I've been studying Embodied Cognition AI and going to conventions since around 2009, it started seriously gaining popularity around 2012-2015, but then you get division between the new followers and the old about how extreme and radical you need to be.

The new people accuse the old of being too hand-wavy and airy fairy, the old people accuse the new of not taking the new ideas seriously enough, and not accepting the criticism of their entrenched views. From this comes progress.

For my money, the best philosophy comes from Dan Hutto, best book being Radicalizing Enactivism (Hutto and Myin, 2012). The best neuroanatomy with regards to consciousness and intelligence came from Walter J. Freeman III, best book being How Brains Make Up Their Minds (Freeman, 1999) and the best up-to the minute AI research is from Tom Froese. See "Referential communication as a collective property of a brain-body-environment-body-brain system: A minimal cognitive model" (Campos and Froese, 2017), and his (personally very interesting) work on the possibility of self-organising governance in Teotihuacan.

If you just want to have an introduction to the distinction between the two approaches to AI then you can do no better than read the snappily named paper "Why Heideggerian AI Failed and How Fixing it Would Require Making it More Heideggerian" (Dreyfus, 2007). It's true this paper appears to skip straight from Symbolic GOFAI to radically embodied dynamical systems, skipping Connectionism, but the issues raised in the paper can easily be used see that neural networks will fail to reach anything like intelligent behaviour unless they begin to draw strongly on the embodiment literature.

I can see from the dates of my recommended publications that I've not been keeping up particularly well, but I've been writing up my thesis on a slightly different subject.

Pretty sure embodied cognition was very much a thing in the mid 90's.

It seems we keep moving the date forward with AI techniques, claiming they are newer than they actually are, are we in denial?

I think its not too controversial a statement to say that Embodied Cognition was kicked with by the publication of The Embodied Mind (Varela, Rosch, Thompson, 1991), but I noticed a significant increase in its popularity in the 2010s.

fair enough :)

I think we don't need to go there yet. There needs to be a stage before that which is the ability to precisely debug the human brain. If you don't understand the brain, then embodied cognition will be impossible to understand. I think the theory needs to be tested in much more depth. Fortunately, you could create AI models to test the theory itself. AI can serve as a testbed of our understanding in biology.

Let me dive in on the idea of debugging the brain.

If we're able to fully record one's brain activity in a precise manner, then we'll understand much better how to create an intelligent system.

This is because very strong advances have been made in machine vision through a similar idea. Scientists didn't need much precise granularity to understand the visual cortex. The structure of the physical cortex is quite understandable: it detects detailed features and integrate them in bigger concepts until you finally 'see'. But I think for more abstract things in the human brain we'd need more fine-grained data and the possibility to replay that data (in the future), so mapping and recreating the structure of a brain (digitally) will also be needed for when I am talking about "the ability to precisely debug the human brain."

What is funny is that AI currently serves as a very crude check to see whether we really understand brains at all. Just rebuild the brain in AI and see if it produces the same result. So part of this ability to precisely debug the human brain comes from AI itself. Since AI can be used as a hypothesis to test our understanding of the brain.

Couple of things: animal brains are cool too, AI can also progress without understanding the brain and this obviously isn't the only thing that will leap AI forward.

But if human brains become more debuggable (either through questionable ethics or technological advances), then it will benefit AI immensely.

Also the ability to have hardware that would be 10,000 times as fast and software that would be optimized for a 10,000 speedup would help. I know that sounds a bit clunky but it does.

One (of many) reasons I agree is because determining causal relationships is far easier when you can perform 'experiments', i.e. make changes to the world see the results.

Another reason is that nearly all of what we call "common sense" is just knowledge about the real world rather than being some kind of abstract reasoning ability.

Note that embodied cognition doesn't require robotics. An agent can act in a simulated environment instead.

Note also that Deep Mind is very heavily focused on embodied agents.

I am not so sure that it is strictly necessary. But i also like the theory.

Here is an interesting, if dated paper i just read [0]. Not so much that it needs embodiment, but that it needs to be trained on the real world.

[0] https://people.csail.mit.edu/brooks/papers/representation.pd...

In my view, intelligence is an information processing problem. Neural Networks are situated in an environment to some degree. You have inputs, outputs, and then some measure of error with respect to the outputs and some objective function. The objective function can be seen as a way of encoding information about the world in which the NN is producing outputs, and provides the basis for the feedback loop that is used to train the NN ( forward-propagation and backward-propagation ) and produce a representation of the world.

I think these theories are great, but unless a theory makes a mathematical argument about information processing, I think they can be highly misleading and confusing. Natural language has been tripping up philosophers for a long time, and I think the lesson has been learned that we must make mathematical arguments if we ever truly wish to get to the bottom of something.

Thanks!

I think what the theory embodied cognition tries to point out is that strong AI is not this cognitive "thing" that can be transferred from container to container. Imagine that we discovered that Dolphins were just as smart as us and also imagine aliens discovered us, embodied cognition says that the cognitive apparatuses in each of these and us is radically different because brains are always wired up to bodies.

It's not that AI needs to be embodied (computation and cognition are always housed in something), it's that what the housing is will affect the AI. In other words, don't think that strong AI means "thinking like a human" because that AI won't have a human body.

At least that my take on it.

Embodied cognition probably matters a lot if you're trying to build a drop-in replacement for humans. But in principle if someone manages to build a disembodied AGI it would probably still be useful for many purposes including ones we haven't even thought of yet. Think of it like a special-purpose dedicated co-processor. We already have those for other purposes like math, physics, graphics, etc to perform specialized tasks that the CPU can't do efficiently.

In practice we're nowhere close to being able to build any sort of AGI so this is just a thought exercise.

I believe this to be true (embodiment as a precursor to cognition). So much of our early years learning is exploring how our various actions lead to a corresponding result. Touching a hot stove vs petting a soft cat, for example. And instead of just training 1 neural system at a time we're training all of our senses at once, which I think gives a significant boost to the brain's 'understanding' of the result of the action.

Well, once anyway. Then you copy that to every elevator and toaster controller, and voila! It understands your need for unburned toast early in the morning.