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"This is going to be a niche device" – Analysts react to the $1,000+ Steam Machine price reveal
Mat Piscatella, Joost van Dreunen, Manu Rosier, and Piers Harding-Rolls offer their thoughts on the price implications for Project Helix and PS6
Valve's Steam Machine ships June 29 for $1,049, but you probably won't be able to buy one yet
Valve says it's using a randomized purchase queue to make the experience "less frustrating and more fair."
Valve: FSR 4 Is Coming to Steam Machine
A huge confirmation that should see significant quality improvements.
Here’s how you can reserve a Steam Machine
Valve’s reservation process is complicated, but fair.
Valve will finally let you build your own Steam Machine with SteamOS for desktop
Valve’s working on official Nvidia support in SteamOS.
Dbrand launches Companion Cube enclosure for Steam Machine, "Poverty Cube" option costs $99.95
The cube now gets another cube. Dbrand has opened preorders for its Companion Cube enclosure for Valve’s upcoming Steam Machine.
Dbrand Is Now Taking Orders for Its Steam Machine Companion Cube Case
Dbrand is now officially selling its Portal-themed Companion Cube case for Valve's yet unreleased Steam Machine.
Steam Machine Companion Cube now up for pre order, arrives next month with matching Steam Controller skin
First teased soon after Valve announced the Steam Machine, dbrand's Companion Cube case is now available to pre-order from $99.95.
AMD has reportedly released FSR 4 for RDNA3 GPUs in Valve Proton Experimental
AMD FSR4 appears to be coming to Valve’s Proton Experimental.
Here’s who’ll win the World Cup, according to science
Statisticians from across Europe teamed up to train a competition-predicting, machine learning algorithm. This is what they found.
AI Startup Midjourney Pivots to Health With Ultrasound Machine
AI startup Midjourney Inc. announced its first hardware project at an event in San Francisco, outlining an unexpected move into the personal health and medical industries.
Researchers discover 500 hidden earthquakes beneath Antarctica: 'Improve our understanding of possible futures'
“As machine-learning tools continue to improve, they could reveal that deep, continental-interior earthquakes are more common than currently recognized.”
Valve Steam Machine Benchmarks Show Near Twice The Uplift Over Steam Deck & Comparable To Ryzen 5 5600X at 30W
New benchmarks of Valve's Steam Machine show performance comparable to a 6-core AMD Ryzen CPU from 2020, but could be decent if priced right.
Scientists stunned by the world’s largest whale “graveyard” millions of years old - Futura-Sciences
The Hidden Depths of the Diamantina Zone Deep beneath the Indian Ocean, researchers from China, Italy, and New Zealand got more than they bargained for during their recent expedition. Exploring the remote Diamantina Fracture Zone—which formed during the separation of Australia and Antarctica—the team operated the Fendouzhe submersible, a machine...
Team's post-op kidney injury risk model could aid prevention
The machine learning-derived model was 88% accurate in ranking preoperative patients according to risk for postoperative acute kidney injury.
Trump wins two, loses one: Georgia billionaire delivers rare blow to endorsement machine
Trump-backed candidates won two of three key GOP runoffs in Georgia and Alabama, but lost the gubernatorial race to billionaire Rick Jackson.
Steam Machine Performance Benchmark Leak Signals Approaching Launch
Since the news broke that the Valve Steam Machine would be delayed, there have been countless conversations online about how the Steam Machine would be dead on arrival because of its expected pricing and aging internals—a six-core Zen 4 CPU and a Radeon Navi 33 GPU with 8 GB of VRAM—but a recent set...
Skill game ruling leaves Pennsylvania businesses facing possible taxes, new regulations
A Pennsylvania Supreme Court ruling that classifies skill games as gambling devices is raising concerns among small businesses that rely on the machines for extra revenue.
AI and brain-computer interface allow speechless ALS patient to work a full-time job
The hardware isn't new, but a UC Davis research team's machine learning-powered method of translating brain activity in an ALS patient into sentences with 92% accuracy is
Steam Machine one step closer to launch as benchmarks surface online, here’s how it compares to the Steam Deck
We can now have an early look at some Steam Machine benchmarks after results were uploaded to Geekbench, suggesting reviews are underway.
NASA Uses Machine Learning to Enhance Flash Flood Warnings
The Transient Artifact and Continuous Learning System (TACLS) leverages data from continuously operating satellite networks coupled with machine learning models to help meteorologists at the National Weather Service forecast flash floods more efficiently.
The Steam Machine Is Likely in Testers’ Hands, as 2 Fresh SteamOS Benchmarks Leak
Steam Machine "Fremont" hits Geekbench running SteamOS, hinting at an imminent Summer launch for Valve's new console
Steam Machine could launch as early as this month, leak claims
After Valve posted a hopeful, yet vague, update on its exciting new console, new leaks point to an official Steam...
Steam Machine reviews may be underway as two new Geekbench listings surface
Valve's Steam Machine was once again spotted on Geekbench with two separate listings, suggesting that reviews may be underway.
Geoffrey Hinton
The machine-learning guru discusses how politics is undermining U.S. science
Chicago City Council to vote on sweepstakes machine ban as bar owners warn of business closures
Chicago alders are divided over a debate on whether to ban or keep sweepstakes machines legal in the city.
Oprah Winfrey blesses new CT scanner she helped fund in Hawaii
Maui Memorial Medical Center, a 214-bed acute care hospital in Wailuku, celebrated the new computed tomography machine with its imaging team on June 8.
