Once this is clear, the discussion becomes healthier: linguists continue to try and understand fundamentals; ML NLP continues to try to get impressive, possibly lucrative results. Logical research can borrow from ML results, or re-use them, and so forth, but I agree with Chomsky that it can't be a shot in the dark always, because once you deploy it and pass it some tough decisions to make, well, what then?
I was pleased to see at least one idea, the recursive structures found in his theories, which is increasingly being accepted by people like Hinton, so that's great.
You can't understand why without considering the environment, the problem is that it's too expensive to train AI agents in reality and simulated environments are too simplistic.
But if such a simulated environment is available, agents can learn general skills.
> Deep mind: Generally capable agents emerge from open-ended play
https://deepmind.com/blog/article/generally-capable-agents-e...
You can't understand why without considering the environment, the problem is that it's too expensive to train AI agents in reality and simulated environments are too simplistic.
But if such a simulated environment is available, agents can learn general skills.
> Deep mind: Generally capable agents emerge from open-ended play
https://deepmind.com/blog/article/generally-capable-agents-e...
In this kind of setup the agents know why they act like they do.
Lots of brilliant people have looked, but the issue at the moment is that the last set of questions and challenges was surprisingly blown away by statistical approaches and we are waiting to find the ceiling on those approaches before being able to formulate the right questions for the next round of deep thinking I suppose. The Winnograd schema were kinda meant to prompt that....
The issue is that obtaining this understanding requires a lot of effort.
OpenAI invested in trying to understand how a simple CNN works.
You can read their results here: https://distill.pub/2020/circuits/
> In the original narrative of deep learning, each neuron builds progressively more abstract, meaningful features by composing features in the preceding layer. In recent years, there’s been some skepticism of this view, but what happens if you take it really seriously?
> InceptionV1 is a classic vision model with around 10,000 unique neurons — a large number, but still on a scale that a group effort could attack. What if you simply go through the model, neuron by neuron, trying to understand each one and the connections between them? The circuits collaboration aims to find out.
Well, there was a good fifty plus years of linguists without access to good statistical methods who also failed to solve the problems as well... And they also didn't come up with the kinda functional answers that we now have from statistical methods, which are now able to do crazy things like translate reasonably well between nearly arbitrary language pairs.
Your statement sounds like a theoretician's sour grapes...
We do understand how and why it works to a certain degree (gradient based input to output approximation). But if you want the meaning of the third neuron in the 10th layer, then yes... But in the same sense we don't understand thermodynamics because we don't account for each particle, just a statistical aggregate of them.
And on the other hand, we don't understand much about how people work or how we are motivated, even though we have first and third person perspectives. Psychology seems to be no further advanced than AI. And yet we work with what we have.
The reason we have not figured out NLP is not because we are incapable, it’s because enough of the right minds have not been looking at it as a puzzle worth solving, possibly because it and other AI related concepts are often introduced at or just before the PhD level and so most minds in science never encounter it (even though they could never encounter any other written concept without it)