Natural language instructions induce generalization in networks of neurons • Hacker News Board

Re: Natural language instructions induce generalization in networks of neurons

#71

Post by coldtea » Wed, Mar 20, 2024, 1:13 AM UTC

Jensson wrote: post #43

lo_zamoyski wrote: post #29

Earlier quoted context omitted.

Expression is not language. What you're having trouble doing is expressing what you understand.

But that would be an impossibility if understanding requires expressing it in language.

The thinking language doesn't have to be the same thing as the expression language.

It can still have a language form (manipulation of groups of symbolic structures, terms, and associations), but doesn't have to be English, or even at the conscious "internal monologue" level.

Re: Natural language instructions induce generalization in networks of neurons

#72

Post by coldtea » Wed, Mar 20, 2024, 1:15 AM UTC

jameshart wrote: post #55

lo_zamoyski wrote: post #28

Earlier quoted context omitted.

The distinction between language and "thought" to me is odd. Language and "thought" are the same thing . The mouth sounds or hand scribbles aren't the language, but expressions of it.

One issue here is semantics. The things that happen in our brains which we can put into words tend to be the things we categorize as ‘thoughts’. But there are things that happen in our brains which we struggle to connect to language too, and we might call those ‘feelings’ or ‘emotions’ or ‘instincts’ instead. So we’re trying to use language to think about how we think about language and I suspect this might be why th…

>But there are things that happen in our brains which we struggle to connect to language too, and we might call those ‘feelings’ or ‘emotions’ or ‘instincts’ instead

Yes, and we could argue that those are not thoughts, while there still being a distinction between thought language (which could very well be subconscious) and inner monologue/spoken language.

Re: Natural language instructions induce generalization in networks of neurons

#73

Post by nyrikki » Wed, Mar 20, 2024, 1:47 AM UTC

sam0x17 wrote: post #63

nyrikki wrote: post #6

>Tasks that are instructed using conditional clauses also require a simple form of deductive reasoning (if p then q else s) > Our models ofer several experimentally testable predictions outlining how linguistic information must be represented to facilitate flexible and general cognition in the human brain. Aren't those claims falsified by more recent studies that show that even in flys, preferred direction to a movin…

> Or that humans can even do xor with a single neuron. having a single neuron that has learned xor != understanding xor Function approximation is trivial, understanding of what said functions can do and when to use them is much harder (though is arguably still function approximation)

Well xor is linearly inseparable, which is impossible with a single perceptron.

> Our models by contrast make tractable predictions for what popu- lation and single-unit neural representations are required to support compositional generalization and can guide future experimental work examining the interplay of linguistic and sensorimotor skills in humans.

Do you see where that causes an issue with supervenience? Especially when mixed with STDP which could change that more?

It is confusing the map with the territory. At least with the extreme strength of their claim.

Re: Natural language instructions induce generalization in networks of neurons

#74

Post by jameshart » Wed, Mar 20, 2024, 2:09 AM UTC

coldtea wrote: post #69

jameshart wrote: post #14

Earlier quoted context omitted.

Strong Sapir-Whorf (linguistic determinism - language constrains thought) became pretty much seen as a joke by the 1980s. Linguistic relativism (weak Sapir-Whorf - language shapes thought) is still respectable (because, I mean, of course it does). Actually, this research might just as well be evidence for linguistic universalism (Chomsky - language enables thought). In general linguistic philosophers have been coming…

> Strong Sapir-Whorf (linguistic determinism - language constrains thought) became pretty much seen as a joke by the 1980s Was there any substantial empirical reason it was "seen as a joke", or just changing philosophical fashion?

In general, as I understand it, there was only ever evidence for a weak version, and many of the cited anthropological examples that make the case turn out to have dubious factual basis - the old ‘Eskimos have hundreds of words for snow’ and ‘there’s a tribe in Africa who have no word for numbers greater than three’ stuff, all filtered through layers of academic anecdote and institutional racism.

Re: Natural language instructions induce generalization in networks of neurons

#75

Post by nickpsecurity » Wed, Mar 20, 2024, 2:41 AM UTC

retskrad wrote: post #16

It's clear that both biological sentient beings and sentient being made in factories in the future will essentially be two sides of the same coin, differing only in their physical composition. Humans today operate as biological AI, powered by cells, and the sentient beings instead operate on transistors. As we progress toward a future where both sentient beings exhibit comparable intelligence, emotions, and learned e…

It’s not clear at all. So far, the only sentient, intelligent beings are the ones God made. His Word (Bible) said He made us for Him with predictions for the end times. AI takeover isn’t in there. Also, it implies (a) we won’t ever observe an act of evolution that produces anything like us and (b) any artificial intelligence will likewise require brilliant, intelligent designers to work and be maintained.

So far, all these AI’s with a tiny fraction of human capabilities require brilliant designers in fine-tuned environments, like we did. We’ve also observed billions of human and non-human births with no new kinds of animals coming out of them. So, the Word of God is supported by billions of observations plus every AI ever designed while evolutionary or singularity-type views are not. That’s despite so many comments in these discussions referencing evolutionary or mechanical explanations as if they’ve been proven instead of disproven by observations.

So, put your trust in Jesus Christ, receive the Spirit of God our Creator, and find out for yourself what’s special about us. Your life will be much more than the product of a biological, cellular process. God is powerful. You’ll see His work in a new light after you personally know Him.

