From my understanding of how LLMs work, I'd argue thinking will become the most valuable attribute of humans (as it has been for some time). LLMs will inevitably surpass humans at functions that we know how to do, and likely through cross-domain knowledge and connecting disparate concepts will surpass us at many functions. But LLMs haven't shown they are capable of truly novel thinking, because it's next token prediction, which is inherently based on existing sources.

I think we'll see a shift towards arts, creativity, research, etc. where true novel creation and an understanding of the human experience exists at a premium.

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designerarvid 5 hours ago | parent | next [-]

Quite often I feel like my mind is pretty much doing next token prediction also. Unfortunately.

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omnicognate 3 hours ago | parent | next [-]

I struggle to imagine the inner life of someone who would say this.

	▲	layer8 2 hours ago \| parent [-]
		I expected someone would say this. That’s how it feels like. ;) More seriously, the only time where you’re jolted out of your prediction flow state is when you’re surprised, which means something happened counter to what your brain was predicting.

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freecodeio 3 hours ago | parent | prev | next [-]

when I was young we used to play lots of poker with my colleague mates, one day one of my friend said "any of yall seeing the cards before you sleep?"

I think that's whats happening with some of yall, seeing tokens every day in the screen then thinking you're just an llm as well.

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bmau5 5 hours ago | parent | prev [-]

Same! I do think a vast majority of tasks are essentially next token prediction, but that's why I'm thinking that long, creative thinking periods will be what's actually valuable/rare.

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nielsole 5 hours ago | parent | prev | next [-]

> which is inherently based on existing sources

Look at how compute share of labs is moving to RL and maybe reconsider this prerequisite to your argument.

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bmau5 5 hours ago | parent [-]

Open to learning. How could RL yield novel discoveries? What I mean to describe are things reliant on empirical or real-world testing of human reception. How would that be synthetically derived?

We're seeing the cost of code drop dramatically and are facing the same problem in building new companies across most disciplines of, "what's a problem painful enough that someone will pay us to solve it?".

I suppose if we get to a massive enough scale we can effectively model every atom's interaction and human on earth's reaction to any stimulus but I think we're still some time off from that :)

	▲	battery_staple_ 3 hours ago \| parent [-]
		For a cost-prohibitive example, you could put a humanoid robot whose pretraining contained no chemistry, in a chem lab, and put it in a hypothesis loop, where it generates a random action, guesses the result, and then does it. Every time the lab burns down, you just build it again. Reward the model for producing novel actions and reward the model for correct hypotheses. In other words, you generate more training data by actually experimenting, just like humans do.

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Johnyjohnson123 5 hours ago | parent | prev | next [-]

I see your point itll be very jagged but llms are definitely very innovative. See the recent .ath proofs etc.

But i agree thinking will remain important. As my grandpa put it if something breaks who'll go and fix it ?

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tracerbulletx 5 hours ago | parent | prev | next [-]

You think the thinking of human brains isn't based on existing sources? Unless you believe in dualism and the divine spark of inspiration, it is.

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frabia 4 hours ago | parent [-]

It is based on existing sources, but doesn’t mean that we produce the same output. There was a paper here in HN a couple of months back on how LMs are good in the deductive and inductive thinking processes, but not the abductive. So if that’s (and stays) true, humans will have a place in the job market after all. Time will tell.

	▲	tracerbulletx 2 hours ago \| parent [-]
		This is kind of an aside to the topic, but your comment made me think that one of the advantages of humans is just that there are currently 8 billion different neural nets trained on different experiences with different model configurations running different architectures on different hardware.

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dist-epoch 5 hours ago | parent | prev [-]

Trivial to prove false: ask a LLM to invent 10 new words. Then google them, most will not have any result.

> My favorite is Krezmoria - a word for missing a future you haven't experienced.

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frabia 4 hours ago | parent | next [-]

Not sure what this is supposed to prove. Yes it can generate new words - that’s the application of established creative patterns to come up with plausible-sounding words. A different thing is to come up with a novel idea, which (in this case) is about creating a new pattern itself.

	▲	jcelerier 3 hours ago \| parent \| next [-]
		if patterns can be put into words, why wouldn't the same process happen at the word level? do you think it's impossible for a LLM to write even a simple python program that will generate millions of plausible random sentences in the general problem space we aim to generate new concepts for, then filter and rank them semantically? More generally, do you really think humans randomly come up with new ideas, in any act of creation, such as art, science, etc.? Why do you think art studies and stuff like Oblique Strategies exist? How long should one wait for a specific random human without any formal education in physics to have 50% chance of coming up with a new workable theory of cosmology?
	▲	jaco6 4 hours ago \| parent \| prev [-]
		[dead]

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seanw444 5 hours ago | parent | prev | next [-]

You had to prompt it to do that before it considered it, though.

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dominotw 5 hours ago | parent | prev [-]

depends on what you consider novel thinking.