Through the magic of "coming soon!" all things are possible
* Paper - coming soon.
* Github repository - coming soon.
* Details about how it works - coming soon.
* Hype - here now.
This is so retro. I went through Stanford CS in the 1980s, just as the "expert system" boom was collapsing, and the "AI winter" was beginning. This is very close to the hype of that era. Many of the Stanford faculty really believed that artificial general intelligence via expert systems was close. If you could just hammer the real world into predicate calculus...
It might be worth revisiting, though. Embedding-based AI may be reaching its limits. If you need to plan or subgoal or compute, it's not quite the right tool for the job. Some other representation may be needed.
(Consider EMACS "org mode". It probably isn't that hard to get an LLM to translate a question into "org mode" form. Then you need a strategy module to decide which subtasks to work on, notice when progress is being made and when it isn't, work on the problem as a tree of subgoals, and combine into a result.)
We have probably reached the limit of scraping publicly available English training data from the Internet. OpenAI is betting on synthetic data. Let's see where that takes them. The sad part is companies like OpenAI only makes money if the answer to AGI is large models and large data. I don't know if they have unknowingly restricted their ideas on AGI based on that
I'm too dense to grasp the site's mathematical propositions (I don't have the mathematical grounding to understand Borel algebra not to speak of Borel Hierarchies). So it was no wonder I had a lot of trouble discerning how what it proposes was materially different from expert systems, inductive logic systems, and Cyc. The associative memory layer seems to be picking up some lessons learned from Generative AI, but I'm not clear on that. It will be interesting to keep an eye on this project and where it takes us, and see if it makes more sense when someone comes along and dumbs it down for Blubs like me.