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0 pointsJohnFen2y ago0 comments

I don't think the reasoning is solid at all. I mean yes, a theoretical superintelligent AI would be very dangerous, but I see exactly no reason to think that current models could get there.

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manojlds2y ago

Yeah feels a bit like we invent planes and worry about wormholes and time travel.

circuit102y ago

I don’t think we’re as far off as you think

tester4572y ago

People had no reason to believe that today's models would exist.

We are on this part of the ai takeoff graph. https://waitbutwhy.com/2015/01/artificial-intelligence-revol...

geraneum2y ago

> People had no reason to believe that today's models would exist.

People had no reason to believe one day we would finally understand what causes the thunder. We finally did, and it is not made by Zeus.

JohnFenOP2y ago

That's not exactly true. There was plenty of reason to believe that. The only question was what the timeline would be.

sebzim45002y ago

Personally, I wasn't expecting anything as good as GPT-4 so soon. So I no longer have any real confidence in how far away 'real AI' is, whatever that means.

I would not be shocked to find out that AGI (using Altman's definition) is more than 50 years away, but I also would not be shocked if it came in 5.

It's really hard to know how scared to be, I think that rationally I should be pretty terrified but I'm not.

circuit102y ago

Well hardware and parameter count are scaling exponentially, so it seems very feasible that it could happen very soon. Of course it's possible that we'll hit a wall somewhere but it seems that just scaling current models up could be enough to get to the point where they can self-improve or gain more compute for themselves

matthewfcarlson2y ago

We've been out of exponential territory for a few years now (https://en.wikipedia.org/wiki/Moore%27s_law). Yes, we are still bounding forward at a crazy pace, but I think the pace is slowing down somewhat

rolisz2y ago

Hardware isn't scaling exponentially anymore (Moore's law is dead). Parameter count isn't really scaling exponentially anymore either. GPT3 had 175b parameters 3 years ago. There are some attempts at training 1 trillion parameter models, but they are not better than GPT3.

lhl2y ago

While I agree we probably aren't getting exponentially increasing parameter counts (GPT4 is by all accounts 1T paramaters and of course, it is significantly better than GPT3) we are still seeing lots of improvements - 3.5 is much better than 3, based "just" on InstructGPT/RLHF training. Models are getting better as well - LLaMA 30B beats/matches GPT-3 on raw eval benchmarks at 1/6 the parameter count.

We're also seeing lots of optimizations with new models (RoPE/RoPER embedding, Swish/GeLU activation, Flash Attention, etc) but I think some the most interesting gains we'll be seeing soon is with inference-optimized training (-70% parameters for +100% compute) [1] combined with sparsity pruning (-50% size w/ almost no loss in accuracy) [2] and quantization [3] which will lead to significantly smaller models performing well.

[1] https://www.harmdevries.com/post/model-size-vs-compute-overh...

[2] https://arxiv.org/abs/2301.00774

[3] https://openreview.net/forum?id=tcbBPnfwxS

JohnFenOP2y ago

What I doubt is that the current approach can lead to AGI at all, regardless of scale. But I'm just speculating along with everyone else. We will see.

blibble2y ago

as moores law is dead it's hard to see more exponential scaling

they're also not going to find another 2, 4, 8, 16 ... internets worth of content to parasitise

circuit102y ago

It’s still exponential, but a little slower. (edit: wait, is that still exponential if it slows down?) Anyway we only need to get to human level (or maybe a bit less) and we’re not that far off (maybe 10 or 20 years at current rates of progress?)

Not all types of AI need external training data, you can train on how effectively a goal is achieved

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manojlds2y ago

Yeah feels a bit like we invent planes and worry about wormholes and time travel.

circuit102y ago

I don’t think we’re as far off as you think

tester4572y ago

People had no reason to believe that today's models would exist.

We are on this part of the ai takeoff graph. https://waitbutwhy.com/2015/01/artificial-intelligence-revol...

geraneum2y ago

> People had no reason to believe that today's models would exist.

People had no reason to believe one day we would finally understand what causes the thunder. We finally did, and it is not made by Zeus.

JohnFenOP2y ago

That's not exactly true. There was plenty of reason to believe that. The only question was what the timeline would be.

sebzim45002y ago

Personally, I wasn't expecting anything as good as GPT-4 so soon. So I no longer have any real confidence in how far away 'real AI' is, whatever that means.

I would not be shocked to find out that AGI (using Altman's definition) is more than 50 years away, but I also would not be shocked if it came in 5.

It's really hard to know how scared to be, I think that rationally I should be pretty terrified but I'm not.

circuit102y ago

matthewfcarlson2y ago

rolisz2y ago

lhl2y ago

[1] https://www.harmdevries.com/post/model-size-vs-compute-overh...

[2] https://arxiv.org/abs/2301.00774

[3] https://openreview.net/forum?id=tcbBPnfwxS

JohnFenOP2y ago

What I doubt is that the current approach can lead to AGI at all, regardless of scale. But I'm just speculating along with everyone else. We will see.

blibble2y ago

as moores law is dead it's hard to see more exponential scaling

they're also not going to find another 2, 4, 8, 16 ... internets worth of content to parasitise

circuit102y ago

Not all types of AI need external training data, you can train on how effectively a goal is achieved

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