Louis XIV chief minister from 1661 to 1683 and traditionally seen as an architect of absolutism and mercantilism. Alexander Hamilton later cited him as an inspiration in 1782. Now his legacy is disputed. Daniel Dessert, the chief critic, sees Colbert as the orchestrator not so much of an absolutist state as of an elite cabal. Would be great to have a good English-language review of this important debate. Source: lhistoire Didn’t know that the Magna Carta included free trade provisions Fighting for the right to the beach in Oregon in 1967: Source: Research Gate RAF Station – British and Polish Flags, c. 1940-45, The Ingram Collection Feliks Topolski, 1907-1989 ink and wash If you’ve scrolled this far, you know you want to click: Subscribe
Repeated violence puts spotlight on divisive political speech
Scholars who study rhetoric and extremism say the country’s political language has grown more aggressive and can trigger violent acts.
AI is getting worse as Google and Anthropic nerf AI models and limit usage
Evidence is mounting that the subsidization of AI is ending in real time. Will businesses still shell out for it?
KernelScript: A Programming Language For Kernel Customization & App Optimizations
Multikernel Technologies Inc has been working on a multi-kernel architecture for the Linux kernel while in addition to that they have been developing KernelScript as a domain-specific language for carrying out Linux kernel customizations and app-specific optimizations.
Rust-Based Wild Linker 0.9 Brings New Platform Coverage, Linker Plugin API
Wild Linker 0.9 was released today as the latest version of this very fast linker for Linux systems that is written in the Rust programming language.
JPL's management contract is suddenly up for grabs for the first time since the 1930s, and NASA's own language shows why this is more than a routine procurement fight
For the first time since the Jet Propulsion Laboratory’s origins in the 1930s, Caltech’s hold on one of the most important institutions in American robotic space exploration is no longer automatic. NASA has announced that it will compete the next contract for managing and operating JPL, the federally funded research and development center in Southern […]
Introducing Shortcuts Playground: Create Apple Shortcuts with Claude Code or Codex
Today, I’m pleased to introduce something I’ve been working on for the past six months: Shortcuts Playground, a plugin for Claude Code and Codex that can create any shortcut for Apple’s Shortcuts app using natural language. With Shortcuts Playground, you can simply prompt Claude Code or Codex with a sentence requesting a shortcut of any
FOX Sports and Fox Corporation Announce FIFA World Cup 2026™ Community Impact Initiatives
/PRNewswire/ -- FOX Sports, America's English-language home for the FIFA World Cup 2026™, and Fox Corporation today announced community impact initiatives...
iOS 27's New AI Voice Control Feature Hints at Major Siri Upgrade Coming Soon
Every May, Apple previews new accessibility features coming to devices like the iPhone, iPad, and Mac. The announcement typically occurs a few days before Global Accessibility Awareness Day, and it offers a glimpse into what will be included in the next major releases of Apple's software platforms, such as iOS, iPadOS, and macOS. This year, one of the new capabilities shown off by Apple is natural language support for the Voice Control feature on the iPhone and iPad.
Why renaming this common hormonal disorder is a huge deal
You probably know someone who has a condition that, until last week, was known as PCOS, or polycystic ovary syndrome. It affects one in eight reproductive-aged women. But despite it being such a common disorder, patients are often misdiagnosed and have delayed treatment. Now, medical professionals are trying to change that. After 15 years of work, the condition has been renamed to PMOS, or polyendocrine metabolic ovarian syndrome. The new name reflects the real driver of the condition: disruptions in hormones. But how much can changing a name really do? Click here to access resources about PMOS in multiple languages. Interested in more science? Email us your question at shortwave@npr.org.Listen to every episode of Short Wave sponsor-free and support our work at NPR by signing up for Short Wave+ at plus.npr.org/shortwave.
