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Comment by throw310822 | Hacker News Reader

I am surprised by how dismissive the whole post sounds. For example:

> OpenAI could in fact have a breakthrough that fundamentally changes the world

Well, it appears to me that OpenAI already has such a breakthrough- it had it roughly 4 years ago with GPT2, and it's still scaling it.

Considering that it's not yet a year since the introduction of the first ChatGPT, and given the pace at which it's evolving, I would say that the current product is already showing great promise to fundamentally change the world. I would not be surprised if just incremental changes were enough to fulfill that prediction. The impact at this point seems more limited by the ability of society to absorb and process the technology rather than intrinsic limits of the technology itself.

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One thing OpenAI has now that it didn't have 4 years ago is a lot more compute power at its disposal. Sam Altman has already said "I think we're at the end of the era where it's going to be these, like, giant, giant models." If that's actually true, then the GPTX tech has largely hit a wall in which throwing more compute at it won't get the same increase in capability. Bill Gates predicted that GTP5 won't be much better than GPT4. So, any "breakthrough" really could be incremental rather than game changing.

I think there are two main source of learning for AI - the web scrape datasets, which contain our historical experiences and communications, and AI feedback generated from deployed agents. The web text is almost exhausted, or we can't scale it 100x more, but the feedback is just starting to ramp up.

Every day millions of chat sessions are recorded, and they are exceptional training examples. They would contain the kind of errors LLMs do, and the kind of demands people have, and include a human reaction to each LLM message.

The OpenAI move to create "GPTs" is showing they are actively working on improving the feedback signals by empowering the LLM with RAG, code execution and API access. In such a setup it is possible to use a model at level N to generate data at level N+1.

The keyword here is learning from feedback, which aligns with recent talk of using RL methods like AlphaZero with LLMs. A RL agent would create its own data as it goes. I think progress will be gradual, as we need to wait for the world to produce the learning feedback signal. Of course in domains where we can speed that up, AI will progress faster.

Interesting thought: by making LLMs available to the public, they are going to assist people in many ways and create effects that will percolate in the next training set: LLM inference -> text -> effects in the world -> text -> LLM training. So there is already an implicit feedback loop when we retrain the base models. GPT-5 will train on data from a world influenced and shaped with GPT-4.

The corollary of this is optimistic though. If increasingly large models don't make a big difference, this is hinting at the fact that data quality is a lot more important than the raw quantity of data. This is good news for open source models, because it would be possible to run or train viable models on less expensive hardware, and having billions of dollars in GPUs isn't as much of a moat as they are suggesting.

The article is from Gary Marcus, a well known AI skeptic. Not surprising if he's being dismissive.

Skeptic and completely reactionary. I had to unfollow him on Twitter because he always has to have a "take" on every AI headline, and he's often contradictory between "AI is useless" and "AI is a huge threat".

It was super weird to see him align with the "regulate AI people" after years of being one of the main "this is going nowhere" guys

The people behind LLMs believe in it so thoroughly that they are pushing it to do things that aren’t safe. So it can easily be true that it’s both overhyped and needs better regulation. The fact that LLMs can’t actually solve the problems they are being used for is, in fact, a problem that may require government intervention.

If LLMs aren't fit for the types of problems they're being used for, what would the government be needed for? Users would pretty quickly give up and move on if LLMs continue to suck at solving the problems people want them to solve.

Not if those people are "true believers". Once you get emotionally attached to the idea that something must work, you don't respond to failures rationally.

If we're delving into the realm of belief and therefore religion, pulling in the government to shut it down goes against everything that America was (supposedly) founded on.

I think his stance is pro-regulation, but not in the same way that AI doomers and OpenAi want.

Yes, skepticism is healthy and useful, but this sounds more like intellectual dishonesty.

I think his position is that "AI is not intelligent but is nevertheless dangerous, and even moreso because people think it's intelligent".

I tend not to listen to people who have no fucking clue what they're talking about.

I don't know, it is an opinion and it seems kind of well-founded (i.e. there's no evidence for groundbreaking research on OpenAI part except for scaling things up).

It seems to me that the breakthrough claim was a desperate attempt of OpenAI staffs to get their beloved boss back to OpenAI. If anything it most probably will be incremental things rather than a breakthrough but definitely not a bad things but just call a spade, a spade [1]. Ironically the breakthrough happened somewhere else in Google and for some unknown reasons as of now, Google has missed the boat of commercializing its very own invention.

