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Machine Learning is just a tool belt. AI is the idea of intelligent machines being able to solve problems (and recognizing them) on their own. Therefore of cour

Machine Learning is just a tool belt. AI is the idea of intelligent machines being able to solve problems (and recognizing them) on their own. Therefore of course AI can utilize the tool belt that is Machine Learning.

jasode 9y ago
>AI is the idea of intelligent machines being able to solve problems (and recognizing them) on their own. From the perspective of a language descriptivist instead of prescriptivist, that strict definition of AI is already long gone. That ship has sailed. Even wikipedia says that colloquial uses of "artificial intelligence" stands for machine learning that mimics intelligence. It's understandable that The NYT and all mainstream non-compsci publications will use "artificial intelligence" that way. One of the researchers (Stephen Weng) cited in the article also uses the term "artificial intelligence" in his LinkedIn page and in the PDF research article itself. If and when "solving/recognizing on its own" truly becomes reality, I suspect the world will adapt and call it "True-AI" or "Lifelike-AI" or "Strong-AI". We'll find another term to distinguish it from the watered down "artificial intelligence".
mortehu 9y ago
I think you might be talking about what other people call Artificial General Intelligence (AGI)? https://en.wikipedia.org/wiki/Artificial_general_intelligence https://en.wikipedia.org/wiki/Artificial_general_intelligenc...
gnaritas 9y ago
The timeless problem of AI, anything that starts out AI ends up being rebranded as not AI once it's understood. It's the no true Scotsman fallacy. Machine learning is AI, it's just not human level AI.
- jgalt212 9y ago
  > Machine learning is AI I disagree. Machine Learning is just super-charged linear regression. If you build a ML system that autonomously chooses it factors, and automatically adjusts to model drift (by either adjusting existing coefficients (easy) or adding/removing factors (hard), then ML drifts into AI.
  - gnaritas 9y ago
    > I disagree. Machine Learning is just super-charged linear regression. Machine learning is neural nets, which started out as an approach to AI and which yes boil down to linear regression, but that's about as useful as saying brains are just super super charged linear regression. And that's the point, AI is a label that keeps getting cast off of things that started out as AI but once understood people decided they no longer were, you are committing the no true Scotsman fallacy. If it started off as AI, it's AI, that doesn't change because you understand the math underneath that it boils down to.
- felippee 9y ago
  I'd disagree, there is more here than the no true Scotsman fallacy. It is equivalent to saying that any magic trick ceases to be magic once exposed as a trick. We don't have a good definition of I in AI therefore such arguments go on forever, yet somehow we know that the "I as in human being "I" is different than the "I" as in "AI" statistical smoke and mirrors. My opinion is that a good definition could be established that would clear such BS once and for all, some attempt here: http://blog.piekniewski.info/2017/04/13/ai-confuses-intelligent/ http://blog.piekniewski.info/2017/04/13/ai-confuses-intellig...
  - gnaritas 9y ago
    You're conflating human level intelligence with artificial intelligence for one, AI doesn't have to be human level to be AI. Secondly "statistical smoke and mirrors" is the Chinese room fallacy; it doesn't matter what it is under the covers, if it behaves intelligently then it's intelligent regardless of whether or not it can be boiled down to some math. Your link covers that near the beginning when he discusses duck typing, but he comes to the wrong conclusion. The article should have ended there, if it behaves intelligently, then it's intelligent, that simply is the truth and looking at the implementation is cheating because we don't know brains aren't doing the same thing. What's really going on is you are branding "human intelligence" special because we don't understand its implementation and labeling everything else not intelligent because we do, for all we know the human mind itself could be nothing more than statistical smoke and mirrors. The only problem here is human ego. A car that can drive me somewhere on its own simply by being given a destination, is AI, not matter how it's implemented as long as it's the computer doing the driving and is operating locally by actually having sensors that see the road. It doesn't have to be able to ponder its own existence to be AI. Neural nets were an attempt to model how the brain works; they are by definition AI regardless of whether they boil down to some maths. Everything a computer does boils down eventually to some maths, that is not an escape hatch to claim something isn't AI. Machine learning is AI. It is not AGI, but it most certainly is AI.
    - mythbuster2001 9y ago
      You are dogmatically defending Turing test which I think is the primary source of this confusion. Turing test says: if it fools humans into thinking its intelligent it is intelligent. That is fair. But once some other humans understand the inner workings of some simple "AI" mechanism it no longer fools humans, since they now know what adversarial questions to ask to uncover it. Therefore it consequently fails the Turing test and we have the AI effect. This test is just a bad idea and it impairs research (for a number of reasons stated in the post which you prematurely dismiss). The coffee criterion for AGI (https://en.wikipedia.org/wiki/Artificial_general_intelligence https://en.wikipedia.org/wiki/Artificial_general_intelligenc...) is much better, since it requires ability to creatively interact with unpredictable reality as a test for intelligence. It avoids all the philosophical bullshit and all the smoke and mirrors, since you cannot fool physics. Somehow the so called "AI researchers" avoid robotics like fire, sine there stuff actually needs to work (not just statistically) and outrageous BS claims cannot be made. And yes, ultimately the human brain may be smoke and mirrors. But frankly, quite sophisticated smoke and mirrors, not anywhere close to the crap that is being put forward right now.

