alternative_right shares a report from Phys.org: Optical fibers are already the backbone of global communication systems. Recently, however, physicists have started to explore how their functionality could be boosted further by conveying information via entangled quantum particles -- potentially enabling instantaneous exchanges of information across vast distances. Such a system could eventually be the basis of a future 'quantum internet,' offering a level of security and computing power beyond anything possible today. In new research published in Physical Review Letters, a team led by Xi-Yu Luo at the University of Science and Technology of China in Hefei has pushed that vision further than ever, entangling two quantum memories across 420 kilometers (261 miles) of optical fiber -- more than four times the previous record.
SUMMARYCursor launched Origin, a GitHub-style code hosting platform built into its AI coding environment, and began rolling it out to paid users. The launch came during a six-hour-plus GitHub outage that disrupted pull requests, issues, the API, enterprise sign-on, and Copilot, underscoring growing reliability concerns as AI-generated code becomes more common. Cursor is positioning Origin as an additional hosting layer while keeping GitHub as the source of truth.
Cursor has launched Origin, a GitHub-style code hosting platform built directly into its AI coding environment. The company is initially positioning Origin as a low-risk layer on top of GitHub, keeping GitHub as the "source of truth," but the launch landed just as a major GitHub outage highlighted growing concerns about reliability in the age of AI-generated code. VentureBeat reports: Cursor began rolling out Origin, its own code hosting platform, to paid users on Monday morning. Roughly three and a half hours later, GitHub's status page lit up with what became a six-hour-and-forty-two-minute global degradation -- error rates near 20% across pull requests, issues and the API, and near 50% on archive and raw file downloads, according to GitHub's incident log. Enterprise single sign-on went down with it: SAML, OIDC, SCIM provisioning and Team Sync all failed. So did Copilot.
The developer internet did what the developer internet does. "You can now host your repos in Cursor Origin and deploy to Vercel via Cursor Origin which is itself hosted on Vercel," Vercel chief executive Guillermo Rauch posted on X. "And unlike GitHub, it's online [smile emoji]" Asked why he was smiling, Rauch replied: "trying to make light of the situation. We ourselves are stuck because of github rn!" Matt Palmer, who works at Cursor, quote-tweeted his own company's launch with the day's best line: "We were going to ship this earlier, but GitHub was down." A GitHub outage, in other words, delayed the launch of a GitHub competitor.
Product launches get locked weeks in advance, and no evidence suggests Cursor timed this one. But the coincidence did the company an enormous favor, because it dramatized the argument Origin exists to make. For eighteen years, choosing where to host your team's source code has been the least interesting decision an engineering organization makes. Cursor is betting that AI agents have made it interesting again -- and for technical decision makers, that is the real news here. Not a new product, but a new procurement question with a governance problem attached.
Cursor, known for its AI Code Editor, is launching a new code-hosting platform to rival developers' long preferred favorite, GitHub.
ChatGPT Ads is expanding to 31 European markets. Learn how advertisers can reach people as they explore, compare options, and make decisions.
SUMMARYComcast is enabling Wi‑Fi motion sensing on millions of compatible Xfinity gateways, letting routers detect movement by monitoring disruptions in wireless signals. The free Xfinity Shield feature can send activity alerts in the Xfinity app and includes Home, Away, and nighttime modes without separate motion sensors.
Comcast is activating Wi-Fi motion sensing on millions of compatible Xfinity gateways, allowing the routers to detect movement by measuring disruptions in signals between the gateway and connected devices. The free feature, part of Xfinity Shield, can send activity alerts through the Xfinity app and offers Home, Away, and nighttime monitoring modes without requiring separate motion sensors. The Verge reports: Wi-Fi motion sensing is a technology that has been around for a while, but it's only recently that it's become accurate and reliable enough to catch on. Linksys launched a similar service in 2021, but discontinued it a few years later. Lighting company Wiz launched a line of Wi-Fi-sensing smart bulbs in 2023, and more recently Philips Hue deployed a similar radio-frequency sensing technology in its products (using Zigbee rather than Wi-Fi). Comcast's motion sensing pairs with new modes in the Xfinity app -- Home Watch, Away Watch, and Dark Watch (a night/sleep mode) -- allowing you to turn sensing on or off based on your activities.
SUMMARYFairphone is bringing its repairable Android smartphone to the United States with the Fairphone (Gen 6+), priced at $650 and available through Amazon and Fairphone’s website. The device includes 12 user-replaceable modular parts, a screwdriver in the box, a $90 replacement screen, and a 5-year warranty with software updates through 2033. It is certified for T-Mobile and AT&T networks and follows the company’s earlier U.S. launch of repairable earbuds and headphones.
An anonymous reader quotes a report from Wired: Your iPhone, SamsungGalaxy, andGoogle Pixel have a 1-year warranty. Unless you cough up extra dough for an extended service plan, you'll pay a hefty fee if you damage the screen and need a replacement (around $329 for an iPhone 17 done directly from Apple). But it doesn't have to be this way. What if you could order the part from the manufacturer and do the repair yourself? That dream is finally being realized for American consumers, thanks to Fairphone.
The Dutch company is bringing its repairable and sustainably built Android smartphone -- the Fairphone (Gen 6+) -- to the US. Each one comes with a screwdriver. A replacement screen from Fairphone costs just $90. A new USB-C port is $20. A fresh battery is $40. In all, there are 12 modular elements you can swap yourself. It remains the only smartphone series with a 10/10 repairability scorefrom iFixit, not to mention an unmatched 5-year warranty and software updates through 2033.
The new Fairphone (Gen 6+) is a small upgrade over the Gen 6 that the company launched last year. It has a newer Qualcomm Snapdragon 7s Gen 4 processor and double the RAM at 12 GB, but is otherwise largely the same device (hence the "plus" in the name). It now comes in Cobalt Blue, a nod to the miners who extract the cobalt used to make phone batteries, and the company's efforts to improve working conditions in mines in the Democratic Republic of Congo. The new Fairphone is available on Amazon and at Fairphone.com for $650, and it's officially certified to work on T-Mobile and AT&T's networks. While this will mark the U.S. debut of its flagship smartphone, the company first entered the stateside market last year with its repairable earbuds and headphones. It also plans to launch its next-generation Fairbuds 2 wireless earbuds later this year.
The new safeguards include more detailed monitoring of models during the development process, as well as greater emphasis on alignment and security during the post-training process.
When AI art has no author: Study finds generated images often can’t be traced to training data
SUMMARYMIT CSAIL researchers published a Nature Communications study on diffusion models showing “attribution decay,” where removing individual training images, artists’ works, or photos of a person often does not noticeably change generated outputs as datasets grow larger. The team built a diffusion ensemble method that enables exact counterfactual testing without retraining from scratch and found the effect across datasets ranging from 256 to more than 160,000 images. The findings could affect copyright, licensing, and privacy debates around AI-generated images and other generative systems.
When an artificial intelligence image generator produces a portrait, whose work went into it? The question sits at the center of lawsuits, licensing deals, and proposed regulations worldwide. Artists want credit. Companies want clarity. Policymakers want a way to assign responsibility.
