[lisper]

> then the statistical models get better

Maybe. The statistical models are definitely better at natural language processing now, but they still fail on analytical tasks.

Of course, human brains are statistical models, so there's an existence proof that a sufficiently large statistical model is, well, sufficient. But that doesn't mean that you couldn't do better with an intelligently designed co-processor. Even humans do better with a pocket calculator, or even a sheet of paper, than they do with their unaided brains.

[YeGoblynQueenne]

If human brains are statistical models, why are human brains so bad at statistics?

Edt: btw, same for probabilistic inference, same for logical inference, and same for any other thing anyone's tried as the one true path to AI since the 1950's. Humans have consistently proven bad at everything computers are good at, and that tells us nothing about why humans are good at anything (if, indeed, we are). Let's not assume too much about brains until we find the blueprint, eh?

[lisper]

> why are human brains so bad at statistics?

That depends on what you mean by being "bad at statistics." What brains do on a conscious level is very different than what they do at a neurobiological level. Brains are "bad at statistics" on the conscious level, but at the level of neurobiology that's all they do.

As an analogy, consider a professional tennis or baseball player. At the neurobiological level those people are extremely good at finding solutions to kinematic equations, but that doesn't mean that they would ace a physics test.

[YeGoblynQueenne]

That is a very big assumption -that brains have conscious and subconscious levels that are good and bad at different things- that needs to be itself proved, before it can be used to support any other line of inquiry.

I'm not well versed in the relevant literature at all but my understanding is that research in the area points to the completely opposite direction: that humans e.g. playing baseball do not find solutions to kinematic equations, but instead use simple heuristics that exploit our senses and body configuration, like placing their hands in front of their eyes so that they line up with the ball etc.

This makes a lot more sense, not only for humans playing tennis, but for animals surviving in the wild, finding sustenance and shelter, and mates, while avoiding becoming a meal. Consider the Portia spider [1], a spider-hunting spider, itself prey to other hunting spiders, with a brain consisting of a few tens of thousands of neurons and still perfectly capable not only of navigating complex environments in all three space dimensions but also making complex plans involving detours.

Just think of how quickly a spider must be able to think that hunts, and is hunted by other spiders -some of the most deadly predators in the animal kingdom. There is no chance of a snowball in hell that such an animal has the time to solve kinematic equations with a few KBs of neurons. Absolutely no chance at all.

For that and many other stuff like that it looks very unlikely to me that human brains, or any brains, are like you say. In any case, that sounds positively Freudian and I don't mean that as an insult, but I so could.

______________

[1] My favourite. No, I don't mean meal. I just love this paper; it's almost the best paper in autonomous robotics and planning that I've ever read:

https://www.frontiersin.org/journals/psychology/articles/10....

[lisper]

> That is a very big assumption -that brains have conscious and subconscious levels that are good and bad at different things- that needs to be itself proved, before it can be used to support any other line of inquiry.

You can't be serious. Do you really doubt that hand-eye coordination and solving systems of kinematic equations on paper using math are disjoint skills? That one can be good at one without being good at the other? That there is in actual fact an inverse correlation between these skills? How do you account for the fact that even people who have never studied math or physics can learn to throw and catch a ball?

[YeGoblynQueenne]

... because they don't need to use maths or physics?

And yes, I'm serious. Can you please be less confrontational?

[lisper]

Sorry about that, I'm dealing with a troll on another thread so I'm on a bit of a hair trigger.

I think we have a fundamental disconnect somewhere, so let's try to diagnose it. Where do you start to disagree in the following series of claims:

1. People can have kinematic skills, like throwing and catching balls, without having math or physics skills, like solving kinematic equations.

2. In order to have kinematic skills, something in your brain must be doing something that can be equated by some mapping to solving kinematic equations, because the actions that your muscles perform when performing kinematic skills are the solutions to kinematic equations, so your brain must be producing those (things that map to) solutions somehow.

3. As far as we can tell, brains don't operate symbolically at the neurobiological level. Individual neurons operate according to laws having to do with electrical impulses, synapse firings, neurotransmitters, etc. none of which have anything to do with kinematics.

4. People with kinematic skills generally have only limited insight into how they do what they do when they apply those skills. Being able to catch a ball doesn't by itself give you enough insight to be able to describe to someone how to build a machine that would catch a ball. But someone with math and physics and engineering skills but no kinematic skills (your streotypical geek) could plausibly build a machine that could catch a ball much better than they themselves could. But the workings of a machine built using knowledge of math would almost certainly operate in a very different manner than the brain of a human with kinematic skills.

I think I'll stop there and ask if there is anything you disagree with so far.

[mistermann]

> That is a very big assumption -that brains have conscious and subconscious levels that are good and bad at different things- that needs to be itself proved, before it can be used to support any other line of inquiry.

Does this assumption itself need to be proven?

Besides, it's not true: you can simply define it as an assumption within a thought experiment and proceed merrily along, or you can just not bother to consider whether one's premises are true in the first place, and proceed merrily along.

The second option tends to be more popular in my experience, perhaps because it is so much easier, and perhaps for some other reasons also.

[TeMPOraL]

> If human brains are statistical models, why are human brains so bad at statistics?

If CPUs are made of silicon, why are they so bad at simulating semiconductors? Or why CPUs are so bad at emulating CPUs?

If JavaScript runs on a CPU, why is it so bad at doing bitwise stuff?

Etc.

What the runtime is made of is entirely separate of what's running on it. Same is with human brain (substrate) and human consciousness (software), or humans (substrate) and bureaucracy (runtime) and corporations (software).

[datascienced]

Your question implies it is obvious that a system of statistical models would (or should) be good at statistics. And that the opposite is a paradox. I would ask why you think that is obvious?

Being good at statistics is more of a knowledge graph of understanding concepts than a statistical model, I think.

Just like understanding a car engine.