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> machine translation, data mining, industrial robotics, logistics, speech recognition, banking software, medical diagnosis and Google's search engine, to name

> machine translation, data mining, industrial robotics, logistics, speech recognition, banking software, medical diagnosis and Google's search engine, to name a few. Do these things really directly descend from pure AI reasearch? Or are they really the result of a bunch of clever, yet extremely specialized algorithms, independently developed, combined with an incredible increase in hardware power? Not to say such things aren't incredible (Google clearly has changed the world), but is it clear that pure AI research has paved the way? From an outside perspective it seems the "God of the gaps" argument - AI is what AI researchers haven't done yet - is used as a smokescreen to cover up that AI research hasn't really done much in the last 30 years. (Counterexamples without hand-waving?) And not only that, but it consistently, wildly, and systematically makes incredible predictions that don't come true. For example, Kurzweil is clearly a genius, but also wildly deluded and wrong about his time-frames. Some of you guys will down-mod me for saying this, and you're the same people who also won't admit my side was correct when, in 20 years, automated translation tools still suck and you'll still be making the same old arguments about how AI is what we haven't done yet... (but in ten years you'll be able to upload your brain!)

xenophanes 17y ago
I agree. Also, even if all those things were the result of AI research, that wouldn't imply AI research made any progress towards inventing an AI, just that it's useful for other stuff.
gruseom 17y ago
Norvig has made this clear: we don't have any better algorithms, only more data.
lliiffee 17y ago
> From an outside perspective it seems the "God of the gaps" argument - AI is what AI researchers haven't done yet - is used as a smokescreen to cover up that AI research hasn't really done much in the last 30 years. Here is what you don't understand: There are very, very, very few people who do research on "AI". People research medical diagnosis, or search, or data mining, or speech recognition, or vision, or chess. If you go to a conference on AI, these are the people you will meet. If you look at who the NSF is funding with their "robust intelligence" area, these are the people you will find. You can only say that "AI research hasn't done much in the last 30 years" if you also say "no one has worked on AI in the last 30 years". update: if you want to see what people in the mainstream (Kurzweil is not mainstream!) are actually working on, try browsing the IJCAI proceedings: http://ijcai.org/papers09/contents.php http://ijcai.org/papers09/contents.php
- WilliamLP 17y ago
  The trouble with that argument is that if you'd have asked anyone (anyone!), in any AI related field in 1980, where AI would be in thirty years what would they have said? Surely not "Well, for instance one of the greatest achievements might be that we will work on chess algorithms, and we will see some incremental improvements resulting from tweaking certain heuristics and more intelligent pruning through more specialized algorithms and hardcoded chess knowledge. The programs still won't be able to learn in any interesting sense, but with the help of several orders of magnitude of hardware speed, they will be 200 ELO stronger than the best human!"
  - berntb 17y ago
    >>if you'd have asked anyone (anyone!), in any AI related field in 1980, where AI would be in thirty years what would they have said? Uhm, Hans Moravec isn't exactly "anyone". :-) http://en.wikipedia.org/wiki/Moravec%27s_paradox http://en.wikipedia.org/wiki/Moravec%27s_paradox Edit: To be clear, Moravec shows that you misrepresent the AI field of that time. But sure, he wasn't the mainstream.
  - deleted 17y ago
    [deleted]
  - lliiffee 17y ago
    My point is that there IS no serious "pure" AI research these days. Your image of lots of pure AI researchers wasting their time is a fantasy-- those people don't exist. People work on applications.
    - chime 17y ago
      And yet there are numerous people who work on pure math and theoretical physics.
    - yummyfajitas 17y ago
      "Those people don't exist" is a bit of an exaggeration. Eliezer Yudkowsky exists, for example. http://news.ycombinator.com/user?id=eyudkowsky http://news.ycombinator.com/user?id=eyudkowsky His research is in making AGI (artificial general intelligence) not go skynet by building in morals. That seems fairly pure to me.
StrawberryFrog 17y ago
Do these things really directly descend from pure AI research? Or are they really the result of a bunch of clever, yet extremely specialized algorithms, independently developed, combined with an incredible increase in hardware power? Is there a meaningful difference? There's a good case to be made that the human mind (AKA "natural intelligence") is "a bunch of clever, yet extremely specialized algorithms, independently developed, combined with incredible increase hardware power"
- WilliamLP 17y ago
  The difference is techniques that are developed for special purposes, but turn out to be useful for more general cases, versus techniques that become more and more specialized, resulting in a decreased possibility of ever using them for anything but increasingly specialized areas. Perhaps the human brain's neural wiring that was required for throwing could have been, and was, subverted into something else. Better negamax alpha-beta algorithms for chess (that require the programmer to know more and more about chess to make any further research progress) will never be useful for anything but chess. What seems to have died is the dream of any kind of useful generalized intelligence. Any kind!