Writing science that humans and machines can read
Large language models are now routinely used to search, summarize and synthesize the literature at scales impossible for any individual researcher—yet scientific publishing has not adapted to that reality.
68 quadrillion underground miles of fungi
With machine learning and a high-resolution imaging robot, scientists measured and mapped the extent of Earth’s carbon circulatory system.
SUN Bucks, MRI machines and more. Here's the latest Louisiana health news.
Health Notes is an occasional listing of health happenings around Louisiana. Have something you'd like to share? Contact us at margaret.delaney@theadvocate.com.
Steam machine reportedly sent to reviewers as embargo date and package contents leak
According to a new leak, the first Steam Machine review units may already have been shipped to reviewers and content creators. The rumored review embargo date and possible package contents have also surfaced.
Valve just imported 13 tons of VR headsets in one day
And probably 141 tons of Steam Machines in the past 50 days.
Latest News In AI Chips - AI-Powered Shift: Transforming Data Into Business Intelligence
Treasure Global Inc. has secured a US$15 million contract to develop an AI-powered Social Listening Platform for Creative World Industries, emphasizing the growing demand for AI systems that convert digital data into actionable insights. This platform leverages machine learning and NVIDIA-powered infrastructure to provide real-time sentiment and trend analytics across various sectors. The deployment underscores a trend in the AI chips industry towards enhancing real-time decision intelligence...
Scientists Measure Earth’s Vast Underground Fungal Webs
With machine learning and a high-resolution imaging robot, scientists measured and mapped the extent of Earth’s carbon circulatory system.
Time for an AI checkup: Flaw found in machine learning for sepsis treatment
Shengpu Tang, Emory assistant professor of computer science, found a flaw in many peer-reviewed studies using the AI method known as reinforcement learning as a theoretical guide for the treatment of sepsis.
Dwayne Johnson on Cancer Scare, Not Talking Politics and Oscar Snub
Dwayne Johnson admits he wishes he got an Oscar nomination for 'The Smashing Machine' and reveals a recent cancer scare.
Bypass Siri AI Waitlist on macOS 27 Golden Gate Beta: Here's How
If you installed the macOS 27 Golden Gate developer beta and have signed up to the Siri AI waitlist but are still waiting to be accepted, there's a Terminal command workaround that enables the new virtual assistant on your Mac immediately. If you're still reading, since you're on the first macOS 27 developer beta, we're assuming you are running it on a test machine, and not on your primary Mac. We're also assuming you're comfortable with using the Terminal app.
Family business grows from one man, one machine to major name in packaging industry
They moved from Minnesota to South Dakota, saw a manufacturing market opportunity -- and evolved into a national leader in their industry.
‘Smart’ public health vending machine expands access to free health resources in York County
Leaders from Penn State College of Medicine, WellSpan Health, York County and local community organizations gathered to celebrate the launch of an interactive public health vending machine.
We can create the future of science right now
Paul Litvak is the founder and Executive Director of the Robyn Dawes Institute and a Visiting Scholar at UC Berkeley. He has a PhD in Behavioral Decision Research from Carnegie Mellon where he studied emotions and the sunk cost bias. Over 15 years in industry, he solved a wide range of challenging technical problems. At Meta he created models optimizing the ad review process. At Google he ran experiments to measure social influence. At Airbnb, he was a product manager leading machine learning teams optimizing search ranking and price suggestions. He also co-founded and led product at Rhythmic Health, a venture-backed biosensing startup, creating an accurate low cost system using a color changing strip and a smart phone to measure salivary lactate. The bottleneck At this point, it is uncontroversial to say that science needs to stop using the PDF article as the unit of knowledge and currency. The unbearable slowness of scientific publishing, the profit motive and margins: I’m not saying anything new. The PDF also sucks because it’s hard to extract structured information, which makes it hard to do evidence synthesis. As a result, we do much less evidence synthesis than is needed. And evidence synthesis ultimately undergirds most policy and medical decision-making. I can see second-by-second real-time odds for any sporting or newsworthy event, but a school board can’t see the best evidence on whether their 8th graders should be taught algebra. As a society, we don’t treat this as an important problem. Again, not controversial. Not only is the problem well understood, but the solution has already been laid out. What we need is AI-assisted living evidence synthesis - (1) an open knowledge graph of atomic claims, (2) claims linked to evidence, (3) assessment and synthesis of each piece of evidence, and (4) continuous updating with new data. What few realize (yet) is that the technical capacity to build this vision for a significant portion of science already exists. Not only that, scientists and startup teams are already building many of these components. I know this because I’ve been surveying the space and talking to many of the builders. There are some missing pieces: for example, evaluations of how well some of the components work. But at this point, most of what’s missing is a fully end-to-end working integration of all of these parts. In the rest of this essay, I’m going to lay out all the parts of a working living evidence layer for science and who is working on them, and propose concrete next steps for building this system. What’s now possible The diagram above outlines the components of a living evidence synthesis platform, including some of the teams working on each component1. Scientific PDFs are processed into claims with associated evidence. The evidence is subjected to a forensic audit, methodological evaluation and robustness and reproducibility checks. Finally it’s given a weight in a continuously updating synthesis. What follows is a description and status of each component and a few of the teams working on them. Document understanding The first thing you need to be able to do is turn an article into structured data. Mostly that means parsing PDFs. There are often multicolumn layouts that confuse non-specialized PDF to text processing libraries. For scientific papers, there is the added complexity of parsing formulas and tables and figures. This is a really hot area - there are startups offering APIs, and it seems like a new open source package gets posted to Github every few weeks. What follows isn’t exhaustive. A package called GROBID was the state of the art for a while, they didn’t update their package for nearly two years until very recently. In