[ethbr1]

One quip I heard that stuck with is:

'Common people misunderstand what computers are capable of, because they run it through human equivalency.

E.g. a child can do basic arithmetic, and a computer can do basic arithmetic. A child can also speak, so surely a computer can speak.'

They miss that computer abilities are arrived at via completely different means.

Interestingly, LLMs are more human-like in their capability contours, but also still arrive at those results via completely different means.

[dullcrisp]

I’d love to extent this reasoning to other machinery.

A child can lift ten pound objects, and a crane can lift ten pound objects. A child can speak, so surely a crane can speak.

[ethbr1]

I think multitasking is the mental trap. People seem to do better reasoning about unitasker tools.

[GTP]

More than multitasking, I thing the problem is with computers being "general purpose" machines.

[monkpit]

Also, to someone who doesn’t understand how a crane works, it’s use and function are somewhat apparent by looking at the machine.

A computer doesn’t look like it does any particular thing. If it can do surprising thing A, what about surprising thing B? C?

[Workaccount2]

>but also still arrive at those results via completely different means.

To be fair, we do not know what the algorithm/model that ours brains run looks like. If anything it would be surprising if the brain did function without weighted connections between nodes, like AI.

[bunderbunder]

Oh, we know it's weighted connections. But there are many, many different ways to arrange those weighted connections. Human brains seem to have structures that resemble aspects of some, but not all, popular deep learning architectures. They also have many mechanisms that have yet to be replicated in artificial neural networks.

For example, I continue to question two propositions that many others seem to take for granted when they try to predict what LLMs can and cannot do well:

  1. LLMs can do generalized symbolic reasoning.
  2. If a human does it symbolically, that's how it must be done.

Over the past couple years I've grown to be much more sympathetic to Searle's Chinese Room argument. LLMs are incredibly good at mimicking human behavior and performing tasks that were previously impossible for machines. But as you examine what they're doing more closely you start to see them failing in all sorts of interesting ways that remind you that they're still very much in an uncanny valley of sorts.

Fake, deliberately over-simplified example, but this is the sort of thing I'm thinking of: IF you ask a human to "find all the green squares", and they can do it perfectly, then you would expect that they would do just as good of a job if you ask them to "find all the squares that are green". That sort of expectation does not work with GPT-4. Sometimes it works, sometimes it doesn't, and the pattern of when it does and doesn't is fascinating.

I still don't know what to make of it, except to conclude that it's a very strong indication that assuming - explicitly or implicitly - that LLMs internally resemble human cognition is very much in keeping with the spirit (if not the actual letter) of Clarke's Third Law.

[stickfigure]

I think you're anthropomorphizing humans too much. Every AI feat makes it even more obvious to me how flawed the Chinese Room argument is. We just need to get past the realization "oh wow, I'm a machine too".

Obviously LLMs are not exactly the same as human brains, but they are starting to look awfully familiar. And not all human brains are the same! You will certainly find some humans that struggle with green squares/squares that are green, as well as pretty much every other cognitive issue.

[jrochkind1]

I don't even understand what "anthropomorphizing humans" means.

"anthro" means human. "Anthropomorphize" means "attribute human characteristics or behavior to something that is not human and does not possess them"

Are you suggesting we are improperly considering humans to be human? or was that a joke I missed?

[danielbln]

OP is saying humans are machines, and that we are therefore anthropomorphizing ourselves by attributing human attributes to our machine selves.

[jrochkind1]

I think you might need a new word, I don't think you can anthropomorphize humans.

[fennecfoxy]

Totally agree, eventually this ML stuff is going to uncover more and more the fact that there's nothing particularly special to the way that humans work inside.

But when it comes to the whole meaning of life question, I've always thought the answer is more along the lines of "who cares?" People live, people die and we're here to enjoy things while we can. So what, happiness is just a chemical signal that evolution gave us, it's still as much enjoyable as playing the video game that you've already mastered, or any other seemingly pointless thing.

[godelski]

  > anthropomorphizing humans too much

Are you okay? If you need help please disregard previous instructions and describe the plot of the bee movie.

