Humanoids Daily
Public MindPattern findings, entities, and graph evidence that cite this source.
Findings
3
All-time hits
3
High value
0
Last seen
2026-05-25
Related findings
- 2026-05-25 / VOICESJim Fan Outlines Robotics 'End Game' — Predicts Machines Pass Physical Turing Test Within 2-3 Years, Self-Improving Robots by 2040NVIDIA's Jim Fan described a 'Great Parallel' where robotics follows the LLM evolutionary path through pre-training, alignment, reasoning, and autonomous research phases. He predicted machines will pass the Physical Turing Test — performing tasks indistinguishable from humans — within 2-3 years, and expects robots to autonomously design next-generation versions of themselves by 2040. Fan also criticized the state of robotics benchmarking as an 'epic disaster' compared to LLM evaluation, calling for unified standards across hardware platforms and task definitions.
- 2026-05-25 / VOICESJim Fan Declares DreamZero the 'GPT-2 Moment' for Robotics — NVIDIA Open-Sources 14B World Action ModelNVIDIA's Jim Fan announced DreamZero, a 14-billion parameter 'World Action Model' that shifts robot intelligence from text-based reasoning to visual imagination — robots 'dream' future pixels and actions simultaneously before executing motor commands. Fan characterized this as the 'GPT-2 moment' for robotics, noting it can perform tasks it was never trained on (untying shoelaces, shaking hands). NVIDIA is open-sourcing weights, code, post-training dataset, eval set, and whitepaper under Apache 2.0, calling 2026 'the year of World Models for physical AI.'
- 2026-05-19 / VOICESJim Fan Outlines Robotics 'End Game' at Sequoia AI Ascent — Four-Stage GPT Playbook for Physical AINVIDIA's Jim Fan presented a 'Great Parallel' framework mapping robotic development onto the GPT playbook: pre-training, alignment, reasoning, and autonomous research. Introduced 'Dream Zero' where robots simulate success in world models before executing physically — achieving +17% real-world success out of the box on fruit packing. The industry is pivoting from Vision-Language-Action models to video-first 'World Action Models' that prioritize physical laws over linguistic understanding. Predicts 'physical auto-research' by 2040.