“I believe advances in LLM alignment research reduce x-risk even if future AIs are different.”

“The general idea is to view agent action and perception as part of the same discrete data stream, and model intelligence as compression of sub-segments of this stream into independent "mechanisms" (patterns of action-perception) which can be used for prediction/action and potentially recombined into more general frameworks as the agent learns.”

“The goal is to ensure a consistent daily flow, converting minimal outputs into a stockpile.”

“The article's focus is on helping users overcome a common hurdle.”

“"Well Sam says the poors (free tier) will be shoved with contextual adds"”

“What if the key to building truly intelligent machines isn't bigger models, but smarter ones?”

“Reinforcement Networks unify hierarchical, modular, and graph-structured views of MARL, opening a principled path toward designing and training complex multi-agent systems.”

“The article likely discusses visual faithfulness within the context of 'slow thinking' in AI.”

“The paper focuses on aligning LLMs with student reasoning.”

“The paper explores how basic arithmetic operations can be leveraged to improve LLM performance.”

“Instead of mimicking other people’s successful trajectories, you should take your own actions and learn from the reward given by the environment.”

“Nam is a neural network emulator for guitar amplifiers.”

“By getting into machine or deep learning I mean building upto a stage to do ML/DL research. Applied research or core theory of ML/DL research. Ofcourse, the path to both will quite different.”

“SATNet bridges deep learning and logical reasoning with differentiable SAT.”