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AI isn’t good enough

skventures.substack.com

281–290 of 374 posts

Re: AI isn’t good enough

#281
post #18

This entire piece is based on one massive, unsupported assertion, which is that LLM progress will cease. Or, as the author puts it, "we are at the tail end of the first wave of large language model-based AI... [it] ends somewhere in the next year or two with the kinds of limits people are running up against." I want to know only one thing, which is what gives him the confidence necessary to say that. If that one stat…

> This entire piece is based on one massive, unsupported assertion, which is that LLM progress will cease. Which is countered by...the assertion that it won't? LLMs won't get intelligent. That's a fact based on their MO. They are sequence completion engines. They can be fine tuned to specific tasks, but at their core, they remain stochastic parrots. > I want to know only one thing, which is what gives him the confide…

> LLMs won't get intelligent. That's a fact based on their MO. They are sequence completion engines.

Where's the proof that sequence completion engines can't be intelligent?

Re: AI isn’t good enough

#282

Earlier quoted context omitted.

>Live systems in nature seem to solve similar problems with way less compute available Do they really? They're certainly more energy-efficient in business-as-usual mode, but a human brain has 86 billion neurons, 600+ trillion synapses(!), and each instance takes 15-20+ years to train to do complex logical tasks. Even if the per-cell work is tiny (and, is it? cells are amazingly complex), 86 billion (or 600+ trillion)…

86 billion (or 600+ trillion) times 20 years is a lot of computation What is that in kilowatt hours? Human brains are remarkably energy-efficient with their compute. We should get credit for that. Comparing one of us to an AI being trained at a data centre with the energy budget of a small city isn’t really fair, is it?

Seems that ~100W is regularly quoted as the average human energy output, so that'd be something like 18 MWh for 20 years (assuming 100% efficiency of food input). I suppose there's also the energy cost of all the training infrastructure (daycare, school teachers, homes, transportation, etc.), and also all the energy consumption of all humans that have come before and built those cities and knowledge infrastructure (although LLMs sit atop that anyway), and then the millions (billions?) of generations of creatures going back to the earliest organisms, who have been optimising how cells work to lower their energy cost.

For that setup cost, you get a single-threaded human, starting to specialise in a single field. They will work in that field for around 7.5 hours every weekday, with ~20 days of holiday a year, for around 45 years, and then retire.

Llama v2 70B cost 1,720,320 GPU-hours[1] at 400W, so 688MWh. Once trained, it can be run 24/7, and you can spin up as many instances as you want on much lower spec hardware. That model produces output faster than a human while consuming around ~30W on my Macbook Pro.

Now, I know we're in pretty shaky spherical cow territory here, comparing a human (albeit a highly educated one) to Llama v2 in logical reasoning... but consider that this is the state of the art in generally-available LLMs after a few decades of research into machine learning & we're using repurposed silicon to compute vs the amazing complexity and physics-leveraging approach of human neurons... and the training cost is only 1 order of magnitude off humans.

Again, I'm not disagreeing with the general point of the OP, I'm not saying that the models we're using right now are the best/right ones (or that the hardware they're running on are the most efficient way of executing them), but I don't think the energy efficiency gap is actually all that high considering

Edit: If you look at the total footprint (considering not just the efficiency of the neurons, but the whole animal) the figures are very close - the llama model card indicates that v2 70B caused the emission of ~300T of CO2, and an average human in the US emits 16T of CO2 a year, so a human would emit ~320T of CO2 in 20 years. I assume children don't have as high a CO2 output, but even so it seems like it's the same order of magnitude.

1: https://github.com/facebookresearch/llama/blob/main/MODEL_CA...

Re: AI isn’t good enough

#283
I hope beyond hope that we continue to iterate on the current GPT-4 model. But statements made by Altman himself make me leery that it might not happen. That, coupled with the fact that it is just so expensive to run make me wonder if this whole thing is sustainable.

Take this into consideration - NVDA’s massive profit in the last couple of quarters is someone else’s expense. When no one is making money on AI, can this continue? I absolutely think companies can make money using AI that people will pay for, but I am not convinced that companies can make money supplying AI services. I see it as almost a commodity type service, like internet access or something. There is no clear path to profitability, and that is ok to some extent, but with the massive costs this could come crashing to a halt very quickly.

Re: AI isn’t good enough

#284

Earlier quoted context omitted.

Anyone who has worked a bit with a top LLM thinks that they learn world models. Otherwise, what they are doing would be impossible. I've used them for things that are definitely not on the web, because they are brand new research. They are definitely able to apply what they've learnt in novel ways.