Taylor Bishop soars toward political career thanks to MSU internships
When Taylor Bishop (Class of 2026) came to Morehead State University from nearly three hours away in Greensburg, he found two things that put him on a clear path to college graduation and his future career. The first was his interest in history growing up, which expanded into political science after he started taking classes at MSU. "I really got interested in politics when I got to Morehead State," Bishop said. "It was like learning a language that I'd never known, and I fell in love with it instantly." The second key factor that shaped much of Bishop's college experience was taking his First-Year Seminar with instructor Rebecca Wright. Wright is also the coordinator of internships for MSU's Center for Career Development & Experiential Education and gave Bishop some important advice. For more information on gaining real-world experience like education abroad and internships at Morehead State University, contact the Center for Career Development & Experiential Education at careerservices@moreheadstate.edu or 606-783-2233. To learn more about MSU's Department of History, Philosophy, Politics, Global Studies and Legal Studies programs, contact hgpl@moreheadstate.edu or 606-783-2655. "You need to get internships wherever you can so it can get you wherever you want." Bishop took those words to heart and for much of his undergraduate career, he benefited from several meaningful experiences outside of the classroom that expanded both his knowledge and his confidence to succeed.
Does emotional intelligence stop us from being rude? Here's what the science says
We often assume that how we respond to a rude comment says something stable about us: our personality, our culture, even the language we are speaking. If someone reacts calmly, we presume they are a patient ...
Climate, food and health challenges call for one conversation, not separate solutions
In New Orleans, food is the language people use to speak to one another. It is how neighbors greet each other, the center of every gathering and one of the main characters in the city’s own story. So when farmers across Louisiana faced a sudden and severe drought in the summer of 2023, they reached … Continue reading Climate, food and health challenges call for one conversation, not separate solutions
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.
AI piplines correctly identify genetic basis for disease
Large language models use reasoning capabilities to identify new genetic factors causing disease - even without any medical training.
Scientists Discover Ancient “Language Switches” Hidden in Human DNA
Researchers have identified tiny genetic “switches” that appear to play a surprisingly large role in human language ability.
Iko Uwais, Joe Taslim Head K-Movie Entertainment’s Indonesian Action Slate at Cannes Market (EXCLUSIVE)
K-Movie Entertainment is at the Cannes Film Market with a focused Indonesian action slate, led by “Pendekar: Warrior” starring Iko Uwais and an Indonesian-language remake of the Korean hit “The Man Fr...
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)
Research repository ArXiv will ban authors for a year if they let AI do all the work
ArXiv is doing more to crack down on the careless use of large language models in scientific papers.
NYU's Joshua Tucker on the politics of training AI
Joshua Tucker, New York University Center for Social Media & Politics co-director, joins 'Squawk Box' to discuss details of the university's new research showing that AI chatbots generate more government-friendly responses when prompted in languages tied to countries with tighter media controls, the intersection of politics and AI, and more.
Outstanding Seniors in the College of Science: Alicia Salazar Contreras
The College of Science is proud to recognize 12 outstanding seniors who went above and beyond during their time as Wildcats. Alicia Salazar Contreras is this semester's recipient from Speech, Language & Hearing Sciences!
For Yale senior, a love of science and of languages opened up new worlds
Some classmates were confused by Audrey Aslani-Far’s interest in studying both science and Russian as a Yale student. But to her, the combination makes perfect sense: both disciplines help a person to better read the world.
Mom moved abroad with my family; we stayed and she returned to US
My 70-year-old mom struggled with the language barrier in Spain and didn't find community overseas as I'd hoped. Now, she's back in the US without us.
Governments May Shape What AI Chatbots Say by Shaping the Data They Learn From
Six studies show how state-coordinated media in AI training data influence model responses about politics—especially in a country’s own language
What’s the Best Way to Brainwash an LLM?
I spent a weekend trying to convince a language model it was C-3PO. Here's what actually worked.
Jack Ma-Backed Ant’s Profit Fell 79% on AI, Health Care Costs
Ant Group Co.’s quarterly profit continued to decline after the company increased investment in artificial intelligence technology for its health care, large language model and payment services.
