But why think "statistics" precludes it from having genuine understanding to some degree. After all, there is a statistical description the human brain but that doesn't seem to preclude understanding.
I keep asking this whenever I see dismissive responses of this sort, and I never get a reply.
I don't know why you think this is true. If statistically B follows A to a high degree, then a sufficiently advanced statistical model will represent "A then B" in some manner. In a predictive language model, at some point the best way to model a text corpus that indirectly references the "A then B" causal structure is to just model that structure and reference it as needed.
The statistical model will die to combinatorial explosion between billions of possible combinations of locations, times, and modes of transport. In various literature in 2 hours you might have travelled across town, across continents, or to the moon. Statistical approach to such problems is dumb.
>Statistical approach to such problems is dumb.
Well, expecting your model to extract a spatial representation of the world from text is a dumb approach indeed. We interact with the spatial information much more directly. But our ability to navigate is fundamentally just a process of capturing regularities in our sensory input.
> But our ability to navigate is fundamentally just a process of capturing regularities in our sensory input.
i don't think this is true at all, many animals have dedicated 'hardware' for navigation that can sense magnetic fields, etc. We seem to be born with spatial awareness that is far beyond what GPT will ever be capable of.
Yes, but suppose A implied B only if C were true. And in the training corpus C were always true (hence learned A=>B) but in the test corpus suppose C is not true, then the learned statistical rule is wrong.
The problem is that to cover all these possibilities will require not a billion documents but something like exp(billion) documents (exaggerating, but to give you a feel for combinatorial explosions) as @ClumsyPilot explains.
And problem is the real world always keeps “changing” (Eg: whether C is valid or not) compared to the examples you trained on. This is referred to in polite company as “distribution shift” where your underlying statistical distribution keeps evolving.
Kahneman and Tversky came up with questions meant to probe that in humans:
> If a baseball bat and a ball cost a total of $1.10, and the bat costs $1 more than the ball, then how much does the ball cost?
Most people arrive at $0.10 quickly (system 1), but you can't arrive at the right answer if you go by your gut, you have to shift into a different mode of cognition (system 2) in order to compute the correct answer.
It's a matter of scale. Adult human brains aren't just trained for 18 years with a static dataset - they've got hundreds of millions of years of mammalian evolution and fault tolerance built into them. Our brains self-reconfigure in response to external stimuli as we age, to the point where we can (in rare cases) lose significant fractions without becoming a vegetable. The biochemistry of a single neuron is likely oodles more complex than the most complex AI we've made.
It's like going from an analysis of a small family to an analysis of a civilization: at some point the sample size crosses a threshold and emergent phenomena start to dominate the system. Yes, it's basically all just statistics (ignoring quantum hocus pocus and the supernatural) but we still don't understand how to go from statistics to intelligence.
>The biochemistry of a single neuron is likely oodles more complex than the most complex AI we've made.
There's a lot of biochemistry in service to supporting the neurons existence exclusive from its functional properties for the brain. In terms of a neurons I/O mapping, deep learning has that covered[1]
>Yes, it's basically all just statistics... but we still don't understand how to go from statistics to intelligence.
But this assumes intelligence isn't an emergent phenomenon of sufficiently general learning. GPT-3 suggests this might be the case.
[1] https://www.biorxiv.org/content/10.1101/613141v1.full.pdf
Does a honeybee (arguably the most intelligent insect) understand that pollen is a necessary component of honey? Or that it is using geometry to convey directions using angles of the sun? Why should we expect a piece of technology with magnitudes less sophistication to be able to mimic higher order lifeforms?
Putting aside these as of yet unanswerable questions it seems to me that, interestingly, the less commonly used definitions of "understand" may increasingly fit the bill with an inverse relation to common use.
From Merriam Webster: 2 : to accept as a fact or truth or regard as plausible without utter certainty 3 : to interpret in one of a number of possible ways 4 : to supply in thought as though expressed // "to be married" is commonly understood after the word engaged
https://fs.blog/2012/01/richard-feynman-on-why-questions/
> Of course, it’s an excellent question. But the problem, you see, when you ask why something happens, how does a person answer why something happens? For example, Aunt Minnie is in the hospital. Why? Because she went out, slipped on the ice, and broke her hip. That satisfies people. It satisfies, but it wouldn’t satisfy someone who came from another planet and knew nothing about why when you break your hip do you go to the hospital. How do you get to the hospital when the hip is broken? Well, because her husband, seeing that her hip was broken, called the hospital up and sent somebody to get her. All that is understood by people. And when you explain a why, you have to be in some framework that you allow something to be true. Otherwise, you’re perpetually asking why. Why did the husband call up the hospital? Because the husband is interested in his wife’s welfare. Not always, some husbands aren’t interested in their wives’ welfare when they’re drunk, and they’re angry.
Honeybees apparently have 1 million neurons compared to GPT-3's 175 billion parameters. Granted, there isn't a 1 to 1 correspondence between biological neuron and parameter. And considering much of the honeybees neuron's would be directed towards biological function and control mechanisms, whereas all of GPT-3's parameters are directed towards text prediction, the relevant expressive power of GPT-3 is plausibly much larger.
[1] https://en.wikipedia.org/wiki/List_of_animals_by_number_of_n...
But the issue here isn't about modelling the human, it's about learning a model that captures understanding. To assume that human or animal biology is necessary for understanding is a big leap that isn't supported by the evidence. For one, the relevance of biology and electrochemistry appears to be to influence the function of neurons in various ways. So there's no reason to think that this influence cannot be captured by a model driven by statistical relationships.