Renormalising Generative Models (RGMs), introduced by Friston and colleagues at Verses.ai in July, 2024, have the potential for dramatically influencing AGI (artificial general intelligence) by providing a scaling method for active inference. Prior to this, we examined Action Perception Divergence (APD) for its potential AGI role. In this vid, we contrast-and-compare RGMs with APD, and suggest resources for learning about both.
The Blogpost for this Vid is at:
https://themesis.com/2024/08/30/contr...
The Themesis three-week short course, "Top Ten Terms in Statistical Mechanics," is designed to give students a gentle-yet-intensive immersion into sufficient statistical mechanics vocabulary so that the student can go on to read papers in generative AI and AGI that were previously inaccessible.
The Themesis three-week short course, "AGI Fundamentals: AI, Generative AI, and AGI" is designed to give students a conceptual (no code, very light math) contrast-and-compare study on how emerging AGI will differ from both generative AI and classic AI, and also identify how active inference is a form of generative AI, extending foundational work in variational inference. Daily readings and reflection exercises; weekly Zoom meetings for scheduled cohorts.
Here's the link to the Themesis Academy, where you can traverse a link to the actual course offerings on themesis.thinkific.com:
https://themesis.com/academy/
Prior to this, we introduced Renormalising Generative Models (RGMs) in THIS YouTube:
"Big AGI Breakthrough! From Active Inference to Renormalising Generative Models"
• Big AGI Breakthrough! From Active Inferenc...
We then did the derivation for Eqn. 3 of the Action Perception Divergence in THIS YouTube:
• AGI: APD (Action Perception Divergence) - ...
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