Xiaomi officially announces new flagship phone with Leica cameras and large display
The Xiaomi 17 Max has been confirmed for a May launch in China, with pre-orders already open. The company has teased the device in a light blue colorway, revealing its design language and a few key highlights. Separately, a leak has shown the device in multiple additional colorways.
The cinema effect: Turning films into a gateway to science
The sci-fi film Project Hail Mary, currently in theaters, is capturing the attention of both audiences and the scientific community for its science-based content. It manages to engage viewers with complex, cutting-edge topics — from astrophysics to language — without sacrificing entertainment. Yet not all films strike this balance. Many, unlike this recent production, have promoted inaccurate or even misleading scientific ideas, and thanks to their wide reach, have contributed to shaping distorted public perceptions of science. Precisely by harnessing the power of cinematic storytelling, Hildrun Walter, Fritz Treiber and colleagues have used films as a tool to foster engagement and dialogue between scientists and the public. Their innovative format, Science & Cinema, described in a new paper published in JCOM, proves both effective and potentially easy to replicate.
Disability Justice: Access, Communication & Belonging in Health Care & Public Health
This session will center on I/DD and deaf communities. From myth-busting to communication best practices to recognizing the intersections with other public health issues and identities.
René Cárdenas, longtime Dodgers Spanish-language announcer, dies
Cárdenas with Dodgers in 1958 was first full-time Spanish-language broadcaster in MLB history
Richard C. J. Somerville on the Science of Climate Change
In his new book, Climate Change Science: An Essential Reader, Richard C. J. Somerville describes cutting-edge climate change science in language that everyone can understand.
Language Lessons: How LLMs Are Transforming Research
Stanford GSB faculty are using AI to analyze text at unprecedented speed and scale.
Learner spotlight: Vivian’s path to rights based mental health advocacy
For Vivian Anyango Aola, mental health is deeply personal. Growing up she witnessed people around her struggling with emotional distress without access to support, understanding or solutions. Those early experiences laid the foundations for her work today as a mental health advocate at St. Daniel Comboni mission in Nairobi, Kenya.“Mental health is about feelings – something we all experience every day,” Vivian explains. “Ensuring people are supported in those feelings is what drives my work.” Creating safe spaces and challenging stigmaIn her role, Vivian works closely with people living with mental health conditions and psychosocial, intellectual and cognitive disabilities. Through dialogue, listening and shared learning, she helps create safe spaces where people feel seen, heard and respected. However, stigma and discrimination remain significant barriers.“Witnessing stigma only strengthens my resolve to fight for inclusion, acceptance and equal treatment for all,” she says. Vivian enrolled in the WHO ‘QualityRights in Mental Health’ online, self-paced course available on the WHO Academy learning platform to strengthen her knowledge and advocacy skills. The six-module course provides the foundation for challenging stigma and discrimination and for promoting person-centred, rights-based approaches in mental health.“The course gave me a new perspective,” she says. “It showed me how to support people in ways that respect autonomy and promote recovery, rather than focusing only on treatment.”The importance of language and cultureSince its launch, more than 141 000 people have enrolled in the course globally, and it is now available in 17 languages on the learning platform – the highest number of languages of any WHO course. For Vivian, accessibility is key to transforming mental health systems. “Language is a bridge between professionals and communities,” she explains. “When mental health resources are available in local languages, they reduce misunderstanding, build trust and encourage people to seek help. It also empowers health workers to deliver care in ways that are culturally sensitive and relevant.”In a global evaluation of the course, which analysed data from over 3000 learners, attitudes toward people with mental health conditions improved by 22.78% overall after completing the course, with even greater shifts in low- and middle-income countries (29.18%) compared to high-income countries (20.58%)[1].“The course equips participants with the essential knowledge and skills to transform mental health into a more rights-based, inclusive and recovery-oriented system,” explains Vivian. “It reinforces that change is possible. When people understand rights, dignity and recovery, the entire system can begin to shift.”Mental Health, Brain Health and Substance Use[1] Breaking stigma, discrimination and promoting rights: global evaluation of the World Health Organization QualityRights e-training on mental health, recovery and community inclusion
Springfield school board to consider new elementary-school science curriculum
Lookout Eugene-Springfield reviewed Springfield’s elementary science curriculum, which has been in process for three years. The curriculum is a mix of teacher-created lesson plans, a science curriculum called Mystery Science and science-themed segments of the district’s English Language Arts curriculum that align with state standards.
