#Scaling-Laws
3 notas · última:
Empiricists vs. Extrapolators
The essay argues that AI progress is better understood by extrapolating from scaling laws and first-principles models than by waiting for more data, because empirical caution often underestimates exponential change.
Raphaël Millière on the Limits of Deep Learning and AI x-risk Skepticism
Raphaël Millière argues that current scaling results do not justify claims about human general intelligence, and that today’s models still fall short of broad generalization and human-level compositionality.
How o3 and Grok 4 Accidentally Vindicated Neurosymbolic AI
The essay argues that pure scaling has hit diminishing returns, while LLMs improve when paired with symbolic tools. It presents this as a belated vindication of neurosymbolic AI.