3 ms·

Doing what has been done in the field would repeat the mistakes. Doing something original might actually be, you know, original.

by downer 19y ago

akkartik 19y ago
"Do not deny the classical approach, simply as a reaction, or you will have created another pattern and trapped yourself there." -- Bruce Lee http://en.wikipedia.org/wiki/Bruce_Lee#Philosophy http://en.wikipedia.org/wiki/Bruce_Lee#Philosophy
- downer 19y ago
  That's referring to refusing to use something you KNOW works, simply because it is traditional. Quite different from seeking your own path. If you follow the classical path, you will become bogged down in the vast field of AI. Ramanujan started from first principles and worked from there. My guess is that the person(s) who create "actual" AI will use that approach.
  - akkartik 19y ago
    That's a valid point. But there's value in studying things that didn't work. A huge number of obvious approaches to AI have been tried already, and the insights on why they don't work, or why they work on this problem but not this other one, are often the result of huge quantities of time spent on subtle traps and dead ends. To ignore them risks wasting your time all over again. I think AI boils down to just the problem of managing complexity that all of cs is about. The eventual solution will be vast, and will require designing lots of separate subsystems that work in very different ways and yet need to communicate in subtle ways. That emphasis on scale goes for research in general, I think. I recommend this video of Malcolm Gladwell talking about the nature of genius and how it's changed over the years. http://www.newyorker.com/online/video/conference/2007/gladwell http://www.newyorker.com/online/video/conference/2007/gladwe... He compares the decoding of the rosetta stone and linear B 40 years ago with Andrew Wiles proving Fermat's last theorem in the past decade, and how fundamentally different their respective approaches are. Both approaches may work, but you have to decide what attitude you want to take. You can either try to be a single monster-mind like Ramanujam sweeping through vast areas of research, or you can assume that won't happen and resign yourself to a lot of effort and learning before you're able to synthesize something useful. To summarize: I agree that you want to avoid cluttering your mind with the ideology of past approaches. There's huge value though in simply studying the episodic history of a field, to be aware of what has been tried, what worked and what didn't.
    - downer 19y ago
      If you follow your own path, you will later find various areas of overlap and intersection, but you may avoid sinking into the tar-pit as you would when starting out with ALL the dead weight. It takes tremendous effort to load your brain with all the existing knowledge, and once you do, you may find yourself only able to think in terms of it, having conditioned yourself to other people's failed modes of thinking.
      - jey 19y ago
        I think that is a function of attitude, and not a function of how much existing material you've read.
        downer 19y ago
        It's pretty hard to learn something just by reading. It takes doing. That's where you get stuck in someone else's mindset.
        akkartik 19y ago
        If you can get stuck in a mindset by following a specific trajectory of action then it doesn't matter whether you're following somebody deliberately or just blindly finding the same path. In fact, reading what somebody did will often help you avoid going down a path. I've read of many many more algorithms, successful and unsuccessful, than I've ever implemented.
        downer 19y ago
        You don't really understand something until you implement it. And AI is unlikely to be an algorithm like those we are familiar with...
        akkartik 19y ago
        > You don't really understand something until you implement it. Yup. We've already established that one has to prioritize what to implement. The priority clearly shouldn't always go towards doing something rather than reading what somebody else did. (Children would never learn to write that way.) Instead we prioritize differently at different times, and we try to have lots of feedback between reading and doing, by things like active reading.

Doing what has been done in the field would repeat the mistakes. Doing something original might actually be, you know, original.

by downer 19y ago

Doing what has been done in the field would repeat the mistakes. Doing something original might actually be, you know, original.

akkartik 19y ago

"Do not deny the classical approach, simply as a reaction, or you will have created another pattern and trapped yourself there." -- Bruce Lee http://en.wikipedia.org/wiki/Bruce_Lee#Philosophy http://en.wikipedia.org/wiki/Bruce_Lee#Philosophy

downer 19y ago
That's referring to refusing to use something you KNOW works, simply because it is traditional. Quite different from seeking your own path. If you follow the classical path, you will become bogged down in the vast field of AI. Ramanujan started from first principles and worked from there. My guess is that the person(s) who create "actual" AI will use that approach.
- akkartik 19y ago
  That's a valid point. But there's value in studying things that didn't work. A huge number of obvious approaches to AI have been tried already, and the insights on why they don't work, or why they work on this problem but not this other one, are often the result of huge quantities of time spent on subtle traps and dead ends. To ignore them risks wasting your time all over again. I think AI boils down to just the problem of managing complexity that all of cs is about. The eventual solution will be vast, and will require designing lots of separate subsystems that work in very different ways and yet need to communicate in subtle ways. That emphasis on scale goes for research in general, I think. I recommend this video of Malcolm Gladwell talking about the nature of genius and how it's changed over the years. http://www.newyorker.com/online/video/conference/2007/gladwell http://www.newyorker.com/online/video/conference/2007/gladwe... He compares the decoding of the rosetta stone and linear B 40 years ago with Andrew Wiles proving Fermat's last theorem in the past decade, and how fundamentally different their respective approaches are. Both approaches may work, but you have to decide what attitude you want to take. You can either try to be a single monster-mind like Ramanujam sweeping through vast areas of research, or you can assume that won't happen and resign yourself to a lot of effort and learning before you're able to synthesize something useful. To summarize: I agree that you want to avoid cluttering your mind with the ideology of past approaches. There's huge value though in simply studying the episodic history of a field, to be aware of what has been tried, what worked and what didn't.
  - downer 19y ago
    If you follow your own path, you will later find various areas of overlap and intersection, but you may avoid sinking into the tar-pit as you would when starting out with ALL the dead weight. It takes tremendous effort to load your brain with all the existing knowledge, and once you do, you may find yourself only able to think in terms of it, having conditioned yourself to other people's failed modes of thinking.
    - jey 19y ago
      I think that is a function of attitude, and not a function of how much existing material you've read.
      - downer 19y ago
        It's pretty hard to learn something just by reading. It takes doing. That's where you get stuck in someone else's mindset.
        akkartik 19y ago
        If you can get stuck in a mindset by following a specific trajectory of action then it doesn't matter whether you're following somebody deliberately or just blindly finding the same path. In fact, reading what somebody did will often help you avoid going down a path. I've read of many many more algorithms, successful and unsuccessful, than I've ever implemented.
        downer 19y ago
        You don't really understand something until you implement it. And AI is unlikely to be an algorithm like those we are familiar with...
        akkartik 19y ago
        > You don't really understand something until you implement it. Yup. We've already established that one has to prioritize what to implement. The priority clearly shouldn't always go towards doing something rather than reading what somebody else did. (Children would never learn to write that way.) Instead we prioritize differently at different times, and we try to have lots of feedback between reading and doing, by things like active reading.