2) There are two aspects to a rhyming poem:

a) It is a poem, so must have a fairly high degree of thematic coherence

b) It rhymes, so must have end-of-line rhyming words

It seems that to learn to predict (hence generate) a rhyming poem, both of these requirements (theme/story continuation+rhyming) would need to be predicted ("planned") at least by the beginning of the line, since they are inter-related.

In contrast, a genre like freestyle rap may also rhyme, but flow is what matters and thematic coherence and rhyming may suffer as a result. In learning to predict (hence generate) freestyle, an LLM might therefore be expected to learn that genre-specific improv is what to expect, and that rhyming is of secondary importance, so one might expect less rhyme-based prediction ("planning") at the start of each bar (line).

I would put it this way: the question "do LLMs, etc understand?" is rooted in a category mistake.

Meaning, I am not claiming that it is premature to answer such questions because we lack a sufficient grasp of neutral networks. I am asserting that LLMs don't understand, because the question of whether they do is like asking whether A-flat is yellow.

Quite a stretch, in my opinion (cf. Plato's Cave).

Syntax has dual aspect. It is both content and behavior (code and execution, or data and rules, form and dynamics). This means syntax as behavior can process syntax as data. And this is exactly how neural net training works. Syntax as execution (the model weights and algorithm) processes syntax as data (activations and gradients). In the forward pass the model processes data, producing outputs. In the backward pass it is the weights of the model that become the data to be processed.

When such a self-generative syntactic system is in contact with an environment, in our case the training set, it can encode semantics. Inside the model data is relationally encoded in the latent space. Any new input stands in relation to all past inputs. So data creates its own semantic space with no direct access to the thing in itself. The meaning of a data point is how it stands in relation to all other data points.

Another important aspect is that this process is recursive. A recursive process can't be fully understood from outside. Godel, Turing, Chaitin prove that recursion produces blindspots, that you need to walk the recursive path to know it, you have to be it to know it. Training and inferencing models is such a process

The water carves its banks

The banks channel the water

Which is the true river?

Here, banks = model weights and water = language

While many of these relationships are related to things we see and do in trivial ways, the vast majority go far beyond anything that can be seen or felt.

What does economics look like? I don't know, but I know as I puzzle out optimums, or expected outcomes, or whatever, I am moving forms around in my head that I am aware of, can recognize and produce, but couldn't describe with any connection to my senses.

The same when seeking a proof for a conjecture in an idiosyncratic algebra.

Am I really dealing in semantics? Or have I just learned the graph-like latent representation for (statistical or reliable) invariant relationships in a bunch of syntax?

Is there a difference?

Don't we just learn the syntax of the visual world? Learning abstractions such as density, attachment, purpose, dimensions, sizes, that are not what we actually see, which is lots of dot magnitudes of three kinds. And even those abstractions benefit greatly from the words other people use describing those concepts. Because you really don't "see" them.

I would guess that someone who was born without vision, touch, smell or taste, would still develop what we would consider a semantic understanding of the world, just by hearing. Including a non-trivial more-than-syntactic understanding of vision, touch, smell and taste.

Despite making up their own internal "qualia" for them.

Our senses are just neuron firings. The rest is hierarchies of compression and prediction based on their "syntax".

This and the rest of the comment are philosophical skepticism, and Kant blew this apart back when Hume's "bundle of experience" model of human subjects was considered an open problem in epistemology.

>All our simple ideas in their first appearance are deriv’d from simple impressions, which are correspondent to them, and which they exactly represent.

>...he is so confident the correspondence holds that he challenges anyone who doubts it to produce an example of a simple impression without a corresponding simple idea, or a simple idea without a corresponding simple impression...

In other words, Hume thought that your ideas about things are a result, and only a result, of your impression of the thing. Knowledge must be, then, a posteriori. Indeed he reduces our "selves" into "bundles", which is to say nothing more than an accumulation of the various impressions we've received while living.

The problem with this is that it raises the question: How do we come up with novel thoughts that are not just reproducing things we have observed? (This is called synthetic a priori knowledge)

You can see at this point that this question is very similar to the one posed to AI right now. If it's nothing more than a bundle of information related to impressions it has received (by way of either text or image corpora), then can it really ever create anything novel that doesn't draw directly from an impression?

Kant delivered a decisive response to this with his Critique of Pure Reason and his Prolegomena to Any Future Metaphysics. He focused initially on Hume's biggest skepticism (about causality). Hume claimed that when we expect an effect from a cause, it's not because we're truly understanding how an effect can proceed from a cause, but rather because we've just observed it often enough that we expect it out of habit. Kant addresses this and expands it to any synthetic a priori statements.

He does so by dispensing with the idea that we can truly know everything there is to know about concepts. We simply schematize an understanding of the objects after observing them over time and use our sense of reason to generalize that collection of impressions into objects in our mind. From there, we can apply categorical reasoning that can be applied without the need for empirical evidence, and then produce synthetic a priori statements, to include expecting a specific effect from a specific cause. This is opposed to Hume, who said:

>It is far better, Hume concludes, to rely on “the ordinary wisdom of nature”, which ensures that we form beliefs “by some instinct or mechanical tendency”, rather than trusting it to “the fallacious deductions of our reason”

Hume's position was somewhat of a dead end, and Kant rescued philosophy (particularly epistemology and metaphysics) from it in many peoples' estimation.

