>It's like another intelligent species opening up a new way of looking at the world (at least for this very specific domain). and much to our surprise, it's a new way that's more powerful than ours.
It's not like this at all; let's not do this sort of thing. Humans are inveterate myth makers (viz. your description of how people conceive the Go board as army units), and our impositions on the world are easily confused for reality.
In this case, there's no "intelligent species" at work other than humans. We made this, and it is not an intelligence, it is a series of mathematical optimization functions. We have been doing this for decades, and these systems, while sophisticated, are mathematical toys that we have applied. We built and trained this thing to do exactly this.
As a student of AI you know that convolutional neural networks are black boxes and are hard to interpret. A different choice of machine would have yielded more insight about how it is operating (for example, decision trees are easier to interpret). The inscrutability of the system is not a product of its complexity; even a simple neural network is hard to understand.
This, actually, is my primary objection to using CNNs as the basic unit of machine learning - they don't help US learn, they require us to put our faith in machines that are trained to operate in ways that are resistant to inspection. In the future I hope that this research will move more towards models that provide interpretable results, so they ARE actually a tool for improved understanding.
> We made this, and it is not an intelligence, it is a series of mathematical optimization functions
You can say the same about your mind too which is a bunch of optimization nodes. If something is intelligent, does it matter if it's evolved in nature or created by a species who is evolved in nature?
> In the future I hope that this research will move more towards models that provide interpretable results
I think it's not really possible to understand in detail how these networks operate on the level of nodes, because emergent behavior is necessarily more complex than the sum of its parts.
It's a bit precious I think to say that a human is a "bunch of optimization nodes". I can write code to create a CNN, and I can draw a graph of how it operates on a piece of paper. We can't even decode a few million rat neurons the same way.
A CNN is a pure mathematical function - if you want, you could write it down that way. Given a set of inputs, it will always produce the same output. We don't call a linear regression model an "intelligence", a CNN is no different.
Of course I agree that humans are built up of billions of tiny machines like this, but let's appreciate the vast difference in scale.
My exaggeration was intentional to point out that if you scale up NN based systems, we are not that different :) I do appreciate it, but let's not forget that we have finite nodes, so at one point a machine can surpass us with "just mathematical functions".
> A CNN is a pure mathematical function
That's their basic property, but who are we to say that our cell based neural network is superior? Cells are just compositions of atoms and they are defined by quantum mechanics, which is... "just" math and information.
I also think that Go might be a great communication tool between AI and humans. If you look at the commentary from this angle if's fun to think about like this.
As a follow up to your idea, we should explore two paths: first create the most powerful AI, second create subsystems devised to be interpretable. The powerful method could be used to train the interpretable method, that is we need an interpreter to translate from machine AI to human AI, and interpretable systems provide a middle ground.
I think training one function to approximate another function wouldn't help much; we'd inevitably lose the subtleties of the higher-order function and any insights that come with it. If we could train a decision tree to do what a CNN does and then interpret the outcome, why not use decision trees in the first place?
I think the answer must be in figuring out how to decompose the black box of a CNN - it is, after all, just a set of simple algebraic operations at work, and we should be able to get something out of inspection.
I have to imagine Hinton et al. have done work in this regard, but this is far afield for me, so if it exists I don't know it.
Having a machine that gives you feedback in the middle of the game perhaps could be used to describe what is the weak point of a decision tree, and in which situations the method is good. It could detect some situations in which decision trees are good, then use that decision tree to understand what is happening and with that new understanding devise a new method in the middle. We could train a decision tree using new very powerful information about the value of the game in the middle of the game, that is new and powerful.
It's not like this at all; let's not do this sort of thing. Humans are inveterate myth makers (viz. your description of how people conceive the Go board as army units), and our impositions on the world are easily confused for reality.
In this case, there's no "intelligent species" at work other than humans. We made this, and it is not an intelligence, it is a series of mathematical optimization functions. We have been doing this for decades, and these systems, while sophisticated, are mathematical toys that we have applied. We built and trained this thing to do exactly this.
As a student of AI you know that convolutional neural networks are black boxes and are hard to interpret. A different choice of machine would have yielded more insight about how it is operating (for example, decision trees are easier to interpret). The inscrutability of the system is not a product of its complexity; even a simple neural network is hard to understand.
This, actually, is my primary objection to using CNNs as the basic unit of machine learning - they don't help US learn, they require us to put our faith in machines that are trained to operate in ways that are resistant to inspection. In the future I hope that this research will move more towards models that provide interpretable results, so they ARE actually a tool for improved understanding.