China’s assault on the Tibetan language
Tighter policies in schools reflect the ‘narrowed’ tolerance towards Tibet from the Chinese state
Scientists discover the hidden physics behind how words spread
Scientists discovered that language change follows hidden statistical patterns similar to the physics of magnets and soap bubbles.
Communication, collaboration essential to the future of US public health, leaders say
The future of U.S. public health will require stronger partnerships, clearer messaging and more public involvement. That was the consensus of leaders who shared their insights this month during a discussion on challenges facing the field.
Indosat CEO Vikram Sinha is building an AI for Indonesia’s local languages. Can he make a business case for sovereignty?
Vikram Sinha wants Sahabat AI to be a platform for Indonesia's startups—even if he admits his team can't quite think of a "business case" right now.
Yakuza Dev's Stranger Than Heaven Drops English Dub Option Due to Bilingual Characters
Language barrier
AI lets chemists design molecules by simply describing them
Creating complex molecules usually requires years of experience and countless decisions, but a new AI system is changing that. Synthegy lets chemists guide synthesis and reaction planning using simple language, while powerful algorithms generate and evaluate possible solutions. The AI doesn’t just compute—it reasons, scoring pathways and explaining which ones make the most sense.
URI Researcher Examines How AI Is Changing the Language of Science
From philosophy conferences to biomedical research, URI Assistant Professor of Cell and Molecular Biology Nic Fisk is examining how computational tools are changing how scientists talk about their research – and what the consequences might be.
Agricultural science ‘doesn’t know politics,’ says Israeli-American, whose nonprofit CultivAid wins Bronfman prize
“When we talk about irrigation or plants, we see that this common language can overcome many political difficulties,” Tomer Malchi told JNS.
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.
Plan to Increase Role of Spanish in AI and Science
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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 ‘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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Q: What draws you to business law? A: Prior to law school, I worked in Cooley LLP’s Private Companies practice group as a corporate paralegal for three years, supporting over 100 venture-backed startups and venture capital funds. I found purpose in helping founders transform their vision into successful companies. It was incredibly rewarding being part of their journeys—from company formation and initial financing to major transactions such as M&A deals. What excites me most about this field is the potential for a small, innovative company to become the next Google or Zoom, shaping how people communicate and access information. I hope to continue supporting entrepreneurs and investors as a corporate attorney. Q: What does it mean to you to have been awarded this scholarship? A: As the first person in my family to attend law school in the United States, receiving this scholarship is a tremendous honor. My parents and I left everything behind so that I could enjoy the freedom to practice my religion without persecution and pursue a career in law as a woman. This award is an acknowledgment of my dedication to business law and motivates me to continue pursuing excellence in this field. It also recognizes years of hard work—learning a new language, navigating the college and law school application process, and ultimately building a career in the legal profession. Q: How will this scholarship help support your future goals? A: This scholarship will provide access to mentorship from leaders in the field, enabling me to develop the skills necessary to offer practical advice and effective representation to future clients. It also eases the financial burden of law school, letting me focus on my coursework and deepen my commitment to corporate law. As an immigrant, breaking into the legal profession has not always been easy, and the time that mentors have invested in me has been instrumental to my progress. This scholarship will allow me to continue building those relationships while positioning me to one day mentor others pursuing similar paths. Alvaro Flores: Inspired by Entrepreneurs The son of Mexican immigrants raised in San Antonio, Texas, Alvaro Flores says he has long been interested in how policy, markets, and culture shape society. He studied international relations and global economy at the University of Southern California, with minors in business finance and legal studies. His experiences span local political organizing, venture capital and startup work, and teaching entrepreneurship. At