The reason it's untrue is because the concept of "I/O channels" does not exist within physics as we know it.

Yes. They most certainly do. The only truly consistent interpretation I know of current physics is information theoretic anyway, but I'm not interested in debating any of that. The fact is I'm communicating to you with physical I/O channels right now so I/O channels certainly exist in the real world.

the true laws of physics make no reference to inputs, outputs, or indeed any kind of agents at all.

Agents are emergent phenomenon. They don't exist on the level of particles and waves. The concept is an abstraction.

"I/O channels" are simply arrangements of matter and energy, the same as everything else in our universe. There are no special XML tags attached to those configurations of matter and energy, marking them "input", "output", "processor", etc. Such a notion is unphysical.

An I/O channel doesn't imply modern computer technology. It just means information is collected from or imprinted upon the environment. It could be ant pheromones, it could be smoke signals, its physical implementation is secondary to the abstract concept of sending and receiving information of some kind. You're not seeing the forest through the trees. Information most certainly does exist.

Why might this distinction be important? It's important because an algorithm that is implemented on physically existing hardware can be physically disrupted. Any notion of agency which fails to account for this possibility--such as, for example, AIXI, which supposes that the only interaction it has with the rest of the universe is by exchanging bits of information via the input/output channels--will fail to consider the possibility that its own operation may be disrupted.

I've explained in previous posts that AIXI is a special case of AIXI_lt. AIXI_lt can be conceived of in an embedded context, in which case; its model of the world would include a model of itself which is subject to any sort of environmental disturbance.

To some extent, an agent must trust its own operation to be correct, because you quickly run into infinite regression if the agent is modeling all the possible that it could be malfunctioning. What if the malfunction effects the way it models the possible ways it could malfunction? It should model all the ways a malfunction could disrupt how it models all the ways it could malfunction, right? It's like saying "well the agent could malfunction, so it should be aware that it can malfunction so that it never malfunctions". If the thing malfunctions, it malfunctions, it's as simple as that.

Aside from that, AIXI is meant to be a purely mathematical formalization, not a physical implementation. It's an abstraction by design. It's meant to be used as a mathematical tool for understanding intelligence.

AIXI also fails on various decision problems that involve leaking information via a physical side channel that it doesn't consider part of its output; for example, it has no regard for the thermal emissions it may produce as a side effect of its computations.

Do you consider how the 30 Watts leaking out of your head might effect your plans to every day? I mean, it might cause a typhoon in Timbuktu! If you don't consider how the waste heat produced by your mental processes effect your environment while making long or short-term plans, you must not be a real intelligent agent...

In the extreme case, AIXI is incapable of conceptualizing the possibility that an adversarial agent may be able to inspect its hardware, and hence "read its mind".

AIXI can't play tic-tac-toe with itself because that would mean it would have to model itself as part of the environment which it can't do. Yes, I know there are fundamental problems with AIXI...

This is, again, because AIXI is defined using a framework that makes it unphysical

No. It's fine to formalize something mathematically. People do it all the time. Math is a perfectly valid tool to investigate phenomena. The problem with AIXI proper, is that it's limited to a context in which the agent and environment are independent entities. There are actually problems where that is a decent approximation, but it would be better to have a more general formulation, like AIXI_lt that can be applied to contexts in which an agent is embedded in its environment.

This applies even to computable formulations of AIXI, such as AIXI-tl: they have no way to represent the possibility of being simulated by others, because they assume they are too large to fit in the universe.

That's simply not true.

I'm not sure what exactly is so hard to understand about this, considering the original post conveyed all of these ideas fairly well. It may be worth considering the assumptions you're operating under--and in particular, making sure that the post itself does not violate those assumptions--before criticizing said post based on those assumptions.

I didn't make any assumptions. I said what I believe to be correct.

I'd love to hear you or the author explain how an agent is supposed to make decisions about what to do in an environment if it's agency is completely undefined.

I'd also love to hear your thoughts on the relationship between math, science, and the real world if you think comparing a physical implementation to a mathematical formalization is any more fruitful than comparing apples to oranges.

Did you know that engineers use the "ideal gas law" every day to solve real-world problems even though they know that no real-world gas actually follows the "ideal gas law"?! You should go tell them that they're doing it wrong!

Decision Theory

by abramdemski, Scott Garrabrant 1 min read31st Oct 201837 comments

101

Ω 24


Crossposted from the AI Alignment Forum. May contain more technical jargon than usual.

(A longer text-based version of this post is also available on MIRI's blog here, and the bibliography for the whole sequence can be found here.)

The next post in this sequence, 'Embedded Agency', will come out on Friday, November 2nd.

Tomorrow’s AI Alignment Forum sequences post will be 'What is Ambitious Value Learning?' in the sequence 'Value Learning'.