It's a stretch to call this discovering language. It's learning the correlations between sounds, spoken words and visual features. That's a long way from learning language.
babies do a lot more. they have feelings. like pain, pleasure. and different verities of them. then they have a lot of things hardcoded. and they have control of their bodies and the environment. for example they quickly learn that crying helps getting what they want.
They do, and from it they learn many other things even before language, including some non-verbal expectation of what is likely to happen next, and that they have some ability to participate in the external world. Until recently, we have not seen anything that has gained some competence in language without having these precursors.
Indeed - a half-billion years of training since the first neurons appeared - but here I'm thinking more of the sort of understanding which leads, for example, to reactions of surprise to incongruous outcomes.
Actually the feedback part of learning is important. There's a famous experiment with cats in baskets that demonstrated that.
But AI isn't an animal so the same constraints don't necessarily apply. I think you'd have to have a particularly anti-AI & pedantic bent to complain about calling this language discovery.
Feedback, yes. Feedback from interacting with the physical reality by means of physical body with flexible appendages? Useful in general, but neither necessary nor sufficient in case of learning language.
Feedback is fundamental to deep neural networks, it's how they're trained. And to be honest, all of the things 'astromaniak mentions can be simulated and made part of training data set too. While the "full experience" may turn necessary for building AGI, the amount of success the field had with LLMs indicates that it's not necessary for the model to learn language (or even to learn all languages).
A lot of a baby's language pickup is also based on what other people do in response to their attempts, linguistically and behaviourally. Read-only observation is obviously a big part of it, but it's not "exactly" the same.
Right. But that happens too with ML models during training - the model makes a prediction given training example input, which is evaluated against expected output, rinse repeat. A single example is very similar to doing something in response to a stimuli, and observing the reaction. Here it's more of predicting a reaction and getting feedback on accuracy, but that's part of our learning too - we remember things that surprise us, tune out those we can reliably predict.
I think he's wrong with the same logic. If sound, visual and meaning aren't correlated, what can language be? This looks like an elegant insight by Mr Hamilton and very much in line with my personal prediction that we're going to start encroaching on a lot of human-like AI abilities when it is usual practice to feed them video data.
There is a chance that abstract concepts can't be figured out visually ... although that seems outrageously unlikely since all mathematical concepts we know of are communicated visually and audibly. If it gets more abstract than math that'll be a big shock.
> The idea of linguistic relativity, known also as the Whorf hypothesis, [the Sapir–Whorf hypothesis], or Whorfianism, is a principle suggesting that the structure of a language influences its speakers' worldview or cognition, and thus individuals' languages determine or influence their perceptions of the world.
Does language fail to describe the quantum regime with which we could have little intuition?
Verbally and/or visually, sufficiently describe the outcome of a double-slit photonic experiment onto a fluid?
Describe the operator product of (qubit) wave probability distributions and also fluid boundary waves with words? Verbally or visually?
I'll try: "There is diffraction in the light off of it and it's wavy, like <metaphor> but also like a <metaphor>"
> If it gets more abstract than math
There is a symbolic mathematical description of a [double slit experiment onto a fluid], but then sample each point in a CFD simulation and we're back to a frequentist sampling (and not yet a sufficiently predictive description of a continuum of complex reals)
Even without quantum or fluids to challenge language as a sufficient abstraction, mathematical syntax is already known to be insufficient to describe all Church-Turing programs even.
Church-Turing-Deutsch extends Church-Turing to cover quantum logical computers just: any qubit/qudit/qutrit/qnbit system is sufficient to simulate any other such system; but there is no claim to sufficiency for universal quantum simulation. When we restrict ourselves to the operators defined in modern day quantum logic, such devices are sufficient to simulate (or emulate) any other such devices; but observed that real quantum physical systems do not operate as closed systems with intentional reversibility like QC.
For example, there is a continuum of random in the quantum foam that is not predictable with and thus is not describeable by any Church-Turing-Deutsch program.
