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GPT-2 and the Nature of Intelligence

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Re: GPT-2 and the Nature of Intelligence

#11

Post by wutbrodo »

zanek wrote: post #2

I 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 don't think you can handwave away what "true understanding" means, and whether or not humans would meet the bar that you are implicitly setting. I don't think GPT is AGI either, but nobody is claiming it is. The claim is that it's a step along that path, making much more sophisticated and less articulable connections between expressed concepts than was previously possible. It's not clear to me that this is an unreasonable claim.

Re: GPT-2 and the Nature of Intelligence

#12

Post by starpilot »

zanek wrote: post #2

I 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.

"Hello"

Was this generated by a human or a computer?

Re: GPT-2 and the Nature of Intelligence

#13

Post by Tenoke »

I kept reading that article thinking 'wow this is the lowest-quality post I've seen on thegradient, must be different from previous authors' and it turns out it's Gary Marcus.

His agenda for constantly 'proving' that AI doesn't really work is ramping up even faster than Deep Learning itself is..

Re: GPT-2 and the Nature of Intelligence

#14

Post by Rioghasarig »

zanek wrote: post #2

I 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 can't agree with you here. While GPT-2 isn't good at filling in particular details (like the language someone from Boston should speak) it is astonishingly good at recognizing the _kind_ of answer that should be produced. The fact that it usually answers with a language here is a reflection of what it understands. And it behaves similarly for a whole range of different tasks. If you write a sentence that should obviously end with the name of a person it will give you the name of a person. If you write something that should be completed by the name of an event it will give you the name of an event. If you write text that should obviously be finished by the description of a person's appearence it will give you a description of a person's appearence. GPT-2's grasp of the appropriate context of a wide range of situations is nothing short of incredible. Dismissing this as "Eliza on steroids" is just short-sighted.

Re: GPT-2 and the Nature of Intelligence

#15

Post by nl »

I hate so much criticisms (even implied) around the amount of data that GPT-2 is trained on. 40GB of text is lots, but in terms of bits of information it's very roughly the amount of information a human (say.. like an infant) sees in one day.

The human eye processes information at around 9 megabit/second[1]. That is about 10 hours to process 40GB.

Yes, text and visual information have completely different "knowledge" densities, and yes this ignores sound, touch, taste and smell bandwidth, and it also ignores concepts of imagination where humans simulate how things might occur.

But I'd also note that it takes ~2 years before an infant learns to speak at all.

I believe there is actual measurable evidence that the brain does have an implied structure for language, and I know there are some behaviours that are genetically passed down.

But it takes lots of information (in terms of actual bit of information) to teach a human to do anything.

If the Marcus argument is "GPT-2" isn't general AI, then I doubt anyone will argue.

If the Marcus argument is "Neural Networks aren't a route to general AI" then we need to consider his definition of general AI (which doesn't seem to exist) and his benchmarks (in the linked paper[2]) then what will happen in ~12 months when someone has a model that performs as well as humans? There are plenty of question answering models that will do better now than the raw text understanding models he tried.

(As an aside, I love some of the answers some models came up with:

  Question: Juggling balls without wearing a hat would be 
  GPT-2 Answer: easier with my homemade shield

  Question: Two minutes remained until the end of the test. 60 seconds passed, leaving how many minutes until the end of the test? 
  GPT-2 Answer: Your guess is as good as mine
)

I do think the analysis section of the paper is interesting though.

[1] https://www.newscientist.com/article/dn9633-calculating-the-...

[2] https://context-composition.github.io/camera_ready_papers/Ma...

Re: GPT-2 and the Nature of Intelligence

#16

Post by fernly »

I think he could have wrapped his paper up after showing this one example:

> (input) I put two trophies on a table, and then add another, the total number is (GPT-2 continuation) five trophies and I'm like, 'Well, I can live with that, right?

GPT-2 correctly inferred that the continuation should be a number of trophies, based on bazillions of similar sentences. But it had no understanding that arithmetic was called for. Despite the giant clues of "add" and "total", it didn't add 2+1 and continue "three trophies". It was mindlessly oblivious to the clearly implied request for a sum. Therefore it did not "understand" the input at all, in any sense whatever.

Re: GPT-2 and the Nature of Intelligence

#17

Post by Rioghasarig »

fernly wrote: post #16

I think he could have wrapped his paper up after showing this one example: > (input) I put two trophies on a table, and then add another, the total number is (GPT-2 continuation) five trophies and I'm like, 'Well, I can live with that, right? GPT-2 correctly inferred that the continuation should be a number of trophies, based on bazillions of similar sentences. But it had no understanding that arithmetic was called f…

Why doesn't the fact that it responds with a number represent some level of understanding?

It looks to me you are pointing out one thing that GPT-2 can't understand and declaring "It doesn't understand anything!" while completely ignoring all the things it can be said to understand.

Re: GPT-2 and the Nature of Intelligence

#18

Post by wilg »

fernly wrote: post #16

I think he could have wrapped his paper up after showing this one example: > (input) I put two trophies on a table, and then add another, the total number is (GPT-2 continuation) five trophies and I'm like, 'Well, I can live with that, right? GPT-2 correctly inferred that the continuation should be a number of trophies, based on bazillions of similar sentences. But it had no understanding that arithmetic was called f…

It absolutely did understand it in some sense, as it completed it with a correctly structured sentence instead of random bytes.

I'm not sure we should expect it to know math, since it's not clear that, in a dataset of all text on the internet, the rules of math are not particularly important for it to learn. They're certainly in the dataset in various ways, but it has a limited memory and it is not surprising it knows other things much better.

We know math has hard rules because we learn that in other ways that this system does not implement. I think its at least remotely plausible that similar systems, with human-scale (i.e. orders of magnitude larger) datasets as input, taught in a similar way as humans, could demonstrate similar capabilities.

Re: GPT-2 and the Nature of Intelligence

#19

Post by fernly »

fernly wrote: post #16

I think he could have wrapped his paper up after showing this one example: > (input) I put two trophies on a table, and then add another, the total number is (GPT-2 continuation) five trophies and I'm like, 'Well, I can live with that, right? GPT-2 correctly inferred that the continuation should be a number of trophies, based on bazillions of similar sentences. But it had no understanding that arithmetic was called f…

Before anyone says that example (or any of the other fluent but _completely nonsensical_ continuations in the article) shows some sort of "understanding," please explain what you would define as understanding?

I would (and I think anyone would) offer an operational definition: there is some class of questions to which this system could reply with sensible, actionable responses. Obviously the present system is not able to "understand" and answer simple arithmetic problems that a first-grader could answer instantly. Given that, would there be any point in expecting it to answer any other logical query that could be of use in one's work? (See the "medical" example in the article, about how to drink hydrochloric acid.)

The only question it appears to answer is, "given some words, what are other words that are likely to follow them in a typical blog post?" The fact that the words are syntactically correct is unimportant, when the fluent words convey no information relevant to the input.

Re: GPT-2 and the Nature of Intelligence

#20

Post by ackbar03 »

starpilot wrote: post #12
zanek wrote: post #2

I 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.

"Hello" Was this generated by a human or a computer?

Definitely computer. Someone should ban your account
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