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Natural language instructions induce generalization in networks of neurons

nature.com

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Re: Natural language instructions induce generalization in networks of neurons

#61

Post by riwsky »

AlecSchueler wrote: post #54
visarga wrote: post #35

Earlier quoted context omitted.

> 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…

> But the real hero here is not the LLM, but the training set. And in the case of windmills the hero is the wind. But the mill is still a fantastic achievement.

And if you rearrange the letters in “mill”? “I, LLM.”

qed

Re: Natural language instructions induce generalization in networks of neurons

#62

Post by KoolKat23 »

ben_w wrote: post #59
KoolKat23 wrote: post #33

Earlier quoted context omitted.

Cell division is a solved problem. Install new ram module and ctrl+c, ctrl+v from backup, done.

That's "solved" in the way that viruses "solved" life; I think we might want higher standards than that. Un/fortunately (depending on who you ask), there's also a lot of automation being developed for every stage of the industrial processes from "where do we even look for the right rocks to get out of the ground?" to "here's the RAM chip you wanted to stick in your socket".

My point is, it's irrelevant. Yes we can't replicate the process of cell replication but in this case it isn't necessary. We can achieve the end objective (which is to repair/extend) through other means, this is akin to just replacing organs with factory build versions (memories pre-installed too).

Re: Natural language instructions induce generalization in networks of neurons

#63

Post by sam0x17 »

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)

Re: Natural language instructions induce generalization in networks of neurons

#64

Post by error_logic »

photonthug wrote: post #53
somewhereoutth wrote: post #38

Earlier quoted context omitted.

You don't need language to catch a ball, but clearly thinking is required to intercept its trajectory correctly. Language is about communication .

There’s an argument that communication which is internal is still communication, and that a language of trajectories required for coordination is still linguistic in a meaningful sense. Most of the ways to differentiate thought from language are probably going to end up splitting hairs. It all comes back to Wittgenstein, and it’s arguable whether the POV is useful, but it’s certainly coherent and defensible.

I think this entire thread of discussion would benefit from remembering multimodal models exist. In other words, pictures are worth a thousand words and have their own place in thought. The existence of a way to translate between modalities doesn't make any of them superior overall--they each have their roles to play.

Re: Natural language instructions induce generalization in networks of neurons

#65

Post by error_logic »

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.

The usefulness is why the term is so widespread in familiarity but I think the term would have existed to describe the linguistic mapping even if they hadn't proven to have direct problem-solving capabilities.

Re: Natural language instructions induce generalization in networks of neurons

#66

Post by error_logic »

robwwilliams wrote: post #49
nothis wrote: post #7

Earlier quoted context omitted.

I'm not pretending to understand half the words uttered in this discussion but I'm constantly reminded of how much it helps me to articulate things (explain them to others, write them down, etc) to understand them. Maybe that thinking indeed happens almost entirely on a linguistic level and I'm not doing half as much other thinking (visualization, abstract logic, etc.) in the process as I thought. That feels weird.

Or is the real thinking sub-linguistic and “you” and those you talk to are the target audience of language? Sentences emerge from a pre-linguistic space we do not understand.

I do find it funny that this discussion thread has tried to represent language as a universal form of thought when it would be messy to encode the inner workings of a LLM (the weightings/relationships) themselves as natural language.

You could sort of represent the deterministic contents of an LLM by compiling all the algorithms and training data in some form, or maybe a visual mosaic of the weights and tokens, or what have you...but that still doesn't really explain the outcome when a model is presented with novel strings. The patterns are emergent properties that converge on familiar language--they're something deeper than the individual words that result.

Re: Natural language instructions induce generalization in networks of neurons

#67

Post by error_logic »

dmead wrote: post #24

I hate that we just turned out to be stochastic machines another not something more interesting.

This reminds me of how 'interesting' it is when people defend their use of language by downplaying the effects on other people. We really do have so many ripple effects from everything we broadcast to others, and tacking a negative, or a double/triple negative into a sentence doesn't change the fact that mentioning pink elephants will color an arbitrary portion of someone's day.

In other words, this is why tone absolutely matters, and I love how this awareness feeds into support for genuine expression. Sarcasm is chaotic with an unknown audience who would be less likely to recognize the intended subversion and toying with meaning, instead taking it in whatever direction the listener feels like without being sure that the speaker is understood.

Re: Natural language instructions induce generalization in networks of neurons

#68

Post by coldtea »

mewpmewp2 wrote: post #60
AlecSchueler wrote: post #54

Earlier quoted context omitted.

> But the real hero here is not the LLM, but the training set. And in the case of windmills the hero is the wind. But the mill is still a fantastic achievement.

But can mills easily move? No, people can, so people are still viable.

>But can mills easily move?

Yes, that's their whole thing: moving when the air blows.

Re: Natural language instructions induce generalization in networks of neurons

#69

Post by coldtea »

jameshart wrote: post #14
sisyphus_coding wrote: post #11

Earlier quoted context omitted.

> comeback Did it fall out of favour?

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?

Re: Natural language instructions induce generalization in networks of neurons

#70

Post by coldtea »

a_gnostic wrote: post #22
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…

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.

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