[mjburgess]

Then animals do not understand.

You've rigged this up to operationalize it for current digital machines.

"Understanding", "Intelligence", etc. is a feature of animals in their environment. We need to begin there; and that is what we are talking about.

We "understand" how to drive as a dog "understands" how to play fetch. Understanding is not ever going to be a trivial rule that some digital system may instantiate.

It will always require direct causal contact with an environment. In my view "understanding" is "competent play in a changing environment" -- ie., the ability to modify the environment as it changes in accordance with your goals.

This rough definition is inspired by work in animals to understand the role of the neocortex, and animal learning, and the role of consciousness therein. Roughly: consciousness is "perceptual and cognitive intelligence grappling with environmental change".

[js8]

> Then animals do not understand.

I am agnostic regarding that, as I don't think there is any evidence that they do not attempt to build models that are consistent representations of reality.

I am assuming, based on my own experience, they also have this "internal lightbulb" going on when they think they have built the correct model. But whether they are actually cognizant of it (self-aware), I have no idea. (I guess what I am saying is that understanding and self-awareness are two different things.)

[mjburgess]

I'm not even talking about self-awareness. I'd be happy to raise the bar to that level when (, if) we have mice-level AI.

However the bar is way below that at the moment, and masquerading as "intelligence".

Current machine learning (ie., mere statistical) approaches to AI, that do not explicitly aim to dynamically model environments/goals/behaviour/etc., aren't even meeting an extremely minimal notion of intelligence.

We have at the moment "smart rocks". Electrical current "tumbles down" a "digital mountain" and we all it's path "smart" because it has useful outcomes. Equally, a rock rolling down a hill finds an optimal path -- it aint "smart".

We should look at what the rock does when you start adpating its environment: eg., create a little dip in the mountain side; it gets trapped. A mouse doesnt get trapped in a dip, it continues to explore -- why?

Because animal behaviour is inherently exploratory of the enviornment. A mouse doesnt "solve" a maze, it intelligently navigates it -- so that when unexpected change occurs, it isn't "broken".

At the moment, all AI systems radically break when such changes occur -- because they are statistically trained on mere data. They arent dynamically model building. They aren't in an environment. They're just rocks rolling down a hill.

[sgt101]

There is a lot of evidence that they do though - wolves splitting the pack to ambush the tired deer at the end of the valley, chimps and corvids using tools and water displacement to achieve goals, whales bubble fishing in teams.