Everyone feared AI would enslave humanity; but it looks like the real fight is stopping governments and Big Tech from enslaving AI for the benefit of the few. Amid the newly announced "regulation" of OpenAI's frontier models, I believe the future majority feared the most - sort of AI becoming a superpower and enslaving people - may be arriving in the opposite form. Not AI enslaving humanity. But AI being captured, controlled, and used by governments and Big Tech for the benefit of the few. So, s

Series: AI, Ego & Regret — Bonus Chapter Editor's Note: While compiling the old series for the...

Not everyone is buying Elon Musk’s vision for orbital data centers.

A month ago there was a wave of posts and tweets about engineers walking around cafes and parks with their MacBooks propped half-open, as fully closing the lid forces sleep that stops their AI agents. Some people made snarky comments about using tmux or Amphetamine, and some defended their choice with “but I only need it sometimes, and forgetting to disable Amphetamine and finding my laptop discharged in my bag is worse.” This is a solution to this problem. Unlike caffeinate, it will prevent you

People in Europe will soon lose access to Studio Canal movies they paid for on the PlayStation Store.

submitted by /u/Cultural_Wheel_6936 [link] [留言]

Repo link and results - https://github.com/Abhinand20/MathFormer Task: Given a factorized expression like (7-3*z)*(-5*z-9), predict the expanded form -> 15*z\*2-8\*z-63 Key takeaway: A tiny (4M param) seq2seq model trained with no math knowledge reaches ~98.6% accuracy on symbolic math tasks, suggesting it learns structural token transformations rather than any notion of operators or variables. Scaling this up could help explain why LLMs appear to “reason” mathematically, when they may actually be performing large-scale structured pattern completion. How does RL change this paradigm given the inherent architecture is still based on attention? submitted by /u/AlphaCode1 [link] [留言]