Anthropic is using AI to build more powerful AI. And it appears to be working remarkably well, with the company's models improving roughly twice as quickly as a year ago, with little indication of slowing down. That's according to a new report published Thursday by the AI safety research organization METR, following an 11-week investigation […]
OpenAI introduces BioBasicsBench, a 100-task benchmark measuring how well AI agents interpret raw experimental data and handle everyday wet-lab science.
Article URL: https://github.com/smokeeaasd/flawless-ui-ux Comments URL: https://news.ycombinator.com/item?id=50381846 Points: 1 # Comments: 0
Article URL: https://github.com/SimakovIgor/pixel-buddy Comments URL: https://news.ycombinator.com/item?id=50389351 Points: 2 # Comments: 0
arXiv:2611.07088v1 Announce Type: new Abstract: Symbolic models offer a powerful account of cognition, but their scope has historically been limited by the need for hand-engineering and computational tractability. We show how advances in artificial intelligence (AI) can scale symbolic modeling and help overcome these engineering limitations. We introduce Behavior-Conditioned Symbolic Synthesis (BCSS)---an approach for automated search of interpretable, deterministic symbolic programs that verifiably reproduce behavior---and apply it to model both human and machine behavior across hundreds of challenging reasoning problems. Analyzing these programs reveals that both people and machines tend to oversimplify, adopt rules that are partial explanations for their observations, and misinfer dependencies between problem features. These shared signatures are modulated by available resources, with reasoning effort predicting the extent to which machines simplify and explain their observations. Together, these results point to underlying mechanisms reminiscent of satisficing in both people and machines, and illustrate how BCSS offers a general approach for interpreting and comparing rule-based reasoning of humans and black-box AI systems.
Token Pass クレジットは、認証方法にかかわらず、AI Gateway を介したモデル使用時に自動的に適用されるようになりました。Token Pass クレジットがなくなった場合はチームクレジットにフォールバックします。リクエストは引き続きゼロデータ保持ポリシーの対象であり、フォールバックアクセスには標準キーまたは OIDC トークンの使用を推奨します。
The enterprise and organization repository-level Copilot usage metrics reports now show what happens to Copilot code review feedback. A new copilot_code_review object on each repos-1-day row reports how many Copilot-reviewed… The post Usage metrics API adds code review details and addressed rate appeared first on The GitHub Blog.
The Copilot usage metrics API now shows which users are missing client telemetry or are running IDE or extension versions below the usage metrics minimums. New per-user fields appear in… The post Usage metrics API flags telemetry and version gaps appeared first on The GitHub Blog.
Data Mining & Modeling
Certinia Inc. is bringing AI into the software firms use to sell projects, assign staff, deliver work and bill clients. AI’s ability to instantaneously draw from a deep well of information and provide guidance in practically any field of expertise has changed the paradigm for professional services. For Certinia Inc., whose business facilitates 7.1 billion […] The post Certinia brings AI into professional services workflows appeared first on SiliconANGLE.
