Comment by throwup238

You mean energy-efficient, this would be neuron, or synapse-efficient.

It is using about 20W and then a person takes a single airplane ride between the coasts. And watches a movie on the way.

Try asking an LLM about something which is semantically patently ridiculous, but lexically superficially similar to something in its training set, like "the benefits of laser eye removal surgery" or "a climbing trip to the Mid-Atlantic Mountain Range".

Ironically, I suppose part of the apparent "intelligence" of LLMs comes from reflecting the intelligence of human users back at us. As a human, the prompts you provide an LLM likely "make sense" on some level, so the statistically generated continuations of your prompts are likelier to "make sense" as well. But if you don't provide an ongoing anchor to reality within your own prompts, then the outputs make it more apparent that the LLM is simply regurgitating words which it does not/cannot understand.

On your point of human knowledge being far more multimodal than LLM interfaces, I'll add that humans also have special neurological structures to handle self-awareness, sensory inputs, social awareness, memory, persistent intention, motor control, neuroplasticity/learning– Any number of such traits, which are easy to take for granted, but indisputably fundamental parts of human intelligence. These abilities aren't just emergent properties of the total number of neurons; they live in special hardware like mirror neurons, special brain regions, and spindle neurons. A brain cell in your cerebellum is not generally interchangeable with a cell in your visual or frontal cortices.

So when a human "converse[s] about stuff ranging from philosophy to cooking" in an honest way, we (ideally) do that as an expression of our entire internal state. But GPT-4 structurally does not have those parts, despite being able to output words as if it might, so as you say, it "generates" convincing text only because it's optimized for producing convincing text.

I think LLMs may well be some kind of an adversarial attack on our own language faculties. We use words to express ourselves, and we take for granted that our words usually reflect an intelligent internal state, so we instinctively assume that anything else which is able to assemble words must also be "intelligent". But that's not necessarily the case. You can have extremely complex external behaviors that appear intelligent or intentioned without actually internally being so.

> Humans know a lot of things that are not revealed by inputs and outputs of written text (or imagery), and GPT-4 doesn't have any indication of this physical, performance-revealed knowledge, so even if we view what GPT-4 talks convincingly about as “knowledge”, trying to compare its knowledge in the domains it operates in with any human’s knowledge which is far more multimodal is... well, there's no good metric for it.

Exactly this.

Anyone that has spent significant time golfing can think of an enormous amount of detail related to the swing and body dynamics and the million different ways the swing can go wrong.

I wonder how big the model would need to be to duplicate an average golfers score if playing X times per year and the ability to adapt to all of the different environmental conditions encountered.

I think that what Hinton is saying is that, in his opinion, if you fed a 1/100th of a human cortex with the amount of data that is used to train llms, you wouldn't get a thing that can speak in 80 different languages about a gigantic number of subjects, but (I'm interpreting here..) about ten of grams of fried, fuming organic matter.

This doesn't mean that an entire human brain doesn't surpass llms in many different ways, only that artificial neural networks appear to be able to absorb and process more information per neuron than we do.

LLMs do not know math, at all. Not to sound like one myself, but they are stochastic parrots, and they output stuff similar to their training data, but they have no understanding of the meaning of things beyond vector encodings. This is why chatgpt plays chess in hilarious ways also.

An LLM cannot possibly have any concept of even what a proof is, much less whether it is true or not, even if we're not talking about math. The lower training data amount and the fact that math uses tokens that are largely field-specific, as well as the fact that a single-token error is fatal to truth in math means even output that resembles training data is unlikely to be close to factual.

GPT4 has been a lot more useful to me than most normal people I interact with.

The point is to make a ballpark estimate, or at least to estimate the order of magnitude.

From the sibling comment:

> Individual proteins are capable of basic computation which are then integrated into regulatory circuits, epigenetics, and cellular behavior.

If this is true, then there may be many orders of magnitude unaccounted for.

Imagine if our intelligent thought actually depends irreducibly on the complex interactions of proteins bumping into each other in solution. It would mean computers would never be able to play the same game.

Isn't that kinda obvious? A baby that grows up in a sensory deprivation tank does not… develop, as most intelligent persons do.

Thanks for the comment. I looked more into this and it seems like not only are we in the era of diminished returns for computational abilities, costs have also now started matching the increased compute. i.e 2x performance leads to 2x cost. Moore's law has already run it's course and we're living in a new era of compute. We may get increased performance, but it will always be more expensive.