the meantime reducto.ai released an AI powered PDF extraction API, PaddleOCR became popular, IBM released a model called Docling, and both Mistral and Gemini created models and libraries. I also know of at least one other well-funded psychology research group working on a paper parser. By contrast, there are few open evals in this space, with no extensive evals for complex table comprehension in particular. Nonetheless, I’m confident this will be a solved problem soon, given the combination of LLM advances and developer interest. Hypothesis level extraction There has been increasing interest in comprehending the extracted text of papers and linking information to evidence for each hypothesis. A lot of work has already been done. Trialstreamer (Marshall et al. 2020) and RobotReviewer LIVE (Marshall et al. 2023) demonstrated automated extraction of trial population, intervention, and outcome at scale on clinical RCTs. PaperQA2 (Skarlinski et al. 2024) and Ai2 ScholarQA (2024) extended this to retrieval-augmented question answering with citation grounding. Elicit, Consensus, and SciSpace operationalized claim-level extraction for end users. OpenEval (Booeshaghi et al. 2026) is the most recent and most ambitious: 1.96 million atomic claims extracted from 16,087 eLife manuscripts using Claude Sonnet 4.5, grouped into ~299,000 results, with LLM evaluations showing 81% agreement with human peer review on a 2,487-paper subset. None of these solutions link claims to test-statistics, as you would need to evaluate randomized controlled trials. This is why I built the evidence.guide API - to extract hypotheses and associated test statistics from behavioral science papers. The best public eval of this kind of extraction I’m aware of comes from the recent SCORE project - they had humans code thousands of psychology papers to extract their claims by hand. It would be extremely helpful to the world if all scientific PDFs were available as structured open data. I’ve been working to make this happen, both directly at Berkeley and through coordination with large entities I can’t yet speak of; as hard as it is to do, I think it’s possible2. Forensic audit A lot of work has been done on forensic audit, but some gaps remain. Of course, for biology papers that rely on images for evidence, there are a variety of tools (notably Proofig and ImageTwin) to spot anomalies. These are still well short of what sleuths like Elizabeth Bik can do on her own, but these tools are constant companions among fraud analysts. There’s someone working on auditing Excel files for anomalies, and a number of teams are automating numerical checks like GRIM and SPRITE, including Scrutiny project, the INSPECT-SR team as well as statcheck. The regcheck team is building a way to use AI to compare preregistrations to analyses in papers, to ensure there aren’t significant deviations. Nonetheless, there are many other kinds of anomalies to screen for, both public and less publicly known. And there are no formal evals for anomaly detection that I’m aware of. Still, there’s a lot to draw from in this space and I’m pretty certain we will be able to scan papers for most kinds of obvious anomalies in the near future. Methodological review This area has been white hot, though I fear for many of the startups in this space, because this capability may become commoditized. There are at least six different AI peer review companies, including Refine.ink, Reviewer3, ReviewerZero.ai, Q.E.D. Science, Paper Wizard, and Isitcredible. Coarse (a pun on refine) was also recently created as an open source alternative. These systems provide qualitative feedback on the content of papers, spotting methodological weaknesses and mathematical errors. They seem to work pretty well, and many academics report bitterly that they exceed the average quality of typical peer reviewers. But there are few evals here either. What evals exist so far involve using LLM-as-judge (circularity problems abound) or comparing against human reviews of questionable quality. What you’d ideally want is an eval that measures capturing known errors in papers3. Reproducibility and robustness Another active area has been using AI agents to automate computational reproducibility4 and robustness5 checks in papers that report numerical results. For more recent papers where data and code are available, AI agents can see whether they can re-run the analyses and produce the numbers reported in the published paper. In addition to a handful of individual academics who have been experimenting using Claude Code for this, the Institute for Replication is a leading group working on building an end to end system. The evals related to this problem are the most mature, with CORE-Bench (Siegel, Kapoor, Narayanan 2024) and PaperBench (OpenAI 2025) available to benchmark agents on this task. There is also work on getting AI agents to test alternative ways of analyzing the data to ensure the results are robust to small analytic design choices. Synthesis This is the most underdeveloped area where significant investment is required. Although some automated evidence synthesis systems exist — for example, otto-sr is building an AI agent to write systematic reviews — none of these incorporate the full range of paper level signals to weight evidence appropriately. Nor is there anything like an eval or a gold standard for a good systematic review. Arguably Cochrane reviews are the closest we have to gold standard human systematic reviews, though I’ve heard academics in the know complain about their uneven quality. A key question for a synthesis platform is how to weight anomalies and methodological issues in assessing the quality of a piece of evidence. This is an unsolved problem and one I’m very keen to work on. Continuous updating There are many pieces of basic infrastructure available for monitoring for new research and initiating updates. OpenAlex is the current open citation graph. Retraction Watch integrated into Crossref in October 2023. Scite tracks how citations support, contrast, or mention prior claims. The Living Evidence Network demonstrated continuous-update workflows in clinical guidelines. Engineering this is a relatively straightforward task. When you look over this technical architecture and all the progress being made, it’s hard not to be optimistic that a living guide to scientific evidence will be built. The stakeholders are ready The social infrastructure for this is starting to coalesce — it’s not just a pie in the sky academic exercise to imagine this coming into existence. Institutions like the Center for Open Science, the Institute for Replication, the INSPECT-SR, the Living Evidence Network and more are all working on scaling work to improve research quality. Funders are also aligned. The Sloan Foundation has funded living evidence work through COS. Coefficient Giving supports the Institute for Replication and COS. The Astera Institute and the Institute for Progress have shown interest in this space. NIH has established an Office for Replication and Reproducibility. Although there are (very unfortunately) serious headwinds