UC Law SF, he is involved in the Business Law Society, Technology & Law Society, and the Latinx Law Student Association. He will join Perkins Coie as a 1L/2L summer associate with the firm’s Emerging Companies & Venture Capital practice. Q: What draws you to business law? A: I’m drawn to stories of entrepreneurs whose simple ideas become companies that shape the economy and society. Spending time in venture capital and working directly with founders has shown me that many important decisions in the scaling process are ultimately legal ones, particularly how companies are structured and how capital is raised. Business law sits at the intersection of innovation and equity. It offers a way to stay close to the pace of technological change while also helping expand who gets to participate in it. There is something special about being part of that process here in the Bay Area, where the American Dream feels like it is reimagined in real time. Q: What does it mean to you to have been awarded this scholarship? A: I am truly grateful for the support and belief this scholarship represents. I see it not just as recognition, but also as alignment. The CBL Scholars Program’s focus on developing individuals who engage deeply with the business and technology ecosystem reflects the path I have been intentionally building. More than anything, it feels like an investment in that journey. I have been thoughtful about each next step–whether teaching, working in venture capital, or building multidisciplinary experiences–and this scholarship reinforces that those efforts come together in a meaningful way. Q: How will this scholarship help support your future goals? A: It allows me to focus more on developing as a business lawyer, without the distraction of financial constraints. Beyond that, the mentorship and access to the Bay Area network are just as valuable. I’m especially interested in working with early-stage companies and investors, so the opportunity to learn directly from people operating at a high level in that space is huge. Tavia Pappaly: Working in the Wine Industry Sparked Interest in Business and Law Tavia Pappaly spent five years in the wine industry before deciding to pursue law school with a goal of becoming a corporate transactions attorney. Pappaly says she wants to use her legal skills to help business grow and thrive. A first-generation law student from Fairfield, California, she is a member of the Legal Education Opportunity Program (LEOP) and will serve as vice president of social planning for the UC Law SF Sports Law Society in her 2L year. She plans to work as a summer associate at Gibson Dunn in San Francisco after her 2L year. Q: What draws you to business law? A: Business law brings together many of my interests and personal experiences. As the daughter of immigrants who grew up around a family-owned small business, I saw firsthand both the opportunities and challenges of building something from the ground up. That perspective, combined with my experience in the wine industry, solidified my interest in helping businesses of all sizes tackle evolving legal challenges. I’m excited to grow within the corporate law space and develop the skills and experience to support businesses at every stage. I’m deeply motivated to give back to immigrant communities and small business owners, whose experiences often intersect. By building a strong foundation in corporate law, I hope to support those working to create generational stability and to contribute in a meaningful way to the communities that have shaped me. Q: What does it mean to you to have been awarded this scholarship? A: Receiving this scholarship is incredibly meaningful to me. I take great pride in my background as a first-generation student and a woman of color, so being recognized for my potential in a field that has historically underrepresented those communities is both affirming and empowering. This program will help connect me to the resources and committed mentors that will help me succeed. Beyond that, it is no secret that law school is a significant financial commitment. This scholarship helps ease both the financial burden and the stress that comes with it, allowing me to focus more on my education and long-term goals. I am incredibly grateful! Q: How will this scholarship help support your future goals? A: This scholarship has already connected me with an incredible network of faculty, business law professionals, and like-minded students. The community fostered by the Center for Business Law is deeply supportive, and I’m excited to grow alongside my cohort. I truly believe this network will play an important role in helping me thrive in law school and position me for success in business law. As I have seen firsthand how impactful both financial scholarship and mentorship are, I hope to give back in this same way later in my career.
