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If you built a good one, and you knew how to look at the dynamics, you'd find that the agent in the computer was in a "liquid" state. Although it's virtualized, so the liquid is in the virtualization layer.
Ah, yes, this took me a long time to grok. It's subtle and not explained well in most of the literature IMO. Let me take a crack at it.
When you're talking about agents, you're talking about the domain of coupled dynamic systems. This can be modeled as a set of internal states, a set of blanket states divided into active and sensory, and a set of external states (it's worth looking at this diagram to get a visual). When modeling an agent, we model the agent as the combination of all internal states and all blanket states. The active states are how the agent takes action, the sensory states are how the... (read 603 more words →)
Yes, you are very much right. Active Inference / FEP is a description of persistent independent agents. But agents that have humans building and maintaining and supporting them need not be free energy minimizers! I would argue that those human-dependent agents are in fact not really agents at all, I view them as powerful smart-tools. And I completely agree that machine learning optimization tools need not be full independent agents in order to be incredibly powerful and thus manifest incredible potential for danger.
However, the biggest fear about AI x-risk that most people have is a fear about self-improving, self-expanding, self-reproducing AI. And I think that any AI capable of completely independently self-improving... (read more)
So, let me give you the high level intuitive argument first, where each step is hopefully intuitively obvious:
The Active Inference literature on this is very strong, and I think the best and most overlooked part of what it offers. In Active Inference, an agent is first and foremost a persistent boundary. Specifically, it is a persistent Markov Blanket, a idea due to Judea Pearl. https://en.wikipedia.org/wiki/Markov_blanket The short version: a Markov blanket is a statement that a certain amount of state (the interior of the agent) is conditionally independent of a certain other amount of state (the rest of the universe), and that specifically its independence is conditioned on the blanket state that sits in between the exterior and the interior.
You can show that, in order for an agent to... (read 387 more words →)
I found your explanations of Bayesian probability enlightening, and I've tried to read several explanations before. Your recent posts on quantum mechanics, much less so. Unlike the probability posts, I find these ones very hard to follow. Every time I hit a block of 'The "B to D" photon is deflected and the "C to D" photon is deflected.' statements, my eyes glaze over and I lose you.