in science funding generally, there is an active group of funders interested in metascience. A brief word about what I’ve been doing at RDI. First, as a Visiting Scholar at Berkeley I’ve been actively figuring out how a non-profit and a public university can conduct and make public the results of large-scale academic article data mining. With some of the money I raised from donors, I commissioned a legal analysis of recent case law and publisher text data mining (TDM) agreements in order to understand whether a massive open data mining of academic articles is possible (with caveats, it is). I’ve also been working to bring together stakeholders in this space, and identify gaps. I’ve also been doing some software development in this space, with more to come. A pilot proposal The assumption undergirding all of this is that an AI, given all this information, would make the right judgment about a scientific claim with lots of conflicting evidence, weighing all the factors appropriately. That’s the hypothesis we need to test. Randomized control trial research is the best place to focus on first. RCTs are used to make many of the important decisions in society - from medical trials to public policy changes. And they use a relatively uniform set of inferential statistics with lots of known and available diagnostics. Behavioral science experiments, within the broader realm of RCTs, should be first used as a testbed whose results can be generalized. Because behavioral science is at the vanguard of open science practices, replications abound (there are thousands of them) to serve as ground truth training data. Key Hypothesis Therefore the pilot would test, in behavioral RCTs that have been replicated, whether the quality of evidence for a claim can be used to accurately predict whether that claim will replicate. Secondary Hypotheses Compared to claims that replicated, non-replicated claims demonstrate a greater share of forensic anomalies in their source literatures. Hypothesis level claim and statistic extraction is accurate enough to scale living evidence without onerous human review costs. Replication prediction is more accurate than prediction markets6 or journal prestige. If all the different quality signals we gather do accurately predict which studies will replicate, then we can use that model to score evidence to power the living evidence layer. Why this is informative regardless of outcome If the pilot succeeds, the architecture extends to medical RCTs (where Living Evidence already operates and integration is mostly about claim representation), then to slices of basic biology with stable replication structure. If it fails, the field learns which quality signals are load-bearing and which ones metascience has oversold. Either result is a contribution to knowing what the literature supports. Implications for funders Because this burgeoning ecosystem of builders already exists, a well-informed philanthropic or government funder could play a crucial catalyzing role in bringing this future about. They could play at least three roles: creating open structured datasets, publishing open benchmarks and incentivizing the solving of key technical challenges. First, open archives of papers that the government maintains, like PubMed, could be turned into structured data amenable to large scale metascience and claim aggregation. I know the US government already has an interest in doing this, though some key open questions remain unanswered. How do you determine the best models and systems for accurately extracting information from papers? How can you establish a robust way to allow researchers to flag errors and correct them? And finally, how do you create a legal regime in working with publishers to maximize the scope of available papers? For the latter, a university or a private philanthropy may be better positioned to make structured data publicly available under journal subscription terms or fair use. Philanthropists or government funders could also coordinate to create or commission benchmarks that evaluate whether important problems have been solved. For example, an open benchmark for claim extraction from a range of different scientific article types would be extremely helpful. Ensuring the underlying data are accurate is vital for creating these evaluations. I’ve discussed opportunistically using various human-created datasets for this purpose, but a consistent problem is that errors in human data make it difficult for them to serve as a gold standard. Finally, with structured data and benchmarks available, the government or private philanthropy could use them to incentivize groups to develop machine learning systems that meet these benchmarks. Prizes are one potentially valuable tool for this. For example, you could establish a prize for a system that accurately updates a living meta-analysis for a small set of claims. Prizes are particularly useful as signals of problem importance, and can help create vibrant ecosystems of public and private research—see, for example the role the government played in kickstarting the current work on nuclear fusion. Conclusion The drawbacks of the current scientific publishing system are known. Scientists agree, metascientists agree, philanthropists agree: the published PDF plus citation graph isn’t the right substrate for maintaining a representation of the evidence base in science. The pieces needed to build the alternative either already exist or are rapidly taking shape. The community is forming around exactly this problem, with concrete partnerships and shared infrastructure. A pilot should start on behavioral science RCTs because that’s the slice of empirical science most amenable to legibility, where replication ground truth is richest, and where the failure modes are best documented. What’s been missing is the galvanizing mission to assemble these pieces into something that works. That’s what I’m proposing to build. *** 1 I have a broader field map that I’ll release publicly soon. This is me, building in public! 2 If this is something that you are excited about, please reach out and talk to me. 3 More on this very soon too! 4 This tests whether, given the code and the data, you can get the same statistics as reported in the published paper. 5 This tests whether the results are the same as a paper’s given alternative analytical decisions in conducting the analysis (like outlier omission). Closely related is the idea of a “multiverse” where you come up with many different ways of answering the same underlying research question with the same data, and test whether the results hold in all those alternative methods. There’s been work on the latter as well. 6 Some of the replications, e.g. those from the SCORE project, had paired the experiments with forecasts from prediction markets. So we get to look at this for free.
The weather and climate science AI revolution isn’t revolutionary
Machine learning has its limits—how is it being used?
Classic race planned in place of excluded sidecars
The Southern 100 Club says it plans to allow classic machines to race instead of modern sidecars.
