Test time adaptation

Building the Automated AGI Lab: Core Automation's Jerry Tworek and Rohan Anil

Building the Automated AGI Lab: Core Automation's Jerry Tworek and Rohan Anil

Jerry Tworek and Rohan Anil, founders of Core Automation, argue that the transformer architecture has reached its limits and the primary bottleneck to smarter AI systems is now architectural. They contend that current models lack continual learning and test-time adaptation capabilities, critical for real-world deployment. They advocate for new architectures that learn from experience more efficiently than current reinforcement learning, optimize pre-training and RL end-to-end, and build a highly automated lab focused on accelerating innovation through kernel generation, aiming for models that can improve themselves without human intervention.

AI Can't Learn The Way Humans Do - This Could Fix That

AI Can't Learn The Way Humans Do - This Could Fix That

This discussion explores world models as a promising path to solving sample efficiency in AI and achieving AGI. It contrasts deterministic control (Newtonian physics) with stochastic environments (RL), explaining the challenges posed by large action spaces in Go, self-driving, and robotics. The episode delves into how synthetic data, video diffusion models, and latent space architectures like JEPA are making world models practical, while also highlighting remaining open problems in physics modeling, real-time adaptation, and rich sensory integration.