ai
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Yann LeCun’s cake on the RL, Supervised & Self-Supervised Learning
I come to the same realization: in the context of AI, what I’m doing is a waste of time. It’s horrifying. The fun has been sucked out of the process of creation because nothing I make organically can compete with what AI already produces—or soon will.
Dustin Curtis on Svbtle • Thoughts on Thinking
on condition that all you do is interpolations in the latent space of human knowledge, and AI not only will be able to, but will - make such interporlations.
I believe an Einstein-level breakthrough in Go would involve something more fundamental, like inventing the rules of Go itself—exploring the vast landscape of possible game rules to create something genuinely more compelling than existing games.
Thomas Wolf • 🔭 The Einstein AI model
think: the glass bead game, inveting the rules of the game of the human culture itself; move 37 in go is not an Einstein level breakthrough
In that compute-constrained world, deployment and development trade off directly. That starts as a lab-internal effect, where deployment and product improvements eat into (compute) resources otherwise earmarked for development.
hard cap on what can be done & recursive self improvement not necessarily happening this decade
Ilya Sutskever discussed the boundary between understanding and awareness, as what comes next, in his latest test of time talk at NeurIPS 2024. To understand is to predict things accurately. To be self-aware is to be able to predict accurately with an understanding of what it is and what its environment is.
Nathan Lambert • The rise of reasoning machines
czym jest świadomość?
LeCun points to four essential characteristics of human intelligence that current AI systems, including LLMs, can’t replicate: reasoning, planning, persistent memory, and understanding the physical world. He stresses that LLMs’ reliance on textual data severely limits their understanding of reality: “We’re easily fooled into thinking they are intel... See more
Azeem Azhar • 🧠 AI’s $100bn question: The scaling ceiling
To create an Einstein in a data center, we don't just need a system that knows all the answers, but rather one that can ask questions nobody else has thought of or dared to ask. One that writes 'What if everyone is wrong about this?' when all textbooks, experts, and common knowledge suggest otherwise.
Thomas Wolf • 🔭 The Einstein AI model
czy ja potrafię tak zadawać pytania? raczej nie. albo momentami, w pojedynczych sytuacjach, jak np. u filomatów na interview
This all goes back to Ilya’s provocation for why next-token prediction is enough on the Dwarkesh Podcast:
Predicting the next token well means that you understand the underlying reality that led to the creation of that token.
The rise of reasoning machines
