GPT-2 and the Nature of Intelligence
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GPT-2 and the Nature of Intelligence
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Re: GPT-2 and the Nature of Intelligence
#2I completely agree with Marcus' assessment of GPT-2 and its ilk. They are simply regurgitating words with zero understanding of any words/meaning.
It seems that OpenAi and others are peddling this AI when its simply a glorified Eliza on steroids.
Re: GPT-2 and the Nature of Intelligence
#3> Literally billions of dollars have been invested in building systems like GPT-2, and megawatts of energy (perhaps more) have gone into testing them
Huh, seems like the bot that produced the article lacks some understanding about the real world. Maybe it just needs more training until it learns to associate megawatts with power instead of energy.
Meanwhile GPT2 completes this sentence to
> Literally billions of dollars have been invested in building systems like GPT-2, and megawatts of power generation to support this project.
On a more serious note GPT2 doesn't learn, it can't iteratively explore the world, doesn't experience time or associate those words with other stimuli or anything like that. Given these and more limitations it's fairly impressive what it does. It's like a child reading advanced physics books without the necessary prior knowledge. Being able to form a coherent-seeming sentence of jargon is all you can expect from it. Of course the path to AGI is long.
Re: GPT-2 and the Nature of Intelligence
#4I don't agree with the conclusion here. It's all about the input data.
GPT-2 is trained on words people actually write on the internet, which is an inherently incomplete dataset. It leaves out all the other information an "intelligence" knows about the world. We know what sources are authoritative, we know the context of words from the visual appearance of the page, and we connect it all with data from our past experiences, school, work, friends, our interaction with the world. Among a million other ways we get data.
How would GPT-2 determine most facts from the input dataset? If the only thing you knew was all the text on the internet, with zero other context, you'd have no way of knowing what is "true", or why that concept is important, or anything else. I bet you'd behave just like GPT-2.
It's a robot that is really good at writing, because that is all it knows. I think it doesn't know anything about how to make sense on a macro scale because I don't think the input data contains that information. It seems to do well when the input data contains relevant information.
Re: GPT-2 and the Nature of Intelligence
#5https://nostalgebraist.tumblr.com/post/189965935059/human-ps... for an orthogonal point of view—-I feel Marcus is a bit too embroiled in this particular debate to make level-headed criticism on the merits/potential of GPT-2
Re: GPT-2 and the Nature of Intelligence
#6https://nostalgebraist.tumblr.com/post/189965935059/human-ps... for an orthogonal point of view—-I feel Marcus is a bit too embroiled in this particular debate to make level-headed criticism on the merits/potential of GPT-2
To elaborate a bit: people like Marcus tend to overload/move the goal posts with what the word “understand” means. I kinda feel like in a world where we have perfectly conversational chat bots that are capable of AI complete tasks—-that if these bots look like Chinese rooms under the hood, he’ll still be complaining that they don’t “understand” anything.
I don’t think it’s unreasonable to say that if you think something that doesn’t “understand” anything can do what GPT-2 can do, then maybe your definition of “understand” doesn’t cut reality at the joints
Re: GPT-2 and the Nature of Intelligence
#7The TLDR:
- there are two opposing views about nature of human intelligence, nativism (that believe a fair deal of intelligence is already encoded in us when we are born, eg we are 'primed' to learn language a certain way according to Chomsky) and empiricism (that believe we mostly learn things from scratch via experience)
- GPT 2 is a recent mega large neural net trained on lots of data to take in a few words or sentences and predict which words are most likely to come next given that input. It was trained on absurdly huge amounts of data with absurdly huge amounts of compute at a fairly large cost.
- GPT 2 is pretty impressive in many ways in that the stuff it predicts is syntactically correct, relevant to input, and very versatile (it can handle and complete text on any subject you can think of). But, its predictions often exhibit a lack of basic common sense.
- Since it lacks common sense and a ton was invested it, the piece posits it is evidence in favor of 'empirical' approaches to intelligence seemingly being wrong and alternatives being a good idea from now on.
