5 ms·

I recommend starting from scratch and ignoring the field. All the field will teach you is very complex ways to NOT have AI.

by downer 19y ago

Kaizyn 19y ago
Why would you want someone to do this? Wouldn't that simply lead one to remake all the mistakes that have been made in the field?
- 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.
pg 19y ago
Those lessons can be useful, though. You might make the same mistakes.
- downer 19y ago
  Or you might come up with something that works, by avoiding having your mind crammed and distorted by the classical mess.
  - akkartik 19y ago
    It's one thing to notice that beginners can sometimes make contributions by luck, just by not knowing how hard something is supposed to be. It's another to use this reasoning to glorify lack of knowledge. Whenever I see someone succeed rapidly I assume it isn't a newcomer, that there are depths of effort I cannot yet see.
    - downer 19y ago
      Many, many people have gone into the field to study the traditional techniques, and glorifying this knowledge has not led to AI. I don't think a beginner is going to create AI by "luck". But a beginner's mind is not cluttered in the same way as an expert's. This may be what it takes to get working AI. Expert knowledge is appropriate when discussing proven solutions. AI is speculative at this point.
      - akkartik 19y ago
        (response: http://news.ycombinator.com/item?id=90476 http://news.ycombinator.com/item?id=90476)
        downer 19y ago
        (response: http://news.ycombinator.com/item?id=90481 http://news.ycombinator.com/item?id=90481)
- anaphoric 19y ago
jey 19y ago
The problem isn't that the field called "AI" is useless, it's just that they accidentally got away with being called "AI". Without the vast body of research done under the banner of "AI" you wouldn't have OCR, speech recognition, fraud detection, search engines, ... That said, I also don't think you need to be an expert neural network wrangler and genetic algorithms shaman if you want to study "true AI". You should however have an understanding of the fundamentals behind the techniques used in classical AI so that you can rationally dismiss them, or hell, maybe you'll even find some amazing lead or inspiration in the existing work. After all, the field of AI did start out with ambitions of creating "true AI" (whatever that is).
- downer 19y ago
  When someone says "AI", I think they mean AI, not e.g. path-finding for writing a Quake bot. Of course a real AI would be much better at all those things you mentioned anyway -- like deciding which of my e-mail messages are spam. Or carrying on a conversation. P.S. I'm an AI.
  - jey 19y ago
    Not in academia. In academia, as far as I can tell, they pretty much exclusively mean narrow AI when talking about "AI". Us lay people might be thinking of HAL and Skynet when we say "AI" though. ;-) Yes, I fully agree that a "real AI" would be able to do those things better, but my point is that we do have real practical utility from the narrow AI tradition, and their work isn't for nothing.
  - neilc 19y ago
    "When someone says "AI", I think they mean AI, not e.g. path-finding for writing a Quake bot." Then you're hopelessly out of touch with what most people mean when they say "AI", and what the person who asked the question wants to learn about ("Learning Algorythms and Decision Making programs").
    - downer 19y ago
      > Then you're hopelessly out of touch with what most people mean when they say "AI" Hopeless? Doc, are you telling me there's no cure?!? In fact, when most people talk about AI, they mean it in the sense of, "do you think computers will one day be as intelligent as humans?". Cf. the moviefilm "AI". If that kind of AI is what you wish to pursue, then the so-called field of AI isn't going to get you there. I'm reclaiming the term AI for actual AI from the minority that's misusing it and we're having a Pride march on the 23rd. Wheel out your servers and "represent".
lkozma 19y ago
Ignoring existing work in AI is as if you would try building a flying machine, ignoring existing knowledge in aerodynamics. You'd be right back where Leonardo was. People are doing that, nevertheless, but if you do that in AI, chances are you'll be just fabricating crackpot theories.
- downer 19y ago
  The difference between the field of AI and the field of aerodynamics is the state-of-the-art in the latter has produced working results -- to say the least! Meanwhile humans farm gold and solve CAPTCHAs.

