[cynusx]

The comparison doesn't really hold.

He is comparing energy spend during inference in humans with energy spend during training in LLM's.

Humans spend their lifetimes training their brain so one would have to sum up the total training time if you are going to compare it to the training time of LLM's.

At age 30 the total energy use of the brain sums up to about 5000 Wh, which is 1440 times more efficient.

But at age 30 we didn't learn good representations for most of the stuff on the internet so one could argue that given the knowledge learned, LLMs outperform the brain on energy consumption.

That said, LLM's have it easier as they are already learning from an abstract layer (language) that already has a lot of good representations while humans have to first learn to parse this through imagery.

Half the human brain is dedicated to processing imagery, so one could argue the human brain only spend 2500 Wh on equivalent tasks which makes it 3000x more efficient.

Liked the article though, didn't know about HNSW's.

Edit: made some quick comparisons for inference

Assuming a human spends 20 minutes answering in a well-thought out fashion.

Human watt-hours: 0.00646

GPT-4 watt-hours (openAI data): 0.833

That makes our brains still 128x more energy efficient but people spend a lot more time to generate the answer.

Edit: numbers are off by 1000 as I used calories instead of kilocalories to calculate brain energy expense.

Corrected:

human brains are 1.44x more efficient during training and 0.128x (or 8x less efficient) during inference.

[wanderingmind]

Not just that the brain of a newborn comes pretrained with billions of years of evolution. There is an energy cost associated with that which must be taken into account

[eloeffler]

Then you must also take that cost into account when calculating the cost of training LLMs, as well as the cost humans operating the devices and their respective individual brain development.

LLMs are always an additional cost, never more efficient because they add to the calculation, if you look at it that way.

[coldtea]

Well, LLMs also pressupose humans and evolution, since they needed us to create them, so their tally is even higher by definition...

[madhatter999]

Also take into consideration the speed of evolution. LLM training might be much faster because a lot of competition power is used for its training. Maybe if it was the same speed as evolution then it would take billions of years, too?

[eru]

Also our brains and our language are co-optimised to be compatible.

ChatGPT has to deal with the languages we already created, it doesn't get to co-adapt.

[thfuran]

Brains are only about half a billion years old.

[AbstractH24]

Those are sunk costs

[bamboozled]

Humans spend their lifetimes training their brain

I don't think this is true personally, ideally as children, we spend out time having fun and learning about the world is a side effect. This borg like thinking applied to intelligence because we have LLMs is unusual to me.

I learned surfing through play and enjoyment, not through training like a robot.

We can train for something with intention, but I think that is mostly a waste of energy, albeit necessary on occasion.

[Jensson]

> we spend out time having fun and learning about the world is a side effect

What do you think "play" is? Animals play to learn about themselves and the world, you see most intelligent animals play as kids with the play being a simplification of what they do as adults. Human kids similarly play fight, play build things, play cook food, play take care of babies etc, it is all to make you ready for an adult life.

Playing is fun since playing helps us learn, otherwise we wouldn't evolve to play, we would evolve to be like ants that just work all day long if that was more efficient. So the humans who played around beat those who worked their ass off, otherwise we would all be hard workers.

[glenstein]

>we spend out time having fun and learning about the world is a side effect

I think the part of this that resonates as most true to me is how this reframes learning in a way that tracks truth more closely. It's not all the time, 100% of the time, it's in fits and starts, its opportunistic, and there are long intervals that are not active learning.

But the big part where I would phrase things differently is in the insistence that play in and of itself is not a form of learning. It certainly is, or certainly can be, and while you're right that it's something other than Borg-like accumulation I think there's still learning happening there.

[pessimizer]

That's like saying that you eat because it tastes good.

[Closi]

I think you would probably have to take into account the full functioning power of a human too.

We don't know how to fully operate a human brain when it's fully disconnected from eyes, a mouth, limbs, ears and a human heart.

[londons_explore]

> At age 30 the total energy use of the brain sums up to about 5000 Wh,

That doesn't sound right... 30 years * 20 Watts = 1.9E10 Joules = 5300 kWh.

[cynusx]

Where did you get the 20 Watt from?

My number is based on calorie usage

[CuriouslyC]

You're doing apples and oranges.