To be fair GPT-2 does have some innate built in structure (it's not just a fully connected neural net, it has the popular Transformer architecture which relies on the fairly recent idea of self attention as a core building block). And it's fair to argue that GPT-2 is just evidence training on word prediction conditioned on input is not enough to get to common sense; perhaps a different task/loss built on top of Transformer model would work just fine. But really the whole research project of Deep Learning has been an exercise in nativism (since most research is trying to find new and better neural net architectures, ie priors for learning, for various tasks), aside from OpenAI which is much bigger on just scaling existing stuff up, so this agrees with current AI trends more or less.
Re: GPT-2 and the Nature of Intelligence
#8This article doesn't make much sense to me, although I admittedly am not familiar with linguistic theory.
> ' One of the most foundational claims of Chomskyan linguistics has been that sentences are represented as tree structures, and that children were born knowing (unconsciously) that sentences should be represented by means of such trees.'
I don't understand how GPT-2 tests or attempts to refute this claim. Can't we view children as being born with a pre-trained network similar to a rudimentary GPT?
> 'Likewise, nativists like the philosopher Immanuel Kant and the developmental psychologist Elizabeth Spelke argue for the value of innate frameworks for representing concepts such as space, time, and causality (Kant) and objects and their properties (e.g spatiotemporal continuity) (Spelke). Again, keeping to the spirit of Locke's proposal, GPT-2 has no specific a priori knowledge about space, time, or objects other than what is represented in the training corpus.'
I'm just very confused. Are nativists arguing that these principles regarding language and "innate frameworks" aren't emergent from fundamental interactions between neurons in the brain?
It seems like either
1. They are arguing these aren't emergent, which seems obviously wrong if we're trying to describe how language actually works in the brain, in which all thoughts are emergent from the interactions of neurons.
2. They are arguing that they are emergent, but pre-encoded into every human that is born. This doesn't seem inconsistent with GPT-2 at all.
This article seems like a fine critique of our performance so far in language modeling, but in no way does it seem to be vindicating nativist views of language, nor do I quite understand how such views apply to GPT-2.
Obviously, the idea that you can encode every thought in a fixed length vector is BS (just because thought space doesn't have a fixed dimensionality it can be reduced to), but seems rather irrelevant to the main point of the article.
Re: GPT-2 and the Nature of Intelligence
#9The TLDR: - there are two opposing views about nature of human intelligence, nativism (that believe a fair deal of intelligence is already encoded in us when we are born, eg we are 'primed' to learn language a certain way according to Chomsky) and empiricism (that believe we mostly learn things from scratch via experience) - GPT 2 is a recent mega large neural net trained on lots of data to take in a few words or sen…
> Since it lacks common sense and a ton was invested it, the piece posits it is evidence in favor of 'empirical' approaches to intelligence seemingly being wrong and alternatives being a good idea from now on.
It is unclear to me what the distinction between an "empirical" vs not approach even means within this context.
Do nativists a la Chomsky suggest that these "language frameworks" are independent of the basic interactions of neurons in the brain?
If you view human evolution as the learning procedure for building brain structure, GPT-2 seems entirely consistent with the 'nativist' approach, no?
Re: GPT-2 and the Nature of Intelligence
#10I completely agree with Marcus' assessment of GPT-2 and its ilk. They are simply regurgitating words with zero understanding of any words/meaning. It seems that OpenAi and others are peddling this AI when its simply a glorified Eliza on steroids.
> I completely agree with Marcus' assessment of GPT-2 and its ilk. They are simply regurgitating words with zero understanding of any words/meaning.
There's a pretty strong argument that most humans also frequently do this.
My go-to example is high school physics. The majority of students merely learns to associate keywords in problem statements with a table of equations and a mapping of what numbers to substitute for what variables in those equations. Only a small handful of students actually understand what those equations represent and have the ability to generalise them beyond the course material.