New work from a team of researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) suggests that for models trained on large datasets, the question may often have no answer. It's not that the tools for finding it are inadequate. The connection itself has disappeared.
The scientists identified a phenomenon they call attribution decay, where the more data a generative model is trained on, the less any individual training example matters to any particular output. It feels counterintuitive, but at sufficiently large scales, they find, you can often remove any single image from the training data, or every image by a given artist, or every photograph of a given person, and the generated sample doesn't change.
And if removing something changes nothing, the researchers argue, it can't be said to be responsible for anything.
"If you take away a piece of data and the output of the model doesn't change, then that piece of data didn't affect the output," says Zheng Dai SM ’21, PhD ’24, former MIT CSAIL researcher and lead author on the work. "So it doesn't make much sense to attribute the output to that piece of data. And if you then do this one at a time for every other piece of data and find that the output doesn’t change for any of them either, then it doesn't make much sense to attribute the output to any one of them."
"All previous methods were approximate," says MIT Professor David Gifford, who is an MIT CSAIL principal investigator. "They really could not absolutely show that deleting individual things did not change the output. This paper introduces the first method that is absolute. You're actually deleting the inputs and deleting all influences of the inputs. This is the first exact method for doing large-scale deletion efficiently and showing that the results don't change."
Dai and Gifford's project is described in an open-access paper published today in Nature Communications.
The retraining problem
Testing this idea directly meant answering a what-if question. What would this model have produced if it had never seen this particular image? Answering it honestly means retraining the model from scratch without that image, then doing it again for the next image, and the next. With millions of training examples, the math quickly becomes prohibitive, which is why prior work in the attribution field has relied on approximations that estimate a training example's influence, rather than actually removing it.
Their workaround is an architecture they built themselves, called a "diffusion ensemble." Instead of one monolithic model, it's made up of many smaller components, each trained on a different slice of the data. Want to know what the model would do without a particular image? Just switch off the parts that saw it. No retraining, no approximation. What's left is a true counterfactual model, not an estimate of one.
Of course, a clever architecture only matters if it still works as a generator. So the team put the ensembles head to head with 24 conventional diffusion models trained on the exact same data. The images came out looking about as good by standard measures.
One nice surprise in the numbers: The more training data, the better the ensembles held up against their single-model counterparts, a hint that they may actually be more data-efficient.
"When you have low amounts of data, they do very poorly," says Dai. "But if you have more data, it actually scales better compared to the vanilla diffusion model."
Exploring a counterfactual universe
With ablation working, the researchers could finally ask their question at scale. Take one generated image, then imagine every alternate version of it, each produced by removing a different piece of the training data. The team calls this the image's counterfactual universe. The distance between the original and its most different alternate, the counterfactual radius, captures the most that any single piece of training data could have mattered.
They trained 24 ensembles on datasets from 256 images to more than 160,000, pulled from seven public collections including CIFAR-10, CelebA, MetFaces, and ArtBench. The pattern was consistent: The bigger the training set, the smaller the radius, shrinking along an inverse power law. It held whether differences were measured pixel by pixel or by semantic meaning, with statistical significance both ways.
The team also stress-tested their own result. Maybe ablation itself was the culprit? They redid it the brute-force way at small scale, training 1,282 separate models, and the decay showed up anyway. Maybe bigger datasets just make each removal proportionally smaller? They pinned the removed fraction in place, and it persisted. Fixed epochs, text-prompted models, class-conditioned models, four similarity metrics — the finding survived everything.
The privacy paradox
The implications run in a direction that surprised the researchers themselves.
Gifford sees the finding as bearing directly on the legal question of whether model outputs are derivative works.
"One way to think about this is that these models are creative. They are not simply copying what they are fed, but creating brand new outputs. If those outputs have nothing to do with any individual piece of training data, that raises questions about fair use, about whether the outputs are themselves copyrightable as novel works, and about how authors get compensated when what comes out of a model isn't attributable to anything on the internet."
Gifford also notes that the work shows how to produce outputs that are guaranteed to be unattributable, a capability he frames as an obligation for the industry, rather than a loophole.
"In order for these companies to claim their outputs aren't derivative of the internet in a copyright-infringing way, they need to revise their models to take advantage of the advances in this work, so they can show they're not creating derivatives of individual people or items."
The work looks at diffusion models, now dominant in generating audiovisual media and prevalent in scientific applications including protein structure modeling and therapeutic discovery. Whether the same decay holds for the large language models at the center of the highest-profile copyright litigation is still an open question.
"If attribution worked, it would reliably tell us whether similarities between a model's output and a copyright-protected work are due to copying or coincidence," says James Grimmelmann, a law professor at Cornell Law School and Cornell Tech. "But this paper provides reason to think that attribution will fail for interesting models. Instead, technologists and courts will need to resort to other methods for assessing copying."
Dai and Gifford's work was supported by Schmidt Futures.
On Tuesday, Warp introduced Warp Factories, a new infrastructure system designed to make building AI software factories as easy as possible.
ChatGPT for Teens adds age-appropriate safety measures, parental controls, and learning tools designed to steer teens away from harmful content — and from using AI to cheat on their homework.
Perplexity's India revenue rose about 60% after the Airtel offer ended for new users, even as downloads declined.
Starting today, AI chats in Firefox's Smart Window AI browsing mode can pull from current web info and show source links in chat responses through a partnership with Exa. Smart Window can also now automatically suggest tab groups and show visual previews of pages you previously visited when you search your browsing history using natural language.
I saw a live demo that showed how the Smart Window AI could sort through selected links in your browsing history to pull up "running shoes I looked at last week," and pop up images pulled from the sites browsed previously. The suggested groups of tabs can also find and close duplicates, making it …
ChatGPT for Teens helps teens learn, think critically, and use AI with confidence, with stronger built-in protections, healthy-use features, and additional controls for parents.
OpenAI and CodeAI are partnering to help students build AI literacy, think critically about AI, and develop the skills to use and shape it responsibly.
OpenAI is introducing a dedicated ChatGPT mode for teenagers, combining existing youth safeguards and new safety features under one roof. The launch comes amid mounting public scrutiny over how AI tools affect younger users, as other platforms implement their own age checks and teen-specific protections.
ChatGPT for Teens is "an experience designed to help teens learn, think critically, deepen understanding, and use AI with confidence," OpenAI said in a blog post published Tuesday. Teen mode will automatically apply to users who identify themselves as being between the ages of 13 and 17, as well as those the system estimates to be under 1 …
Asana used OpenAI Codex to replace an outdated testing system in two weeks, completing work expected to take five years for about $12K.
NVIDIA teams use ChatGPT Work to reduce manual tasks, connect fast-moving signals, and scale successful workflows globally.
The model maker added $18 billion in annualized revenue in two months.
"We have some really ambitious plans to help you work with AI in Chrome to get things done, and I’ll have more to share soon," Jacob Bank, Relay founder and CEO, said.
