And now I'm transforming that through the concept of taking a photograph and applying the clone tool via a light airbrush.
Repeat enough times, and you get uncompilable mud.
LLMs are not going to generate improvements.
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.
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...
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.
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.
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.
> 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.
There's basically a few axes you can view this on:
- Number of connections and complexity of connection structure: how much information is encoded about how to do the calculations.
- Mutability of those connections: these things are growing and changing -while doing the math on whether to fire-.
- How much calculation is really needed to do the computation encoded in the connection structure.
Basically, brains are doing a whole lot of math and working on a dense structure of information, but not very precisely because they're made out of meat. There's almost certainly different tradeoffs in how you'd build the system based on the precision, speed, energy, and storage that you have to work with.
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.
As long as the bottleneck is the fab capacity as wafers per hous, the number of operations per second per chip area determines who will produce more compute with best price. It's a good measure even between different technology nodes and superchips.
Nvidia is leader for a reason.
If manufacturing capacity increases to match the demand in the future, FLOPS or TOPS per Watt may become relevant, but now it's fab capacity.
I know they can provide creative new solutions to totally novel problems from firsthand experience… instead of assuming what they should be able to do, I experimented to see what they can actually do.
Focusing on the simple mechanics of training and prediction is to miss the forest for the trees. It’s as absurd as saying how can living things have any intelligence? They’re just bags of chemicals oxidizing carbon. True but irrelevant- it misses the deeper fact that solving almost any problem deeply requires understanding and modeling all of the connected problems, and so on, until you’ve pretty much encompassed everything.
Ultimately it doesn’t even matter what problem you’re training for- all predictive systems will converge on general intelligence as you keep improving predictive accuracy.