Everything from the sorites paradox to leaky abstractions; everything real defies precise definition when you look closely at it, and when you try to abstract over it, to chunk up, the details have an annoying way of making themselves visible again.
You can get purity in mathematical models, and in information systems, but those imperfectly model the world and continually need to be updated, refactored, and rewritten as they decay and diverge from reality.
These things are best used as tools by something similar to LLMs, models to be used, built and discarded as needed, but never a ground source of truth.
Cracking that is a huge step, pure multi-modal trained models will probably give us a hint, but I think we're some ways from seeing a pure multi-modal open model which can be pulled apart/modified. Even then they're still train and deploy not dynamically learning. I worry we're just going to see LSTM design bolted onto deep LLM because we don't know where else to go and it will be fragile and take eons to train.
And less said about the crap of "but inference is doing some kind of minimization within the context window" the better, it's vacuous and not where great minds should be looking for a step forwards.
Starting with the sophomoric questions of the optimist who mistakes the possible for the viable: how definite of a thing is "the world", how knowable is it, what is even knowledge... and then back through the more pragmatic: by whom is it knowable, to what degree, and by what means. The mystics: is "the world" the same thing as "the sum of information about the world"? The spooks: how does one study those fields of information which are already agentic and actively resist being studied by changing themselves, such as easily emerge anywhere more than n(D) people gather?
Plenty of food for thought from why ontologies are/aren't a thing. The classical example of how this plays out in the market being search engines winning over internet directories. But that's one turn of the wheel. Look at what search engines grew into quarter century later. What their outgrowths are doing to people's attitude towards knowledge. Different timescale, different picture.
Fundamentally, I don't think human language has sufficient resolution to model large spans of reality within the limited human attention span. The physical limits of human language as information processing device have been hit at some point in the XX century. Probably that 1970s divergence between productivity and wages.
So while LLMs are "computers speak language now" and it's amazing if sad that they cracked it by more data and not by more model, what's more amazing is how many people are continually ready to mistake language for thought. Are they all P-zombies or just obedience-conditioned into emulating ones?!?!?
Practically, what we lack is not the right architecture for "big knowing machine", but better tools for ad-hoc conceptual modeling of local situations. And, just like poetry that rhymes, this is exactly what nobody has a smidgen of interest to serve to consumers, thus someone will just build it in their basement in the hope of turning the tables on everyone. Probably with the help of LLMs as search engines and code generators. Yall better hurry. They're almost done.
> how many people are continually ready to mistake language for thought
This is a fundamental illusion - where, rote memory and names and words get mistaken for understanding. This was wonderfully illustrated here [1]. Few really grok what understanding actually is. This is an unfortunate by-product of the education system we have.
> Are they all P-zombies or just obedience-conditioned into emulating ones?!?!?
Brilliant way to state the fundamental human condition. ie, we are all zombies obedience conditioned to imitate rather than understand. Social media amplifies the zombification, and now LLMs too.
> Starting with the sophomoric questions of the optimist who mistakes the possible for the viable
This is the fundamental tension between operationalized meaning and imagination wherein a grokking soul gathers mists from the cosmic chaos and creates meaning and then continually adapts it.
> it's amazing if sad that they cracked it by more data and not by more model
I was speaking to experts in the sciences (chemistry) and they were shocked that the underlying architecture is brute force, and they expected some theory which turns out to be compact and information compressed not by brute-force but by theorization.
> The physical limits of human language as information processing device have been hit at some point in the XX century
2000 years back when humans realized that formalism was needed to operationalize meaning, and natural language was too vague to capture and communicate it. Only because the world model that natural language captures encompasses "everything" whereas for making it "useful" requires to limit the world model via formalism.
I don't disagree that the world is full of fuzziness. But the problem I have with this portrayal is that formal models are often normative rather than analytical. They create reality rather than being an interpretation or abstraction of reality.
People may well have a fuzzy idea of how their credit card works, but how it really works is formally defined by financial institutions. And this is not just true for software products. It's also largely true for manufactured products. Our world is very much shaped by artifacts and man-made rules.
Our probabilistic, fuzzy concepts are often simply a misconception. That doesn't mean it's not important of course. It is important for an AI to understand how people talk about things even if their idea of how these things work is flawed.
