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To your first question, yes it's just a style of solving a problem that can be implemented in software for 90%+ of the time. For your second, I think that's pa

To your first question, yes it's just a style of solving a problem that can be implemented in software for 90%+ of the time. For your second, I think that's part of the problem. Most people confuse how a human brain learns with what is actually running in a machine learning program. It's similar at some level, but not really doing the same thing at another.

gbolcer 6y ago
BTW, Google Brain posted this on the subject of "brute force" solutions yesterday. I thought it would be of interest here. The big idea here is that enough brute force will lead to better compute-use techniques which will in turn make it possible to do more with less compute. But the current reality is that these systems don’t tend to justify their existence when compared to greener, more useful technologies. It’s hard to pitch an AI system that can only be operated by trillion-dollar tech companies willing to ignore the massive carbon footprint a system this big creates.
xpe 6y ago
> To your first question, yes it's just a style of solving a problem that can be implemented in software for 90%+ of the time. What insight is gained by this statement? What does it explain? What does it downplay? I'm not currently seeing much value in it. I'll explain why. Saying 'just a method of solving a problem' comes across as reductive without being useful. Imagine if someone said 'flying is just a means of movement' in the context of studying a hummingbird's agility. It says more about the speaker than the subject. It suggests the person is uninterested or focused on other things. So with regards to your statement, it suggests you don't care and/or don't appreciate what makes learning difficult. I'm talking about learning theory. About generalization given data. This is certainly not easy. Yes, it can be encoded in an algorithm, but that does not make it less interesting.

gbolcer 6y ago

BTW, Google Brain posted this on the subject of "brute force" solutions yesterday. I thought it would be of interest here. The big idea here is that enough brute force will lead to better compute-use techniques which will in turn make it possible to do more with less compute. But the current reality is that these systems don’t tend to justify their existence when compared to greener, more useful technologies. It’s hard to pitch an AI system that can only be operated by trillion-dollar tech companies willing to ignore the massive carbon footprint a system this big creates.

xpe 6y ago

> To your first question, yes it's just a style of solving a problem that can be implemented in software for 90%+ of the time. What insight is gained by this statement? What does it explain? What does it downplay? I'm not currently seeing much value in it. I'll explain why. Saying 'just a method of solving a problem' comes across as reductive without being useful. Imagine if someone said 'flying is just a means of movement' in the context of studying a hummingbird's agility. It says more about the speaker than the subject. It suggests the person is uninterested or focused on other things. So with regards to your statement, it suggests you don't care and/or don't appreciate what makes learning difficult. I'm talking about learning theory. About generalization given data. This is certainly not easy. Yes, it can be encoded in an algorithm, but that does not make it less interesting.