How AGIs Will Differ from (Simple) AI - and Impact Our Lives

AGIs (artificial general intelligence) will be different from existing AIs in that they will be more like "persons," whereas AIs are best characterized as "tools" (even though they can sometimes act as persons). As we move towards AGI emergence, we each need to think through what we can offer that is distinct from an AI "tool" that we can use, and an AGI "person" that can support our work. One key feature of an AGI architecture will be a Markov blanket, separating the observed external world from the internally-constructed AGI representation of that world, and the model that the AGI creates of its representation.

THIS BLOGPOST gives you details regarding papers mentioned in the YouTube, etc.:
https://themesis.com/2025/09/12/how-t...

THIS is a link to my 60++ page tutorial on Karl Friston's most basic, starting equation for active inference:
https://arxiv.org/abs/1906.08804

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. This course gives 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. (Information will be opened up on this course shortly.)

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/

Here's the link to the Themesis Muse offerings:
https://themesis.com/muse/

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