Definition
Reinforcement learning
Training an AI through trial and error: it takes actions, gets rewards or penalties, and learns which choices lead to better results.
Learn the language of AI-native founders
Every week: the new terms, tools, and workflows founders are using, straight from the podcast.
Related terms in AI Fundamentals
RAG (Retrieval-Augmented Generation)A technique where a model first looks up relevant information from documents or databases, then uses it to write a more accurate, up-to-date answer.RationalistsAn online community focused on reasoning and AI risk.Reasoning modelA model built to work through hard problems step by step before answering, trading extra time for better accuracy.Red teamingAdversarial testing that attacks your own systems to find vulnerabilities before someone else does.Responsible AIThe principles and practices for developing and deploying AI ethically and safely, including fairness, transparency, and accountability.RWKVAn attention-free, linear architecture Eugene co-created, now under the Linux Foundation, designed to lower inference cost.Scaling lawsPredictable relationships showing how model performance improves as you increase model size, training data, and compute.Searchable MemoryThe idea that your recorded thoughts, decisions, and emotional context can later be queried like a database.