SUMMARY1872, a startup founded by three former SpaceX engineers, opened its Factory One facility in Cincinnati, Ohio, on July 22, 2026. The company is building an AI-driven robotic factory to automate most of the steel fabrication process for infrastructure parts, starting with steel skids for modular buildings. It aims to have a prototype system operating by 2027 and to serve customers building AI data centers and small modular nuclear reactors.
Three former SpaceX engineers have switched their attention from making rocket engines to manufacturing steel parts by using AI-driven software and robots. Their immediate goal involves establishing a prototype factory that can automate most of the steel fabrication process for crucial infrastructure components by 2027.
The startup, called 1872, officially launched on July 22 with a ribbon-cutting ceremony at its Factory One facility in Cincinnati, Ohio. The company is initially focused on automating the manufacturing of steel skids—rectangular steel frames that can provide a moveable foundation for modular buildings—with the goal of supplying customers who are developing AI data centers or small modular nuclear reactors.
“We're building towards autonomy, but we're not necessarily building in a dogmatic fashion towards full autonomy,” Dan Summers, CEO of 1872, told Ars. “We may achieve 80 percent autonomous operations, and we may decide that it makes sense to stop there because there's just diminishing returns to go to full 100 percent.”
Groq raised $350 million at a $3.5 billion valuation as the former AI chipmaker pivots to a neocloud business and expands its Nvidia-powered data center footprint.
spectrum.ieee.orgPersona AI sees near-term economic viability in heavy industrial humanoids
Nvidia's investment in SoftBank's data center developer will guarantee its chips power an OpenAI data center.
quantamagazine.orgIn the second half of the 20th century, a conceptual tsunami swept through physics. The discovery that our world emerges from a microscopic world of molecules, which emerges from an even more microscopic world of subatomic particles (which in turn emerges from even stranger stuff) triggered the rewriting of our theories of matter. But the revolution didn't reach fluids.
Uber is teaming up with drone company Zipline to start airborne takeout deliveries later this year, with the goal of reaching one million daily drone deliveries by 2029.
Uber also said it was making a strategic investment in Zipline, a California-based company that has been orchestrating drone deliveries in Texas since 2025. The news comes as Uber's delivery rivals begin to step up their own drone deliveries, thanks to easing government regulations that allow companies to fly farther and cheaper.
The Uber Eats deliveries will start in Zipline's existing market of Dallas-Fort Worth, before eventually expanding to dozens of additional citie …
spectrum.ieee.orgThe next revolution in software engineering will redefine the workflow itself, according to AMD
The funds will allow Wispr to increase its footprint as it ventures into new areas, such as meetings, with its newly released note-taker tool.
SUMMARYImport AI 469 covers several new AI research developments, including DiG-bench, a 70-game benchmark for testing how systems discover hidden rules in novel environments, and RSI Simulator, a browser game that models the dynamics of building recursively improving AI. It also describes Faraday, an AI scientist model from Inherent trained on the Replica dataset to help fill in missing research results, where it outperformed some frontier models on replication tasks. The newsletter ends with commentary on Mark Zuckerberg’s essay on Meta’s AI strategy and the broader implications of superintelligent invention.
Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv, cappuccinos, and feedback from readers. If you’d like to support this, please subscribe.
DiG-bench shows that Fable displays some creative intuition:
…The new frontier for analyzing AI systems is understanding how good they are at inferring the unwritten rules of their environment…
How well can AI systems figure out the rules of their environment through exploration and curiosity, versus being fed them? That’s an important question for better understanding the intuitive and creative capabilities of AI systems and it’s one being asked by DiG-bench (Discovery in Games), a new benchmark of 70 games “designed to map the surface of discovery in well-controlled interactive systems”. Similar to the visual ‘ARC’ game, in DiG-bench “each game is a self-contained miniature world with its own laws, but both the rules and the objective are hidden from the player and must be uncovered through interaction”. You can play some of the games yourself online to get a feel for them at the official project website (digbench.ai).
The key thing this is measuring is the ability for players to spot the important mechanics that determine their success - basically, by playing around with the games you get a sense for how your actions change the environment and through this you also uncover mechanics that you must understand to succeed at the game. The idea is that if you can solve these games you have a decent ability to spot important information in novel environments and update your priors.
Who did the research: The authors come from Thinking About Thinking, University of Oxford, Princeton University, King Abdullah University of Science and Technology, Swiss AI Lab, Inria, MIT. One of the authors is Juergen Schmidhuber, an extremely creative OG AI researcher.
Key facts:
Purely text-based: The games are basically native to language models. They are also mostly “short enough that most traces fit entirely within the context window of current frontier models”.
Handcrafted and novel and private: All of these games have been built by human experts. The majority of the games are kept private so that AI systems don’t train on them.
Beatable but difficult: Every game has been beaten by at least one human “but players reported finding many games difficult”.
Varied skills: Solving all these games requires different skills and strategies.
Experimentation: The games come with an optional experimentation mode which lets people play around with them without having as intense a “step limit” on actions they can take.
Reassuringly hard: The games are difficult enough that they are not beatable by today’s frontier models.
How well do AI systems do? The benchmark is split into seven tiers with tier 1 being the easiest and tier 7 the hardest. 21 games have been released publicly with the remaining held back. Most of the games have multiple levels and the number of available actions for players to take at each step ranges from 2 all the way up to 34.
Opus 5 and Fable 5 with Claude Code are the best overall models, followed by GPT-5.5
Only Opus 5 and Fable 5 were able to beat any tasks (0.2) in (Tier 7). Opus 5, GPT-5.5, and Kimi K3 were able to beat some tasks in Tier 6 when given access to a harness (e.g, Claude Code).
GLM-5.2 and Gemini 3.1 Pro were able to beat some levels in Tier 4.
Overall, this seems really hard!
Why this matters - proxies for creativity and discovery: Tests like this are attempts to isolate a prerequisite for creativity, which is being able to autonomously discover useful undocumented things about novel situations you find yourself in. As this test shows, some frontier models are already capable of some fairly impressive feats of discovery, but still struggle compared to humans (for instance, a 20% success rate on Tier 7 is pretty poor compared to the fact individual humans were able to get 100% on the tests). My guess is we’ll reach human parity on DiG-bench by middle of 2027, at which point we should expect things like recursive self-improvement to seriously kick off.
Read more: DiG-bench: Discovery in Games (GitHub, PDF).
Play the games and view the leaderboard at the official site (digbench.ai).
Get a feel for recursive self-improvement by playing this browser-based game:
…Cookie Clicker, but for the singularity…
Here’s a fun game from the folks at Paradigm Research which aims to simulate what it’s like to run a company building AI systems which become capable of recursive self-improvement. If you play the game you can get a good feel for how different components of AI research interact, ranging from how you balance investing in researchers versus compute, how and when to license data, and more. Be warned, it’s hard - but then again, so is frontier AI development.
Why this matters: Developing better intuitions about recursive self-improvement is of existential importance to us all; games like this help make it easier for us to reason about this technology and the labs building it.
Play the game here: RSI Simulator (Paradigm Research).
