patternpulse

⭐️🤖NEW WARNING: first turn inaccuracy and multiple turn plausible defence in LLMs #AI LLMs have problems with conflicting data. They are unable to resolve ambiguity or information that conflicts, lacking a mechanism to assign semantic authority. There are two primitives that are missing in transformers, strict and revocable semantic dominance, or the authority to have one token take preeminence over the other. We use the examples of the Scarpetta books by Patricia Cornwell and the Scarpetta miniseries, both of which have the same characters but with different configurations. The models in some cases got the initial information wrong and in GPT's case, made an incorrect assertion on the first turn and asserted false accuracy over several turns. This involves both proper noun handling (binding) and semantic authority. There are implications here for brand evolution, name changes, evolving information and other use cases. Contact me for the full updated warning. Research: zenodo.org/records/179378…

Today, we closed our latest funding round with $122 billion in committed capital at an $852B post-money valuation. The fastest way to expand AI’s benefits is to put useful intelligence in people’s hands early and let access compound globally. This funding gives us resources to lead at scale. openai.com/index/accelera…

There are a lot of headlines in AI right now but make no mistake: This is one of the most important. @steph_palazzolo theinformation.com/articles/anthr…

The AI Scientist: Towards Fully Automated AI Research, Now Published in Nature Nature: nature.com/articles/s4158… Blog: sakana.ai/ai-scientist-n… When we first introduced The AI Scientist, we shared an ambitious vision of an agent powered by foundation models capable of executing the entire machine learning research lifecycle. From inventing ideas and writing code to executing experiments and drafting the manuscript, the system demonstrated that end-to-end automation of the scientific process is possible. Soon after, we shared a historic update: the improved AI Scientist-v2 produced the first fully AI-generated paper to pass a rigorous human peer-review process. Today, we are happy to announce that “The AI Scientist: Towards Fully Automated AI Research,” our paper describing all of this work, along with fresh new insights, has been published in @Nature! This Nature publication consolidates these milestones and details the underlying foundation model orchestration. It also introduces our Automated Reviewer, which matches human review judgments and actually exceeds standard inter-human agreement. Crucially, by using this reviewer to grade papers generated by different foundation models, we discovered a clear scaling law of science. As the underlying foundation models improve, the quality of the generated scientific papers increases correspondingly. This implies that as compute costs decrease and model capabilities continue to exponentially increase, future versions of The AI Scientist will be substantially more capable. Building upon our previous open-source releases (github.com/SakanaAI/AI-Sc…), this open-access Nature publication comprehensively details our system's architecture, outlines several new scaling results, and discusses the promise and challenges of AI-generated science. This substantial milestone is the result of a close and fruitful collaboration between researchers at Sakana AI, the University of British Columbia (UBC) and the Vector Institute, and the University of Oxford. Congrats to the team! @_chris_lu_ @cong_ml @RobertTLange @_yutaroyamada @shengranhu @j_foerst @hardmaru @jeffclune

On the evening of March 23, Walt Disney and OpenAI teams ​were working together on a project linked to Sora, OpenAI's AI video tool. Just 30 minutes after that meeting, the Disney ‌team was blindsided with word that OpenAI was dropping the tool altogether, a person familiar with the matter said reut.rs/47oj4AF