  - berntb 17y ago
    >>What seems to have died is the dream of any kind of useful generalized intelligence. Any kind! This looks strange, could you elaborate? Right now, we seems to be just a few years away from a new age in robotics. They will have some self learning, but at first not be much smarter than insects. For instance, there are cheap systems that can (roughly) understand what they see. And yes, the robot vision systems are specially built for that -- but the same functionality in animals has afaik also lots of specially built hardware. Does it really matter if we have to specially build systems, if we can e.g. make system-building-systems as smart tools? Edit: Some syntax and word choices, etc. Also, on consideration, I make the same point as the GP (StrawberryFrog), but he does it better. Edit 2: Hmm... Another argument, then: Even if generalized learning will work in practice, it will probably be inferior to networked systems where problems are automatically found and then solved (and updated) from a central location -- like bugs in operating systems. Since everything will be on the net soon, all future generations of robots will probably work like this.
hegemonicon 17y ago
EURISKO is a development in the last 30 years (though just barely). http://en.wikipedia.org/wiki/Eurisko http://en.wikipedia.org/wiki/Eurisko
dlwh 17y ago
Successes in Speech, Machine Translation, medical diagnosis and data mining successes have all descended from sound theoretical research in statistics and information theory. For instance, Machine Translation (as we know it today) was originally inspired by the "noisy channel" for speech recognition that is based on Bayes Rule. In speech recognition, the probability of some words given some input waveform is proportional to the probability of someone saying those words times the probability of hearing some wave form given those rules. The same model led to statistical machine translation: if I speak to you in French, what I'm really doing is speaking to you in English, but the "channel" is so noisy that it comes out sounding like French. Today, the parallels are less clear (for instance, German and English have substantially different word order, and in speech you don't usually have reordering in the channel--though there's actually some cool new research in MT to bring it closer in line with modern speech processing!). Yes, there are huge amounts of specialization and hacks for each of these fields, but they are (mostly) based around core good statistical ideas. In fact, some people are worried that the AI community is so focused on log-linear models and the like that we're in some kind of local minimum, and that we're unlikely to work our way out any time soon. That said, Kurzweil is still wildly deluded, as you suggest.
giardini 17y ago
You're saying a lot of things here but one of your questions is worth answering: >Do these things really directly descend from pure AI reasearch(sic)? With the exception of Google's search engine [with whose internals I am not familiar], I can answer an emphatic "Yes". And so would any knowledgeable current or past researcher in those fields. The early AI researchers did a hell of a lot of good work and much of it remains relevant. As you demonstrate, many if not most people have no idea of what was actually done back then, much less the lineage of their income-tax software or the control system for their digital camera. Despite funding cuts AI continued to be an interesting and productive field, and remains so today.
plinkplonk 17y ago
"pure AI reasearch? Or are they really the result of a bunch of clever, yet extremely specialized algorithms," This presumes a dichotomy between "pure AI research" (whatever that is) and "clever algorithms".
JulianMorrison 17y ago
A lot of "narrow AI" (what real AI researchers spend their time doing) has made its way into a lot of products. However I doubt this is what you were thinking of as pure AI. There's more, though. Neuroscience continues piecing together how brains work. I've heard that the brain's embodied algorithms are recognizable from eg computer vision research. This seems to imply that there is more of a natural ramp-up from "narrow AI" into "humanlike AI" than at first it would appear.

xenophanes 17y ago

I agree. Also, even if all those things were the result of AI research, that wouldn't imply AI research made any progress towards inventing an AI, just that it's useful for other stuff.

gruseom 17y ago

Norvig has made this clear: we don't have any better algorithms, only more data.

lliiffee 17y ago

> From an outside perspective it seems the "God of the gaps" argument - AI is what AI researchers haven't done yet - is used as a smokescreen to cover up that AI research hasn't really done much in the last 30 years. Here is what you don't understand: There are very, very, very few people who do research on "AI". People research medical diagnosis, or search, or data mining, or speech recognition, or vision, or chess. If you go to a conference on AI, these are the people you will meet. If you look at who the NSF is funding with their "robust intelligence" area, these are the people you will find. You can only say that "AI research hasn't done much in the last 30 years" if you also say "no one has worked on AI in the last 30 years". update: if you want to see what people in the mainstream (Kurzweil is not mainstream!) are actually working on, try browsing the IJCAI proceedings: http://ijcai.org/papers09/contents.php http://ijcai.org/papers09/contents.php