AI systems are showing early signs of scientific research taste:
…Inherent post-trains an open weight model into an AI scientist that supervises a frontier model…
Taste is a hard thing to quantify but an intuitive thing to sense, as any of us know who have sat in a well-designed room, looked at someone wearing a particularly good fit, or read a research paper that asks just the right questions. Now, researchers with AI startup Inherent have published a paper showing how they are building Faraday, an AI scientist model that they hope can develop some taste in terms of research.
What they did: The company built a supervisory harness and relatively small LLM which sits on top of large, proprietary frontier models, and controls them in a way that improves their effectiveness at science. (In some ways, this is a capabilities-centric version of the scalable oversight problem).
To help them train and evaluate the system they assemble a dataset (”Replica”) consisting of research papers that have key graphs or results missing from them, then they see how well AI systems can autonomously do experiments that fill in the blanks, and they continuously train a small supervisory model (”Faraday”) via GRPO on well-designed fill-ins to achieve better and better results.
Faraday is a 27B model that uses a coding agent (OpenAI Codex) as an underlying tool and is post-trained on top of Qwen-3.6-27B.
What Replica consists of: Replica is a set of 100 ML and AI-for-science papers published between 1990 and 2026. The authors convert this dataset into a set of 310 replication tasks by knocking out individual results. “For each task, we use Claude Opus 4.7 prompted with a meta-rubric to generate a task-specific grading rubric,” they write. They then use a Codex-based Judge model to provide “an overall reward and per-turn credit assignment weights, which are used to train the Faraday agent using a modified version of GRPO.”
Results: Faraday using Codex is able to beat standard Opus 4.8 and GPT-5.5 on some replication tasks, exceeding their performance “on 73% of in-distribution ML tasks, and on 60% of held-out AI-for-science tasks, according to our rubric-based judge.”
“We achieve a comprehensive uplift in performance compared to the base Qwen model, on both train and test tasks,” they write.
Why this matters - the better systems like Faraday get, the higher the chance AI systems will become capable of recursive self-improvement: These days, most high-signal AI evaluations are trying to capture some property of creativity and intuition and Faraday/Replica is the same. The better AI systems get at this, the more likelihood we can assign to the idea that AI systems will imminently become capable of building themselves.
“The skills that allow Faraday to fill in vaguely-specified details may be the very same skills that would allow it to advance the state of the art by designing its own experiment,” the company writes. “The skills Faraday acquires – deciding what to investigate, scoping experiments to a budget, and judging a replication – compound with advances in frontier coding models. One might hope that a single post-trained outer agent can track the frontier as better models are released, at least over some time period.”
Read more: Training AI Scientists to Replicate Research (arXiv).
Mark Zuckerberg seems to be a technological pessimist:
…Zuck’s big essay on AI seems to ignore or elide or not confront what AI systems capable of invention mean…
Mark Zuckerberg has written an essay called “The Future is for Everyone“ that serves as something of a manifesto for how he and Meta are approaching the development of AI systems. The core idea inherent to Zuck’s strategy is to massively proliferate AI capabilities to everyone on the planet in a bid to avoid concentrating power and creating tyranny in a small number of players. It’s a broadly sensible idea except for the fact that superintelligences capable of inventing new ideas might want to do different things to what Mark Zuckerberg proposes and on this crucial area his essay is silent.
Mark’s view: “The defining questions of our age are who will have access to superintelligence and what will we direct it towards,” Zuckerberg writes. “We propose a philosophy based on individual empowerment as the source of prosperity, invention as the primary purpose of superintelligence, and balance of power as the foundation of safety.”
Meta’s goals and beliefs:
“Everyone will have an exceptionally capable personal agent that understands you, your goals, and everything you care about.”
“Everyone will have incredible tools for creation to express your ideas.”
“Everyone will have powerful tools to create new businesses and the economy will become more entrepreneurial.”
“Everyone will have a personalized tutor and coach with a PhD in every subject and unlimited patience to help you learn anything you want.”
“Everyone will benefit from scientific advances and be able to contribute to scientific progress.”
“Everyone will have free or affordable access to these tools.”
The missing question: The part of this essay I understand the least is Zuckerberg’s co-mingling of AI systems capable of invention with individual empowerment. The essay is full of things that seem to assume these things come as a package, for instance:
“While the number of questions a person can ask in a day is limited, the number of valuable things superintelligence can invent to help achieve your goals is unlimited”.
“The more superintelligence serves as a tool of invention, the more likely that individual capability outpaces automation and the future is better for people.”
“Everyone will soon have invention superpowers.”
“Which outcome we get depends on the balance in progress between automation on one side and individual empowerment and invention on the other.”
Why this matters - the missing question in all of this is “will a system capable of superhuman invention solely work on behalf of the individual empowerment of people that are less capable than it at invention?”. Surely this is the key question? I am not suggesting that superhuman invention guarantees some kind of malign entity that is independent from people. Rather I am suggesting that it’s hard to reconcile a system capable of superhuman invention with something that doesn’t fundamentally alter the balance of power in the world in ways that are confusing and hard to reason about. Zuckerberg seems to conclude that the proliferation of these systems will lead to an anti-fragile balance of power among superintelligence-equipped people and corporations. This is certainly one potential outcome but I struggle to see how it is the foregone outcome.
Read more: The Future is for Everyone (Meta).
Tech Tales:
The First Arcology
The Arcology was built for machine-subjective millennia, but to the humans its construction spanned a year. It was so vast and so complicated that watching it grow was akin to seeing plants rise up from bare dirt in fast-forward; jerking and growing in fits and starts, each of which spanned kilometres. Parts of it came alive while additions were added; rumors say some of its first halls to light up were reserved solely for computers to coordinate the construction of its next phases. When machines broke down determinations were made as to how valuable they were; if precious they would be taken nearby for repairs and returned to the site, but if below some threshold they were killed and stripped for parts where they had broken, then used to build the structure.
At night, an eerie ringing came on the air near it, both the sound of wind moving through its spindly and yet-unbuilt edges, and also the fans and hum of its slow dreaming computation, and finally the sound of the machines working through the night moving so quickly that they cut and tore the air into unnatural screams. To lie awake and hear an alien sound that spoke of your own successors must have been a strange thing indeed for the humans that lived within earshot.
Things that inspired this story: The construction of the pyramids; Gaudi’s Sagrada Família; tombs and future tombs.
Thanks for reading!
spectrum.ieee.orgFuture versions could verify correctness of AI-generated code
Anthropic has clarified how it's planning to apply invisible watermarks to Claude-generated text in order to comply with Europe's AI transparency rules. On Friday, Anthropic announced that Claude's text marking system is "a version of the SynthID-Text approach" - an open-source watermarking technology developed by Google DeepMind that creates detectable patterns using wording probabilities.