Personally I have been recently have been using the ChatGPT-4 on a daily basis, and I considered myself a long time and ardent user of Google products (mainly search) for the past two decades. However it becomes increasingly frustrating for the past several years since it is getting more difficult to perform "search that matters" (going to trademark this motto). What frequently irked me the most is not when doing random or targetted search with Google, is when looking for something that you discovered previously but it's forgotten and apparently it does really matter now. It was quoted that about 10% of our time looking for items that we lost or misplaced, and the same can be said to our acquired knowledge and information. Some times we crave so much for the knowledge or the info we had but very difficult or impossible to recall. ChatGPT-4, in particular the online search with Bing feature, is extremely useful in this regard but at the same time is very limited since it is not a well supported features with many failed attempts, perhaps due to limited online data scrapping capability, and thus sub-par results. This specific feature, I call it search that matters, is like Google search with steroid and the fact that Google, with its Deepmind subsidiary, failed to utilize and monetize on this opportunity until now is just beyond me. If ChatGPT, call it ChatGPT-5 if you like, can perform this operation intuitively and seamlessly it will be a game changer but not a breakthrough. Apparently according to ChatGPT-4 you can have a game changer not a breakthrough.

The breakthrough, however, is to fundamentally improve AI or LLM itself as mentioned by Stephen Wolfram in his tutorial article on ChatGPT, not merely enhancing its existing operations [2]:

When it comes to training (AKA learning) the different “hardware” of the brain and of current computers (as well as, perhaps, some undeveloped algorithmic ideas) forces ChatGPT to use a strategy that’s probably rather different (and in some ways much less efficient) than the brain. And there’s something else as well: unlike even in typical algorithmic computation, ChatGPT doesn’t internally “have loops” or “recompute on data”. And that inevitably limits its computational capability - even with respect to current computers, but definitely with respect to the brain.

[1] Eight Things to Know about Large Language Models:

https://wp.nyu.edu/arg/eight-things-to-know-about-large-lang...

[2] What Is ChatGPT Doing and Why Does It Work:

https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-...

Must agree with you. Even when GPT-2 not impressed me much, but with GPT-3 and GPT-4 we constantly see significant progress, not seen on near every other technologies.

Even if GPT-5 will be fatally expensive, but still show such leap as GPT-4, it will be enough to consider OpenAI most successful AI research in human history.

And for about expenses, we just need to wait some 10 years, and GPT-3 class tech will be in every smartphone, and in 30 years, GPT-4 will become also affordable for everyone, GPT-5 will be just ordinary business workhorse.

How expensive is a "typical" GPT-4 query in Azure pricing?

Edit: Oh I see: https://azure.microsoft.com/en-us/pricing/details/cognitive-...

>Gary Marcus

>I am surprised by how dismissive the whole post sounds.

I wouldn't be surprised, it's Gary Marcus. He's an academic with a lot of prestige to lose if the LLM approach is actually good/useful/insightful, who's only widely publicly known now because AI has had a backlash and media needed an expert to quote for "the other side". Same as the computational linguistics researchers who always get quoted for the same reason.

In general, academics in competing fields whose funding threatens to get tanked or eclipsed by research approaches that work on principles they have fundamental academic disagreements with are going to talk negatively about the tech, no matter what it is achieving. Where I think it can be valuable to listen to them is when they're giving the technology credit - generally they'll only do that when it's something really undeniable or potentially concerning.

> it's Gary Marcus. He's an academic with a lot of prestige to lose if the LLM approach is actually good/useful/insightful

I wouldn't bet on loss of perceived expert on the other side status or prestige no matter how poorly his gloomy LLM forecasts fare. It hasn't happened from any of his previous consistently pessimistic predictions around deep learning since the beginning of its resurgence (which has clearly far surpassed its state in 2012, even if currently overhyped). To quote Gary Marcus quoting Gary Marcus:

> Yet deep learning may well be approaching a wall, much as I anticipated earlier, at beginning of the resurgence (Marcus, 2012)

This is such a case of a bad-comment that seems clever and insightful. It boils down to saying we don't need to debate or even consider the content of his arguments because we can assume he's only motivated by prestige and money (but without considering the second-order effects on his credibility and funding if he actually turns out to be proven substantially wrong in the future).

I don't know how right or wrong he is - none of us do. That's why it's all still being debated.

The one thing I know is that we can only truly understand a topic by fully understanding arguments for and against all the claims. I also know the pro-LLM set have way more money (double-digit billions as we saw just this week) and credibility to lose over this topic than Gary Marcus does.

I completely understand your comment, it's reasonable to want sources. I didn't have enough time when I made my original comment to do some good sourcing, but I had that time now. In the interest of not making this comment absurdly long and not repeating work that's already been done, I'll try to be brief and link to secondary sources that contain accurate analysis where possible, rather than linking to primary sources with mostly my own analysis.