And then there is the sort of semi-formal language used in legal or scientific contexts that often has to be translated into formal models before it can become effective. Law makers almost never write algorithms (when they do, they are often buggy). But tax authorities and accounting software vendors do have to formally model the language in the law and then potentially change those formal definitions after court decisions.
My point is that the way in which the modeled, formal world interacts with probabilistic, fuzzy language and human actions is complex. In my opinion we will always need both. AIs ultimately need to understand both and be able to combine them just like (competent) humans do. AI "tool use" is a stop-gap. It's not a sufficient level of understanding.
> Our probabilistic, fuzzy concepts are often simply a misconception.
How eg a credit card works today is defined by financial institutions. How it might work tomorrow is defined by politics, incentives, and human action. It's not clear how to model those with formal language.
I think most systems we interact with are fuzzy because they are in a continual state of change due to the aforementioned human society factors.
But ultimately I agree with you that this entire societal process is just categorically different. It's simply not a description or definition of something, and therefore the question of how formal it can be doesn't really make sense.
Formalisms are tools for a specific but limited purpose. I think we need those tools. Trying to replace them with something fuzzy makes no sense to me either.
I don't see how LLMs are significantly different. I don't think the formalisms are an "other". I believe they could be tools, both leveraged and maintained by the LLM, in much the same way as most software engineers, when faced with a tricky problem that is amenable to brute force computation, will write up a quick script to answer it rather than try and work it out by hand.
I believe what makes the current Transformer based systems different to humans is that they cannot reliably decide to simulate a deterministic machine while linking the individual steps and the outcomes of that application to the expectations and goals that live in the fuzzy parts of our cognitive system. They cannot think about why the outcome is undesirable and what the smallest possible change would be to make it work.
When we ask them to do things like that, they can do _something_, but it is clearly based on having learned how people talk about it rather than actually applying the formalism themselves. That's why their performance drops off a cliff as soon as the learned patterns get too sparse (I'm sure there's a better term for this that any LLM would be able to tell you :)
Before developing new formalisms you first have to be able to reason properly. Reasoning requires two things. Being able to learn a formalism without examples. And keeping track of the state of a handful of variables while deterministically applying transformation rules.
The fact that the reasoning performance of LLMs drops off a cliff after a number of steps tells me that they are not really reasoning. The 1000th rules based transformation only depending on the previous state of the system should not be more difficult or error prone than the first one, because every step _is_ the first one in a sense. There is no such cliff-edge for humans.
Maybe at university, but not at a trillion dollar company. That job as chief scientist is leading risky things that will work to please the shareholders.
Yes but he was hired in the ZIRP era where all SV companies were hiring every opinionated academic and giving them free reign and unlimited money to burn in the hopes that maybe they'll create the next big thing for them eventually.
These are very different economic times right now, after the FED infinite money glitch has been patched out, so now people do need to adjust to them and start actually making some products of value for their seven figure costs to their employers, or end up being shown the door.
Also, like… it’s Facebook. It has a history of ploughing billions into complete nonsense (see metaverse). It is clearly not particularly risk averse.
Things known to work are not risky. Risky things can fail by definition.
Well then you and he are clearly dead wrong.
Talking to these people is exhausting, so I cut straight to the chase: name the exact, unavoidable conditions that would prove AGI won’t happen.
Shockingly, nobody has an answer. They’ve never even considered it.
That’s because their whole belief is unfalsifiable.
Corporate R&D teams are there to absorb risk, innovate, disrupt, create new fields, not for doing small incremental improvements. "If we know it works, it's not research." (Albert Einstein)
I also agree with LeCun that LLMs in their current form - are a dead end. Note that this does not mean that I think we have already exploited LLMs to the limit, we are still at the beginning. We also need to create an ecosystem in which they can operate well: for instance, to combine LLMs with Web agents better we need a scalable "C2B2C" (customer delegated to business to business) micropayment infrastructure, because as these systems have already begun talking to each other, in the longer run nobody would offer their APIs for free.
I work on spatial/geographic models, inter alia, which by coincident is one of the direction mentioned in the LeCun article. I do not know what his reasoning is, but mine was/is: LMs are language models, and should (only) be used as such. We need other models - in particular a knowledge model (KM/KB) to cleanly separate knowledge from text generation - it looks to me right now that only that will solve hallucination.