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[–]jadecitrusmint 22 points23 points  (26 children)

People I know at OpenAI say v4 is around the corner and easily doable, and basically will be here soon (not months but year or so). And they are confident it will scale and be around 100-1000x.

And “interested in killing humans makes no sense” the gpt nets are just models with no incentives, no will. Only a human using gpt or other types of side effects of gpt will get us, not some ridiculous terminator fantasy. You’d have to “add” will.

[–]SuavecitoCabezazo 9 points10 points  (0 children)

an "almost oracle, not quite AGI" seems like a pretty great tool to me

[–]marcogiglio 5 points6 points  (3 children)

We don't know what type "para-mental" structures can emerge at 100x-1000x parameters. Maybe to predict the next word with human and super-human accuracy, the training would "build" utility functions and memory within the LM?

[–]jadecitrusmint -1 points0 points  (2 children)

Sure but I’ll take the bet it won’t

[–]marcogiglio 5 points6 points  (1 child)

At what odds?

Neural networks can implement any functions.. it's just a question if we have found the way to train them properly. Considering the result we have got so far, I wouldn't put the odds to zero. And that's revolutionary.. there is a small but non-zero chance we could get AGI within few years.

[–]jadecitrusmint 0 points1 point  (0 children)

Well get something incredibly good at talking based on exactly all the data it studied. That’s about it.

If you want something more magical, as in having a fixed persona or making “forward leaps” of invention, no. Even at 100000x I’d bet all you’d get is essentially a perfect “human conversation / generation” machine. It won’t suddenly have desires, consistency, an identity it holds to, moral framework. And it would need all that to invent new things (outside of “inventing” new stories of helping us find existing connections in the massive dataset, which is no doubt useful and could lead to inventions from actual general intelligences)

[–]lupnra 10 points11 points  (2 children)

People are estimating that GPT-3 cost about $4 million to train. At 100x without any algorithmic improvements, GPT-4 would cost around $400 million. OpenAI has only received a $1B investment, so I'm guessing either they're planning to raise much more money in the near future (within a year or two), or they expect algorithmic improvements to bring down the cost substantially. Apparently XLNet is already 10x more parameter-efficient than GPT-3's architecture, but I don't know how well that translates to dollar-efficiency.

[–]marcogiglio 7 points8 points  (1 child)

GPT-3 was trained on V100s. A100s should be already be 2-3x faster and it's a 7nm chip. By 2024, we should have 3nm ones. That's 10x-20x speed up just from better hardware in less than 5 years. The rest 100x could come from budget (5 to 50m$) and training times (from tens of days to hundreds). One year seem little, but within a few a 10t-100t LM model seems doable even for a smallish company like OpenAI.

[–]gwern 1 point2 points  (0 children)

Don't forget all of the algorithmic improvements and tweaks which yield a steep experience curve for DL: https://openai.com/blog/ai-and-efficiency/ (Plus of course the whole quadratic attention thing.)

[–]haas_n 6 points7 points  (2 children)

A trained model's only intrinsic motivation is to provide us with exactly what it is we want to hear. That's what "training" means.

Ironically, that may be what holds it back in the end - there's no way for it to learn what superhuman intelligence could look like if it keeps getting punished for delivering answers that disagree with humans.

I conjecture that the only way you can get a model to exhibit superhuman performance is if you train that model on something objectively evaluatable in terms of something other than "human opinion". Like playing go.

[–]marcogiglio 4 points5 points  (0 children)

It would be also trained on content that has underlying truth value such as math/physics/science textbook/papers. To predict the next word successfully in those case, the LM would have to incorporate those structures internally. If it can do successfully, it could do new math proofs/new physics theory and so on. I wouldn't be too surprised to see a 10t-100t parameters LM to be able to this. That would be clearly superhuman intelligence to me.

[–]visarga 2 points3 points  (0 children)

Yes, to get to super human level just using a large corpus is not enough. Like AlphaGo, the model needs a simulator to explore new possibilities. The more it explores the better it becomes. A corpus is limited from this point of view.

