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At least to some extent, how you sees this relates to what you want/think AI to eventually be. A while back, I heard a particularly curmudgeonly interview with

At least to some extent, how you sees this relates to what you want/think AI to eventually be. A while back, I heard a particularly curmudgeonly interview with Noam Chomsky, just commenting on the state of AI and recent AI programs (Watson, Deep blue..). Chomsky basically called all these statsistical AI programs brute force parlour tricks, nothing to do with computer science or artificial intelligences. He stopped just short of calling the whole thing fraud. Real AI are things like bird flocking algorithims (did he work on something like this, can't remember). I think the jist was that to be an AI program, a program must embody a theory of intelligence. The theory-less nature of modern statistical algorithims seemed to really peeve him. maybe he's right. Anyway... I don't remember what point 9if any) i was driving at. I think there are abstract values at play, in how we narrate progress. Since we still don't know what inteligence "is," or even if it is a distinct thing, it's hard to know what the real milestones are. Personally, I like the fully realized, running at scale "proofs," but that probably says more about my ability to understand theory than anything else.

braythwayt 8y ago
At some level: Alice: “Computers aren’t doing real intelligence, because they don’t think the way we theorize we think.” Bob: “Computers are doing real intelligence, because we don’t think the way we theorize we think.”
nkozyra 8y ago
> Chomsky basically called all these statsistical AI programs brute force parlour tricks, nothing to do with computer science or artificial intelligences. The counterargument is our own brains use brute force parlor tricks to solve problems. We learn by a priori (pre-built models), by experience (reinforcement learning) and viewing and weighing features that we observe against prior experience (NNs/ANNs). The entire point of ML is to produce approximately the same result a human does. Whether it's a parlor trick or a perfect simulation of the brain is irrelevant. There's no advantage to something novel and more "organic" unless it produces better results. This is a common problem I see - people are always trying to compare human reasoning to artificial reasoning rather than looking at the output. The output is all that matters.
- dalbasal 8y ago
  Thinking back, I wonder if that interview mentioned Turing test-like.. tests. I wonder what Chomsky thinks of these, as gauges of progress.
- darepublic 8y ago
  This 'common' problem is addressed by the distinction between 'strong' AI and 'weak' AI.
  - nkozyra 8y ago
    While nice, the general public is not exposed to the details of what "AI" is and how it's designed. It's that uphill battle against expectations that makes comments like "AI is a bust" or "AI hasn't really advanced since the 60s" difficult to counter.
- henrikschroder 8y ago
  > This is a common problem I see - people are always trying to compare human reasoning to artificial reasoning rather than looking at the output. The output is all that matters. Entire schools of philosophy disagree completely. There was also a fantastic article here on HN a while ago [1] by Douglas Hofstadter where he ranted against google translate and similar, by showing that the current state-of-the-art statistical brute force word nearness ML cluster doesn't understand the text it's translating, and therefore will always be lacking, always have cases that it simply cannot solve. He's basically saying that Weak AI can only get to 99% when it comes to translating, but we would need Strong AI to get to 100%, and Strong AI is probably impossible. [1] https://news.ycombinator.com/item?id=16296738 https://news.ycombinator.com/item?id=16296738
  - dekhn 8y ago
    Given that we now have superhuman image recognition (in particular, even highly trained experts have a hard time classifying dog breeds with extensive time per sample while modern NNs can classify them with 99+% accuracy), I don't think this is true. We will probably exceed human's ability to come up with reliable training data before we reach strong AI.

braythwayt 8y ago

At some level: Alice: “Computers aren’t doing real intelligence, because they don’t think the way we theorize we think.” Bob: “Computers are doing real intelligence, because we don’t think the way we theorize we think.”

nkozyra 8y ago

> Chomsky basically called all these statsistical AI programs brute force parlour tricks, nothing to do with computer science or artificial intelligences. The counterargument is our own brains use brute force parlor tricks to solve problems. We learn by a priori (pre-built models), by experience (reinforcement learning) and viewing and weighing features that we observe against prior experience (NNs/ANNs). The entire point of ML is to produce approximately the same result a human does. Whether it's a parlor trick or a perfect simulation of the brain is irrelevant. There's no advantage to something novel and more "organic" unless it produces better results. This is a common problem I see - people are always trying to compare human reasoning to artificial reasoning rather than looking at the output. The output is all that matters.

dalbasal 8y ago
Thinking back, I wonder if that interview mentioned Turing test-like.. tests. I wonder what Chomsky thinks of these, as gauges of progress.
darepublic 8y ago
This 'common' problem is addressed by the distinction between 'strong' AI and 'weak' AI.
- nkozyra 8y ago
  While nice, the general public is not exposed to the details of what "AI" is and how it's designed. It's that uphill battle against expectations that makes comments like "AI is a bust" or "AI hasn't really advanced since the 60s" difficult to counter.
henrikschroder 8y ago
> This is a common problem I see - people are always trying to compare human reasoning to artificial reasoning rather than looking at the output. The output is all that matters. Entire schools of philosophy disagree completely. There was also a fantastic article here on HN a while ago [1] by Douglas Hofstadter where he ranted against google translate and similar, by showing that the current state-of-the-art statistical brute force word nearness ML cluster doesn't understand the text it's translating, and therefore will always be lacking, always have cases that it simply cannot solve. He's basically saying that Weak AI can only get to 99% when it comes to translating, but we would need Strong AI to get to 100%, and Strong AI is probably impossible. [1] https://news.ycombinator.com/item?id=16296738 https://news.ycombinator.com/item?id=16296738
- dekhn 8y ago
  Given that we now have superhuman image recognition (in particular, even highly trained experts have a hard time classifying dog breeds with extensive time per sample while modern NNs can classify them with 99+% accuracy), I don't think this is true. We will probably exceed human's ability to come up with reliable training data before we reach strong AI.