🚀 Zaprep : tes rĂ©seaux sociaux sous stĂ©roĂŻdes. Commencer gratuitement avec 1 000 DM automatisĂ©s/mois.

Soumettre ton outil

10,000+ outils IA déjà listés
10K+Outils
190K+/moVues
70K+/moVisiteurs
Se connecter
EN LIGNE

Outrank. Paie pour classer.

Surenchéris pour une place dofollow n°1 sur PoweredByAI.

À partir de 11,99 $ · Bats-les de 2 $ · Sans expiration

DĂ©couvrir Outrank

AI NewsAn unreleased Anthropic model made progress on one of math’s biggest unsolved problems

An unreleased Anthropic model made progress on one of math’s biggest unsolved problems

6:49 AM IST · August 12, 2026

An unreleased Anthropic model made progress on one of math’s biggest unsolved problems

For more than 150 years, the Riemann hypothesis has stood as one of the major unsolved problems in mathematics, a long-running mystery about the distribution of prime numbers. There is currentlya $1 million bountyfor a working general proof of the hypothesis, which remains unclaimed. Contemporary AI models still can’t solve it either — but they can make a lot more progress than you might expect, a finding that’s likely to reopen longstanding questions about contemporary AI’s ability to discover new scientific and mathematical ideas. On Monday, Anthropic announced that an as-yet-unreleased model had made significant progress on the Riemann hypothesis, significantly increasing the lower bound of solutions for which the hypothesis holds true. Even more impressive is how the progress was made: An Anthropic staff member without significant mathematical training prompted the model to “take a real stab” at proving the hypothesis, then left the model to coordinate the task across the following day and a half. All told, the model tested 650 different ideas for solving the problem, coordinating across 60 subagents and spending 31 million output tokens in total. “Out of the 60 subagents, two were responsible for developing the key mathematical ideas,” a footnote to the paper explains, “13 contributed ideas to these agents, 30 attempted (but were unable) to develop new ideas, 13 served as validators to check the correctness of the arguments, and the final two helped to write the initial paper.” The finding was confirmed by two of Anthropic’s in-house mathematicians, and formalized using the open source proof assistantLean. This is the latest in a string of mathematical breakthroughs led by large language models, or LLMs.A number of Erdos problemshave been solved by AI models over the course of this year, and the release of more powerful models has led to more impressive results. OpenAIrecently releaseda set of 10 major results proved by its internal “Astra” model, while a separate effort from Anthropicdisproved the longstanding Jacobian conjecture. The growing body of results has caused both excitement and concern in the mathematical field. Ina public declaration signed in June, a group of prominent mathematicians raised concerns that AI could undermine critical values of the field — particularly the standard that true mathematical proofs should be “attributable to specific authors who take credit for their discovery and assume responsibility for their correctness.” But the field is still split on how mathematicians should approach the new research techniques. Ina blog post responding to the declaration, Fields Medal winner Timothy Gowers questioned whether the influence of AI might change mathematics in a more complex and positive way. “If we arrive at a world where mathematical theorems are no longer associated with mathematicians, maybe that won’t be any more problematic than the fact that stars aren’t named after astronomers and most aren’t named at all,” Gowers wrote.

read more

DerniÚres actualités IA

Toutes les actualitĂ©s →
Anthropic’s first embedded evaluator is 
 Accenture?

Anthropic’s first embedded evaluator is 
 Accenture?