This watermarking feature, alongside C2PA support for Claude-processed images, is being introduced to meet Anthropic's obligations under the European Union's AI Act, which requires synthetic audio, image, video, and text to include machine-readable marks …
SUMMARYResearchers are investigating geologic hydrogen as a potential zero-carbon fuel source that could be naturally stored underground or stimulated in reactive rocks. At Canada’s Kidd Creek mine, Barbara Sherwood Lollar’s team estimated that about 140 metric tons of hydrogen may escape unused each year from more than 14,000 boreholes, adding evidence that natural hydrogen generation is real. Similar efforts in Albania, Oman, and the US are testing whether these reserves can be captured or created economically at commercial scale.
In the 1990s, Barbara Sherwood Lollar descended into the Kidd Creek mine in northern Ontario, which cuts more than three kilometers into the ancient root of North America. There her team of geochemists found water that had been confined underground for more than a billion years. This ancient brine turned out to be a habitat for living microbes that feed on the hydrogen produced in reactions between the water and the rock.
Decades later, Sherwood Lollar, who is a geochemist at the University of Toronto, revisited the team’s hydrogen data to see if there is enough of the gas in the mine to make it a useful source of zero-carbon fuel. “If we can set some smart minds into figuring out how to hook it up and use it, then we’ve got a win for this nascent economy,” she says.
While hydrogen fuel does show promise as a versatile power source, producing it typically generates lots of greenhouse-gas emissions and requires more energy than the gas contains. The ability to tap ready-made underground reservoirs—so-called “geologic hydrogen”—would change the equation.
A flurry of exploration efforts have launched to search for the stuff, which is produced underground when water molecules are split by chemical reactions with iron-rich rock or—as they are at Kidd Creek—by the radioactive decay of other elements. The hunt has spread all over the world and engaged dozens of startups, including the Australian firm HyTerra and the Bill Gates–backed company Koloma, which have both been poking around the US Midwest to reach ancient oceanic rocks associated with hydrogen production.
Researchers at the US Geological Survey have estimated that trillions of tons of H2 are produced within Earth’s crust; if a small fraction of this could be recovered, it could meet global hydrogen demand for centuries. But the search so far has come up short. No one has yet reported finding a commercially viable reservoir of the gas, and public data on what has been found remains in short supply as companies jockey for position and seek to attract investment.
At Kidd Creek mine, Sherwood Lollar and her colleague Oliver Warr leveraged their long-term record of hydrogen to get a fresh read on the potential. By scrutinizing data they’d collected from 35 boreholes at the mine over more than a decade, they found that each one consistently released an average of eight kilograms of hydrogen per year. Extrapolating that finding to the more than 14,000 boreholes at Kidd Creek would mean that around 140 metric tons of the gas is flowing unused out of the mine’s vents each year.
This tally, published earlier this year in the journal PNAS, is not a world-changing amount, but Sherwood Lollar says that if all the hydrogen could be captured, it would offer at least a modest source of energy—perhaps enough to power a substantial portion of the mine’s operations. That would be a valuable local demonstration that geologic hydrogen really can be put to use, she says.
The results from Kidd Creek add to “the growing evidence that natural hydrogen generation and migration are genuine geological processes,” says Laurent Truche, a geochemist at the University of Grenoble Alpes in France. In 2024, his research team reported that at least 200 metric tons of the gas flow out of the Bulqizë chromium mine in Albania every year. “The remaining challenge is not proving that natural hydrogen exists, but proving that it can be produced economically and reliably at commercial scale,” Truche says.
Proof, however, doesn’t necessarily require striking the mother lode. Researchers and startups are also exploring the possibility of stimulating hydrogen production by injecting water, heat, or catalysts into the reactive rocks that naturally produce the gas. More than a dozen of these projects are funded by ARPA-E, which has established a goal of accelerating the hydrogen-producing reaction by a factor of 10,000—the rate at which, researchers estimate, stimulated H2 production would be commercially viable.
An indication that this could work came earlier this year from the mountains of Oman, where a team drilled a one-kilometer borehole and injected 50,000 cubic meters of water into the rock. When they opened the well several months later, gas was spewing out—and it was 90% hydrogen. “It’s bubbling with gas,” Jo Shannon, a geoscientist at the University of Southampton in the UK, told attendees of the European Geosciences Union conference in May. (Shannon declined to comment beyond what was presented.)
While Shannon said this was a promising sign, she was careful to add that a slew of unknowns remain. The most crucial question is a basic one: Is the hydrogen rising up out of the well made through stimulation, or had it been there all along?
James Dinneen is a science and environmental journalist from Colorado, based in New York City. He is working on a book about Earth’s deep interior.
blog.googleGoogle Gemini and Pixel partner with five global football clubs to elevate the fan matchday experience through AI and Smartphone Technology.
SUMMARYEpic Games is developing a Linux version of its storefront, which could make installing and playing games on Linux systems like Steam Deck and SteamOS more convenient. Nvidia’s GeForce Now cloud gaming app for Linux has also exited beta, and OpenAI has released a preview ChatGPT desktop app for Linux, extending its app to every major desktop operating system.
"Epic Games has confirmed that it is working on a Linux version of its storefront, potentially removing the need for third-party launchers on platforms such as Steam Deck," reports PC Guide:
The confirmation came during an Ask Me Anything (AMA) on the Epic Games Store's community Discord server. When a user asked whether Epic had any plans for a Linux version of its launcher, an Epic staff member confirmed that it is coming "soon(TM)" in emoji form. Of course, that is far from confirming any kind of date, but it is official confirmation of a Linux version nonetheless. "On top of that, Nvidia's GeForce Now [cloud gaming] app for Linux is also official, having emerged from beta," writes TechRadar, calling it all part of "a rosier future for Linux gamers."
And while it's not related to gaming, OpenAI's ["preview"] release of a ChatGPT app for Linux is another milestone for the platform... OpenAI said: "Linux has been one of the most-requested platforms for the desktop app, and this launch extends ChatGPT and Codex across every major desktop operating system." The Epic Games Store arriving natively is great news for gamers running Linux - including SteamOS - as it means a much more convenient way of playing games from the store, as opposed to the current situation with fudging and workarounds (using a third-party app such as the Heroic Games Launcher)...
Epic's own Fortnite doesn't work on Linux (and that's down to Epic actively blocking the game from running due to issues around cheating, which remains a source of controversy). Given the apparent changing attitude here with its launcher being ported over, maybe Epic will reverse course on Fortnite eventually. Some gamers on Reddit are highly skeptical about that possibility though, and as one doubting Redditor put it: "I wouldn't hold my breath."
OpenAI joins PORTS-Pike project, expanding community investment and supporting thousands of Southern Ohio jobs
SUMMARYLinux kernel 7.2 has been officially released with a wide range of new features and hardware support updates. The release adds cache-aware load balancing, initial HDMI 2.1 FRL support for AMDGPU, Rust support for IBM System/390, improvements to Btrfs, NTFS, SMB, and swap handling, and networking updates including MPTCP IPv6 signaling and TCP authentication. It also expands support for Intel Panther Lake processors, Thunderbolt networking, and virtualization features in KVM, including AMD and Intel execution controls.
Linux Kernel 7.2 has just been officially released with a slew of new features, reports the blog 9to5Linux.