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“AI polls” are fake polls
A few weeks after Donald Trump’s second presidential win, I took the train up from London (where I was living at the time) to Oxford to attend a conference on polls and forecasts of the 2024 election. Most of the attendees were pollsters or academics, but I also watched presentations from Aaru and Electric Twin, two companies that do what is interchangeably called synthetic sampling, silicon sampling, or synthetic audiences. Stripped of startup jargon, that means they use large language models (LLMs) to simulate responses to public opinion polls by having AI agents take on the role of survey respondents. I had already heard of Aaru thanks to some articles with eye-catching headlines like “No people, no problem: AI chatbots predict elections better than humans” in the months leading up to Election Day. The founders were making some big, some might even say far-fetched claims, such as: “within two years, we will simulate the entire globe — from the way crops are grown in Ukraine to how that impacts production of oil in Iraq, trade through the strait of Malacca, and elections for the mayor of Baltimore.” When Semafor asked Aaru’s cofounders — Cameron Fink and Ned Koh — about my boss, they said “we respect all those who came before us.” Nate (as he so often does) shared his thoughts on Twitter: Fink and Koh were relatively good-natured about this back-and-forth when we spoke at Oxford. They even offered to mail me one of the t-shirts featuring Nate’s quote they apparently had made. I never took them up on the offer, which I now somewhat regret. These synthetic sampling companies fell off my radar for a while, but they do still exist. In fact, Aaru recently received a $1 billion valuation. Is what they’re doing anywhere close to the most important frontier in AI development? Not by a longshot, especially when Anthropic just developed a model so adept at exploiting software vulnerabilities that it’s only being released to 40 companies. Still, silicon sampling is increasingly finding its way into public polling. Axios reported in March that “a majority of people trust their own doctors and nurses” based on findings from Aaru — without mentioning that the “people” in that sentence were actually LLMs. Around the same time, the Public Sentiment Institute “boosted” their online sample of 373 real survey respondents with 114 AI agents.1 (Spoiler alert: even the co-founder of Electric Twin doesn’t think that’s a particularly defensible approach.) Polling companies like Qualtrics and Ipsos are also developing synthetic data panels. So, what should we make of these … “polls”? Let’s get one thing out of the way: whatever they are, they’re not polls in the way that term is usually defined. Subscribe You can’t replace polls with AI On one hand, using LLMs to essentially make up fake survey respondents sort of sounds like the dumbest idea ever, one that will at best imperfectly replicate real polls while introducing all sorts of biases. On the other hand, with LLMs improving at a remarkable, perhaps even alarming rate, maybe that means I’m a dinosaur at the ripe old age of 24 because I still want to rely on polls that talk to actual people. I’m not going to argue that synthetic samples are completely useless. In fact, as I’ll return to later, there is evidence that some techniques can replicate topline survey results quickly and cheaply. But the marketing from certain companies can be slightly optimistic. “No traditional poll will exist by the time the next general election occurs,” said Fink in 2024. We’re just 206 days away from the midterms, and based on the fact that I still have to collect a bunch of polls every day, I’d say he should have run that prediction by a sample of AI agents before the interview.2 To see why synthetic samples can’t replace polls, here’s a quick primer on how they work. The simplest version of these models involves taking a LLM (like ChatGPT or Claude), giving it a demographic profile (e.g., a white, college-educated woman who lives in Utah and makes $70k a year), and then asking it to respond to a survey question. You repeat that process a few thousand times using different demographic profiles and end up with a sample of synthetic survey responses. The actual models used by private companies are more sophisticated than this, usually because they incorporate more hypothesized demographic characteristics for each agent and provide them with extra information. Aaru, for example, feeds agents a diet of news and information they’d be likely to consume, while Electric Twin incorporates their customers’ proprietary data about the audience they’re trying to replicate. The way Ben Warner, the co-founder of Electric Twin, explained it to me was “we have a large amount of data on […] for instance, 5,000 people. Can we make an accurate prediction of how they would respond to another question?” Still, it should be obvious why synthetic samples can’t replace polls. Polling is fundamentally a data collection process. We might use surveys to make predictions by feeding them into election forecasts, but the main purpose of a poll isn’t prediction, it’s gathering new data about what people think and how they