[dekhn]

Except for a short window around the release of GPT-4 (especially the inflated claims around beating expert trained humans at legal and math tests, as well as "replacing google"), I think people have more or less right-sized their expectations for large language models and generative AI. Clearly it can do interesting and impressive things but it's not superintelligence, and the folks predicting we're just around the corner have been recognized once again as shysters, hucksters, and charlatans. It doesn't help that state of the art ML researches have gotten so good at over-hyping the actual abilities of their technology.

However, I do think we'll continue to see impressive advances in the areas of media consumption and production, with complex reasoning on hard problems being a likely area of improvement in the near (1 decade) future. While I once never expected to see something like HAL in my lifetime, I feel that many aspects of HAL (voice recognition, ship automation, and chess-playing) have been achieved, if not fully integrated into a single agent. We can expect most applications to be banal- the giants who have the largest data piles will train models that continue to optimize the addictivity of social media, and click-thru rates of ads.

I am also quite impressed at the recall of information by language models for highly factual and well-supported things (computer reviews in particular).

[aurareturn]

I don't think anyone thought it was super intelligence.

I think it's impressive that we went from LLMs not being useful at all to GPT3.5 shocking the world to GPT4 becoming super useful for many things in around 7 months time.

LLM progress have slowed down a bit. But I think we're just getting started. It's still really early. It's only been 1 year since GPT4 came out. Even at the level of GPT4, scaling it would have immense benefits. But my sense is that we'll have a few more levels of great leaps in LLM capabilities that will shock people in the next 3-4 years.

[dekhn]

My text written above makes it quite clear that I don't think most people were saying GPT-4 was superintelligence, but the implication, especially from the charlatans, was there.

I'm referring to the OpenAI white paper on GPT4 that shows exam results. https://cdn.openai.com/papers/gpt-4.pdf figure 4, and surrounding text.

Clearly not superintelligence, as I would define it (see my other comment about scientific discovery, which I consider the best test), but these are tests actual humans take, and we know how most humans would score on these tests (thru some amount of memorization, along with parsing word problems and doing some amount of calculation). But many people who looked at the testing results concluded that GPT-4 was actually a reasoning agent, or that reasoning agents were just around the corner.

The press picked up on that, and my LinkedIn stream was absolutely filled with second-class influencers who thought that superhuman capabilities were not far away. For a while there, looking at some of the test results specifically on moderately challenging math problems, I suspected that LLMs had some sort of reasoning ability.

[aurareturn]

No one said it was super intelligence. I've never seen anyone say that about GPT4 in the media or on Hacker News/Reddit/X.

Yes, I'm sure if you google "GPT4 super intelligence", you'll find a stupid source that says it is. But I've never seen anyone reputable say it is.

[sailingparrot]

> and the folks predicting we're just around the corner have been recognized once again as shysters, hucksters, and charlatans

Why? Can you see the future? No one (serious) was claiming that GPT-4 is superintelligence, it’s about the rate of improvement.

There has only been 6 years between GPT-1 and GPT-4, and each iteration brought more and more crazy emergent behaviour. We still don’t see any sign of the scaling laws slowing down.

I work in ML research, and personally don’t believe ASI is just around, but I talk everyday to researcher that believe so, they don’t say that to swindle anyone’s money (they have extremely well paid 9 to 5 jobs at Goog/MS/OAI, they aren’t trying to raise money from VCs), they only believe so due to the rate of improvement.

Claiming, barely 18 months after GPT-4, when we haven’t yet seen any result from the next jump in scale, that it’s all baloney is a bit premature.

Btw in research time, 10 years from now is « around the corner ».

Now for the VC-chasing folks, their motivation is an entirely different story.

[dekhn]

I'm pretty good at estimating the future; I started working in ML around 1993 and my last work in ML was on TPU hardware at Google (helping researchers solve deep problems when hardware goes wonky), and a number of my ideas (like AlphaFold's capabilities) were predicted by me at CASP in ~2003.

I just continue to think that Vinge was a bit optimistic both on the timeline and acceleration rate. Everybody who cares about this should read https://edoras.sdsu.edu/~vinge/misc/singularity.html and consider whether we will reach the point where ML is actively improving its own hardware (after all, we do use ML to design next gen ML hardware, but with humans in the loop).