I recommend starting from scratch and ignoring the field. All the field will teach you is very complex ways to NOT have AI.

by downer 19y ago

I recommend starting from scratch and ignoring the field. All the field will teach you is very complex ways to NOT have AI.

Kaizyn 19y ago

Why would you want someone to do this? Wouldn't that simply lead one to remake all the mistakes that have been made in the field?

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.

pg 19y ago

Those lessons can be useful, though. You might make the same mistakes.

downer 19y ago
Or you might come up with something that works, by avoiding having your mind crammed and distorted by the classical mess.
- akkartik 19y ago
  It's one thing to notice that beginners can sometimes make contributions by luck, just by not knowing how hard something is supposed to be. It's another to use this reasoning to glorify lack of knowledge. Whenever I see someone succeed rapidly I assume it isn't a newcomer, that there are depths of effort I cannot yet see.
  - downer 19y ago
    Many, many people have gone into the field to study the traditional techniques, and glorifying this knowledge has not led to AI. I don't think a beginner is going to create AI by "luck". But a beginner's mind is not cluttered in the same way as an expert's. This may be what it takes to get working AI. Expert knowledge is appropriate when discussing proven solutions. AI is speculative at this point.
    - akkartik 19y ago
      (response: http://news.ycombinator.com/item?id=90476 http://news.ycombinator.com/item?id=90476)
      - downer 19y ago
        (response: http://news.ycombinator.com/item?id=90481 http://news.ycombinator.com/item?id=90481)
anaphoric 19y ago

jey 19y ago

The problem isn't that the field called "AI" is useless, it's just that they accidentally got away with being called "AI". Without the vast body of research done under the banner of "AI" you wouldn't have OCR, speech recognition, fraud detection, search engines, ... That said, I also don't think you need to be an expert neural network wrangler and genetic algorithms shaman if you want to study "true AI". You should however have an understanding of the fundamentals behind the techniques used in classical AI so that you can rationally dismiss them, or hell, maybe you'll even find some amazing lead or inspiration in the existing work. After all, the field of AI did start out with ambitions of creating "true AI" (whatever that is).

downer 19y ago
When someone says "AI", I think they mean AI, not e.g. path-finding for writing a Quake bot. Of course a real AI would be much better at all those things you mentioned anyway -- like deciding which of my e-mail messages are spam. Or carrying on a conversation. P.S. I'm an AI.
- jey 19y ago
  Not in academia. In academia, as far as I can tell, they pretty much exclusively mean narrow AI when talking about "AI". Us lay people might be thinking of HAL and Skynet when we say "AI" though. ;-) Yes, I fully agree that a "real AI" would be able to do those things better, but my point is that we do have real practical utility from the narrow AI tradition, and their work isn't for nothing.
- neilc 19y ago
  "When someone says "AI", I think they mean AI, not e.g. path-finding for writing a Quake bot." Then you're hopelessly out of touch with what most people mean when they say "AI", and what the person who asked the question wants to learn about ("Learning Algorythms and Decision Making programs").
  - downer 19y ago
    > Then you're hopelessly out of touch with what most people mean when they say "AI" Hopeless? Doc, are you telling me there's no cure?!? In fact, when most people talk about AI, they mean it in the sense of, "do you think computers will one day be as intelligent as humans?". Cf. the moviefilm "AI". If that kind of AI is what you wish to pursue, then the so-called field of AI isn't going to get you there. I'm reclaiming the term AI for actual AI from the minority that's misusing it and we're having a Pride march on the 23rd. Wheel out your servers and "represent".

lkozma 19y ago

Ignoring existing work in AI is as if you would try building a flying machine, ignoring existing knowledge in aerodynamics. You'd be right back where Leonardo was. People are doing that, nevertheless, but if you do that in AI, chances are you'll be just fabricating crackpot theories.

downer 19y ago
The difference between the field of AI and the field of aerodynamics is the state-of-the-art in the latter has produced working results -- to say the least! Meanwhile humans farm gold and solve CAPTCHAs.