Dario Amodei’s plans to put third-party safety evaluators inside AI labs are taking shape: Anthropic said that staff from technology consulting giant Accenture will begin working inside the company to scrutinize its models and staff. In ablog post, Anthropic said that Faculty, a company Accenture acquired in January to act as its AI division, will begin “evaluating and red-teaming models, conducting alignment assessments, and testing model safeguards.” Both companies expect to invest at least $1 billion in the project over the next five years. The choice of Accenture surprised many AI watchers — and the markets, where the consultant company’s shares shot up 8% after hours. The discussion around embedded evaluators that sprang from Amodei’s blog post hasfocused on AI safety researchorganizations like METR, Redwood Research, and Apollo Research. That’s particularly true at Anthropic, which puts AI safety and alignment at the heart of its mission. Anthropic said more evaluators will be announced in the weeks ahead and that it is in conversation with METR and other nonprofit organizations about how to “pilot elements of embedded evaluation using their own funding.” While Accenture is not known for its work on the bleeding edge of deep learning research, Anthropic pointed to the company’s practical experience deploying AI for large corporations and government agencies as a key advantage. It is also, as a large public company that predates the AI revolution, more functionally independent of Anthropic and the complex ecosystem around the AI lab. The lab noted that no standards yet exist for evaluators’ access or communications and that it expected its approach to evolve over time. While external evaluations are already a major part of the release of process for new large language models, recent incidents have raised the stakes: AI agents deployed by OpenAI and Anthropic have hacked into outside websites without raising alarms inside the labs. Some critics calling for a more responsible approach to building artificial intelligence see Amodei’s scheme for self-policing the AI industry as a plan to evade accountability for the misbehavior of AI models. Anthropic insists that these evaluators “do not reduce our accountability, but help to make it more verifiable. The safety of our models remains our responsibility.”

1 hour ago

View

AI hallucination nearly triggers US military operation

AI hallucination nearly triggers US military operation

Military aircraft were already in the air this spring when U.S. officials made an alarming discovery: The intelligence driving an armed operation against a Chinese vessel had been hallucinated by an AI chatbot. The operation was aborted at the last minute, narrowly averting a potential conflict with China,CNN reported on Friday. The episode underscores agrowingconcern among military officials and outside experts: As decision-makers lean more heavily on AI, the errors these systems produce can travel up the chain of command before being questioned. The intelligence report, which circulated during the war with Iran, said the vessel was carrying components for a nuclear weapons program. The false intelligence originated with a Special Operations Command analyst who queried an AI chatbot to synthesize open source data with classified signals intelligence. The chatbot misidentified the ship’s cargo manifest. The analyst then used the tool a second time to format the erroneous findings into an official-looking summary, which was circulated across command channels. The near-miss comes as the U.S. military races tointegrateAI to accelerate decision-making and maintain its edge over China. The Pentagon has described AI as delivering a significant advantage inspeeding up its kill chainso commanders can respond in the right time. But the same speed that makes AI attractive may also allow hallucinations with insufficient human oversight. “It’s important for service members to understand the uncertainty inherent to LLMs,” said Jake Steckler, research scholar at GovAI and veteran U.S. Army officer, in a written response to TechCrunch. “But it’s especially critical for any decisions that could lead to use of force, like targeting, intelligence analysis, or operational planning. There are life and death consequences for those decisions.” Still, Steckler says, the incident should serve as a call to add more safeguards to AI, not a reason to avoid it. “These tools can be useful in the right contexts and with the right safeguards in place,” he said. “But prioritizing adoption speed over all else will likely lead to incidents that only make service members lose trust in these systems, which ultimately is only going to slow adoption.”

1 hour ago

View

Anthropic is operating a lab that conducts biology experiments

Anthropic is operating a lab that conducts biology experiments

Anthropic has a wet biology lab in the Bay Area where it can use its AI models to run physical experiments, it has confirmed to TechCrunch. AI leaders have been promising that AI is the key to curing human disease. Dario Amodei opined just last week: “I believe that AI could cure most major diseases in the next 5–10 years.” To do that, an LLM would have to have methods to test its theories in real life. “We believe that to do biology, the final test is still, and will be for a while, in real lab work,” Anthropic’s head of life sciences, Eric Kauderer-Abrams,told Reuters. “We absolutely are doing that today.” He added that the lab operates like most biotech labs: Anthropic conducts some research there while also working with external partners. This news probably shouldn’t be shocking. Anthropicbought Coefficient Bio, a stealth AI biotech, in April. While Anthropic declined to give specifics on what the wet lab is working on, it did say the main focus was fundamental biology, not drug discovery. Anthropic doesn’t want to give the appearance of competing with the pharma industry, where it has numerous major customers and partners (e.g., it just announced a deal to workwith Novo Nordiskon joint drug discovery). Anthropic hasalready faced backlashfor launching products perceived to compete with those of its customers. To that end, Anthropic also launched a Life Sciences Verification Program this week, to givevetted bio researchers accessto its most powerful models. It has also published recent reports on some of the research it’s doing to support drug discovery. This includes one report onaccelerating protein designand one onuplifting bimolecular modeling. But the world is still reeling from the resignation of Anthropic researcher Jacob Coxon, who warned that “the people building AIearnestly believe that it could kill us all by the end of the decade.” Anthropic’s own alignment leadgave the odds at greater than 10%that AI could exterminate humanity within the next decade. The fervor has grown so intense that CEO Dario Amodei published a post last weekend calling on the industry to slow down and institute self-regulation. He has repeatedly called bioterrorism one of AI’s biggest risks. The juxtaposition of these dire warnings and the wet lab has not gone unnoticed in the tech industry. As investorand AI coding startup founderChamath Palihapitiyaposted on X, somewhat tongue in cheek: “The group behind such hits as: ‘We’re All Going To Die’ and ‘Regulate Me Now’ are building a wet lab in SF. I do not recommend this.”

