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

CheatGPT

blog.humphd.org

Post reply on HN
Print view View original

331–340 of 544 posts

Re: CheatGPT

#331

Post by JohnFen »

iameli wrote: post #326
JohnFen wrote: post #310

Earlier quoted context omitted.

It's because whatever you and I are doing, it's not simply statistical analysis. That is all that ChatGPT is doing. It may be possible to create a machine that flirts with actual intelligence, but this is simply not it. There's not even room for doubt about this.

I can't claim to understand everything my brain is doing, but accepting input and filtering it through a bunch of neuron chains to result in some kind of output sounds like "statistical analysis" to me. You seem convinced that ChatGPT will never have "actual intelligence" — care to make a prediction about something that LLMs will never accomplish? We know they can write code, write essays, generate artwork, and play…

An LLM will never be able to produce something that cannot be statistically derived from its training data.

So... creating a new language, maybe?

Re: CheatGPT

#332

Post by jki275 »

Just tell students to use it like they might use StackOverflow or even a textbook. It might help you to learn, but it's not safe to try to just use it to give you the answer. You need to know the topic better than the machine does, because it will lie to you like nobody's business and doesn't care if it does.

Re: CheatGPT

#333

Post by JohnFen »

zeckalpha wrote: post #329
JohnFen wrote: post #305

Earlier quoted context omitted.

Because ChatGPT isn't thinking. It's not reasoning at all. It's assembling sentences that are statistically predicted from using existing writings as the template. Accuracy isn't a part of the process except in terms of how accurate the training data is. ChatGPT is not making any sort of truth or accuracy determination, let alone doing so poorly.

Can you point to where the thinking happens in a human?

Nope. But I don't have to do that to understand that LLMs do not assess truth or accuracy of anything.

Re: CheatGPT

#334

Post by grugagag »

stuckinhell wrote: post #283
throw310822 wrote: post #209

I don't understand how most of the comments here seem to be along the lines of "these interview questions are useless now" or "we need to rethink education, it hasn't kept up". These all seem absurdly myopic to me. What we're seeing is the first instance, still very limited and imperfect, of AGI. This is not going to make some interview questions obsolete, or give students more tools to cheat with their homework. It…

I agree 1000000%. Watching all the ART AI's change so radically fast, Watching ChatGPT being adopted by so many people at my company. Watching people say art will shift to prompt engineering, then watching prompt engineering being automated away already! Watching the rise of stable diffusion tooling like invokeai, controlnet, and more. Watching youtube videos of people integrating openai and stable diffusion together…

It is a paradigm shift and whoever happens to be on the good side of this may survive just a bit longer before they too become redundant.

Re: CheatGPT

#335

Post by sacrosancty »

jacobjjacob wrote: post #279
throw310822 wrote: post #209

I don't understand how most of the comments here seem to be along the lines of "these interview questions are useless now" or "we need to rethink education, it hasn't kept up". These all seem absurdly myopic to me. What we're seeing is the first instance, still very limited and imperfect, of AGI. This is not going to make some interview questions obsolete, or give students more tools to cheat with their homework. It…

I think what an LLM is best at is fooling people into thinking it’s intelligent. It is really good at saying things in a natural sounding way, and statistically often getting it right, because certain strings of tokens are encoded. But it’s clear when you start poking that it just as easily tells you that 5/2=3, or that 2+2 != 4. it doesn’t model math or any sort of knowledge at all.

[dead]

Re: CheatGPT

#336

Post by dcow »

corbulo wrote: post #112
dcow wrote: post #81

Earlier quoted context omitted.

> A bit like forcing students to use an abacus while calculators are around. I said allow students to use ChatGPT. Just make it clear that pasting answers from it verbatim, just like doing so from SO, is called plagiarism and does not benefit the academic community or themselves. There will always be cheats. Agree about shifting evaluation methods.

What about when it gives the 'best' word choice? Should a student change it arbitrarily?

The same is true for plagiarism. What you do in that case is quote and cite the source.

Re: CheatGPT

#337

Post by TexanFeller »

jacobjjacob wrote: post #279
throw310822 wrote: post #209

I don't understand how most of the comments here seem to be along the lines of "these interview questions are useless now" or "we need to rethink education, it hasn't kept up". These all seem absurdly myopic to me. What we're seeing is the first instance, still very limited and imperfect, of AGI. This is not going to make some interview questions obsolete, or give students more tools to cheat with their homework. It…

I think what an LLM is best at is fooling people into thinking it’s intelligent. It is really good at saying things in a natural sounding way, and statistically often getting it right, because certain strings of tokens are encoded. But it’s clear when you start poking that it just as easily tells you that 5/2=3, or that 2+2 != 4. it doesn’t model math or any sort of knowledge at all.

