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.
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.
> 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.
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.
Human intuition and to certain extent, creativity are like this as well.
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.