AI Model Thinks Without Tokens

In the YouTube video titled “New AI Model ‘Thinks’ Without Using a Single Token”, Matthew Berman discusses a groundbreaking research paper that explores how large language models can perform internal reasoning in latent space before generating any output tokens. Berman contrasts this new approach with traditional Chain of Thought models, which rely on token output for reasoning. He highlights insights from Yan LeCun, who argues that current language models lack true reasoning capabilities. The video explains the architecture of the new model, its benefits, and how it allows for more efficient reasoning without the need for extensive training data. Berman emphasizes the potential of this model to enhance AI’s reasoning and planning abilities, suggesting it could be a step toward achieving true artificial general intelligence.