> Gödel's incompleteness theorems are two theorems of mathematical logic that are concerned with the limits of provability in formal axiomatic theories. These results, published by Kurt Gödel in 1931, are important both in mathematical logic and in the philosophy of mathematics. The theorems are widely, but not universally, interpreted as showing that Hilbert's program to find a complete and consistent set of axioms for all mathematics is impossible.
ASM (Assembly Language) is still not the lowest level representation of code before electrons that don't split 0.5/0.5 at a junction without diode(s) and error correction; translate ASM to mathematical syntax (LaTeX and ACM algorithmic publishing style) and see if there's added value
> Even without quantum or fluids to challenge language as a sufficient abstraction, mathematical syntax is already known to be insufficient to describe all Church-Turing programs even.
> In practice, this [complex analytic continuation of arbitary ~wave functions] is often done by first establishing some functional equation on the small domain and then using this equation to extend the domain. Examples are the Riemann zeta function and the gamma function.
> The concept of a universal cover was first developed to define a natural domain for the analytic continuation of an analytic function. The idea of finding the maximal analytic continuation of a function in turn led to the development of the idea of Riemann surfaces.
> Analytic continuation is used in Riemannian manifolds, solutions of Einstein's [GR] equations. For example, the analytic continuation of Schwarzschild coordinates into Kruskal–Szekeres coordinates. [1]
But Schwarzschild's regular boundary does not appear to correlate to limited modern observations of such "Planc relics in the quantum foam"; which could have [stable flow through braided convergencies in an attractor system and/or] superfluidic vortical dynamics in a superhydrodynamic thoery. (Also note: Dirac sea (with no antimatter); Godel's dust solutions; Fedi's unified SQS (superfluid quantum space): "Fluid quantum gravity and relativity" with Bernoulli, Navier-Stokes, and Gross-Pitaevskii to model vortical dynamics)
> The theorems are widely, but not universally, interpreted as showing that Hilbert's program to find a complete and consistent set of axioms for all mathematics is impossible.
If there cannot be a sufficient set of axioms for all mathematics,
can there be a Unified field theory?
What’s bizarre to me is that the entire field of “AI” research, including the LLM space, seem to be repeating the exact same mistakes of the ecosystem models of the 1920’s and later. Gross oversimplifications of reality not because that’s what is actually observed but because that’s the only way to make reality fit with the desired outcome models.
It’s science done backwards which isn’t really science at all. Not that I think these models have no use cases, they’re simply being used too broadly because the people obsessed with them don’t want to admit their limitations.
I missed all the replies yesterday. My point is that I don't think that learning the correlation between some words and visual concepts qualifies as discovering language. It may be that that's as far as this approach can go so it never discovers more sophisticated constructs of language. In that case it's not different to recognizing a barking dog which is surely below "language". I am not a linguist, so not sure what qualifies as "language" officially, but intuitively this falls short.
This is exactly how I taught myself to read at age 3, and by age 5 my reading comprehension was collegiate, so I don't really understand what you mean. Language is literally communication through patterns. Patterns of speech, patterns of thoughts, archetypes, it's all patterns.
Same, I could read before kindergarten just from my parents reading to me. My mom was shocked when she found out I could read, they hadn't started intentionally teaching me yet.
I just instructed my grandmother to read the same kid's book to me over and over again each night for a few weeks and matched the phonemes to the letters. At that point I'd already had a decent grasp of language so it felt like a very natural transition.
It bit me for a while later on in grade school when I took an English class and realized I didn't know anything about how language was constructed, it was all purely intuition. Formalizing language took a concerted effort on my part, and my teachers didn't understand how to translate the material... because I can't just be told things at face value, there is always a nested mass of "but why?" that must be answered or I fundamentally don't understand.
Once I finally got over that hill, it was smooth sailing again. It also made learning foreign languages very hard in school, I failed both foreign language classes I took, until I again took my own time to learn the fundamentals and now it all seems to stick.
