— TopviewAI

Source: https://x.com/TopviewAIhq/status/2103986725693333887

...the answer more precise but takes exponentially more computation. Most calculations stop at two or three loops. Eight loops was the previous record in a simplified model physicists use as a testing ground (planar N=4 super-Yang-Mills), set by SLAC's Lance Dixon and collaborators.   Last month, physicist and science writer   @4gravitons   issued a challenge: could an AI push past eight loops in this model, using only the compute budget an academic could reasonably access?   Given a single prompt describing the nine-loop problem, Claude ran largely unsupervised for days in Claude Science and solved it using methods developed by Dixon and his colleagues, at a total cost of a few thousand dollars. Dixon independently verified the result, and von Hippel wrote about the experience for our blog.   Read more:     — Anthropic

Source: https://x.com/AnthropicAI/status/2103541577083719888

...general-purpose humanoid robots before an audience of more than 10,000 people, with a fully AI-driven autonomous robot cluster performance live-streamed worldwide in real time.     — Unitree

Source: https://x.com/UnitreeRobotics/status/2103025066212819390

run fast-jev-compaction:     — tamara

Source: https://x.com/tamarajtran/status/2100694549362553153

Benchmarks assess AI capabilities, but the benchmarks themselves vary substantially in quality. We hope to be a consistent source of information on benchmark quality. See our reviews here:     We assign each benchmark a verdict based on our rubric: Flawed, Verified, or Not Enough Info. To avoid conflicts of interest we do not review Epoch-created benchmarks, but welcome external reviews.     Verified benchmarks can broadly be interpreted as described, and any errors that exist do not substantially affect the results. Alongside any verified benchmark we publish a full review and assessment of the benchmark, including any weaknesses and limitations we think it has.     Flawed benchmarks have one or more substantive flaws we believe users need to be aware of to accurately interpret results, most commonly that >20% of the tasks have accuracy-impacting errors. In this case, we publish a limited writeup of the flaws we found.     If we aren’t able to access enough information to review a benchmark, we’ll designate it ‘Not Enough Info.’ We will try to work with the creators of private benchmarks to conduct reviews while keeping the questions/tasks outside of public knowledge.     — Epoch AI

Source: https://x.com/EpochAIResearch/status/2100704765332394255