On Distinguishing Capability Elicitation from Capability Creation in Post-Training: A Free-Energy Perspective
This paper explores the distinction between eliciting existing AI capabilities and creating new ones, viewed through the lens of a free-energy perspective. It is a theoretical work examining foundational aspects of advanced AI development rather than reporting on specific empirical results or practical tools.
A new theoretical paper, "On Distinguishing Capability Elicitation from Capability Creation in Post-Training: A Free-Energy Perspective," delves into the fundamental differences between uncovering latent AI capabilities and engineering entirely new ones after initial training.
Authored by Yuhao Li and another researcher, the paper offers a free-energy perspective on this critical distinction in AI development. This work is primarily theoretical, focusing on foundational concepts rather than empirical results or practical applications.
The research, submitted to arXiv, falls under the category of Artificial Intelligence (cs.AI). It provides a framework for understanding how AI systems acquire and demonstrate advanced functionalities.
Related articles
The AI world is getting ‘loopy’
AI models are taking a significant leap forward with the adoption of "agentic loops," where AI agents continuously prompt each other to improve code and solve complex problems. This approach, though potentially resource-intensive, promises to unlock new levels of autonomous problem-solving and efficiency in AI applications.
Codex-maxxing for long-running work
Codex is increasingly being used by organizations to support long-running projects that go beyond a single prompt. This whitepaper by Jason Liu offers practical strategies for leveraging Codex as a persistent workspace, managing complex workflows and sustaining progress.
Nobel laureate John Jumper is leaving DeepMind for rival Anthropic
Nobel laureate John Jumper is departing Google DeepMind to join its competitor, Anthropic, after dedicating nearly nine years to DeepMind, where he led the AlphaFold team. Jumper, who shared a Nobel Prize for his work on AlphaFold, expressed gratitude for his time at DeepMind while looking forward to new endeavors.
