<span class="vcard">/u/44th--Hokage</span>
/u/44th--Hokage

Neuralink Co-Founder Max Hodak: The Future Of Brain-Computer Interfaces | Y Combinator Podcast

Synopsis: YC alum Max Hodak is the co-founder of Neuralink and founder of Science, a company building brain-computer interfaces that can restore sight. Science has developed a tiny retinal implant that stimulates cells in the eye to help blind pa…

Why AlphaEvolve Is Already Obsolete: When AI Discovers The Next Transformer | Machine Learning Street Talk Podcast

Robert Lange, founding researcher at Sakana AI, joins Tim to discuss Shinka Evolve — a framework that combines LLMs with evolutionary algorithms to do open-ended program search. The core claim: systems like AlphaEvolve can optimize solutions to f…

Tencent Announces ‘HY-World 1.5’: An Open-Source Fully Playable, Real-Time AI World Generator (24 Fps) | "HY-World 1.5 has open-sourced a comprehensive training framework for real-time world models, covering the entire pipeline and all stages, including data, training, and inference deployment."

HY-World 1.5 has open-sourced a comprehensive training framework for real-time world models, covering the entire pipeline and all stages, including data, training, and inference deployment. Tl;DR: HY-World 1.5 is an AI system that generates inte…

DeepMind: Demis Hassabis On ‘The Future Of Intelligence’ | Google DeepMind Podcast

Synopsis: In our final episode of the season, Professor Hannah Fry sits down with Google DeepMind Co-founder and CEO Demis Hassabis for their annual check-in. Together, they look beyond the product launches to the scientific and technological qu…

OpenAI & Apollo Research Are On The Road To Solving Alignment | Introducing: ‘Stress Testing Deliberative Alignment for Anti-Scheming Training’ | "We developed a training technique that teaches AI models to not engage in ‘scheming’ — secretly pursuing undesirable goals — and studied it rigorously."

Anti Scheming Definition: We suggest that any training intervention that targets scheming should: 1. Generalize far out of distribution 2. Be robust to evaluation awareness (models realizing when they are and are not being evaluated) 3. Be robust…