My 10-year-old started a vending machine business
Christina Nicholson says her son Landon launched a vending machine business at 10 and quickly learned lessons about profit and patience.
Steam Machine summer launch confirmed in hopeful update, but still no price
The Steam Machine and Steam Frame have been hit by rising component costs even before launch, but Steam still seems...
Here’s an early look at the Steam Frame Welcome Tour after Valve officially announces summer release window
Valve's Steam Frame is arriving this summer alongside the Steam Machine. Users have managed to get an early glimpse of its setup procedure.
Scientists are seriously asking if bees and ChatGPT are conscious
New studies suggest consciousness can't be judged solely by behavior, whether it's a chatbot discussing philosophy or a bee searching for nectar. Researchers are increasingly focusing on the internal mechanisms of brains and computers, concluding that today's AI is likely not conscious while leaving open the possibility for both conscious insects and future machines.
Valve Confirms Steam Machine Will Ship This Summer
The company also confirmed that its standalone VR headset will arrive in that same timeframe
Is an Online Master’s Degree in AI a Good Idea?
A look at the real-world value of online graduate AI programs, combining hard data with firsthand experience of a big tech machine learning engineer
Pulte Pick Shows How Trump Is Consolidating Power
Plus, the president’s candidate is defeated in Iowa, and inside Trump’s takeover of the American regulatory machine.
The 7 AI Agent Guardrails Every Business Needs Before Things Go Wrong
AI agents promise to automate work, make decisions and transform business operations, but giving machines more autonomy also creates new risks.
Recent mock draft links Rockets to shot-blocking machine
A recent mock draft completed by Jonathan Wasserman has the Houston Rockets selecting Ugonna Onyenso with the No. 39 overall pick.
Where Forensic Science Meets AI: eSleuth, Inc. Names Former ATF Director Ken Melson to Its Advisory Board
eSleuth, Inc., the AI-driven investigative platform putting the power of advanced data analysis and machine learning directly in the hands of investigators, today announced the appointment of Kenneth E. Melson, former Acting Director of the Bureau of Alcohol, Tobacco, Firearms and Explosives, to its Advisory Board. Melson brings a rare depth of expertise spanning federal prosecution, agency leadership, and forensic science policy that directly strengthens eSleuth AI's mission to transform how la
Ghost in the Machine review – entertaining AI polemic dives into its dark history in race politics and eugenics
The roots of AI in rightwing ideology is examined in Valerie Veatch’s enjoyable doc, including an array of colourful, often crazed, figures
Four researchers win data harmonization competition
The U.S. National Science Foundation National Synthesis Center for Emergence in the Molecular and Cellular Sciences (NCEMS) at Penn State has awarded four researchers from across the world for their efforts in a competition to build machine learning-based approaches for streamlining publicly available datasets for reuse.
Willard’s ‘small ball’ tactics a recipe for success
WILLARD–The Willard baseball program has been a winning machine, under Head Coach, Scott McGee. The Tigers will play in their 7th State Semifinal, in McGee’s 19th and final season, Friday night. The bulk of their success, offensively, has come from a method that is “outdated” by today’s standards — ‘small ball.’ Rather than adapt to […]
AI is devoid of meaning and humanity. That’s why its vapid voice suits this political moment
For ease and speed, we are degrading our ability to connect and to organise our societies. We must assert our trust in humans over machines, says Guardian columnist Nesrine Malik
SMU's president has rightly embraced political neutrality
Too many U.S. colleges became political machines. SMU is making space for real debate and the exploration of tough ideas.
Steam Machine launch appears near as Valve adds Welcome Tour to Steam backend
Valve recently added the Steam Machine's Welcome Tour to Steam's backend through a recent update, which suggests the company is finally geting ready to launch the device.
Scientists got mouse eyes to perform photosynthesis — and no, they didn't turn green
Special eye drops containing photosynthetic machinery from spinach leaves have helped combat dry eye, a new mouse experiment reveals.
"This market is not behaving normally" - analysts react to Valve's Steam Deck price increases, and what it means for the cost of the upcoming Steam Machine
Video games analysts chat with Eurogamer about the recent price increase for the Steam Deck, and what this means for Steam Machine.
Key Chemistry Question Answered, No Quantum Computer Required
Do we need quantum computers to fully understand complex chemical reactions? A new result, decades in the making, shows the surprising power of ordinary “classical” machines.
Cultivating Data-Driven Approaches with Plant Science
Launched this winter, the Data-Driven Plant Science course bridges experimental biology with embedded sensing, bioinformatics, and machine learning.
Protein traffic jams may explain aging, memory loss, and Alzheimer’s
Scientists at Stanford may have uncovered a hidden reason our brains decline with age. Studying the ultra-short-lived turquoise killifish, researchers discovered that the cellular machinery responsible for building proteins begins to jam and malfunction over time. Tiny structures called ribosomes start colliding and stalling while reading genetic instructions, triggering a chain reaction that leads to faulty proteins and harmful clumps linked to diseases like Alzheimer’s.
Call Of Duty Warzone Is Shutting Down On PS4 And Xbox One
The popular battle royale will no longer be playable on the older machines later this year
Steam Deck's Massive Price Hike May Not Bode Well for Steam Frame
Valve announced that Steam Deck is getting a sizable price hike, bringing an increase of $240 to $300 to its handheld gaming PC–not exactly a vote of confidence for those of us waiting for Steam Frame, its upcoming standalone VR headset. The company revealed the 512 GB OLED Steam Deck has increased from $549 to […]
Exclusive | The Trump Administration Is in Talks to Fund U.S. Drone Companies
Neros and Donald Trump Jr.-linked Unusual Machines are among the companies in deal talks.
