Intro topic: Running
News/Links:
* Flow matching versus diffusion models + https://youtu.be/firXjwZ_6KI?is=QMq8DcCsXTTktOuE * OpenCV 5 + https://opencv.org/opencv-5/ * Claude fable beats pokemon with no harness + https://youtu.be/Ty_50J84fMY?si=EJ1KjZCZegipfCsV * Can the stockmarket swallow Anthropic, SpaceX and OpenAI? + https://archive.ph/nKEVw

Book of the Show* Patrick + Strength of the Few - James Islington - https://amzn.to/4pmr10T * Jason + Descender - Jeff Lemire - https://amzn.to/3QKhp3l

Patreon Plug https://www.patreon.com/programmingthrowdown?ty=hTool of the Show* Patrick + No Man’s Sky * Jason + Paperlib https://paperlib.app/en/

Topic: World Models
* Making decisions with AI + Action-Value (called a Q model): What is the long-term value of making a decision at a position + Policy: What action should I take (must be a distribution) + Value (called a V model): What is the value of a position (depends on policy) + Advantage/Disadvantage: difference in value given two policies + When advantage is +, do that more. * Model-Free + Look at the current situation and suggest an action + Run that action in the real world and measure the effect + Use that measurement to suggest better actions next time * Model-Based + Observe rollouts (sequences of situations) and learn the dynamics + Choose an action, use your dynamics model to measure the consequence + Potentially do MPC (try many actions and choose the best) * World Models + Observe many many rollouts and learn a full forward model (how to create the input in the future) + Train a policy & value inside the world model + Deploy the policy and fine-tune based on the real world