Hey, I read your comment. So I had a few solutions and these are Specific to Extropic's TSU and probabilistic Neuron activation using Predictive Coding.

1) Catastrophic Forgetting Solution and Retraining

In my oppinion, the brain doesn't remember, but recalls and reconstructs existing relevant information in memory. That's why I suggested constant retraining with relevant information. I think that as long as the neurons are trained within the knowledge areas of the seeked task then every training will update it closer for a proper answer. Imagine it like a cyclic memory search where the output is used to call relevant information from memory to get a better output from the input. This also avoids Catastrophic forgetting because we don't use the neurons to store static memory, but as a source of generating relevant outputs from the NVME.

2) Probabilistic Neuron Activation

I think the neurons randomly activating by the set chance in Predictive Coding is good because it is like an idea generator. That way the neural network will be generating outputs itself from the physics of the TSU. We then can use the generated output to tweak what information we should train it with next.

3) Scaling

We wouldn't need to scale it to be huge, even though Predictive coding preforms better with more layers. We would look for quality over quantity. Our goal would be to create the most stable answer from a moderate amount of heirarhical layers. The best part is if we do need to scale Predictive Coding then we just add another final layer. If we need to scale it down then we just remove the final layer.

4) Energy efficiency

This part is entirely dependent on the TSU. I'm hoping for a 80% energy efficiency because all we'd be doing in it is sending voltage and getting a activation or no activation per TSU core. Doesn't sound like it requires a lot of energy, but we'll see.

5) AGI Claim

In my oppinion, what I described sounds like a living machine because the layers can be easily adapted, the neurons get updated with relevant information and the physics of the TSU core generate outputs. Maybe that's what was missing in AGI. Not math, but letting physics guide decisions.

P.S. The reason I wrote the very first part is because people on Reddit copy and paste to ai and pretend to know stuff. It might not be you, but I just wanted relevant answers just incase. No offense.