I could say the same things about some of my coworkers...

Re: CheatGPT

#338

Post by nemo44x »

jraph wrote: post #316
Al-Khwarizmi wrote: post #308

As a CS professor myself, I don't think ChatGPT really creates entirely new problems for assessment, but rather makes problems that we had been sweeping under the carpet more obvious and harder to ignore. Any assignment that can be cheated by using ChatGPT could already be cheated before by asking a friend, an expert or paying someone else to do it. But most teachers assumed this doesn't happen often, and thus acted…

> the continuous assessment fad that has always been rather disastrous for equity Can you expand on this? I lived the two models as a student and still can't make up my mind on which is best. I feel like it just takes catching some bad cold the week of the exams or a bad exam subject / topic to fail them which seems pretty unfair, but I also know my shit better and have more perspective the week of the exam and prefe…

They’re saying that people that were allowed into their programs/universities because of quotas and not merit struggle to prove competency through assessment.

Re: CheatGPT

#339

Post by wokwokwok »

JohnFen wrote: post #317
adastra22 wrote: post #241

Earlier quoted context omitted.

Have you actually used any of these products? GPT et al are perfectly capable of taking knowledge from any one domain and applying it towards the solution of any other problem domain, through various kinds of data abstraction, reasoning by analogy, and other techniques similar to what humans do. It makes plenty of goofups along the way, just like humans do. But if your requirement is that it performs absolutely perfe…

> through various kinds of data abstraction, reasoning by analogy, and other techniques similar to what humans do. No, that's exactly not how LLMs work. They are extremely good at predicting what sentences resemble the sentences in their training data and creating those. That's all. People are getting tripped up because they are seeing legitimate intelligence in the output from these systems -- but that intelligence…

I feel like there are two parallel discourses going on here, and it's crazy.

On the one hand, we have LLM, and people arguing that they are simply memorizing the internet and what you're getting is a predictive regurgitation from what actual people have said.

On the other hand, you have AI Art, and people arguing that it's not just copy-pasting the images it's recognized, and it's actually generating novel outputs by learning 'how to draw'.

Do you see a commonality?

It's that people are arguing whatever happens to be convenient for them.

If a model can generate human-like responses, and it has a large input token size that effectively allows it to maintain a 'memory' by sticking the history in as the input rather than being a one-shot text generator...

Really.

What is the difference between that and AGI?

Does your AGI definition mean you have to have demonstrated understanding of the underlying representations that are put in as text?

Does it have to be error free?

What fundamental aspect of probabilistic text generation means that it can't be AGI?

...because, it seems to me that it's incredibly convenient to define AGI as something that can't be represented by a LLM, when all you have really is a probabilistic output generator, and a model that currently doesn't do anything interesting.

...and it doesn't. It's not AGI. Right now; but your comment suggests that because of the technical process that the output is generated by that LLMs are fundamentally unable to produce AGI; and I think that's not correct.

The technical process is not relevant; it's simply that these models are not sophisticated enough to really be considered AGI.

...but a 5000 billion param model with a billion character token size? I dunno. I think it might start looking pretty hard to argue about.

Re: CheatGPT

#340

Post by madmax96 »

JohnFen wrote: post #317
adastra22 wrote: post #241

Earlier quoted context omitted.

Have you actually used any of these products? GPT et al are perfectly capable of taking knowledge from any one domain and applying it towards the solution of any other problem domain, through various kinds of data abstraction, reasoning by analogy, and other techniques similar to what humans do. It makes plenty of goofups along the way, just like humans do. But if your requirement is that it performs absolutely perfe…

> through various kinds of data abstraction, reasoning by analogy, and other techniques similar to what humans do. No, that's exactly not how LLMs work. They are extremely good at predicting what sentences resemble the sentences in their training data and creating those. That's all. People are getting tripped up because they are seeing legitimate intelligence in the output from these systems -- but that intelligence…

I do agree. But being able to combine old ideas in new ways is also intelligence. LLMs have memorized a ton of information, and learned “information combinators” to compose them. All that’s missing is a clean way for LLMs to engage in the scientific method.
Post reply on HN