Re: Natural language instructions induce generalization in networks of neurons

#76

Post by throwaway_6142 » Wed, Mar 20, 2024, 2:41 AM UTC

smokel wrote: post #21

worstspotgain wrote: post #15

Birds inspired planes. Later, aerodynamics fed back into ornithology. I've been waiting for LLMs to be evaluated as a model of human thought. Complexity and scale have held neuroscience back. It's nowhere close to building a high-level brain model up from biological primitives. Like ornithology, it could use some feedback. All arguments about AGI aside, a machine was built that writes like a human. Its design is very…

> Later, aerodynamics fed back into ornithology. You mean to imply that birds have learned from jet fighter designs? I fail to understand the point you're making.

Knowledge learned from airplane propeller development, as well as from jet plane wing aerodynamics, has had a great impact on the design of wind turbine blades, which are now affecting the evolution of birds.

Re: Natural language instructions induce generalization in networks of neurons

#77

Post by interroboink » Wed, Mar 20, 2024, 2:41 AM UTC

coldtea wrote: post #70

a_gnostic wrote: post #22

Earlier quoted context omitted.

Oftentimes I find myself understanding complex concepts before I can describe them, even internally . I am sure everyone has this, as I often read comments praising others' submissions for formulating their thoughts efficiently. So thoughts occur independent of language, but need it to be expressed and shared, even if through pictures and sounds.

> So thoughts occur independent of language Independent of language as the conscious surface level mechanism, maybe - as in, they don't have to be in English, say. But independent of language altogether, including symbolic language encoded into brain structures, I wouldn't be so sure. Language doesn't have to mean conscious internal monologue.

> Language doesn't have to mean conscious internal monologue.

Agreed, but boy do I wish we had better words (ha!) for this.

Calling everything "language" even if someone internally has a more visual or tactile or some other kind of "internal grammar" really gives an unfortunate tilt to casual conversation.

For most people, in everyday discussion, "language" means words/text. I wish we had some term for "structured knowledge" that did not rely on the words/text analogy, since it can leave different-minded people feeling a bit sidelined.

Re: Natural language instructions induce generalization in networks of neurons

#78

Post by throwaway_6142 » Wed, Mar 20, 2024, 2:45 AM UTC

jameshart wrote: post #74

coldtea wrote: post #69

Earlier quoted context omitted.

> Strong Sapir-Whorf (linguistic determinism - language constrains thought) became pretty much seen as a joke by the 1980s Was there any substantial empirical reason it was "seen as a joke", or just changing philosophical fashion?

In general, as I understand it, there was only ever evidence for a weak version, and many of the cited anthropological examples that make the case turn out to have dubious factual basis - the old ‘Eskimos have hundreds of words for snow’ and ‘there’s a tribe in Africa who have no word for numbers greater than three’ stuff, all filtered through layers of academic anecdote and institutional racism.

> institutional racism

are you absolutely certain that your thoughts are not being constrained by your language?

Re: Natural language instructions induce generalization in networks of neurons

#79

Post by hackerlight » Wed, Mar 20, 2024, 2:45 AM UTC

nerdponx wrote: post #50

zer00eyz wrote: post #3

Earlier quoted context omitted.

I hate the reductive nature of the concept of "latent spaces". A good enough formula for a task isn't a solution for every task. Yes Newtonian mechanics work, but Einstein is a better reflection of reality.

The entire innovation (discovery?) of LLMs is that a good formula for the task of sequence completion turns out to also be a good formula for a wide range of AI tasks. That emergent property is why language models are called language models.

That's an inductive bias, not an emergent property. The inductive bias of transformers is that they're good at integrating global context from different parts of a sequence without a particular bias towards recent time steps or localized regularities. And that happens to be a good fit for many (but not all) real-world sequence learning tasks.

The "emergent property" aspect is when LLMs are good at a task at scale X*3 but were incompetent at scale X.

Re: Natural language instructions induce generalization in networks of neurons

#80

Post by nickpsecurity » Wed, Mar 20, 2024, 2:47 AM UTC

visarga wrote: post #35

worstspotgain wrote: post #15

Birds inspired planes. Later, aerodynamics fed back into ornithology. I've been waiting for LLMs to be evaluated as a model of human thought. Complexity and scale have held neuroscience back. It's nowhere close to building a high-level brain model up from biological primitives. Like ornithology, it could use some feedback. All arguments about AGI aside, a machine was built that writes like a human. Its design is very…

> a machine was built that writes like a human But the real hero here is not the LLM, but the training set. It took ages to collect all the knowledge, ideas and methods we put in books. It cost a lot of human effort to provide the data. Without the data we would have nothing. Without GPT we could use RWKV, Mamba, S4, etc and still get similar results. It's the data not the model. > the intuition that language is cent…

Indeed, they are mostly parroting that knowledge with some reasoning from combinations of those patterns. That counts for something. It’s not what we do, though. Or not all we do.

Humans can just sit around aimlessly toying with stuff, reading books, etc. They’ll figure out some of these patterns on their own. Whereas, we have to give these things a ton of highly-curated, pre-processed data made by human minds of all kinds. Then, it’s usually 800GB-4TB for the good ones. They’re appear to be not in our league yet as learning machines.

We’ll be able to assess it better as multimodal models come online. We can train them like infants, then children, on books, random observations through cameras, TV, people reading to them, supervised feedback… all the stuff we do with humans. Then see if and how they match up in performance.