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The arrival of AGI | Shane Legg (co-founder of DeepMind)

The arrival of AGI | Shane Legg (co-founder of DeepMind)

Shane Legg, Chief AGI Scientist at Google DeepMind, outlines his framework for AGI, predicting 'minimal AGI' within years and 'full AGI' within a decade. He details a path to more reliable systems and introduces 'System 2 Safety' for building ethical AI. Legg issues an urgent call for society to prepare for the massive economic and structural transformations that advanced AI will inevitably bring.

The arrival of AGI | Shane Legg (co-founder of DeepMind)

The arrival of AGI | Shane Legg (co-founder of DeepMind)

Shane Legg, Chief AGI Scientist at Google DeepMind, outlines his framework for AGI levels, predicts a 50% chance of minimal AGI by 2028, and discusses the profound societal and economic transformations that will follow.

Google DeepMind Lead Researchers on Genie 3 & the Future of World-Building

Google DeepMind Lead Researchers on Genie 3 & the Future of World-Building

Google DeepMind researchers Jack Parker-Holder and Shlomi Fruchter detail the creation of Genie 3, a model that generates interactive, persistent worlds from text in real time. They cover its breakthrough spatial memory, emergent physical intuition, and its potential to revolutionize gaming, robotics, and AI agent training.

Gpt-oss, Genie 3, Personal Superintelligence and Claude pricing

Gpt-oss, Genie 3, Personal Superintelligence and Claude pricing

The panel discusses OpenAI's strategic release of open-weight models (`gpt-oss`), the implications of Google DeepMind's immersive 3D world generator (`Genie 3`), the economic realities behind Anthropic's `Claude Code` rate-limiting, and the competing visions of "Personal Superintelligence" from major players like Meta, OpenAI, and Anthropic.

DeepMind's Secret AI Project That Will Change Everything [EXCLUSIVE]

DeepMind's Secret AI Project That Will Change Everything [EXCLUSIVE]

Google DeepMind's Genie 3 is a new generative interactive environment that creates photorealistic, controllable 3D worlds from text prompts in real-time. This summary explores its architecture, the concept of emergent consistency, and its primary application as a powerful simulator for training embodied AI agents.