LessWrong 2.0 Reader

You can play the same game in the other direction. Given a cold source, you can run your chips hot, and use a steam engine to recapture some of the heat. 

The Landauer limit still applies. 

>But GPT4 isn't good at explicit matrix multiplication either.

So it is also very inefficient. 

Probably a software problem. 

yup, sorry for missing that, thanks!

And as a follow-up in case the current sleeper agent does admit when it's doing something dangerous: I'd be interested whether you can train a sleeper agent that never admits it's doing something dangerous if the "2024" trigger is in the prompt (even if after backdoor insertion, the blue team finetunes it to answer truthfully in cases with dangerous code but without the presence of that trigger). For such a sleeper agent, you could again check whether a probe works.

Humans suck at arithmetic. Really suck. From comparison of current GPU's to a human trying and failing to multiply 10 digit numbers in their head, we can conclude that something about humans, hardware or software, is Incredibly inefficient. 

Almost all humans have roughly the same sized brain. 

So even if Einsteins brain was operating at 100% efficiency, the brain of the average human is operating at a lot less.

ie intelligence is easy - it just takes enormous amounts of compute for training.

Making a technology work at all is generally easier than making it efficient. 

Current scaling laws seem entirely consistent with us having found an inefficient algorithm that works at all. 

Like chatGPT uses billions of floating point operations to do basic arithmetic mostly correctly. So it's clear that the likes of chatGPT are also inefficient. 

Now you can claim that chatGPT and humans are mostly efficient, but suddenly drop 10 orders of magnitude when confronted with a multiplication. But no really, they are pushing right up against the fundamental limits for everything that isn't one of the most basic computational operations. 

A cooked food could technically be called a processed food but I don't think that adds much meaningful confusion. I would say the same about soaking something in water.

Olives can be made edible by soaking them in water. If they're made edible by soaking in a salty brine (an isolated component that can be found in whole foods in more suitable quantities) then they're generally less healthy.

Local populations might adapt by finding things that can be heavily processed into edible foods which can allow them to survive, but these foods aren't necessarily ones which would be considered healthy in a wider context.

mirroring much or our seemingly idiosyncratic cognitive biases, quirks, and limitations.

True. 

They also have a big pile of their own new idiosyncratic quirks. 

https://www.lesswrong.com/posts/aPeJE8bSo6rAFoLqg/solidgoldmagikarp-plus-prompt-generation [LW · GW]

These are bizarre behaviour patterns that don't resemble any humans. 

This looks less like a human, and more like a very realistic painted statue. It looks like a human, complete with painted on warts, but scratch the paint, and the inhuman nature shows through. 

The width of mindspace is completely irrelevant.

The width of mindspace is somewhat relevant. 

At best, we have found a recipe, such that if we stick precisely to it, we can produce human-like minds. Start making arbitrary edits to the code, and we wander away from humanity. 

At best we have found a small safe island in a vast and stormy ocean. 
 

The likes of chatGPT are trained with RLHF. Humans don't usually say "as a large language model, I am unable to ..." so we are already wandering somewhat from the human. 

Biological cells operate directly at thermodynamic efficiency limits:

Well muscles are less efficient than steam engines. Which is why hamster wheel electricity is a dumb idea, burning the hamster food in a steam engine is more efficient. 

If GPT5 actually comes with competent agents then I expect this to be a "Holy Shit" moment at least as big as ChatGPT's release. So if ChatGPT has been used by 200 million people, then I'd expect that to at least double within 6 months of GPT5 (agent's) release. Maybe triple. So that "Holy Shit" moment means a greater share of the general public learning about the power of frontier models. With that will come another shift in the Overton Window. Good luck to us all.

then we should not expect moore's law to end with brains still having a non-trivial thermodynamic efficiency advantage over digital computers. Except that is exactly what is happening. TSMC is approaching the limits of circuit miniaturization, and it is increasing obvious that fully closing the (now not so large) gap with the brain will require more directly mimicking it through neuromorphic computing^[2].

This is a clear error. 

There is no particular reason to expect TSMC to taper off at a point anywhere near the theoretical limits.

A closely analogous situation is that the speed of passenger planes has tapered off.  And the theoretical limit (ignoring exotic warp drives) is light speed. 

But in practice, planes are limited by the energy density of jet fuel, economics, regulations against flying nuclear reactors, atmospheric drag etc. 

This isn't to say that no spaceship could ever go at 90% light speed. Just that we would need a radically different approach to do that, and we don't yet have that tech.

So yes, TSMC could be running out of steam. Or not. The death of moores law has been proclaimed on a regular basis since it existed. 

"Taiwanese engineers don't yet have the tech to do X" doesn't imply that X is physically impossible.