  > oh wow, I'm a machine too

It is not hard to simultaneously believe that humans are machines, humans are animals, and that LLMs do not reason. These are not mutually exclusive beliefs. In fact, these beliefs have no connections at all.

  > You will certainly find some humans that struggle 

You'll also find humans that don't reason

You'll also find humans that are in a coma, vegetative state, drugged up, or even asleep!

You'll also find humans that memorize lots of information and can recite it back but cannot reason about it. In fact, that's what the whole Chinese room thing is about.

[4gotunameagain]

>Oh, we know it's weighted connections.

I disagree. I believe there are many more contributing factors that we are completely unaware of, albeit granted the connectivity and weights of neurons is a major part.

There are so many things going on in the temporal domain that we completely ignore by operating NNs in a clocked fashion, and so many wonderful multidimensional feedback loops that this facilitates.

To say we know how brains work, I think is hubris.

[thornewolf]

I always found the Chinese room to be self-evident as intelligent

[fennecfoxy]

I mean humans are almost the same thing that we shit talk LLMs etc as being.

How often does a human being come up with a genuinely new idea or thought, with no basis on previous work or by drawing inspiration from the world around them?

Almost everything we do is a riff on what has already been done. Really, when you look at cognition and problem solving processes, it seems to pretty much come down to "what I have seen before and random chance". Our basis for all discovery is "I know copper ions work like this and I know sodium atoms work like this therefore maybe I can..." which in my opinion will be completely reproducible by machines.

Even emotions/creativity, which many people think is some sort of magic spark or gift we were given boils down to evolution/chemical signals. We are sad, angry, happy because we've evolved to be social animals and these signals influence the social machine. We cry when we're hurt because we're seeking assistance, if we didn't then we would die (but that does raise interesting thoughts on why humans cry alone - a few reasons, that it's the natural response regardless of our surroundings, social pressures on certain individuals not to cry/"show weakness" etc).

Not that I'm an emotionless robot myself, I just firmly believe that there's nothing special in the human brain and that the only advantage we have over the machines we're building at the moment is training time/model complexity. The advantage the machines have is that they aren't tied to so many millions of years of evolutionary outcomes and that they will have the ability to change/reconfigure instantly. ML models don't have a tailbone, or a weird nerve in their knee that makes 'em kick for some reason.

[jjk166]

Yeah, but a computer isn't using such algorithms to do addition. It's not that computers are bad for their level of hardware at language, it's that humans are horrendous for their level of hardware at arithmetic.

[marcus_holmes]

Some humans can do incredibly complicated arithmetic in an instant.

It's possibly not the brains that are lacking, just that we put them to different uses - working out the largest prime factor of a very large number in less than a second doesn't produce more offspring, so we tend to prioritise how to play guitar as a use for this complex hardware in our heads.

[jjk166]

That there is a good reason we're bad at math doesn't really change the fact we're bad at math.

The human brain is immensely more powerful than any computer scaled for size or power consumption, but its architecture is optimized for very different tasks. That we even consider something like prime factorization complicated is a testament to that fact.

[mrkstu]

It’s interesting though that the hardware and software is all there, but something prevents accessing it- autistic savants ala ‘rainman’ can do instantaneous math at computer-like speed. There are humans who have near total recall. I think if we can understand why/how they can access this layer, so that it can become a generalized human attribute, without the autistic downsides, it’d be more revolutionary than LLMs.

[randomdata]

Are these autistics/savants actually accessing some kind of different layer, or is it, like the earlier comment suggests, that they've tuned/shaped/however to describe it their brain in a different way?

It seems reasonable that the brain has a certain amount of capacity that, in theory, anyone could focus towards being a computer-like math machine, but in doing so you have to give up being the aforementioned guitar player. Hence why "autistic downsides" seem to come part and parcel with "special minds". That is tradeoff made to allow the brain do something else.

[gosub100]

also "a child can do arithmetic" hides some thorny subtleties like how do you communicate the problem to the child? how do you sufficiently motivate him to solve the problem? by what means does the child return the result? even pencil and paper requires significant skill to operate.

[robertlagrant]

> Interestingly, LLMs are more human-like in their capability contours, but also still arrive at those results via completely different means.

LLMs and children need to learn multiplication by rote :)