Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

And yet It Understands

borretti.me

Post reply on HN
Print view View original

101–110 of 231 posts

Re: And yet It Understands

#101

Post by lonelyasacloud »

Certhas wrote: post #86

Human intelligence evolved with the goal to survive and procreate. GPT intelligence evolved to mimick human speech. Both tasks require a conceptual understanding of the world humans inhabit, but otherwise the two tasks that gave rise to these intelligences are utterly different. We should expect these intelligences to be completely different.

Not sure.

It seems plausible that human intelligence could originally have started as a trajectory prediction system for catching prey and/or avoiding being eaten, that evolution has preserved and generalised over the aeons. In which case, at root, how different?

Re: And yet It Understands

#103

Post by rain1 »

quonn wrote: post #49

I think it would be useful for some HN readers to get some basic philosophy training, specifically on the philosophy of mind. I asked myself many of these questions around 2005 or something and started to read up and there are many experiments that have been done and ChatGPT does not change much for the theory. It is interesting because of it‘s possible economic impact etc. Not because because of any supposed moral c…

Do you have any recommendations on where to learn this? Ideally some online course or just a single really good textbook to study?

Re: And yet It Understands

#104

Post by majewsky »

_dain_ wrote: post #92
ivanbakel wrote: post #72

Earlier quoted context omitted.

>I don't see how it can do that unless it really has some kind of understanding. One possibility is that the model itself has learned that tokens are related across languages based on translation examples. If the appended training changes the model's treatment of tokens in one language, that could have a statistical knock-on effect on the weights between similar tokens in different languages. Similarly, if you train…

At the scale/complexity GPT operates at, how is that different from "understanding"? It seems like you've just rephrased it but with more words.

Yeah, but then it goes back to GP's original argument. If relations between translated tokens are classified as "understanding", that would mean that translation AIs are already capable of understanding:

> in which case you could just use machine translation as your example to show that some computational model is capable of understanding, and leave ChatGPT out of it.

Re: And yet It Understands

#105

Post by ivanbakel »

_dain_ wrote: post #92
ivanbakel wrote: post #72

Earlier quoted context omitted.

>I don't see how it can do that unless it really has some kind of understanding. One possibility is that the model itself has learned that tokens are related across languages based on translation examples. If the appended training changes the model's treatment of tokens in one language, that could have a statistical knock-on effect on the weights between similar tokens in different languages. Similarly, if you train…

At the scale/complexity GPT operates at, how is that different from "understanding"? It seems like you've just rephrased it but with more words.

Because it's a purely statistical explanation that doesn't require understanding. Put differently, it's possible that GPT doesn't "understand" language itself as a concept, and instead tokens in the same language are just highly-correlated when it comes to prediction. When affecting weights between tokens, it wouldn't be surprising that those weights have effects across languages, much in the same way they work within languages - after all, it's all just probabilities to GPT.

Google Translate works in a comparable way, and nobody suggests that it is sentient.

Frankly, the argument that "GPT operates at a scale that means it must be sentient" is begging the question.

Re: And yet It Understands

#106

Post by rng_civ »

kypro wrote: post #76
maister wrote: post #6

I've been thinking a lot about the ability of neural networks to develop understanding and wanted to share my perspective on this. For me it seems absolutely necessary for a NN to develop an understanding of its training data. Take Convolutional Neural Networks (CNNs) used in computer vision, for example. One can observe how the level of abstraction increases in each layer. It starts with detecting brightness transit…

I mean, isn't this the whole point of large + deep NNs? To model complex relationships in data? It's odd so many people seem to deny this with GPT and try to trivialise what it does by saying, "it just predicts the next word". This idea that GPT only works at the level of words and develops no deeper understanding of the concepts in language seems silly given its behaviour. And at the very least it's not what we obse…

Here's my abitrary line in the sand: if you give the prompt to a human, they could give a similar reply, but the prompt would also trigger other reactions such as:

* Who's Daisy?

* Why would Daisy do that?

* Daisy is rude.

etc. that imply the existence of some sort of abstract object on which relations and other facts can be plugged into. For me, the existence of that abstract object is "reasoning."

We do not know if GPT is capable of forming abstract objects in its network, and I do not think it is reasonable to infer that from its text output. In my non-expert opinion, it seems possible that the output can be achieved via knowledge regurgitation through the use of sentiment analysis, word correlations, and grammar classification.

So in this framing, it's not reasoning about Daisy nor hallucinating facts. It's regurgitating knowledge about the relationship between sentiment, words, and grammar. (An interesting experiment to run would be to change 'Daisy' to a random noun or even nonsense tokens to see what would happen).

You might argue that the ability to mechanically model that relationship counts as reasoning, and that's a stance I won't outright dismiss. However, it does seem strictly less powerful that mechanically modeling on top of abstract objects.

Re: And yet It Understands

#107

Post by alienicecream »

- the AI is intelligent in a way that's different from us and that we don't understand but is very sophisticated

Also

- the AI cares about what happens to a fictitious child like someone from Reddit

Something here doesn't pass the smell test. It seems more likely that someone wants to believe that the AI has a naive child like consciousness, like you see in pop culture depictions of AIs.

Re: And yet It Understands

#108

Post by _dain_ »

majewsky wrote: post #104
_dain_ wrote: post #92

Earlier quoted context omitted.

At the scale/complexity GPT operates at, how is that different from "understanding"? It seems like you've just rephrased it but with more words.

Yeah, but then it goes back to GP's original argument. If relations between translated tokens are classified as "understanding", that would mean that translation AIs are already capable of understanding: > in which case you could just use machine translation as your example to show that some computational model is capable of understanding, and leave ChatGPT out of it.

Show me a pre-GPT machine translation model that can do what I've described.

Re: And yet It Understands

#109

Post by famouswaffles »

wizofaus wrote: post #80
civilized wrote: post #53

Earlier quoted context omitted.

Nobody is saying the intelligent output is by chance. This is a machine that is fed terabytes of intelligent inputs and is able to produce intelligent outputs. So one explanation of its producing intelligent outputs is that it's basically regurgitating what it was fed. The way to test that, of course, is to give it problems that it hasn't seen. Unfortunately, because GPT has seen so much, giving it problems it defini…

It's not that hard to give it problems it hasn't seen - you can take a classic description of a logical thinking exercise the text for which does occur online, then mix it up in ways that doesn't change the underlying pattern of reasoning necessary to solve it, and at least from the tests I've done it will confidently tell you the incorrect answer (along with some semi-plausible but fatally flawed description of the…

This is a failure mode of people as well. Rewriting so it doesn't bias common priors or at least in the case of Bing, telling it it's making a wrong assumption works.

Re: And yet It Understands

#110

Post by _dain_ »

ivanbakel wrote: post #105
_dain_ wrote: post #92

Earlier quoted context omitted.

At the scale/complexity GPT operates at, how is that different from "understanding"? It seems like you've just rephrased it but with more words.

Because it's a purely statistical explanation that doesn't require understanding. Put differently, it's possible that GPT doesn't "understand" language itself as a concept, and instead tokens in the same language are just highly-correlated when it comes to prediction. When affecting weights between tokens, it wouldn't be surprising that those weights have effects across languages, much in the same way they work withi…

No, you're the one who's begging the question. Why can't a "purely statistical" process have an understanding? If you a priori assume it can't, then nothing could ever persuade you GPT understood anything, no matter how it performed.

And again, this magical word "just". "Just highly correlated", "just probabilities". Putting the word "just" in front of something doesn't mean you've explained it.

Post reply on HN