jasode 9y ago

>AI is the idea of intelligent machines being able to solve problems (and recognizing them) on their own. From the perspective of a language descriptivist instead of prescriptivist, that strict definition of AI is already long gone. That ship has sailed. Even wikipedia says that colloquial uses of "artificial intelligence" stands for machine learning that mimics intelligence. It's understandable that The NYT and all mainstream non-compsci publications will use "artificial intelligence" that way. One of the researchers (Stephen Weng) cited in the article also uses the term "artificial intelligence" in his LinkedIn page and in the PDF research article itself. If and when "solving/recognizing on its own" truly becomes reality, I suspect the world will adapt and call it "True-AI" or "Lifelike-AI" or "Strong-AI". We'll find another term to distinguish it from the watered down "artificial intelligence".

mortehu 9y ago

I think you might be talking about what other people call Artificial General Intelligence (AGI)? https://en.wikipedia.org/wiki/Artificial_general_intelligence https://en.wikipedia.org/wiki/Artificial_general_intelligenc...

gnaritas 9y ago

The timeless problem of AI, anything that starts out AI ends up being rebranded as not AI once it's understood. It's the no true Scotsman fallacy. Machine learning is AI, it's just not human level AI.

jgalt212 9y ago
> Machine learning is AI I disagree. Machine Learning is just super-charged linear regression. If you build a ML system that autonomously chooses it factors, and automatically adjusts to model drift (by either adjusting existing coefficients (easy) or adding/removing factors (hard), then ML drifts into AI.
- gnaritas 9y ago
  > I disagree. Machine Learning is just super-charged linear regression. Machine learning is neural nets, which started out as an approach to AI and which yes boil down to linear regression, but that's about as useful as saying brains are just super super charged linear regression. And that's the point, AI is a label that keeps getting cast off of things that started out as AI but once understood people decided they no longer were, you are committing the no true Scotsman fallacy. If it started off as AI, it's AI, that doesn't change because you understand the math underneath that it boils down to.
felippee 9y ago
I'd disagree, there is more here than the no true Scotsman fallacy. It is equivalent to saying that any magic trick ceases to be magic once exposed as a trick. We don't have a good definition of I in AI therefore such arguments go on forever, yet somehow we know that the "I as in human being "I" is different than the "I" as in "AI" statistical smoke and mirrors. My opinion is that a good definition could be established that would clear such BS once and for all, some attempt here: http://blog.piekniewski.info/2017/04/13/ai-confuses-intelligent/ http://blog.piekniewski.info/2017/04/13/ai-confuses-intellig...
- gnaritas 9y ago
  You're conflating human level intelligence with artificial intelligence for one, AI doesn't have to be human level to be AI. Secondly "statistical smoke and mirrors" is the Chinese room fallacy; it doesn't matter what it is under the covers, if it behaves intelligently then it's intelligent regardless of whether or not it can be boiled down to some math. Your link covers that near the beginning when he discusses duck typing, but he comes to the wrong conclusion. The article should have ended there, if it behaves intelligently, then it's intelligent, that simply is the truth and looking at the implementation is cheating because we don't know brains aren't doing the same thing. What's really going on is you are branding "human intelligence" special because we don't understand its implementation and labeling everything else not intelligent because we do, for all we know the human mind itself could be nothing more than statistical smoke and mirrors. The only problem here is human ego. A car that can drive me somewhere on its own simply by being given a destination, is AI, not matter how it's implemented as long as it's the computer doing the driving and is operating locally by actually having sensors that see the road. It doesn't have to be able to ponder its own existence to be AI. Neural nets were an attempt to model how the brain works; they are by definition AI regardless of whether they boil down to some maths. Everything a computer does boils down eventually to some maths, that is not an escape hatch to claim something isn't AI. Machine learning is AI. It is not AGI, but it most certainly is AI.
  - mythbuster2001 9y ago
    You are dogmatically defending Turing test which I think is the primary source of this confusion. Turing test says: if it fools humans into thinking its intelligent it is intelligent. That is fair. But once some other humans understand the inner workings of some simple "AI" mechanism it no longer fools humans, since they now know what adversarial questions to ask to uncover it. Therefore it consequently fails the Turing test and we have the AI effect. This test is just a bad idea and it impairs research (for a number of reasons stated in the post which you prematurely dismiss). The coffee criterion for AGI (https://en.wikipedia.org/wiki/Artificial_general_intelligence https://en.wikipedia.org/wiki/Artificial_general_intelligenc...) is much better, since it requires ability to creatively interact with unpredictable reality as a test for intelligence. It avoids all the philosophical bullshit and all the smoke and mirrors, since you cannot fool physics. Somehow the so called "AI researchers" avoid robotics like fire, sine there stuff actually needs to work (not just statistically) and outrageous BS claims cannot be made. And yes, ultimately the human brain may be smoke and mirrors. But frankly, quite sophisticated smoke and mirrors, not anywhere close to the crap that is being put forward right now.