Exclusive | The Trump Administration Is in Talks to Fund U.S. Drone Companies
Neros and Donald Trump Jr.-linked Unusual Machines are among the companies in deal talks.
You can print, slice, and engrave using this desktop crafting machine
Get your Etsy sticker shop off the ground with this single machine.
ASUS ROG Zephyrus Duo review: Outrageously expensive, totally awesome
The ASUS ROG Zephyrus Duo is a gamer’s dream machine, but its price is a nightmare.
Nanoparticles genetically modify several human cell types
In a demonstration that helps pave the way for gene therapies with fewer side effects, several human cell types were genetically modified with protein nanoparticles designed at University of Michigan Engineering and Michigan Medicine.
Loyola’s Data Science in Everyday Life
Loyola University Maryland will hold “Data Science in Everyday Life: Applicability, AI, and Other Trends,” a networking and panel event exploring data science from a range of perspectives across industry and government.A keynote address delivered by data science industry expert David Norton, MBA ’95, will begin the event. Norton is the chairman and chief marketing officer of GALE Partners, a digital and analytics-based branding agency. He was named Chief Marketing Officer of the Year in 2010 and wrote “The High Roller Experience,” a book about data-driven marketing.Norton will moderate the panel discussion, which includes:• Elise Beisecker, senior data scientist in the U.S. Government Accountability Office• Amanda Muller, Ph.D., chief of responsible technology at Northrop Grumman• Franklin Hernandez, MBA ’13, M.S. ’20, senior lead machine learning engineer at Booz Allen Hamilton• Joseph Girsch, M.S. ’21, machine learning analyst at The Johns Hopkins University Applied Physics Laboratory• Mark Wicks, retired vice president of data science, data scientist, machine learning engineer and software developerHernandez and Girsch each earned an M.S. in Data Science from Loyola.The event is free and open to the public. Networking and appetizers will begin at 5:30 p.m. followed by the keynote address and panel discussion at 6:30 p.m. and audience questions and closing remarks at 7:30 p.m. To learn more and register to attend or stream the event online, visit the event webpage.
Jim Cramer Reveals Why International Business Machines (IBM) Should Be Doing Better
We recently published Jim Cramer Discussed These 10 Stocks Including A Hidden Gem & An AI Short. International Business Machines Corporation (NYSE:IBM) is one of the stocks discussed by Jim Cramer. International Business Machines Corporation (NYSE:IBM)’s shares have struggled recently as they are down by 1.9% over the past year and by 12.9% year-to-date. RBC […]
China’s machinery giant XCMG eyes bigger scoop of global sales
State-owned firm banks on intelligent manufacturing, green technology, product integration and digital services to boost sales overseas.
6 kitchen gadgets that make adulting feel easier
From a robot stirring your soup to a bread machine that kneads your dough, here are 6 gadgets that may make you feel like you’ve won adulthood.
Kyle Juszczyk Spills Kyle Shanahan's Secret to Offensive Success
The 49ers fullback reveals how the head coach’s mastery of defensive schemes allows him to exploit opponent assignments and maintain an elite, foolproof offensive machine.
Man killed when machine malfunctions at N.J. business
Firefighters extricated the man but he was pronounced dead at the business.
International Business Machines (IBM) Is Up 15.8% After Announcing $2 Billion Anderon Quantum Chip Foundry Partnership
Earlier this week, IBM and the U.S. Department of Commerce announced a Letter of Intent to create Anderon, America’s first pure-play quantum chip foundry in Albany, New York, backed by US$1.00 billion in CHIPS incentives and a matching US$1.00 billion cash commitment from IBM plus intellectual property and staff. This partnership not only cements IBM’s role at the core of the U.S. quantum ecosystem but also creates a dedicated 300‑millimeter quantum wafer facility intended to supply multiple...
Steam Machine appears on official Vulkan 1.4 conformant products list
Steam Machine listed with NAVI33 and Vulkan 1.4 support Mesa/RADV listing confirms Vulkan 1.4 CTS pass for AMD Steam Machine Valve’s upcoming Steam
Galaxy Zoo: Clump Scout II
Identify star-forming clumps in galaxy images, and help train machines to do the same.
Hunter Flynn Signs With Big Machine Records/Nashville Harbor Records & Entertainment
Singer-songwriter Hunter Flynn has signed with Big Machine Records/Nashville Harbor Records [...]
UNT launches new Bachelor of Science in AI
The University of North Texas is launching a new Bachelor of Science in Artificial Intelligence, preparing students for careers in machine learning, AI systems and emerging technologies.
AI & Science: What Is the Future of Discovery?
Continued progress in artificial intelligence, its expanding usefulness in science, and its contributions to landmark advances suggest that we may have entered a new era of AI for science.The breakthroughs so far—such as predicting the structure of practically every known protein, with profound implications for our understanding of biology, health, and the treatment of disease—are notable not only for what was achieved but also how it was achieved and what that suggests for scientific progress. This special double issue of Dædalus poses the question: What is the future of scientific discovery in this new age of AI? Thirty-three scientists responded. Bringing perspectives from life sciences and medicine, cognitive science and neuroscience, the physical and earth sciences, chemistry and materials science, computer science, mathematics and the social sciences—they draw on their work at the frontier of AI and science. The authors write with an eye to the future, not just the present. They explore what is being achieved and what possibilities lie ahead; examine AI’s limitations and efforts to move forward; and investigate the larger implications of AI-assisted science—on how science is done, the role of the scientist, and the scientific method, as well as the challenges and complexities involved.The authors together exemplify a long-standing bidirectional relationship: AI advancing science, while science advances AI. Where that relationship will take us—a golden age of discovery? New scientist-machine collaborations? Autonomous labs? Discoveries without human understanding?—is a future we are only beginning to imagine, and one we must also shape if the beneficial possibilities are to be realized.Image: UMAP sculptures visualizing three Dædalus volumes on artificial intelligence. © 2026 by Refik Anadol.
