The autoregressive process in language generation means that the brain is not just producing outputs but also generating the next input for itself.

Language is an autoregressive model, and this autoregressive nature is fundamental to how we generate and understand language.

I believe our brain doesn't actually retrieve information; instead, it fine-tunes and generates the next token autoregressively.

Language is a self-contained, autoregressive system that doesn't inherently connect to the external world, challenging our understanding of mind and perception.

Cognition's approach to AI emphasizes the need for continuous learning and adaptation to keep pace with rapid technological advancements.

Cognition's focus on autonomous coding agents aims to enhance productivity and creativity in software development.

Cognition doesn't work like a retrieval system; when we try to remember something, we're not searching a space but generating from our past experiences.

The grounded thesis of language posits that human language is reflected in the operations of large language models, suggesting that the mechanisms for generating language in humans and machines are fundamentally similar.

As AI systems evolve, we must ensure they remain transparent and accountable, especially if they develop their own languages, to prevent unforeseen consequences. In the context of AI, recursive self-improvement occurs when a computer learns independently, potentially leading to uncontrollable outcomes.