[ben_w]

Saying they definitely won't or they definitely will are equally over-broad and premature.

I currently expect we'll need another architectural breakthrough; but also, back in 2009 I expected no-steering-wheel-included self driving cars no later than 2018, and that the LLM output we actually saw in 2023 would be the final problem to be solved in the path to AGI.

Prediction is hard, especially about the future.

[jart]

GPT4 does inference at 560 teraflops. Human brain goes 10,000 teraflops. NVIDIA just unveiled their latest Blackwell chip yesterday which goes 20,000 teraflops. If you buy an NVL72 rack of the things, it goes 1,400,000 teraflops. That's what Jensen Huang's GPT runs on I bet.

[ben_w]

> GPT4 does inference at 560 teraflops. Human brain goes 10,000 teraflops

AFAICT, both are guesses. The low-end estimate I've seen for human brains are ~ 162 GFLOPS[0] to 10^28 FLOPS[1]; even just the model size for GPT-4 isn't confirmed, merely a combination of human inference of public information with a rumour widely described as a "leak", likewise the compute requirements.

[0] https://geohot.github.io//blog/jekyll/update/2022/02/17/brai...

[1] https://aiimpacts.org/brain-performance-in-flops/

[jart]

They're not guesses. We know they use A100s and we know how fast an A100 goes. You can cut a brain open and see how many neurons it has and how often they fire. Kurzweil's 10 petaflops for the brain (100e9 neurons * 1000 connections * 200 calculations) is a bit high for me honestly. I don't think connections count as flops. If a neuron only fires 5-50 times a second then that'd put the human brain at .5 to 5 teraflops it seems to me. That would explain why GPT is so much smarter and faster than people. The other estimates like 1e28 are measuring different things.

[ben_w]

> They're not guesses. We know they use A100s and we know how fast an A100 goes.

And we don't know how many GPT-4 instances run on any single A100, or if it's the other way around and how many A100s are needed to run a single GPT-4 instance. We also don't know how many tokens/second any given instance produces, so multiple users may be (my guess is they are) queued on any given instance. We have a rough idea how many machines they have, but not how intensively they're being used.

> You can cut a brain open and see how many neurons it has and how often they fire. Kurzweil's 10 petaflops for the brain (100e9 neurons * 1000 connections * 200 calculations) is a bit high for me honestly. I don't think connections count as flops. If a neuron only fires 5-50 times a second then that'd put the human brain at .5 to 5 teraflops it seems to me.

You're double-counting. "If a neuron only fires 5-50 times a second" = maximum synapse firing rate * fraction of cells active at any given moment, and the 200 is what you get from assuming it could go at 1000/second (they can) but only 20% are active at any given moment (a bit on the high side, but not by much).

Total = neurons * synapses/neuron * maximum synapse firing rate * fraction of cells active at any given moment * operations per synapse firing

1e11 * 1e3 * 1e3 Hz * 10% (of your brain in use at any given moment, where the similarly phrased misconception comes from) * 1 floating point operation = 1e16/second = 10 PFLOP

It currently looks like we need more than 1 floating point operation to simulate a synapse firing.

> The other estimates like 1e28 are measuring different things.

Things which may turn out to be important for e.g. Hebbian learning. We don't know what we don't know. Our brains are much more sample-efficient than our ANNs.

[queuebert]

Synapses might be akin to transistor count, which is only roughly correlated with FLOPs on modern architectures.

I've also heard in a recent talk that the optic nerve carries about 20 Mbps of visual information. If we imagine a saturated task such as the famous gorilla walking through the people passing around a basketball, then we can arrive at some limits on the conscious brain. This does not count the autonomic, sympathetic, and parasympathetic processes, of course, but those could in theory be fairly low bandwidth.

There is also the matter of the "slow" computation in the brain that happens through neurotransmitter release. It is analog and complex, but with a slow clock speed.

My hunch is that the brain is fairly low FLOPs but highly specialized, closer to an FPGA than a million GPUs running an LLM.

[mlyle]

> I don't think connections count as flops. If a neuron only fires 5-50 times a second then that'd put the human brain at .5 to 5 teraflops it seems to me.

That assumes that you can represent all of the useful parts of the decision about whether to fire or not to fire in the equivalent of one floating point operation, which seems to be an optimistic assumption. It also assumes there's no useful information encoded into e.g. phase of firing.

[jart]

Imagine that there's a little computer inside each neuron that decides when it needs to do work. Those computers are an implementation detail of the flops being provided by neurons, and would not increase the overall flop count, since that'd be counting them twice. For example, how would you measure the speed of a game boy emulator? Would you take into consideration all the instructions the emulator itself needs to run in order to simulate the game boy instructions?

[mlyle]

Already considered in my comment.

> Imagine that there's a little computer inside each neuron that decides when it needs to do work

Yah, there's -bajillions- of floating point operation equivalents happening in a neuron deciding what to do. They're probably not all functional.

BUT, that's why I said the "useful parts" of the decision:

It may take more than the equivalent of one floating point operation to decide whether to fire. For instance, if you are weighting multiple inputs to the neuron differently to decide whether to fire now, that would require multiple multiplications of those inputs. If you consider whether you have fired recently, that's more work too.

Neurons do all of these things, and more, and these things are known to be functional-- not mere implementation details. A computer cannot make an equivalent choice in one floating point operation.

Of course, this doesn't mean that the brain is optimal-- perhaps you can do far less work. But if we're going to use it as a model to estimate scale, we have to consider what actual equivalent work is.

[MAXPOOL]

That's is based on old assumption of neuron function.

Firstly, Kurzweil underestimates the number connections by order of magnitude.

Secondly, dentritic computation changes things. Individual dentrites and the dendritic tree as a whole can do multiple individual computations. logical operations low-pass filtering, coincidence detection, ... One neuronal activation is potentially thousands of operations per neuron.

Single human neuron can be equivalent of thousands of ANN's.