Comment by lukeschlather | Hacker News Reader

Comment by lukeschlather | Hacker News Reader

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We don't know how efficient modern GPUs are compared to brains. It's possible that neurons are highly networked in a way that allows significantly more computation along more pathways.

Additionally, development has slowed in the past 20 years. It's unclear how much hardware will improve in the next 20 years. it's unclear if current GPUs are capable of running something "smarter" than GPT4. It's unclear if current GPU architecture is capable. OpenAI claims they've hit a wall with throwing more hardware at the situation (a little questionable.)

All this to say that a hypothetical AI is limited by the amount of hardware it has, and lacking a "true human-level AGI" we can't say how much compute it needs. If an AI needs to be the size of a warehouse to match a human, it's not going to be much smarter or better at research than humans.

I still do think, I would not be surprised if we wake up tomorrow and discover that hyperintelligent AIs are suddenly all around us. But also it could be decades, it's just very unpredictable, there are a lot of unknowns.

Meatspace human brains work on 20W. This is enough proof that we have ways to go to optimize silicon.

I agree that it is unknown when such an AI will come around. But hardware limitation problem is more nuance than just GPU power. The brain is analog and in a way, the current LLMs and neural networks are an approximation of an analog computer. There are current research into making an AI dedicated chip or even an analog computer specifically for neural networks. Digital was easy to work with but when it comes to fuzzy stuff like AI, there are better ways.

We used to think 100mph was the fastest speed we could achieve even with machine (back when trains were the cutting edge). New tech will change things quickly, all we need is one breakthrough and things will fall into place.

We don't really know any of that for certain. We don't know what relationship LLMs and neural networks have to the actual functioning of a human brain. We don't know what relationship current computer architectures have to the architectures that allow us to create properly versatile AGIs. It could be the laptop I'm typing this on is plenty powerful. It could be you need a GPU with 100 petaflops and a few terabytes of RAM, in a similarly sized package to this laptop because the speed of light is too slow when you're working across GPU arrays.

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