Parallels in the ways that humans and ML models acquire language skills
quantamagazine.org
quantamagazine.org
"Here's a phenomenon I was surprised to find: you'll go to talks, and hear various words, whose definitions you're not so sure about. At some point you'll be able to make a sentence using those words; you won't know what the words mean, but you'll know the sentence is correct. You'll also be able to ask a question using those words. You still won't know what the words mean, but you'll know the question is interesting, and you'll want to know the answer. Then later on, you'll learn what the words mean more precisely, and your sense of how they fit together will make that learning much easier. The reason for this phenomenon is that mathematics is so rich and infinite that it is impossible to learn it systematically, and if you wait to master one topic before moving on to the next, you'll never get anywhere. Instead, you'll have tendrils of knowledge extending far from your comfort zone. Then you can later backfill from these tendrils, and extend your comfort zone; this is much easier to do than learning "forwards". (Caution: this backfilling is necessary. There can be a temptation to learn lots of fancy words and to use them in fancy sentences without being able to say precisely what you mean. You should feel free to do that, but you should always feel a pang of guilt when you do.)"
Reminds me of the attention mechanism in transformers!
http://math.stanford.edu/~vakil/potentialstudents.html
And for any parents with toddler age children, seeing the way that toddlers relate to language, and that people relate to toddlers about language, leads to lots of fun observations that remind me of LLM related concepts.
I have made a point over the years of hanging out with people that are far more intelligent and talented than myself, many of whom are in completely different fields to myself.. and I realise that I've always done this!
Whether it's art, music, or the future of power generation, I've been able to hold many conversations that have an aha moment halfway through, where some nugget clicks and backfills the conversation to that point.
And yes, I feel a pang of guilt when entertaining these conversations, but I've made solid friends off of a number of these interactions, so I figure I can't be a completely unbearable bore!
Or maybe I'm a bot.
> There can be a temptation to learn lots of fancy words and to use them in fancy sentences without being able to say precisely what you mean. You should feel free to do that, but you should always feel a pang of guilt when you do.
GPT - as far as we know - feels no guilt pangs whatsoever.
Synapses are one way. Biological neurons have many synapses.
Outside the cortex, and even moreso outside the brain, there is a far greater diversity of structures. Two neurons plugged into each other form an excellent basis for a timer, damper, clock, or even primitive stimulus response. There are structures taking proprioceptive info from the joints and performing integrals on them. It's neat because the circuits are so small we can actually understand them completely! But within the brain things definitely are not laid out right for backprop.
I'd be curious what other sounds look like for both.
chomsky isn't going to like this
I'm not sure about this. We've probably designed general-purpose "neurons" to talk to us, even if we didn't think of it that way. They aren't emulators of physical neurons, they're abstractions of speculative neurons. The way we figure out if they work is by making them talk to us.
What he showed was that children don't get nearly enough examples of language use to learn it from an a priori blank slate.
Thus, the way to disprove his argument would be to take a blank slate model and give it the kind of input a human child would receive, and show that it can learn to the same level as the human child. Ideally, you would also give it some language that is completely non-human and show that it learns that as well, to prove that nothing in its structure accidentally biased it towards human language (since part of the argument is that children always pick up a certain type of language that obeys certain rules of Universal Grammar).
Of course, we are very far away from any experiment of this kind, given that current models actually require much more examples of language than any human has ever been given for an entire lifetime. Which itself tends to lend credence to Chomsky's argument.
It's also very unlikely that Chomsky is wrong, given that all other parts of human and animal ability work via ingrained structures in the brain - look at how horses are able to run a few minutes after being born. So the right prior is that language acquisition, same as sight, spacial awareness, balance, movement, digestion, hormone balance, pain avoidance etc. is not learned from scratch by each newly born, it is only fine tuned based on an existing structure (which was itself trained by evolution of course, over millions of years).
He won't accept machine learning or counter-examples from existing languages, so I wonder if anything could disprove Chomsky save for building an artificial human brain or building a closed form version of human learning - things we hopefully won't do for ethical reasons.
Whether it's true? IMHO, there's definitely human specializations, but there doesn't seem to be a single universal grammar. The underlying model is much lower level and it doesn't necessarily show in the high level language details.
However, speaking about the argument itself, I don't think processing power would matter. The basis of the poverty of the stimulus argument is that the amount of examples given to a child are insufficient mathematically to uniquely determine a particular language in the space of all mathematically possible languages. Even with an infinity of processing power, if I give you only two sentences you can't uniquely deduce the rest of the language I intended if you're not making a good deal many other assumptions about it: many different rule sets will accept those two sentences as correct. Chomsky is arguing that it is those assumptions which are the built-in component.
If you showed that an infinitely powerful supercomputer can pick the same language a baby does from the space of all languages then one of two things must be true: either the assumptions snuck into the computer's construction accidentally, or the argument is wrong. If you then additionally show that, with the same amount of sentences chosen from a non-human language, the computer can pick out the intended non-human-like language, then that proves that the computer is not relying on the same assumptions as humans are in the other case. So, the only remaining possibility is that the argument itself is wrong.
Of course, it would be interesting to imagine what could replace this argument. Perhaps the amount of input given to a toddler is actually large. Perhaps the assumptions do exist, but they are of a more fundamental nature and not specific to humans (e.g. perhaps human languages are the simplest possible rule sets, in some quantifiable mathematical sense, that match those sentences). Either way, the answer could be quite interesting.
So, IF such a machine could be built, it would open the possibility that universal grammar, even if it exists, is only an accident of history (just as much as Indo-European influence on most European languages), not one of human genetics.
Doesn't take much to imbue grammar, coherent completions and basic reasoning.
It's still a large amount of data in the training set compared to what children get (3GB of pure text is many more words than can be said in a lifetime) but it's still a tiny sliver of what GPT-3 was trained on, so it's a very very interesting step in the direction I was thinking of.
Still, the paper has me wondering if we could train a physicist model as brilliant as Einstein with much less compute if we curriculumed the data and restricted it to a physics/physics adjacent dataset.
To the best of my knowledge, Chomsky argued quite the opposite: If you are going to contend that language acquisition work the same in humans and "New AI", show that "New AI" is incapable of learning "impossible languages" just like humans are incapable of learning them.
The paper is not addressing that issue.
I thought this was common knowledge. I mean if we'd come with already specialized hardware for language at birth we'd speak directly just as a new born puppy barks. Or if we'd have specialized software then children of geniuses would be geniuses themselves. And both cases, are obviously not happening in real life.
Just like babies walk directly because they have legs.
There are plenty of examples of clearly specialized hardware/software that nevertheless needs some fine-tuning and is not immediately available in newborn children. Newborns also can't digest solid foods, don't have teeth, aren't able to produce offspring, don't have breasts etc. And yet no one is arguing these systems, many of which will only be apparent months and years later, are not built in.
Even for other cognitive abilities: newborns have fully functioning retinas, yet are unable to see at all for a few days or weeks, and are unable to notice color for a good few months. However, the visual processing areas of the brain are virtually identical in all adults later: they are clearly part of our genetic makeup.
Also, you're confusing the ability to acquire language with general intelligence in your argument about geniuses. This argument is doing the opposite : it's saying that acquiring language is not some feat of intelligence, it is merely a specialized evolutionarily-acquired capacity that all humans share, just like visual and auditory and motor processing.