>but without considering the second-order effects on his credibility and funding if he actually turns out to be proven substantially wrong in the future

He has continuously made predictions about how bad deep learning is and how it would never achieve much, unlike his "much better" neurosymbolic theories that have never shown results anywhere near as significant (there might be good ideas there, doesn't mean that his continuous goalpost shifting is good science). Those predictions have continuously been proven wrong for the last decade, including a number of predictions specifically about LLMs. This is relatively normal in the academy, people have hobby horses, but he's now engaging in public discourse and advocacy about an actually contentious topic and there are different standards here, so I'm going to respond in that context. If you're not familiar with him at all, that's fine, here's some of his track record and explanations of why he's wrong:

For deep learning in general, in case you want an overview of where he's coming from (he stands by everything in this article today, don't let the age inspire sympathy, he's a deep learning skeptic to the core): https://www.newyorker.com/news/news-desk/is-deep-learning-a-...

For GPT-2 (a moderately important retrospective is in the comments): https://www.lesswrong.com/posts/ZFtesgbY9XwtqqyZ5/human-psyc...

For GPT-3: https://www.tumblr.com/nostalgebraist/628024664310136832/gar...

CTRL-F "Gary Marcus" on this page to find a bunch of sourced, specific claims from Marcus alongside evidence they are wrong: https://gwern.net/gpt-3-nonfiction

He has never stopped, and probably never will. He's the student of the original guy who first buried neural nets back in the sixties or seventies; he's royalty in symbolic AI. The only concession I've ever seen him make to the continued progress of deep learning and LLMs in spite of his continuous early proclamations of death is, bizzarely, that he is concerned about existential risk from current AI research. I don't personally see how he squares "Deep learning and LLMs are all nonsense, have hit a wall, off-ramp on the road to AGI" with "Current AI research [which is overwhelmingly deep learning, even if he doesn't say that part out loud] presents an existential risk", but he does it somehow.

Basically, no matter what new AI research or artifacts come out, if they are made with neural nets he will explain:

1. They are not that important

2. It's a dead end

3. They're harmful because people might wrongly get the impression that they work

4. They're nowhere near as good as his favoured AI architectures are, if only his favoured theories had as much funding as that broken deep learning crap

OK, sure, it's a better answer given that it does actually try and address his arguments and includes sources.

But then you've included some very unfair/invalid lines of attack. You've used quote marks when attributing arguments to him, but these are not actual quotes. You've asserted that he argues that "current AI research ... presents an existential risk" - which he recently said he specifically doesn't believe [1].

For the record, I've been aware of Gary Marcus and his positions on AI research since he was on EconTalk in 2014 [2]. I don't consider him the most compelling skeptic on contemporary AI, but I find it beneficial to hear and consider what he has to say. I'm also very familiar with ‘Mr Wern’ and his approach to discussing this and other topics. I've personally debated him on this website over a different topic, and whilst I benefited by learning new things about that topic, which was good, I was also surprised how much somebody of that persuasion would resort to emotion-charged, flawed or fallacious arguments when pressed.

And this is the main issue I'm wanting to confront: whether it's in this thread, or on LW/OB, or Sam Altman himself (in this tweet [3] that to me was a pretty alarming mask-slip for someone who has worked so hard to portray himself as a good operator over the years), it's striking how quickly the arguments turn personal and nasty rather than just pointing to simple proof that he's wrong. Which is just further evidence that discussion about this topic is so much more driven by emotion and grandiosity than sober examination of evidence.

As for Gary Marcus, sure, for someone who speaks/writes publicly a lot, you can always find specific details on which they might be wrong at a point in time. And where he's clearly factually wrong he should be corrected and he should welcome that. But I'm yet to see any slam-dunk evidence that the central position that he's continued to argue and explain for years is wrong.

Indeed, this quote from a post of his from 2018 [4] seems to be identical in substance to what Sam Altman said at Cambridge this very month [5]:

"Possibly. I do think that deep learning might play an important role in getting us to AGI, if some key things (many not yet discovered) are added in first."

[1] https://www.france24.com/en/live-news/20230604-human-extinct...

[2] https://www.econtalk.org/gary-marcus-on-the-future-of-artifi...

[3] https://x.com/sama/status/1512471289545383940

[4] https://medium.com/@GaryMarcus/in-defense-of-skepticism-abou...

[5] https://www.reddit.com/r/singularity/comments/17wknc5/altman...

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