Large Language Models Are Neurosymbolic Reasoners

I think it's interesting how the human mind can "know" whether things that are unknown can be modeled, or not, and I happen to believe that this phenomenon occurs within reality (where I believe the comments within this thread are), bending that portion of it. I also believe that this phenomenon is fundamental.

But then, there "is" "no evidence" for any of this...and we all know what that means!

Since computer science isnt a science, but a form of (applied, discrete) mathematics it encourages people to think in terms of functions with purely mathematical semantics, as if 2 + 2 = 4 were the same thing whether it modelled the divison of cells or the printing of a book.

What causes people to speak is our intentions, desires, theory of mind, representational ability, imagination... etc. We speak because we are in a shared world of social intentions, and we desire to communicate something about ourselves or this world to others.. we translate, clumsily, these features of our experience into text tokens; and hope that the agency we are speaking to can recover our mind from those tokens.

Over a million years we have specialised a culture of communication to enable this illusion to take place: the illusion that meaning is in the patterns of the symbols we use.

You can, of course, build a system to perfectly immitate these patterns; just as a video game, if you hold the viewer fixed, my appear to contain a world with a table and a glass. But if you reach for that glass of water, it isnt there: it's an illusion.

This is all statistical AI is: a trick. It's a replaying back of our own conversations to each other, as if it was a real conversation with us.

We can determine, as certain as you like, that there are no goals, intensions, desires, imagination, counterfactual reasoning -- no body, no observation. The machine is not in the world with us, and it not responsive to the world -- the machine generates text, it does not speak.

You inclination to analogise the machine to a person is just on the grounds that you are strapped into you chair, and observing the video game, believe you can grab the glass of water inside.

I am not strapped into a chair, nor do you have to be. You can do science: you can build real experimental explanations of how we form representations, intentions, goals, desires etc. And it is trivial to explain AI, there is no mystery to "compress reddit and query over its space of text tokens". There is only the illusion that the user is subject to -- the belief that the agency lies in this querying process, and not in the redditors who had cause to speak to each other about their experiences of hte world .

But LLM's are not the whole of AI research.

A lot of your arguments are based on 'embodied' reasoning. Humans live in the world, they need to eat and survive. LLM's just compress what humans generated in the world. Correct, current LLM's are mostly regurgitating, but they don't "speak because we are in a shared world of social intentions".

I'd say game worlds are the frontier, because they are able to simulate a lower resolution world for current AI's to learn in. And in that world, they do embody it and have purpose (rewards/goals), they need to survive.

DeepMind's AlphaGo was when I switched. https://www.wired.com/2016/03/two-moves-alphago-lee-sedol-re... Move 37, it was called alien, creative, inhuman intelligence.

Now, scale that up to our world, with admittedly, thousands/millions of more variables. Put it in a robot body, with vision (the latest studies show AI vision building a world model context). Add some goals. Bam, some dangerous stuff, AI embodied in the world, with a goal to survive.

It might be far away, but where we are now was supposed to take another hundred years. So who knows.

The military is already running world simulations where the AI's goal function lead it to kill the soldier operating the AI in order to bypass him. It 'learned' to bypass the operator by killing them to achieve its goal.

Yes, Hyperbole. But really, not by much.

But back to our discussion. At that point, does the robot have an internal subjective perspective? Did AlphaGO when it was reasoning about its small low variable world?

I'll assume you're asking in good faith. Using NNs allows this project to stand on the shoulders of giants: philosophically, mathematically, programmatically, but also I expect this to plug in to OSS LLMs, and leverage their knowledge, similarly to how a human child learns in a Pavlovian/intuitive response, and only later starts to learn to reason.

Wrt inefficiency, training will be inefficient, but the programs can be extracted to CPU instructions / CUDA kernels during inference. Also, I'm interested in using straight through estimators in the forward pass of training, to do this conversion in training too.

Cyc looks cool, but from my cursory glance, is it capable of learning, or is its knowledge graph largely hand coded? Neurallambda is at least as scalable as an RNN, both in data and compute utilization.