[sailingparrot]

Sure, you can think a lot of people are too optimistic (and as stated in my previous post I agree with you), but calling them shysters, hucksters, and charlatans implies a hidden motive to lie for personal gains, which isn’t there (again, in the ML research side). No one working on GPT-2 thought it would be such a leap on GPT-1, no one working on GPT-3 knew that that was the scale at which 0 shot would start emerging, no one working on GPT-4 thought it was going to be so good, so let’s just not pretend we now what GPT-5 or 6 scale model will and won’t do. We just don’t know, we all have our guesses but that’s just what it is, a guess. People making a different guess might be wrong ultimately, that doesn’t make them charlatans.

[dekhn]

OK, fair. I will stop saying shysters, hucksters, and charlatans when referring to Hinton (https://mitsloan.mit.edu/ideas-made-to-matter/why-neural-net... and https://www.nytimes.com/2023/05/01/technology/ai-google-chat...). Perhaps "doomsayer" is the most apt term?

Altman, however, whenever I read what he says, I think falls within the "snake oil salesman" spectrum, although again, that's not precisely the word. A person who intentionally overstates the capabilities (and future capabilities) of a system with the intended goal of personal gain.

[tim333]

>folks predicting we're just around the corner have been recognized once again as shysters, hucksters, and charlatans

is a bit vague as to how long round the corner is and which folk you are thinking of but there have been very non charlatan predictions that you'd be getting something like HAL around now based on the Moore's law like improvements in hardware performance which have kept going for a century and are currently accelerating due to the vast amounts of cash being thrown in.

Probably the best of them in terms of reasoned thinking and being ahead of the curve is Hans Moravec, a robotics guy at the Robotics Institute of Carnegie Mellon who argued computers would be reaching this point around now in his 1988 book - graph here https://imgur.com/a/moravec-graph-V3S2XoK and there's more detail in his 1998 paper https://jetpress.org/volume1/moravec.pdf

The reasoning is very down to earth based on his research attempts at robot vision and comparing the hardware needed to that of the retina - not much hucksterism.

In the graph in the paper on page 5 or so he has computer power roughly going from the equivalent of a lizard to a monkey to a human over about two decades so going by that and assuming they are around human level now they should be about monkey to human level better than us in a decade or so. Not sure if that counts as superintelligence around the corner? This is all independent of which particular algorithms are used.

[karaterobot]

I agree with what you say above, but my perception is that most people still view the current crop of models as a step or two away from superintelligence. That superintelligence, or AGI, is a matter of continued improvement along the current lines, rather than along entirely different lines.

[torginus]

I like to think of the 'car factory' analogy - it's populated by robots that are in some respects far superior to humans, and are doing 90% of the labor. Some ancient futurist, not having seen one before, could correctly predict that 9 out of 10 jobs will be done by robots, and arrive at the incorrect conclusion that robots have rendered humans obsolete.

In actuality, humans are still needed for the 10% the robots can't do well, or serve to enhance the productivity of humans.

I predict AI is like this and going to be for a while - it can clearly do some stuff well and sometimes better than humans, but humans will have their niches for a while.

[dekhn]

I call this the "filter changing problem". No matter how complex you make the technology, somebody still has to change the oil filter (or do whatever other maintainence is required to keep the system running). Sort of like ML-SRE, for those who are familiar with the concept.

[sseagull]

This is related to Moravec’s Paradox: https://en.wikipedia.org/wiki/Moravec%27s_paradox

“it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility”

[dekhn]

I don't think we can really say what path would lead to superintelligence (for whichever definition you desire) in the near future. Perhaps it is technically possible to achieve merely by making an embodied agent with enough different tricks in a single model (which I see as a matter of continued improvement along current lines), or maybe it requires several new things we haven't conceived yet.

