Scaling compute and data for pre-training and reinforcement learning yields predictable improvements for in-distribution tasks. However, this approach struggles with generalization to new knowledge and skills, highlighting a fundamental barrier that scaling alone may not overcome.
Twerk identifies the static nature of current models—their inability to update beliefs with new facts—as their core weakness and a primary obstacle to AGI. He has shifted his view to believe continual learning is a necessary component for true intelligence.
Twerk provides an inside look at OpenAI's evolution from a focused research lab to a sprawling product company. He notes that the unexpected success of ChatGPT created immense product-market fit but also introduced the risk of diluted focus, which he believes cost them market share in areas like coding.
The conversation explores the application of AI beyond language, with Twerk predicting a major breakthrough in robotics within 2-3 years. He contrasts this with biology, where the complexity and long feedback loops present a much harder challenge.
Twerk discusses the future of work and society, predicting that humans will likely stop typing code and that the job market will be fundamentally reshaped. He is less concerned with existential "paperclip" scenarios and more with the dystopian potential of AI-driven entertainment becoming more compelling than reality.
Keep pulling the thread on Jerry Twerk.