The big difference between us and LLMs is that (right now), LLMs don't have a thinking component that transcends their empirical data modeling. It is conceivable that someone might produce an "AI" system that uses the LLM as a sensory apparatus and combines it with some kind of pure logic reasoning system (ironically, the kind of thing that old school AI focused on) to give it that kind of reasoning power. Because without something applying reasoning, all we have is some statistical patterns that we hope can give the best answer, but which can't guarantee anything.

You're making a slightly different point from the person you're answering. You're talking about the combination of words (with intelligible content, presumably) and the syntax that enables us to build larger ideas from them. The person you're answering is saying that LLM work on the principle that it's possible for intelligence to emerge (in appearance if not in fact) just by digesting a syntax and reproducing it. I agree with the person you're answering. Please excuse the length of the below, as this is something I've been thinking about a lot lately, so I'm going to do a short brain dump to get it off my chest:

The Chinese Room thought experiment --treated by the Stanford Encyclopedia of Philosophy as possibly the single most discussed and debated thought experiment of the latter half of the 20th century -- argued precisely that no understanding can emerge from syntax, and thus by extension that 'strong AI', that really, actually understands (whatever we mean by that) is impossible. So plenty of people have been debating this.

I'm not a specialist in continental philosophy or social thought, but, similarly, it's my understanding that structuralism argued essentially the one can (or must) make sense of language and culture precisely by mapping their syntax. There aren't structulists anymore, though. Their project failed, because their methods don't work.

And, again, I'm no specialist, so take this with a grain of salt, but poststructuralism was, I think, built partly on the recognition that such syntax is artificial and artifice. The content, the meaning, lives somewhere else.

The 'postmodernism' that supplanted it, in turn, tells us that the structuralists were basically Platonists or Manicheans -- treating ideas as having some ideal (in a philosophical sense) form separate from their rough, ugly, dirty, chaotic embodiments in the real world. Postmodernism, broadly speaking, says that that's nonsense (quite literally) because context is king (and it very much is).

So as far as I'm aware, plenty of well informed people whose very job is to understand these issues still debate whether syntax per se confers any understanding whatsoever, and the course philosophy followed in the 20th century seems to militate, strongly, against it.

We are talking about LLMs and the debate seems to be around whether learning about non-verbal concepts through verbal patterns (i.e. syntax that includes all the rules of word use, including constraints reflecting relations between words meaning, but not communication any of that meaning in more direct ways) constitutes semantic understanding or not.

In the end, all the meaning we have is constructed from the patterns our senses relay to us. We construct meaning from those patterns.

I.e. LLMs may or may not “understand” as well or deeply as we do. But what they are doing is in the same direction.

Appears quite bold. What sense-relays inform us about infinity or other mathematical concepts that don't exist physically? Is math-sense its own sense that pulls from something extra-physical?

Doesn't this also go against Chomsky's work, the poverty of stimulus. That it's the recursive nature of language that provides so much linguistic meaning and ability, not sense data, which would be insufficient?

A waterfall that never seems to run dry. The ocean. Outer space. Time.

I think infinitude, i.e. a property of something which never ends, is a simplifying abstraction for the many things we come across in reality for which we can’t know where it ends or, in a specific context, we don’t care.

A great point. A fantastic question.

My guess is:

1. We learn useful patterns that are not explicitly in our environment, but are good simpler approximations to work with.

Some of these patterns only mean something in a given context, or are statistical happenstance.

But some of them are actual or approximate abstractions, potentially applicable to many other things.

2. Then we reason about these patterns.

Sometimes we create new patterns that reveal deeper insights about our environment.

Sometimes we create nonsense, which is either obviously nonsense, fools those who don't reason carefully (i.e. bullshit). And some nonsense is so psychologically attractive that it helps some of us pose and believe we are special and connected to higher planes.

And sometimes we create patterns that offer deeper insights into patterns themselves. I.e. abstractions, like counting numbers, arithmetic, logic, and infinity.

--

It is worth considering, that the progression of abstractions, from unary counting, to more scalable number notations, zero as a number, negative numbers, etc. took a couple hundred thousands years to get going. But once we got going, every small new abstraction helped progress compound faster and faster.

At the level of abstract thinking, I view humans as intelligent as a species, not as individuals. Even the greatest minds, a very small proportion of us, had to stand on innumerable inherited abstractions to make significant progress.

Now many people contribute to new abstractions, but we have inherited powerful abstractions about abstractions to work with.

Erase all that accumulated knowledge for a new generation of humans, and very few would make much or any accumulated progress in explicit abstractions for a very long time.

The truth is, syntax and semantics are strongly intertwined and not cleanly separable. A "proof" is merely a syntactically valid string in some formal system.

I.e. those higher concepts are kept in mind as a goal. It is healthy: it keeps the aim alive.

This does not show complex thinking behavior, although there are probably better examples. Translation just isn’t really one of them.

The "water story" is a pivotal moment in Helen Keller's life, marking the start of her communication journey. It was during this time that she learned the word "water" by having her hand placed under a running pump while her teacher, Anne Sullivan, finger-spelled the word "w-a-t-e-r" into her other hand. This experience helped Keller realize that words had meaning and could represent objects and concepts.

As the above human experience shows, aligning tokens from different modalities is the first step in doing anything useful.