This statement on "learning world models" lies between overhyping, nitpicking and wishful thinking. There are many different ways we represent world knowledge, and llms are great in problems that relate with some of them, and horrible at others. For example, they are really bad with anything that has to do with spatial relations, and with logical problems where a graphical approach helps. There are problems that grad…

> For example, they are really bad with anything that has to do with spatial relations, and with logical problems where a graphical approach helps

Of course they are, they haven't been trained on anything spatial, they've only been trained on text that only vaguely describes spatial relations. A world model built from an anemic description of the world will be anemic.

Re: AI isn’t good enough

#285
post #117
post #18

This entire piece is based on one massive, unsupported assertion, which is that LLM progress will cease. Or, as the author puts it, "we are at the tail end of the first wave of large language model-based AI... [it] ends somewhere in the next year or two with the kinds of limits people are running up against." I want to know only one thing, which is what gives him the confidence necessary to say that. If that one stat…

There has actually been research that found that there are strong diminishing returns in terms of at least expanding parameter sizes. While I think there are still breakthroughs to be made in terms of window sizes and workarounds like Mixture of Experts, I'm not sure how much farther we will get here in the long term in terms of raw performance of the LLM itself. FWIW, Sam Altman agrees and has a surprisingly similar…

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Re: AI isn’t good enough

#286

Earlier quoted context omitted.

It seems to me that the real question here is what is true human intelligence. Ai has made it plain to see, by being able to replicate it so convincingly, that much of what we have considered intelligence has been pattern matching or acting as complex parrots. There is much more to the abilities of human body-mind-emotional-experiential being, but it is only slowly becoming mainstream. (Edit: Of course there are also…

> It seems to me that the real question here is what is true human intelligence. IMHO the main weakness with LLMs is they can’t really reason. They can statistically guess their way to an answer - and they do so surprisingly well I will have to admit - but they can’t really “check” themselves to ensure what they are outputting makes any sense like humans do (most of the time) - hence the hallucinations.

As the other poster said, they can check themselves but this requires an iterative process where the output is fed back in as input. Think of LLMs as the output of a human's stream of consciousness: it is intelligent, but has a high chance of being riddled with errors. That's why we iterate on our first thoughts to refine them.

Re: AI isn’t good enough

#287

Earlier quoted context omitted.

I can take a bet that it haha already failed - the hype cycle has already made a promise that LLMs can’t keep. Hallucinations to the normal person are a bug. The issue is that only humans can hallucinate. We know there is a “reality”. For an LLM, everything it does is a hallucination. That’s why you have more POCs than production goods. Your “hallucination rate” is unknown. Yesterday Ars has an article that described…

And while you mention one article with a negative experience, tons of positive article came out too. GitHub copilot is really good and useful. All demos I saw which use LLMs were spectacular. The ai race started this year for everyone which means we will continuesly see progress. And while you only mention LlM the whole ai space is crazy. There is a high chance that the architecture from LLMs will change. And we have…

Sorry, I didn’t want to talk about what I have personally done to test this. I felt it would be better to refer to other people.

In the past months I have used Gen AI to create multiple proof of concepts, including labelling and summarization tools. In addition, to make sure I took a project to conclusion, I built a website from scratch, without any prior knowledge - using Gen AI as extensively I could.

I am being pretty conscientious with my homework. The results of those experiments are why I am confident in this position. Not just because of the articles.

I am also pointing out that its not the tech, its the expectations in the market.

People expect Chat GPT to be oracular, which it just cant - the breathless claims from proofs of concepts fan the flames.

I leave it to you to recall the results and blame, when unrealistic expectations were not met.

Re: AI isn’t good enough

#288

Earlier quoted context omitted.

"Guy calls humans flawed why claiming the same flawed species will build superhuman intelligence...", pretty good for a bunch of dumb LLMs in "meat bags"...

Humans have repeatedly built things that are beyond their own physical and intellectual capabilities. A calculator can do math problems much more quickly than any human being.

We've yet to build a single machine that is intellectually capable beyond our own understanding.

Re: AI isn’t good enough

#289

>We are bumping against many of its >limits He keeps saying that as a fact with absolutely no proof and based his whole argument on it.

Conversely, if you don't know what the limits are yourself (and you don't) you wouldn't know either way. So I'm not quite sure if you'e added anything here unfortunately.

I haven't written an article asserting my argument with certainty. He did. Big difference.

Re: AI isn’t good enough

#290

Earlier quoted context omitted.

Yes, I have noticed that a lot of extreme AI cynics have been arguing that any and every example of reasoning or thinking that an LLM displays is just some variant of memorisation.

The biggest evidence that LLMs can’t reason is hallucinations. If it could reason it would have rejected fictional generated output that make no sense.

> The biggest evidence that LLMs can’t reason is hallucinations.

If I asked you a question and you had to respond with a stream of consciousness reply, no time to reflect on the question and think about your reply, how inaccurate would your response be? The "hallucinations" aren't a problem with the LLM per se, but how we use them. Papers have shown that feeding the output back into the input, as happens when humans iterate on their own initial thoughts, helps tremendously with accuracy.

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