Humans who spend a long time doing inference have not fully learned the thing being inferred - unlike LLMs, when we are undertrained, rather than a huge spike in error rate, we go slower.

When humans are well trained, human inference absolutely destroys LLMs.

[cheema33]

> When humans are well trained, human inference absolutely destroys LLMs.

This isn't an apt comparison. You are comparing a human trained in a specific field to an LLM trained on everything. When an LLM is trained with a narrow focus as well, human brain cannot compete. See Garry Kasparov vs Deep Blue. And Deep Blue is very old tech.

[cynusx]

Depends on the person I guess, but yes. Humans are more accurate for now.

[greenthrow]

The article is a bit of a stretch but this is even more of a stretch. Humans can do way more than an LLM, humans are never in only learning mode, our brains are always at least running our bodies as well, etc.

[glenstein]

Exactly right - we are obviously not persistently in all-out training mode over the course of our lifetimes.

I suppose they intended that as a back-of-the-envelope starting point rather than a strict claim however. But even so, gotta be accountable to your starting assumptions, and I think a lot changes when this one is reconsidered.

[bufferoverflow]

Also, human brains come pre-trained by billions of years of evolution. It doesn't start as a randomly-connected structure. It already knows how to breathe, how to swallow, how to lean new things.

[bognition]

If we’re going to exclude the cortical areas associated with vision, you also need to exclude areas involved in motor control and planning. Those also account for a huge percent of the total brain volume.

We probably need to exclude the cerebellum as well (which is 50% of the neurons in the brain) as it’s used for error correction in movement.

Realistically you probably just need a few parts of the lambic system. Hippocampus, amygdala, and a few of the deep brain dopamine centers.

[philipov]

A lot of our cognition is mapped to areas that are used for something else, so excluding areas simply because they are used for something else is not valid. They can still be used for higher-level cognition. For example, we use the same area of the brain to process the taste of disgusting food as we do for moral disgust.

[pama]

Thanks. So after your corrected energy estimate and more reasonable assumptions it appeaars that the clickbaity title of the article is off by more than 7 orders of magnitude. With the upcoming NVidia inference chips later this year it will be off by another log unit. It is hard for biomatter to compete with electrons in silicon and copper.

[mirekrusin]

Also you can't cp human brain.

[vasco]

We can clone humans at current level of technology, otherwise there wouldn't be agreements about not doing it due to the ethical implications. Of course its just reproducing the initial hardware and not the memory contents or the changes in connections that happen at runtime.

[marginalia_nu]

You kinda can do a sort of LoRA though. Reading the right book can not only change what you hold true, but how you think.

[Rinzler89]

The plot of The Matrix would beg to differ.

[phantompeace]

Not yet, anyway.

[freehorse]

> representations for most of the stuff on the internet

Yes we have learnt far more complex stuff, ffs.

[jryan49]

How about the fact that llm's don't work unless humans generate all that data in the first place. I'd say the llm's energy usage is the amount it takes to train plus the amount to generate all that data. Humans are more efficient at learning with less data.

[Closi]

Humans also learn from other humans (we stand on the shoulders of giants), so we would need to account for all the energy that has gone into generating all of human knowledge in the 'human' scenario too.

i.e. not many humans invent calculus or relativity from scratch.

I think OP's point stands - these comparisons end up being overly hand-wavey and very dependent on your assumptions and view.

[dist-epoch]

For every calorie a human consumes, hundreds or thousands more are used by external support systems.

So yeah, you do use 2000 calories a day, but unless you live in an isolated jungle tribe, vast amounts of energy are consumed on delivering you food, climate control, electricity, water, education, protection, entertainment and so on.

[b112]

By that metric, the electricity is only part of it. The cost of building the harsware, the cost of building the roof and walls for the datacentre, the cost of clearing the land, cost of humans maintaining the hardware, the cost of all the labour making the linux kernel, libc6, etc, etc. Lots of additionals here too.

[greenthrow]

Are you going to include all the externalities to build and power the datacenters behind LLMs then? Because i guarantee those far outweigh what it takes to feed one human.

[unyttigfjelltol]

Including support from ChatGPT. It really is a comparison of calories without ChatGPT and calories with, and that gets to the real issue of whether ChatGPT justifies its energy intensity or not. History suggests we won't know until the technology exits the startup phase.