feel. Silicon sampling, on the other hand, produces no new data. It’s simply a model: you input LLM training data, demographic prompts, and a bunch of other information, and it spits out a prediction for what a poll would say. We love models here, but models aren’t polls. That difference is an important philosophical sticking point for most pollsters I talk to. “I think politics should stay away from [synthetic sampling], because we’re trying to […] represent the voice of the people,” said Natalie Jackson, a vice president at GQR Insights. Democratic pollster John Hagner told me: “I think I’m just incredibly skeptical of this idea. I don’t think it’s research. At that point, you’re asking the machine to tell you what you already believe.” Hagner has seen some presentations of early synthetic sampling experiments, but so far, “if it’s being used in a campaign, people are keeping it incredibly quiet.”3 But Eli, I hear you saying, aren’t polls themselves increasingly governed by modeling decisions? Indeed they are: pollsters’ choices on which sampling method to use, how to define their likely voter models, and how to weight their samples can and do lead to dramatic differences in the results they publish. Aaru even referenced these limitations in the methodology statement included with that maternal mortality “poll” — although I’m using the term “methodology statement” loosely here, because it doesn’t really explain how the model works at all. We can ignore the (frankly preposterous) implication that synthetic sampling isn’t subject to a separate set of biases. The important point is that there’s still a meaningful difference between using weighting and other statistical techniques on actual polling data and using a model to predict what a poll would say. The latter is far closer to election forecasts or techniques like MRP — potentially useful models, but not a replacement for polls.4 To be fair, other synthetic sampling companies are perfectly happy with the distinction between polls and models. Warner compared polling and synthetic sampling to different tools in a toolbox. “The mistake I think we make is we think that these new tools should either work in exactly the same way or somehow replace these old tools,” he said. “Rather than thinking of it as, okay, so we’ve always had the hammer, we’ve always had the screwdriver, now we’ve got a saw. But don’t use a saw to try [to] do the job of a hammer.” A quick comment from Nate Eli didn’t ask me for a comment — rather rude of him, don’t you think? But since I’m editing this story, I figured I’d add a few quick thoughts rather than putting words in his mouth. Beyond the frequently misleading marketing, what bothers me about the AI “poll” hype is that as AI tools make statistical inference cheaper and/or better (note that these are not synonyms) that actually increases the comparative value of collecting original data. You might be able to train a model to make a reasonable estimate of what some hard-to-reach poll respondent would say — say, a young Black man who voted for Trump. (Such a person checks a number of boxes for a voter who is usually hard to reach in surveys.) Indeed, this is closely related to what models like the Silver Bulletin forecast already do. They essentially smooth out the kinks in noisy survey data by making inferences based on past voting patterns or national polls or surveys of other states. But you don’t actually know what these voters think unless you’re reaching them directly. If there’s a shift in opinion among this subgroup, you’re not going to detect it. So if I were running a campaign, I’d invest more in going the extra mile to find a representative sample of those voters. And then I’d hire some smart quants — or Claude? — to figure out the implications for campaign strategy based on proprietary data that my competitors didn’t have access to. -Nate Silver Are these models any good? If synthetic surveys are just a new type of model, the next obvious question is whether the models are at least accurate. The answer very much depends on who you ask. On one end of the spectrum, you have the maximalist argument that synthetic sampling is more accurate than actual polls. “It’s an incredibly challenging problem to go to someone and say ‘hey, we’re going to be more accurate at predicting human behavior than you, even when you talk to your customers directly’,” Koh recently told CNBC. In his view, synthetic sampling isn’t a saw to polling’s hammer, it’s “magic.” There’s certainly evidence that synthetic samples can replicate certain survey toplines. But if Aaru does have any examples of their approach outperforming the polls, they’re keeping those to themselves.5 Aaru’s 2024 election model, for example, had Kamala Harris leading in Michigan, Nevada, Pennsylvania, and Wisconsin on November 4th. And although they’ve since taken down their forecast page, they gave Harris a 50.5 percent chance of winning the race on November 2nd.6 After the election, Fink told Semafor he was happy enough with those results because they were “within margin of error,” a term that is completely meaningless when applied to a “sample” of AI agents. And of course, Aaru says their models have improved since 2024, so supposedly now they’d be