Amulya Vishwanath, who spent seven years in product marketing for Nvidia's robotics platform, founded early-stage firm Techable VC, focusing on physical AI in robotics, materials science and life sciences.
Amulya Vishwanath, who spent seven years in robot platform product marketing at Nvidia, founded early-stage firm Techable VC to invest in physical AI. She says robotics is in its "Lego era," with building blocks like actuators, grippers, software and safety standards still being rebuilt, rather than nearing a "ChatGPT moment." She also defines physical AI as broader than robots, covering molecules, materials, drugs and machines on factory lines.
Latent Space hosted Dyna Robotics co-founder and chief scientist Jason Ma for a deep dive into the Dyna-2 world action model, pretrained on over 1 million hours of human video, combining a diffusion transformer with flow matching to predict future video and generate robot actions.
In a piece produced with Aventine, MIT Technology Review reports that despite optimism about humanoid robots from Musk, Jensen Huang, and others, robotics researchers broadly believe current AI methods can't support general-purpose robots.
Import AI issue 474 covers three items: Michael Levin has published a paper proposing that the relationship between mind and brain is like that between mathematical patterns and physical outcomes—the mind is a non-physical pattern from "Platonic space" and the body is merely its interface into the physical world, citing experiments with xenobots, anthrobots, and more as evidence. The issue also discusses how robotics is preparing for an LLM-like moment but still lacks a general-purpose algorithm, and Zhipu AI's progress in starting an outer RSI (recursive self-improvement) loop.
TrendForce released the first installment of its humanoid robot industry analysis series, noting that the US is betting on AI agent ecosystems while China is leveraging its complete supply chain to accelerate scaled deployment, such as AgiBot growing output from 1,000 units to 5,000 units in about a year, then reaching 10,000 units in three more months.
NVIDIA launched ENPIRE, letting frontend coding agents drive real robots to continuously self-improve policies through a closed loop of automatic evaluation and automatic reset, reaching 99% success on dexterous manipulation tasks like PushT and pin tidying, with GPT-5.5 (Codex) and Opus 4.7 (Claude Code) performing best. The issue also covers China's 10,000-GPU cluster and an essay noting humans have historically struggled to predict technology's impact.
King's College London, Fudan University, and The Alan Turing Institute released the SocioHack benchmark, containing 72 sandboxed social environments that test LLMs' ability to exploit loopholes in real rule systems after RL training; RL let LLMs rediscover historically patched loopholes with 61.25% recall and 90.85% precision.