191–200 of 374 posts
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Are you using GPT-4? If not, it's understandable. If you don't pay for ChatGPT, you get GPT-3.5. You can also get access to GPT-4 if you use the playground.
Why is it always the same reply? Yes, GPT4 is as useless as GPT3.5 on any non trivial task.
I keep going around telling people that 1+1=3, why do they always give me the same nonsense about the number '2'?
I blame Sam Altman.
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I see them more as creative artists who have very good intuition, but are poor logicians. Their world model is not a strict database of consistent facts, it is more like a set of various beliefs, and of course those can be highly contradictory.
That maybe sufficient for advertising, marketing, some shallow story telling etc., it is way too dangerous for anything in the physical sciences, legal, medicine, ...
It's almost like we need our AI's to have two brain parts. A fast one, for intuition, and a slow one, for correctness. ;-)
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Geoffrey Hinton, Andrew Ng, and quite a few other top AI researchers believe that current LLMs (and incoming waves of multimodal LFMs) learn world models; they are not simply 'stochastic parrots'. If one feeds GPT-4 a novel problem that does not require multi-step reasoning or very high precision to solve, it can often solve it.
A typical parrot repeats after you said something. A parrot that could predict your words before you said them, and could impersonate you in a phone call, would be quite scary (calling Hollywood, sounds like an interesting move idea). A parrot that could listen to you talking for hours, and then provide you a short summary, would probably also be called intelligent.
At night when he is awake (he sleeps in our room in a covered cage) he knows not to vocalize anything more "Dear" when my wife gets up - he says nothing when I do this as he is not bonded to me.
When I sit at my computer and put on my headset he switches to using English words and starts having his own Teams meetings.
When the garage door opens or we walk out he the back door he starts saying Goodbye - Seeya later and then does the sound of the creaky outside gate.
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You are ignoring the fact that a toddler, once their musculature develops, is able to learn to walk after several tries. Show me a humanoid robot that can do that.
Not sure about humanoids but there are RL algorithms that can learn to walk from scratch in a quadruped in minutes.
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That maybe sufficient for advertising, marketing, some shallow story telling etc., it is way too dangerous for anything in the physical sciences, legal, medicine, ...
On their own, yes. But if you have an application where you can check the correctness of what they come up with, you are golden. Which is often the case in the hard sciences. It's almost like we need our AI's to have two brain parts. A fast one, for intuition, and a slow one, for correctness. ;-)
For some industries where I understand the cost stacks with lower and higher skilled workers, I'd say it only takes out the "cheap" part and thereby not taking out a large chunk of costs (more like 10% cost out prior to paying for the AI). That is still a lot of cost reduction, but something that also will potentially be relatively quickly be "arbitraged away", i.e., will bleed into lower prices.
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Geoffrey Hinton, Andrew Ng, and quite a few other top AI researchers believe that current LLMs (and incoming waves of multimodal LFMs) learn world models; they are not simply 'stochastic parrots'. If one feeds GPT-4 a novel problem that does not require multi-step reasoning or very high precision to solve, it can often solve it.
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.
You can very easily give "evidence" of gpt4 being anywhere between emerging super-intelligence and a naked emperor depending what you ask it to solve. They do not learn models of the world, they learn models of some class of our models of the world, which are very specific and already very restricted in how they represent the world.
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Why is it always the same reply? Yes, GPT4 is as useless as GPT3.5 on any non trivial task.
My guess is people feel the need to self-justify their $20/month subscription.
We do a lot of experiments involving gpt3.5, 4, claude-v2, titan-large, and palm2, and for what it's worth, on our real production workloads gpt4 shines. We can make Palm2 produce decent results with a lot of extra effort, and claude-v2 is passable but gpt4 does not disappoint. This is low-grade knowledge management stuff, and we are not using it as a information-retrieval system - but for basic 'cognitive' tasks where all the information needed is provided in the prompt. I'd not rely on it for info retrieval tasks such as the examples quoted above - its knowledge base is highly compressed, after all.
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> LLMs won't get intelligent. That's a fact based on their MO. They are sequence completion engines. A system that could perfectly predict what I would do in response to any particular stimuli, as a continuing sequence, would be exactly as intelligent as me. > They can be fine tuned to specific tasks, but at their core, they remain stochastic parrot Othello GPT was an attempt at answering this exact question, it's a…
A system that could perfectly predict what I would do in response to any particular stimuli, as a continuing sequence, would be exactly as intelligent as me. That's certainly interesting but it's not a depiction of a LLM is it ? LLM's are not deterministic, and (perhaps) so are we so two non-deterministic systems can only occasionally align (or so I assume). Intuition says they may get "close enough", whatever that m…
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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>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)…
You are ignoring the fact that a toddler, once their musculature develops, is able to learn to walk after several tries. Show me a humanoid robot that can do that.
Its a joy to watch a child grow up, but also its super interesting watching them figure out the most basic shit. Would highly recommend if you get the opportunity.