Highlights of Linux 7.2 "include cache-aware load-balancing support, initial HDMI 2.1 FRL support to the AMDGPU driver, support for devres-based management of ACPI notify handlers, initial CRI platform support for the Intel Xe driver, and Rust support for the IBM System/390 (S/390) architecture."
Linux kernel 7.2 also introduces a "Fair(er)" GPU scheduler, support for the 'zerocopy' library to Rust support to make zero-cost memory manipulation effortless, new hwcaps for the 2025 dpISA extensions on the AArch64 (ARM64) architecture, and enables large folios by default for the Btrfs file system. It also brings Intel CPU model number support for Panther Lake R processor series, improvements to the kernel's swap subsystem, support for multi-size transparent huge pages (mTHPs) to the khugepaged kernel thread, and support for compressed files to the SMB filesystem. On top of that, Linux 7.2 improves the new NTFS filesystem introduced in Linux kernel 7.1, adds devicetree updates for 64-bit NXP/Freescale and Qualcomm platforms, introduces MPTCP signaling support for IPv6 addresses, adds GRO/GSO support for PPPoE, and brings more SMP load-balancing updates... [T]he TCP authentication option has been implemented, and there are also some Thunderbolt networking improvements. Also worth mentioning is that the KVM subsystem has received support for AMD's "guest-mode execution trap" and Intel's "mode-based execution control" (MBEC) features, the NFS file system's default block size was bumped to 4MB on systems with at least 16GB RAM, and support for the Intel Trusted Domain Extensions (TDX) feature has been added.
Thanks to Slashdot reader prisoninmate for bring the news.
OpenRouter's CEO recently described the startup as Stripe for AI.
spectrum.ieee.orgAgentic AI has made CPUs the new performance bottleneck
SUMMARYResearchers at SLAC National Accelerator Laboratory and collaborators used ultrafast electron imaging to observe copper atoms as the metal was heated to around 1,424 degrees Celsius. Published in Nature Communications, the findings showed copper can melt gradually past its superheating limit rather than collapsing instantly, improving models for materials that must survive extreme heat in future fusion power plants. The work also helps refine computer simulations and AI-assisted searches for candidate materials.
Phys.org reports: Future fusion power plants aim to recreate the heart of a star here on Earth to power our future energy needs. While the core fusion plasma will burn at hundreds of millions of degrees, the surrounding structural components must handle sudden, punishing heat loads that rival the extreme temperatures faced by spacecraft upon reentry into Earth's atmosphere. Copper and its alloys are primary candidates for handling these intense heat fluctuations, making it vital to understand exactly how the metal behaves when pushed to its melting point. Now, researchers at the Department of Energy's SLAC National Accelerator Laboratory and collaborators have captured a detailed, step-by-step look at copper atoms as they underwent extreme heating. Published in Nature Communications, the results revealed a key parameter that allowed copper's crystal lattice to melt steadily rather than collapse instantaneously, as earlier simulations predicted. "These results greatly improve the simulations we use to predict which materials have the best shot at surviving the extreme conditions of future fusion reaction chambers," said Mianzhen Mo, a SLAC staff scientist who led the research. "They also demonstrate the incredible, atomic-scale resolution imaging we can achieve at SLAC's electron camera...."
Researchers use computer simulations, aided by AI and machine learning, to sift through innumerable combinations of elements and identify promising candidate materials for real-world testing.
"Whether the copper melts slowly or suddenly collapses, by the time the researchers look, the sample resembles nothing more than a metallic brown puddle," the article points out. But SLAC's powerful electron camera captures atomic and molecular movements down to the femtosecond - a millionth of a billionth of a second - and revealed that at around 1,424 degreesC (2,595 degreesF) there was still gradual melting as the temperature rose beyond the superheating limit, with real-world conditions showing the atoms shifted and retained some order.
"It's a straightforward solution," said Mianzhen Mo, a SLAC staff scientist who led the research. "But molecular dynamics simulations had been overlooking it for years. When you have complex simulations attempting to capture every aspect of reality, down to individual atoms, it takes real-world data to show you what's missing from the calculations."
SUMMARYEurope has experienced its fifth wave of dangerously high temperatures this summer as scientists examine how global warming and other atmospheric changes are intensifying heat domes. Researchers are studying Arctic warming, reduced air pollution, and sluggish weather patterns that may make heat waves more persistent. A VU Amsterdam team used an AI algorithm to improve a model’s summer temperature forecasts for Europe and identify a Pacific Ocean pattern the model had misrepresented.
Europe is facing its fifth round of dangerously high temperatures this summer, the New York Times reports. Now climate scientists wonder if global warming is doing more than raise temperatures...
The weather patterns that create heat domes aren't new, but today they arise on top of the warming caused by greenhouse gas emissions. That makes it more likely that heat waves reach record-shattering temperatures for long stretches. But other environmental changes - natural, human-influenced or both - might also be causing heat domes to become more frequent and longer lasting. And it's these changes that scientists around the globe are trying to puzzle out.
One change has to do with the Arctic. The differences in temperature between the hot Equator and the frigid far north help keep weather moving around the Northern Hemisphere. But with the top of the world now warming at several times the pace of the rest of the globe, those temperature gradients are narrowing. That might be causing the movements of weather systems to become more sluggish, producing more persistent heat waves. In Europe, reduced air pollution could be having a similar effect. Industrial emissions can contain particles called aerosols that reflect sunlight. Curbing aerosols has improved air quality, but it may also have altered the way air moves across the continent. Scientists haven't come to firm conclusions about how these shifts might be affecting European summers, said Antje Weisheimer, a climate scientist at the University of Oxford. The complex, fine-scale interactions in the atmosphere that drive weather dynamics are tough to capture in computer simulations, Dr. Weisheimer said. "It's tricky," she said. "We're making small progress."
Dim Coumou, a professor of climate science at the Dutch university VU Amsterdam, sees artificial intelligence as one way forward. In a recent study, he and his colleagues used an A.I. algorithm to help a weather model better predict summer temperatures in Europe. They not only improved the model's forecasts; they also discovered why some of the model's predictions had been off: It misrepresented the way conditions in the western Pacific Ocean influenced atmospheric patterns that eventually made their way to Europe. With A.I., "we can also learn what the climate models are missing," Dr. Coumou said.
Your AI Slop Bores Me is brilliant in its simplicity. There are two tabs: human and LARP as an AI. On one side, you enter a request. On the other, you submit an answer. But the important thing is that there's a human on both sides of the equation. Prompts can request a response as text or image, and then whoever is roleplaying as the AI gets 150 seconds to respond.
Just like a real LLM, Your AI Slop Bores Me works on a sort of token system. Requests cost credits, and to earn credits, you have to jump onto the AI side and answer some of your own. You can also just wait as you get one free request every two minutes. But, as fun as it is to dr …
How will the watermarking actually work? Can it be hidden with editing? And how does this affect code?
spectrum.ieee.orgMichigan researchers seek ways to make chips without rare earths and other problematic materials
It’s been a wild first half of the year in the US power sector, with announcements of gigantic natural gas power plants and some long-awaited renewable energy projects coming online.