more accurate than the polls? Still, their stronger argument is on cost: “We are significantly faster and cheaper than traditional polling, and still more accurate,” said Fink. The first two claims are undeniably true, but the third brings us to the opposite end of the spectrum. Both Jackson and Hagner are skeptical that these models are reliable for anything beyond replicating common survey toplines. “I just […] don’t think the machines are what we want when we’re looking for nuanced views. My example on this is people in Arizona and Nevada in 2024 who voted for Trump and voted for expanding abortion in their states on ballot initiatives,” said Jackson. Hagner identified another issue. Maybe the synthetic respondents, like sycophantic LLMs, are inhuman in one important way: they’re too nice. “The reports that have come through at the meetings that I’ve been at are that the early experiments on this, they cannot get respondents to be as racist or sexist or, frankly, as negative as human respondents,” he said. Academic research mostly agrees on this point. While there are some papers that show promising results when using LLMs to replicate polling data, most show that LLMs suffer from various quirks like producing too few “don’t know” responses and can seriously overpredict the favorability of politicians like Donald Trump and Kamala Harris. They also seem to struggle with too little variation between demographic subgroups, so the difference in predicted opinion between Democrats and Republicans, for example, is too small. When I asked Warner about these studies, his response to these papers was that just because academics can’t get synthetic sampling to work doesn’t mean that the technique doesn’t work in general. “Actually, the argument is, okay, yours does not [work]. That does not mean […] for this complex set of machinery, which uses a lot of investment, a lot of time, a lot of money, you can’t get it to work.” Cards on the table, I’m somewhat sympathetic to this argument because academics aren’t exactly great at making election forecasts. Usually, the people with skin in the game are the most accurate. Warner’s argument is that the approach Electric Twin takes — which includes, for example, making multiple predictions for each synthetic respondent using different models and prompts and subsequently averaging those to get a final prediction in a sort of ensemble forecast — produces better results than the simpler academic models. Warner shared a comparison between his method and the method from a recent academic paper with me, and Electric Twin was indeed able to get more accurate replication. But even still, he acknowledged that synthetic sampling “is not a crystal ball.” “If you asked me, do I think using other data sources will be more accurate than asking somebody who they will vote for, I would probably say no. But if you asked me ‘would your system be useful for our turnout modeling today?’ I would say yes.” For better or worse, it looks like the method is already getting more popular in the market research world. Most of the clients Aaru touts these days are businesses like EY and McDonald’s. And AI will probably begin to pop up in other parts of the political polling process. Pollsters are already using it to code open-ended survey responses, and some firms, like YouGov, are testing using LLMs to ask survey respondents questions. More worryingly, one danger to actual polls is that AI agents can be used to infiltrate online surveys. Most online polls use various checks to prevent that from happening, but there’s conflicting evidence on how effective those filters are and how prevalent AI agents currently are in online panels. If those agents ever become impossible to detect, it might spell the end of online polling, but the solution isn’t to replace all of your respondents with ChatGPT. Silver Bulletin is a reader-supported publication. To receive new posts and support our work, consider becoming a subscriber. Subscribe 1 That particular poll obviously doesn’t meet Silver Bulletin standards for aggregation. But we exclude all Public Sentiment Institute polls from our averages because we classify them as an amateur polling firm. 2 You could argue that Fink meant the next presidential election, but (a) I’m also confident we’ll still have real polls in 2028 and (b) in that case he should have asked an LLM to define “general election.” 3 Quick caveat: that’s reporting from a Democratic pollster. It’s possible that Republicans are more willing to use AI in political campaigns. 4 Indeed, Silver Bulletin does not include “polls” produced by MRP in our forecasts or averages, and we think it’s extremely misleading when their practitioners describe them in a way that suggests original data had been collected among a large number of states or Congressional districts. 5 A recent report from Aaru and EY did show two examples of a synthetic estimate being closer than a survey to a real-world benchmark — but I’d take those findings with a grain of salt because the report reads more like an ad and didn’t involve any sort of prediction being made ahead of time. 6 For comparison, our odds for Harris on the same day were 48.2 percent.
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