[gibsonf1]

Unfortunately, this effort fully misses the boat. Human cognition is about concepts, not language, and that's where one must start to understand it. Language simply serializes our conceptual thinking in multiple language formats, the key is what's being serialized and how that actually works in conceptual awareness.

[buzzy_hacker]

Maybe they can’t be so fully separated. https://en.m.wikipedia.org/wiki/Linguistic_relativity

[gibsonf1]

I think the key point is that serialized words symbolize concepts and other logic such that if you can't retrieve that concept into your awareness, you will not understand the word. Learning and forming the concepts comes prior to attaching common word symbols to them based on the region you live in. So if you start with words, you never get anywhere, hence the complete lack of any intelligence in the LLM approach.

[taliesinb]

Exactly. Thought is prior to language, and much confusion happens when you conflate the them. In particular the surface syntax of language tells you next to nothing about the "syntax" of thought, which is hypergraphical, not tree-structured.

[canjobear]

Read more carefully. Their "language of thought" is not a natural language, it's a variant of lambda calculus with probabilistic semantics.

[gibsonf1]

Right, derived from word pattern statistics. The CYC project tried first order predicate calculus with complete failure. This is not how we think or how conceptual awareness works. The key give away is what they don't talk about, Concepts.

[canjobear]

They talk about concepts several times, defined as probabilistic functions within their LOT.

[gibsonf1]

Except human concepts are not probablistic functions.

[drdeca]

What's a concept?

[YeGoblynQueenne]

In general, in AI, when we talk about "concepts" we're talking about the things machine learning models are trained to, well, to model.

In PAC-Learning terms, specifically, a "concept" is a set of instances (which may be vectors or whatever).

Note that a "concept" is not the same as a "class", as in classification. Instead a concept belongs to a class of similar concepts and a learner is trained on instances of concepts in a class. Then a learner is said to be capable of learning the concepts in a class if it can correctly label instances of a concept in the class with some probability of some error.

For a more concrete example, a "class" of concepts is the class of objects represented as subsets of pixels in digital images. A "concept" of that class is, for example, the concept "dog". An image classifier can be said to be able to learn to identify objects in images if it can correctly classify subsets of the pixels in an image as "dog" (or "not dog").

Since the article above is coming from Josh Tenenbaum's group, that's the kind of terminology you should have in mind, when you're talking about "concepts". These guys are old-school (and I say that as a compliment).

[gibsonf1]

I think modeling a human concept would be a far better approach.

[YeGoblynQueenne]

That is indeed the general idea. That's why they're called "concepts", they're meant to be things, or categories of things, that we perceive. But "concept" also has a technical sense, of the assumed representation in machine learning.

That is, in machine learning a concept is represented as a set of instances. Inside the human mind, who knows.