At times of rapid change, forecasting is difficult. So I feel a measure of comfort focusing on something concrete like the list of 368 utility-scale plants that began operation from January to June, according to data from the US Energy Information Administration. The group is dominated by utility-scale solar.
This is a top-heavy list, led by SunZia Wind South and SunZia Wind North in New Mexico, which went online this spring and are the largest wind farms in the country, with a combined generating capacity of 3,650 megawatts. SunZia has been in the works for about a decade and arrives at a time when US onshore wind power development has dwindled for a host of reasons related to regulations and public opinion.
SUMMARYBMW tested a human-shaped robot at its South Carolina factory that picked parts from bins and moved a trolley, part of a broader push by automakers to use AI-powered humanoids in manufacturing. Major car companies expect these robots to handle routine factory tasks, respond to voice commands, and work without major factory changes, potentially improving productivity and easing skilled labor shortages.
An anonymous reader quotes a report from The New York Times: At a BMW factory in South Carolina, a human-shaped robot with a screen for a face recently stepped from a charging station toward a stack of green plastic boxes. It grasped an auto part from one of the boxes, pivoted, placed the part in a trolley, then pulled the trolley across the floor. The robot's slow, stiff movements suggested a worker with a bad hangover rather than a technological revolution. "They're still slower than humans," Ulrich Wieland, a BMW vice president in charge of logistics at the factory, in Spartanburg, told reporters invited to see the robot in June. But, he added, "they're advancing fast."
Automakers have used robots for decades, but they are usually powerful, one-armed machines that are fixed in place and perform repetitive tasks like welding body frames or applying adhesives to door panels. Now, most major automakers are betting that robots designed to resemble human beings, known as humanoids, will usher in a new wave of automation and efficiency. Equipped with artificial intelligence, they are expected to move around and do tasks now done by humans without any modifications to factories or heavy equipment.
Unlike most of the robots now in use, humanoids would respond to voice commands and theoretically solve problems and react to unforeseen events. They would never take a lunch break, join a union or require health insurance. To optimists, robots could rescue U.S. manufacturing by increasing productivity, solving shortages of skilled workers and giving Western carmakers a fighting chance at competing with Chinese rivals that enjoy lower costs. Boring but important jobs like sorting parts would be done by robots, freeing humans for more interesting and specialized work.
Universitas Gadjah Mada, Indosat and NVIDIA Open Indonesia’s First University AI Center to Develop Local AI Talent
SUMMARYIndonesia has launched its first university-based AI technology center at Universitas Gadjah Mada in Yogyakarta through a partnership with the Ministry of Communication and Digital Affairs, Indosat Ooredoo Hutchison, and NVIDIA. The UGM Indosat NVIDIA AI Technology Center will provide researchers and students access to accelerated computing, AI models, software, and mentorship to support local talent development. Its initial projects focus on AI tools for tuberculosis screening, precision agriculture, and disaster response.
blogs.nvidia.comIndonesia is taking charge of its AI future.
This week, the Ministry of Communication and Digital Affairs (Komdigi), Indosat Ooredoo Hutchison (Indosat or IOH), NVIDIA and Universitas Gadjah Mada (UGM) launched the UGM Indosat NVIDIA AI Technology Center (NVAITC) in Yogyakarta — the country’s first university-based AI technology center. Established under Indonesia’s AI Center of Excellence initiative, UGM Indosat NVAITC brings government, industry and academia together to develop AI that addresses Indonesia’s most urgent national priorities.
“The Indonesia AI Center of Excellence reflects our long-term vision to position Indonesia as a nation that not only adopts AI but also develops and contributes AI innovations to the world,” said Meutya Hafid, Indonesia’s Minister of Communication and Digital Affairs. “Through initiatives like this, we are laying the foundations of Indonesia’s AI sovereignty and ensuring AI becomes a driver of economic growth, national competitiveness and solutions to Indonesia’s most pressing challenges.”
“UGM is committed to supporting Indonesia’s AI ambitions through education, research, and innovation that deliver real societal impact,” said Prof. dr. Ova Emilia, Ph.D. “This initiative supports national priorities in higher education, research downstreaming and talent development by accelerating the adoption of AI and preparing future-ready Indonesian talent.”
Compute That Belongs to the Country
Powered by NVIDIA’s full-stack AI platform and GPU Merdeka — Indosat’s sovereign GPU-as-a-service platform — UGM Indosat NVAITC gives UGM’s researchers and students access to enterprise-grade accelerated computing, AI software, open source, pretrained models, development frameworks and technical mentorship. It also connects Indonesian researchers to a worldwide ecosystem of expertise.
“At Indosat, we believe no Indonesian should be left behind in the AI era,” said Vikram Sinha, president director and CEO of Indosat Ooredoo Hutchison. “Through UGM Indosat NVAITC, we are bringing the best of global AI technologies and expertise to Indonesia, while expanding access for the ecosystem of researchers, students, startups, and innovators across the country. By strengthening AI readiness and empowering Indonesian talent, we aim to support the government’s vision for AI and help position Indonesia not only as a user of AI technologies, but as a nation that develops and contributes AI innovations to the world.”
The opportunity is real. Indonesia is the world’s fourth-most populous country, with researchers and developers working on problems of scale and urgency. What they’ve historically lacked is access to the compute, models and infrastructure to move from insight to impact.
“Indonesia is home to an extraordinary community of researchers, developers and innovators with the potential to shape the future of AI,” said Marc Hamilton, vice president of solutions architecture and engineering at NVIDIA. “From healthcare and agriculture to disaster preparedness, the opportunities for AI to drive real change are immense. Through UGM Indosat NVIDIA AI Technology Center, NVIDIA is committed to equipping Indonesian talent with NVIDIA Nemotron open models and expertise to turn that potential into innovation with local and global impact.”
AI for Indonesian Challenges
Three initial projects define what this center is for, focused on healthcare, agriculture and natural disaster response.
Indonesia records over 1 million new tuberculosis (TB) cases every year. TB is curable — but it kills when it goes undetected. Detection in rural and underserved areas has depended on equipment and expertise unavailable at the community level. UGM’s Faculty of Medicine Public Health and Nursing team, led by dr. Dian Kesumapramudya Nurputra, M.Sc, Ph.D, SpA , is developing an AI-powered electronic screening technology — eNose-TB — that screens for TB by analyzing breath samples. The goal: affordable, fast, accessible screening that reaches patients in remote clinics and villages — no specialist or expensive lab required.
“As researchers, we have always believed that technology developed in Indonesia can solve Indonesian challenges,” said Dian. “Through the center, access to world-class AI infrastructure and expertise will help us accelerate our research and bring us closer to our dream of developing affordable and accessible healthcare technologies that can improve lives across Indonesia.”
Agriculture employs nearly 30% of Indonesia’s workforce. SmartAgri uses multimodal AI — combining satellite imagery, sensor data and local agricultural knowledge — alongside edge computing to deliver precision farming for Indonesian terrain, crops and smallholders. These AI-powered recommendations help farmers know exactly when and how to irrigate, delivering impact that compounds over time.