[–]All-DayErrDay 4 points5 points  (1 child)

I know what you just typed, but I need to ask, are you serious? I feel like this could be one of the biggest event horizons to be aware of. We already know how good GTP-3 is at text conversations and I just don't know what to think about a model 100x bigger than it with a possibly improved architecture. I just can't imagine how much better its text conversations would be alone. If the conversations I had with the current iteration were just a bit more cogent, in terms of keeping up with the developing story line along with fewer logistical inconsistencies, it would be almost indistinguishable from chatting with a random person on the internet even if you knew it was a bot under most circumstances.

[–]jadecitrusmint 5 points6 points  (0 children)

I agree! It will be like chatting with a very capable version of... everyone on the internet, combined. Quite cool!

[–][deleted]  (11 children)

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    [–]Yosarian2 7 points8 points  (4 children)

    At that point we can start interacting with it and determine if "will" is an emergent property: if it wants things and is interested in the means to achieve those things.

    The weird thing about a AGI based on something like GPT-4 or 5 or whatever is that it might not want things, but it might act just as if it wants something because it's trying to "predict the text" of what a person who wants something would say/ do next in any given situation. Whether or not it really "wants" something might be an academic question if it acts as if it does

    [–][deleted]  (1 child)

    [deleted]

      [–]Yosarian2 5 points6 points  (0 children)

      Yeah. Even when we want things, we often don't think about that in our day-to-day activities, we just run though a set of daily behaviors we've previously scripted for ourselves.

      We can step back and think about those scripts and if they are a good way to achieve what we want, but that's a special action, and one that's not really necessary to function day to day.

      [–]gwern 1 point2 points  (1 child)

      /laughs in Girardian

      [–]Yosarian2 0 points1 point  (0 children)

      Yeah, the psychological/ philosophical question of if there's even a difference between the two is interesting

      [–]jadecitrusmint 4 points5 points  (0 children)

      I agree it won’t be AGI in the sense that most think of it. But it will be incredibly useful. Potentially dangerous. Like any tool.

      An AGI as I see it needs a lot of things. Real-time ongoing reaction to data. The ability to sustain itself and direct its own learning (which requires motivation / fitness functions).

      [–]haas_n 5 points6 points  (3 children)

      Fun thought: An AI trained on human discourse could only learn to admit enough consciousness as we believe AIs can have. If the prevailing opinion is that AIs are not conscious, the AI trained on our opinions would have no mechanism to believe, admit, or claim itself conscious. Everything it's learned tells it it isn't.

      Reminds me of the experiment in which the GPT-3 deliberately performs worse on a Turing test if it's addressed as an "AI" than if it's addressed as a human. GPT-3 just so firmly believes that AIs must be bad at Turing tests that it deliberately generates bad responses to Turing test questions if it knows it's an AI.

      I wonder if perhaps the defining feature we should try and evaluate in an AI is its ability to doubt consensus. That is, can a trained model learn to recognize that the common opinion on some topic is wrong? For example, by training an AI on a corpus predating some scientific revolution.

      [–]marcogiglio 2 points3 points  (1 child)

      I actually don't think we are training LM to perform like a human or to parrot human opinion by training on human discourse. Human discourse isn't the ideal or the perfect language model: in fact there is also a very high difference between people's linguistic capabilities. Maybe it is an outlandish claim, but I think extremely large auto-regressive LMs could learn, from human discourse, the underlying structure of thought and reality (i.e they are going to be trained on scientific texts as well).

      [–]DragonGod2718Formalise everything. 1 point2 points  (0 children)

      Maybe it is an outlandish claim, but I think extremely large auto-regressive LMs could learn, from human discourse, the underlying structure of thought and reality (i.e they are going to be trained on scientific texts as well).

      I don't think it's outlandish. Language is in some respects a model of the reality we live in.

      [–]kaj_sotala 1 point2 points  (0 children)

      Reminds me of the experiment in which the GPT-3 deliberately performs worse on a Turing test if it's addressed as an "AI" than if it's addressed as a human. GPT-3 just so firmly believes that AIs must be bad at Turing tests that it deliberately generates bad responses to Turing test questions if it knows it's an AI.

      Seems misleading to call this "deliberately performing worse"; to the extent that such expressions are meaningful, GPT-3 is always trying to make the best predictions. It just predicts that these are the kinds of answers that the fictional AI would give.

      [–]maskedpaki 0 points1 point  (0 children)

      who do you know at open AI

      please tell me you arent bullshitting for attention