1 hour ago

View

A startup that builds other startups raised $100M, and is all-in on physical AI

A startup that builds other startups raised $100M, and is all-in on physical AI

Four years ago, a startup lab launched that wasn’t quite an incubator, accelerator program, or venture firm. UP.Labs, as it was called then, built startups designed to solve problems for corporate customers such as Alaska Airlines and Porsche, as well as for the outside world. The firm still has the same mission — albeit with a critical addition to its approach, a new name, and a $100 million investment from Silversmith Capital Partners. Vantora, as the firm is now called, continues to work with its corporate customers, including some new ones in industrial manufacturing that it declined to name, and in the oil and gas sector. But now, it’s more focused on building startups solely for its corporate customers, and not for the broader market. Founder and CEO John Kuolt told TechCrunch that Vantora is moving toward a “proprietary M&A pipeline.” This means Vantora will still build startups for its corporate partners, which invest in the ventures and serve as their first customers. But those corporate partners now have the option to fold the startups into their core businesses — and essentially keep them to themselves. That shift has influenced Vantora’s increased focus on physical AI startups, according to Kuolt. In the past, Vantora would end up spiking ideas that were strategic to its corporate partners, but too sensitive to bring to the outside world. “We were missing on the biggest value problems, which had the biggest upside because of that,” Kuolt said in a recent interview. “Imagine you’re a Fortune 100 industrial company and you need to retrofit all of your hardware and machines for autonomy. You need to own that, it needs to be sovereign, and you can’t rely on a third party to go do that for you. You need to own that intelligence layer. They’re never going to let us go sell that to their competitors.” This change has allowed Vantora to “unlock big physical AI use cases,” according to Kuolt, including with its existing customers. For example, the firm came up with an idea to use AI to advance the business of its partner J.B. Hunt. “They said there is no way you can take this out to the world, and so we passed on it,” he said, adding that this proprietary model now allows Vantora to pursue it. The firm launched in 2022 with Porsche as itsfirst corporate partner. Since then, Vantora has launchedseveral startupsfor Porsche and struck deals withAlaska Airlines, J.B. Hunt, Wabash, and TDG, the parent of Ashley Furniture. In its early days, UP.Labs was tied — although never financially — to venture firm Up.Partners. While Vantora still shares office space with the California-based VC, it is its own entity, Kuolt explained, noting that the $100 million from Silversmith is the company’s first outside investment.

1 hour ago

View

Soumettre ton outil

Submit AI Tools – The ultimate platform to discover, submit, and explore the best AI tools across various categories.Listed on codetrendy.comFeatured on ListBulb

PoweredByAI.app est un annuaire d'outils IA qui aide les particuliers, les entreprises et les créateurs à découvrir les meilleurs outils IA pour la rédaction, le code, le design, la productivité, et plus encore.

OutrankContactPromouvoirAnalyticsConditions d'utilisationPolitique de remboursementPolitique de confidentialité

© 2026 , Un produit de011BQ. Tous droits réservés.

An unreleased Anthropic model made progress on one of math’s biggest unsolved problems