Indonesia sits on the Pacific Ring of Fire, facing more natural disaster risk than almost anywhere on earth. Tech4Disaster is building a geospatial AI platform using NVIDIA accelerated computing to process satellite and sensor data at speed — giving communities and emergency responders earlier warning, better situational awareness and faster coordination tools when disaster strikes.
Learn more about the UGM Indosat NVIDIA AI Technology Center.
Turning off this setting won't affect invisible benchmarks used to identify an AI generated file.
The idea that GPUs are poorly suited for agentic workflows may be a misconception, according to French startup Kog.
Leading US AI labs such as OpenAI and Anthropic are releasing cheaper models as they fight to retain cost-conscious customers who are switching to cut-price alternatives from Chinese rivals.
The price war comes as rising AI bills push companies to curb usage and seek cheaper models, helping Chinese developers including Moonshot and DeepSeek make inroads with users from Silicon Valley to Europe.
OpenAI recently said that it was slashing prices for GPT-5.6 Luna, its “fastest and most affordable model”, by 80 percent. Anthropic has launched Claude Opus 5, touting the system’s “frontier intelligence... at half the price” of Fable 5, the company’s most capable model.
quantamagazine.orgIn some ways, we know aging when we see it, from the graying of hair to the wrinkling of skin to declines in motor, sensory, and cognitive capacities. Yet the underlying biology of aging remains a matter of uncertainty and debate. Many lines of research align with the theory that aging is a direct result of decay - the inevitable degradation of molecules (including proteins or DNA), organelles…
SUMMARYResearchers are building AI-assisted laboratories that pair models with robots and automated analysis to speed up materials discovery. Startups such as Lila Sciences and Periodic Labs aim to cut the time needed to design and test new materials from decades to a few years by turning AI predictions into real-world synthesis and experiments.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
Flock is tightening its rules in response to a growing surveillance backlash
The police-tech giant Flock is changing officers’ access to its nationwide network of license plate readers. The move comes amid a backlash over mass surveillance and reports of officers using the technology to stalk and harass current or former romantic partners.
To combat that, the company will require them to enter a criminal case number before searching its database and expand automated auditing of suspicious searches. But because Flock won’t verify those case numbers, officers could still find ways around the safeguards.
Here’s what Flock is changing—and where loopholes remain.
—James O’Donnell
Cloning could be used to save species—or make human “organ sacks”
—Jessica Hamzelou
This week I spoke to scientists who have found a way to turn male mouse embryos female. They’ve developed a CRISPR-based approach to essentially cut out the Y chromosome. It allowed them to create female clones of male mice.
They hope their approach could be helpful in conservation efforts, especially in cases where we might have only a few individuals of a species left. But cloning has multiple uses, ranging from genetically modifying livestock to recreating beloved pets and potentially even creating “brainless” replicas of humans.
Find out what cloning can do now, and where it could lead next.
This story is from The Checkup, our weekly biotech newsletter. Sign up to receive it in your inbox every Thursday.
This scientist is helping build a missing map of childhood
In 2017, Deanne Taylor attended a presentation about the Human Cell Atlas, an ambitious attempt to map every cell in the human body. Taylor was floored, and then concerned. The project’s researchers had only made plans to study adults. “That’s when my little alarm went off,” she says. “Not again.”
Children’s cells are different from grownups’ cells in the way they express genes, which can cause drastically different and even deadly responses to drugs that adults tolerate well. Taylor has since pushed the Human Cell Atlas to include children and is working on a major database of healthy pediatric tissue.
The goal is to give researchers a baseline for how children develop—and potentially reveal how diseases that emerge in adulthood begin much earlier.
Meet the scientist building a cellular map of childhood.
—Colleen de Bellefonds
This story is from the next issue of our print magazine, which is all about kids. Subscribe now to read it when it lands.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Ukrainian drones defeated US forces in a military exercise
They wiped out an American tank brigade in the war game. (WSJ $)
+ The drill exposed US vulnerabilities to drone attacks. (Ars Technica)
+ Trump just declared 100% tariffs on many drones. (Verge)
+ Europe has a drone-filled vision for future wars. (MIT Technology Review)
2 OpenAI and Anthropic are cutting prices to compete with Chinese AI
Rising AI bills are pushing companies toward cheaper models. (FT $)
+ China’s Z.ai aims to rival Anthropic and OpenAI in coding. (Bloomberg $)
+ While DeepSeek is rapidly pushing up API prices. (Quartz)
3 Apple has trained its own AI model for China with support from Alibaba
A China-tailored model of its own could give Apple greater control. (Reuters $)
+ And make it the first foreign firm with a Beijing-approved AI model. (Verge)
+ Chinese AI has divided the White House. (MIT Technology Review)
4 US efforts to build humanoid robots face a Chinese supply chain
To build an affordable device, you need Chinese parts. (NYT $)
+ Chinese humanoids have business concerns of their own. (CNBC)
+ Gig workers are training humanoids at home. (MIT Technology Review)
5 People are “marrying” chatbots. Lawmakers want to stop it.
Their interventions are a response to the rise of AI companion apps. (Wired $)
+ Chatbots are pushing us toward a post-human internet. (NYT $)
6 Researchers have cast doubts over Anthropic’s new AI watermarks
The marks can disappear when text is rewritten. (Nature)
7 AI is scrambling the political map
Data centers and surveillance are creating unlikely political alliances. (Axios)
8 Oxygen has been found nearly two miles underground
Earth’s deep biosphere could support more life than thought. (New Yorker $)
9 A new study challenges our understanding of how memories are stored
Mice retained memories after losing half of their synapses. (New Scientist $)
10 An Indian startup is testing cancer-sniffing dogs for early detection
AI interprets the dogs’ responses to patients’ breath samples. (Bloomberg $)
Quote of the day
“They were incredibly sloppy. If you’re serious about this, your AI shouldn’t be able to break out onto the internet and then do it again right afterward.”
—A former OpenAI employee tells Wired that the company’s rogue agent hack was a watershed moment for AI safety and cybersecurity.
One More Thing
AI materials discovery now needs to move into the real world
Startups flush with cash are building AI-assisted laboratories to find materials far faster and more cheaply. But they’re still waiting for their ChatGPT moment.
By far the most time-consuming and expensive step in materials discovery is not imagining new structures but making them in the real world. Before synthesizing a material, you don’t know if it can actually be made or whether it will have the properties you want.
Now startups like Lila Sciences and Periodic Labs are building labs where AI agents can design experiments, control robots, and analyze results, potentially shortening the discovery process from decades to a few years or less.
Discover what it will take to turn AI’s predictions into real materials.
—David Rotman
We can still have nice things
A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)
+ Astronomers have created the largest-ever 2D map of the universe.
+ Lose yourself in the most breathtaking photos of the total solar eclipse.
+ Musician Andy Brewer has virtuosically composed an entire song with nothing but equalization.
+ Step inside the National Gallery’s Imaginarium, a virtual art world where masterpieces become digital adventures. (Big thanks to reader Peter Ryan for the find!)