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> If you go this route, the amount of things you just don't know is innumerable.

Maybe so, but there are things we do know. For example, LLMs are only in a state that looks like consciousness when they are fed input. IOW, their "consciousness" can be switched off and on like a light switch without them being aware of it.

Biological beings have no such off/on switch. At all.

To me the argument is moot; their "consciousness" is a collection of files on a computer, which can be moved, duplicated, erased, edited, paused, sped up, slowed down, examined in detail.

The question is not "Can LLMs be conscious", it's "Are we prepared to call files on a USB stick 'conscious'", because if we are prepared to do that, then the word loses all meaning.

I'll open vim and create a consciousness now, or maybe if I engrave a slab of rock with a bunch of weights, that rock suddenly gains consciousness? How about if I chant all the weights into the wind - is the wind now conscious.

Whether they are conscious or not is a dumb question, because if they are, then everything else is as well.


> On a related note, I’ve seen videos of astra getting depressed when a creeper blew up its chest full of precious items.

The interesting question here is, would it still get depressed if depression was not in any of its training material?

Would it be able to claim to be happy if the entire concept was missing from its training corpus?

With humans, at least, they can express happiness and delight before they know that such a thing exists. Every human has done this, when they were a baby and matured to the point of being able to laugh.

With LLMs, though, if it doesn't exist in the training corpus, it will never express that it "feels" that missing emotion.


I don’t know what I don’t know. I know astra does go through Minecraft grief by farming potatoes.

>> A conscious machine that always answer the very exact same thing, formulated the exact same way, bit for bit, to a query is, well, quite a weird kind of "consciousness".

> You'll need to explain why.

Where are you going with this? I don't see a conclusion for this line of questioning.

I mean, you can't explain why a machine that reliably and predictably produces the same result is "the normal kind of conscious", can you? So why expect someone else to explain why it's a "weird" kind of conscious?


I ask because I truly don't understand what he's saying. Clearly we have different ideas about consciousness and I have no idea where we diverge.

He seems to be saying that determinism and consciousness are incompatible. But we already have examples that break that rule (us).


> But we already have examples that break that rule (us).

No, we don't. Where are you reading your research papers?


Please don't insult me.

> I don't know how well it is studied, but I suspect it is possible there is language-related complexity constraints to the effectiveness of the LLM algorithms.

I believe that's obvious - humans don't think in words. Neither do animals. A machine that only thinks in words is obviously going to be deficient in some things, no matter how proficient it is in everything else.


Aren't the new models thinking in neuralese?

> Aren't the new models thinking in neuralese?

Where did you read that?


Multiple places

> Solving cancer also has an unusual level of specification.

Where did you read that?

"Cancer" is not just a single disease, even though we layman use the term that way. Cancer is a family of diseases, each probably having their own specific solution, but even in each of these individual diseases, there is no specification at the level of any open maths problem.


I know what cancer is. Many scientists believe that all human adults above the age of about 30 have "cancer".

I really meant solving a specific cancer disease for a specific individual, which will require individualized medicine, which will require us to leverage AI to make it possible to do for the general population as opposed to doing it just for Lance Armstrong and the like.

I know I'm an optimist, but I really think AI is gonna result in drastically improved healthcare for a much lower price.


> The navier-stokes shows us what mathematical problem can be solved when $10m worth of compute is thrown at something.

> It also makes one wonder: What could AI solve if we managed to orchestrate billions worth of agents to take on a specific problem?

We need to have robotics automation catchup first. The math and coding problems are problems in written-space only: you can set up feedback loops to test what worked and what didn't, then try to resolve the defects, maybe back up and try a different path, etc.

What solved coding and maths problems weren't the damn models; open up a chat interface to a SOTA model and you'll see they are pretty limited in producing a solution without a feedback loop.

Instead, it was the harness around the models: it let them explore a space and use feedback to control and direct that exploration.

Until we can do it in meatspace, it's kinda pointless sinking a ton of money into large problems facing mankind...

Like establishing a colony on mars (so the next rock to hit earth isn't an ELE).

Or moving us to a post-scarcity utopia, ending the concept of money.

Or designing and building better batteries for transport that uses only electricity (so that we stop using fossils as fuel).

Or actually building mass-housing. Or mass-farming. Or both, potentially ending homelessness and starvation.

Those are all worthwhile problems to solve, but where's the point of getting a solution on paper? There's no exploratory mechanism there, even for humans, to come up with a solution.

So, all we are left with then is making knowledge workers obsolete: another ELE, but of a different, self-inflicted kind.


You just invented Skynet.

> And how many years of direct play and study does it take for a human to get good at chess or any other game?

Time is irrelevant to training; the more relevant comparison is "how many games does a human need to play to get diminishing returns".


> Why should that matter?

Because we want to use this as a replacement for humans, and the average human can learn the rules of chess without needing to see the rules explained hundreds of thousands of times in millions of games.

So, yeah, it matters if a model has millions of examples of something in its training set and still cannot follow the rules.


We're not talking about learning the rules of chess here, but playing a competent game from just being shown the rules. Why is it so hard for people to keep track of the thread of discussion?

> We're not talking about learning the rules of chess here, but playing a competent game from just being shown the rules.

Okay, lets go with that: it's the "shown the rules" bit that we are arguing about.

The argument is that a human may play maybe a dozen games after learning the rules, after which they won't be inadvertently attempting illegal moves. What we are observing with SOTA models is that, even after seeing millions of chess rules, rulebooks, actual games, etc, they still attempt illegal moves.

This does not point to generalisable and adaptable intelligence, such as we see in the average human.


This is not good reasoning. Humans need at least dozens if not hundreds of reinforcement sessions to only make legal moves, and still occasionally fail (consider pins, discovered check, failing to respond to check). LLMs must one-shot a competent game after imbibing a mass of disconnected units of information about chess. Nothing about the two are similar.

See my comment here for more: https://news.ycombinator.com/item?id=49725306


But we are. The models can't even follow the rules: they try illegal moves all the time.

> The LLM's tests passed, which is fine and dandy, but how do I know the tests are truly valid if I do not understand what is fundamentally being tested?

Do what I do sometimes: use opaque pointers (in C, or C++), and don't give the LLM the implementation, give them only the header, when asking it for tests.

I've lost count of the number of times I'll give only the header, but the LLM insists it needs to see the implementation as well in order to write the tests.

If you're not using C or C++, well, then write interfaces, give that to the LLM.


> In a few years we'll look at this the same way as people who unicycle or blacksmith.

It's equally likely that in a few years we see everyone who doesn't have skill the same way we view people who follow the Kardashians and other influencers: vacuous and incapable of non-augmented thought.


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