AMD Announces Ryzen AI Halo, the Compact DGX Spark and Mac Mini Rival
AMD today released Ryzen AI Halo, a compact AI development computer designed by AMD to serve as a nearly full-stack machine for AI development, inferencing, and generative AI content creation. Ryzen AI Halo is designed to strike a unique balance of price, performance, and capabilities that rival App...
Can we harness quantum effects to create a new kind of healthcare?
Experiments hint that quantum mechanisms are vital to the machinery of life. Now researchers are exploring if these effects help to explain the success of an array of puzzling health treatments
NASA Transfers Management of Lunar Science Instruments
NASA has transferred management of two lunar science instruments to Intuitive Machines, due to the instruments' principal investigators and science team
Bridging Systems: How Kentucky is Improving Response to Emerging Health Threats
Learn how Kentucky is integrating public health and health care emergency programs to improve their emergency readiness in this blog.
New MatterChat Model Helps AI to ‘See’ the Language of Science
From writing emails to generating computer code, much of the artificial intelligence prevalent in our daily lives has succeeded by mastering one domain: text. However, this leaves a major blind spot in the physical sciences, where models depend on the high-resolution, three-dimensional data of the physical world, like the intricate lattice of atoms in a crystal. Delivering on the promise of using AI for science requires teaching these data-driven text models to seamlessly “talk to” physics-based models. Now, a new AI framework from Lawrence Berkeley National Laboratory (Berkeley Lab), called MatterChat, solves this problem by creating a specialized “bridge.” It connects the conversational power of a Large Language Model (LLM) with a physics-based AI that models “interatomic potentials”: the complex physical forces between atoms. The resulting system already significantly outperforms general-purpose AI tools like GPT-4 at predicting material properties, and the team hopes it can accelerate scientific discovery by serving as a robust research partner that provides grounded insights and generates step-by-step instructions for synthesizing novel materials. A paper describing this work was recently published in Nature Machine Intelligence. “Traditional simulations can provide the physical rigor required for materials science, yet their computational cost remains prohibitive for high-throughput screening. Conversely, while LLMs excel at rapid knowledge synthesis, they inherently lack the ‘structural vision’ to interpret materials directly from their underlying atomic coordinates,” said Yingheng Tang, a postdoctoral researcher in Berkeley Lab’s Applied Math and Computational Research Division (AMCR) and lead author on the paper. “MatterChat was built to solve this dilemma, empowering LLMs with a structural ‘vision’ that allows researchers to leverage their full potential for solving complex, real-world materials challenges.” Empowering language models to solve complex challenges in materials science To build MatterChat, the Berkeley Lab team drew inspiration from technologies like Vision Question Answering (VQA) and Text-to-Image (T2I) generation. In these tasks, AI must translate high-level text concepts into visual images, or vice versa. Doing so requires developers to build tools that “bridge” two fundamentally different forms of data. The researchers adapted this concept to the physical sciences. With MatterChat, they created a “bridge model” that successfully connects an LLM’s general knowledge with the deep understanding of the atomic-scale world encoded in scientific interatomic potentials. Until now, researchers using LLMs to solve materials problems usually had to feed them raw data files as if they were just strings of text. It’s like asking an AI to understand a complex 3D engine based only on a parts list: the LLM can read the names, but it can’t “see” how the atoms fit together in space. MatterChat solves this by training a specialized AI bridge model, pre-trained on millions of crystal structures and an LLM, to align the LLM’s representation of the world with the interatomic potential’s representation of the world. The bridge model can translate physical insights into a format the LLM can actually understand. By giving the LLM these “scientific eyes”— a scientific “inductive bias” in the terminology of AI — the Berkeley Lab team has transformed it into a robust research tool capable of providing grounded scientific insights into complex materials challenges, such as predicting thermal stability or analyzing electronic band gaps. “We think of atoms as living in a physical space, but from a machine learning perspective, they are just vectors living in some very non-trivially structured manifold in a high-dimensional Euclidean space; and, of course, the same is true for the sentences and paragraphs that we use to express our ideas about those atoms,” said co-author Michael Mahoney, Berkeley Lab’s Scientific Data Division (SDD) AI Initiative Research Lead. “The bridge model basically gets those two structures to ‘talk with’ each other.” As a proof-of-concept of this general approach, the team trained their bridge model on a dataset curated by pairing nearly 143,000 stable atomic structures from the Materials Project with their corresponding physical properties. This training data was automatically assembled using the Materials Project’s API and deliberately enriched with properties fundamental to microelectronics design — like formation energy and bandgap — allowing MatterChat to learn the complex patterns connecting a material’s atomic blueprint to its functional performance. To validate their model, the researchers benchmarked MatterChat against a suite of other AI systems, from general-purpose LLMs to other specialized scientific AI methods. The results show that MatterChat consistently outperformed its competitors across a range of tasks. The model was more accurate in classifying material types and demonstrated superior precision in predicting numerical properties. For example, it excelled at predicting a material’s bandgap, a property critical for designing new electronics from high-capacity energy storage to next-generation computer chips. “Our