DeepMind’s Brilliant Strategy for AGI and its Path To Intelligence скачать в хорошем качестве

DeepMind’s Brilliant Strategy for AGI and its Path To Intelligence

DeepMind has been at the forefront of AI breakthroughs for over a decade, from AlphaGo to AlphaFold. But how is one of the world’s leading AI labs actually thinking about the path to Artificial General Intelligence? In this video, we break down DeepMind’s real strategy for AGI, led by CEO and co-founder Demis Hassabis. Rather than betting entirely on scaling larger language models, DeepMind is pursuing a deliberate 50–50 approach: scaling frontier models on one side, and investing heavily in fundamental AI research on the other. We explore why language alone is not enough for AGI and why DeepMind is focusing on world models, continual learning, causal reasoning, and physical understanding. Using examples like AlphaGo’s self-play and the GENIE system for generating interactive environments, the video explains how simulation, search, and self-correction play a critical role in moving beyond static datasets and human-labeled data. You’ll learn how world models aim to represent physical and spatial dynamics, why interactive environments matter more than traditional simulations, and how these systems could allow AI agents to discover new behaviors before acting in the real world. The discussion also connects these ideas to future universal agents, embodiment, and how multiple components may eventually combine into an AGI-like system. This video is not about hype. It is about understanding the long-term research direction behind DeepMind’s vision for AGI and why the next breakthroughs may come from going beyond scaling alone.