One of the main things that makes it interesting to me is that around 25-30 mins in, ot computationally goes through the main reason why I don’t think we will have agentic behaviour from AI in at least a couple of years. GPTs just don’t have a high IIT Phi value. How will it find it’s own boundaries? How will it find the underlying causal structures that it is part of? Maybe this can be done through external memory but will that be enough or do we need it in the core stack of the scaling-based training loop?
A side note is that, one of the main things that I didn’t understand about IIT before was how it really is about looking at meta-substrates or “signals” as Douglas Hofstadter would call them are optimally re-organising themselves to be as predictable for themselves in the future. Yet it does and it integrates really well into ActInf (at least to the extent that I currently understand it.)
I thought this was an interesting take on the Boundaries problem in agent foundations from the perspective of IIT. It is on the amazing Michael Levin’s youtube channel: https://www.youtube.com/watch?app=desktop&v=5cXtdZ4blKM
One of the main things that makes it interesting to me is that around 25-30 mins in, ot computationally goes through the main reason why I don’t think we will have agentic behaviour from AI in at least a couple of years. GPTs just don’t have a high IIT Phi value. How will it find it’s own boundaries? How will it find the underlying causal structures that it is part of? Maybe this can be done through external memory but will that be enough or do we need it in the core stack of the scaling-based training loop?
A side note is that, one of the main things that I didn’t understand about IIT before was how it really is about looking at meta-substrates or “signals” as Douglas Hofstadter would call them are optimally re-organising themselves to be as predictable for themselves in the future. Yet it does and it integrates really well into ActInf (at least to the extent that I currently understand it.)