design is significantly more efficient because we don’t have to build a massive AI model from the ground up,” said co-author Zhi (Jackie) Yao, a research scientist in Berkeley Lab’s AMCR. “Instead, we take two powerful, pre-trained models — a structural encoder for materials physics and an open-source LLM — and use them off-the-shelf. The only component we actually train is the lightweight ‘bridge model’ that translates between them. It’s the difference between building an entire car factory and simply designing a smart adapter that connects a world-class engine to a world-class navigation system. This approach is not only computationally efficient, but also makes the system modular, so we can easily upgrade components or adapt the bridge for other scientific domains in the future.” Crucially, this modular design highlights exactly how institutions like Berkeley Lab and the Department of Energy are carving out a highly valuable niche in the booming AI landscape. Rather than competing with Silicon Valley tech giants to build ever-larger language models from scratch, the lab is focusing on the specialized connective tissue that makes commercial AI useful for hardcore science. Because the bridge model approach underlying MatterChat is forward-compatible, it is perfectly positioned to leverage these parallel tracks of innovation. As Mahoney pointed out, “We expect that industry will continue to develop improved LLMs, and we expect domain scientists and facilities will continue to generate new data. An important part of scientific machine learning is not simply to solve problems on today’s data, but instead to develop general methods that will be forward-compatible with orders of magnitude more data, whether from scientific domains or from LLMs.” According to Yao, the MatterChat project, which was initially developed and enhanced with funding from a Berkeley Lab Laboratory Directed Research & Development (LDRD) Program, will now expand its capabilities. In a collaboration with Fermilab, MatterChat is already contributing to a U.S. Department of Energy Genesis Mission project — called Accelerating eXtreme Environment Specs-to-Silicon (AXESS) — that aims to speed up the development of next-generation, high-speed, radiation-hardened detectors for challenging particle physics experiments by using advanced 3D integrated circuits (chiplets) and AI-driven data analysis. In addition to the LDRD support, the team also credits supercomputing resources at the National Energy Research Scientific Computing Center (NERSC), located at Berkeley Lab, with MatterChat’s success. “We are incredibly grateful to NERSC; this research simply would not have happened without access to the Perlmutter supercomputer through their AI for Science program,” said Tang. Wenbin Xu, a NERSC postdoctoral fellow at the time, was also a major co-author of the work, as was Benjamin Erichson, a research scientist in Berkeley Lab’s SDD, highlighting the benefits of AMCR-SDD collaboration on AI for science. ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.
Sunday Preview – Mighty war machines in the Age of Darkness
Tracked reinforcements arrive for the Legiones Astartes in next week’s pre-orders.
Scientists discover why some cancers survive chemotherapy
Scientists have uncovered a surprising new trick used by one of cancer’s most notorious proteins. MYC, already infamous for fueling runaway tumor growth, also appears to help cancer cells survive by repairing their damaged DNA — including damage caused by chemotherapy and radiation. Researchers found that MYC can rush directly to broken DNA and recruit repair machinery, effectively helping tumors recover from treatments meant to destroy them.
Prep Sports Report: Brossart scoring machine takes team title in Class 1A girls region track meet
By Terry Boehmker NKyTribune sports reporter The Brossart girls track and field team was a scoring machine that produced points in nearly event during the Class 1A, Region 4 meet on Friday at Brossart. In 11 of the 18 events, Brossart picked up 10 points or more and finished with a 166 team total to...
Kalihi machine shop closing after 80-plus years in business
Takeguchi owns and runs the family machine shop that his parents, Ted and Barbara Takeguchi, opened on Queen Emma Street in downtown Honolulu in 1944.
Inside the furor plaguing Democratic National Committee leader Ken Martin
Democrats keep winning at the ballot box. And yet Ken Martin, the man leading the Democratic National Committee, is facing a crisis of confidence among party officials who are increasingly concerned about the health of their political machine barely a year into his term.
Political Inequality Isn’t Responsible for Economic Inequality
Jennifer Gaudette finds that election officials can increase trust in elections by informing citizens of basic facts, such as that election machines are not connected to the Internet.
Class of 2026: Data Science Student Ready to Support Small Businesses and Underserved Communities Through Cybersecurity and AI
By Kelsey Kendall For Makendra Crosby (B.S. ’24), data science and cybersecurity are not just about numbers and computers, they are about people. As she prepares to graduate with her master’s in data science and analytics with a concentration in artificial intelligence and machine learning, she is focused on using her skills to serve others and make technology more accessible.
Indiana Jones and the Great Circle Nintendo Switch 2 tech analysis, including frame rate and resolution
Digital Foundry has provided its technical analysis of Indiana Jones and the Great Circle on Nintendo Switch 2, which goes over the game’s frame rate, resolution, and more in detail. Overall, MachineGames has produced what looks to be a solid port. The game even retains ray tracing and other graphical features present in other versions. Here’s the full roundup of...
International Business Machines Corporation (IBM): Using Blockchain to Build Trust In AI
International Business Machines Corporation (NYSE:IBM) is one of the most undervalued blockchain stocks to buy now. On May 5, International Business Machines Corporation (NYSE:IBM) announced a broad expansion of its products and capabilities at its Think 2026 conference. In all this, there was a big focus on AI. For instance, the company unveiled expanded capabilities […]