Personally, my area of interest is scientific discovery. Could a model not dissimilar from what we have today, if asked a cogent question, not answer it with an experiment that could be carried out? For example, one of the most important experiments, Avery-MacCleod, which proved (to the extent that you can prove anything in biology) that DNA, not protein, was the primary element of heredity, is not all that complicated, and the mechanical details seem nearly in reach of modern ML techniques. Similarly, could the ML model provide a significant advance in the area of understanding the molecular function in intimate detail of proteins as determined by their structure (which AlphaFold does not do, yet), complete with experimental instructions on how to verify these hypotheses? As of this time, my review of modern ML methods for science suggest we have made some advances, but still have not passed the "phase transition" demonstrating superscientist-level understanding of any field. But perhaps it will just fall out naturally from improved methods for media generation/parsing and ad targeting.

I continue to remain hopeful that within my remaining 20-40 or so years (I'm a typical american male, age 51, with a genome that contains no known risk factors) I will see something like what Vinge describes in https://edoras.sdsu.edu/~vinge/misc/singularity.html in a way that is demonstrable and safe, but honestly, I think it could go in any number of directions from "grim meat-hook future" to "unexpected asteroid takes out human life on the planet, leaving tardigrades to inherit the earth" to "kardyshev-scale civilization".

[tim333]

Thanks for the link to Vinge's thing - I hadn't read that. Shame he didn't get the superintelligence within thirty years of 1993 and died in 2024. Still soonish. I've always been interested in physics and have a hunch that we can't figure things like quantum gravity due to mind limitations. If an AI could do that that would really be superintelligence.

[johnthewise]

>and the folks predicting we're just around the corner have been recognized once again as shysters, hucksters, and charlatans.

Do you think Sutskever,Hinton or Sutter are charlatans?

[Bjorkbat]

I wouldn't take it that far myself, but I'm kind of perplexed by how the smartest people in the room at any given time have very optimistic timelines regarding when we'll have AGI. This isn't a modern phenomenon either. Both AI winters were preceded by experts more-or-less claiming that AGI was right around the corner.

It's as if the easiest people to fool are the researchers themselves

[dekhn]

I think Sutskever is a charlatan outside of his area of expertise, Hinton (with whom I worked loosely at Google) is a bit of a charlatan (again, outside his area of expertise; clearly he and LeCun both did absolutely phenomenal work) and I don't know who Sutter is.

If I wanted to predict the next ten years, I'd bring in Demis Hassabis, Noam Shazeer, and Vincent Vanhoucke, from what I've read of Demis's work, and my interactions with the latter, they seem to have very realistic understanding and are not prone to hype (Demis being the most ambitious of the three, Vincent being the one who actually cracked voice recognition, and Noam because ... his brain is unmatched).

[CamperBob2]

I think Sutskever is a charlatan outside of his area of expertise, Hinton (with whom I worked loosely at Google) is a bit of a charlatan (again, outside his area of expertise; clearly he and LeCun both did absolutely phenomenal work) and I don't know who Sutter is.

What do you think of Vizzini?

[dekhn]

He was right about not getting involved in a land war in Asia, but not so much with iocaine powder?

[dekhn]

Based on comments elsewhere in this thread and some re-reading of the definitions of those terms, I think those weren't quite right. I'm mulling over doomsayer (Hinton), and hypster (Altman). It's very similar to the folks at Google Quantum (https://en.wikipedia.org/wiki/Hartmut_Neven) who claimed quantum supremacy over a toy benchmark.

[exsomet]

I’ve been looking at it in the same sense as something like Docker. When containers first became a big hype, everyone everywhere was using them for everything, including things like trying to containerize full desktop environments, which outside of a couple niche businesses makes almost no sense.

Similar to containers, my feeling is that the truth is LLMs are wildly overkill for almost everything going on today. You don’t need an LLM to sort some already structured data when a basic python library or something will work equally fast, predictably, and with less black box magic. There’s probably a small number of use cases that it makes sense for but for everyone else it’s just silly to try and force the technology. It doesn’t help that the people who are selling the shovels in this gold rush are extremely good at extending the hype train every few months, but eventually when these models stop being sold at a loss and businesses have to start facing down with the bill to run them and/or make these API calls, it will correct itself real fast.

[jasfi]

The most impressive thing about LLMs is their definite progress.