WilliamLP 17y ago
The trouble with that argument is that if you'd have asked anyone (anyone!), in any AI related field in 1980, where AI would be in thirty years what would they have said? Surely not "Well, for instance one of the greatest achievements might be that we will work on chess algorithms, and we will see some incremental improvements resulting from tweaking certain heuristics and more intelligent pruning through more specialized algorithms and hardcoded chess knowledge. The programs still won't be able to learn in any interesting sense, but with the help of several orders of magnitude of hardware speed, they will be 200 ELO stronger than the best human!"
- berntb 17y ago
  >>if you'd have asked anyone (anyone!), in any AI related field in 1980, where AI would be in thirty years what would they have said? Uhm, Hans Moravec isn't exactly "anyone". :-) http://en.wikipedia.org/wiki/Moravec%27s_paradox http://en.wikipedia.org/wiki/Moravec%27s_paradox Edit: To be clear, Moravec shows that you misrepresent the AI field of that time. But sure, he wasn't the mainstream.
- deleted 17y ago
  [deleted]
- lliiffee 17y ago
  My point is that there IS no serious "pure" AI research these days. Your image of lots of pure AI researchers wasting their time is a fantasy-- those people don't exist. People work on applications.
  - chime 17y ago
    And yet there are numerous people who work on pure math and theoretical physics.
  - yummyfajitas 17y ago
    "Those people don't exist" is a bit of an exaggeration. Eliezer Yudkowsky exists, for example. http://news.ycombinator.com/user?id=eyudkowsky http://news.ycombinator.com/user?id=eyudkowsky His research is in making AGI (artificial general intelligence) not go skynet by building in morals. That seems fairly pure to me.

StrawberryFrog 17y ago

Do these things really directly descend from pure AI research? Or are they really the result of a bunch of clever, yet extremely specialized algorithms, independently developed, combined with an incredible increase in hardware power? Is there a meaningful difference? There's a good case to be made that the human mind (AKA "natural intelligence") is "a bunch of clever, yet extremely specialized algorithms, independently developed, combined with incredible increase hardware power"

WilliamLP 17y ago
The difference is techniques that are developed for special purposes, but turn out to be useful for more general cases, versus techniques that become more and more specialized, resulting in a decreased possibility of ever using them for anything but increasingly specialized areas. Perhaps the human brain's neural wiring that was required for throwing could have been, and was, subverted into something else. Better negamax alpha-beta algorithms for chess (that require the programmer to know more and more about chess to make any further research progress) will never be useful for anything but chess. What seems to have died is the dream of any kind of useful generalized intelligence. Any kind!
- berntb 17y ago
  >>What seems to have died is the dream of any kind of useful generalized intelligence. Any kind! This looks strange, could you elaborate? Right now, we seems to be just a few years away from a new age in robotics. They will have some self learning, but at first not be much smarter than insects. For instance, there are cheap systems that can (roughly) understand what they see. And yes, the robot vision systems are specially built for that -- but the same functionality in animals has afaik also lots of specially built hardware. Does it really matter if we have to specially build systems, if we can e.g. make system-building-systems as smart tools? Edit: Some syntax and word choices, etc. Also, on consideration, I make the same point as the GP (StrawberryFrog), but he does it better. Edit 2: Hmm... Another argument, then: Even if generalized learning will work in practice, it will probably be inferior to networked systems where problems are automatically found and then solved (and updated) from a central location -- like bugs in operating systems. Since everything will be on the net soon, all future generations of robots will probably work like this.

hegemonicon 17y ago

EURISKO is a development in the last 30 years (though just barely). http://en.wikipedia.org/wiki/Eurisko http://en.wikipedia.org/wiki/Eurisko

dlwh 17y ago

Successes in Speech, Machine Translation, medical diagnosis and data mining successes have all descended from sound theoretical research in statistics and information theory. For instance, Machine Translation (as we know it today) was originally inspired by the "noisy channel" for speech recognition that is based on Bayes Rule. In speech recognition, the probability of some words given some input waveform is proportional to the probability of someone saying those words times the probability of hearing some wave form given those rules. The same model led to statistical machine translation: if I speak to you in French, what I'm really doing is speaking to you in English, but the "channel" is so noisy that it comes out sounding like French. Today, the parallels are less clear (for instance, German and English have substantially different word order, and in speech you don't usually have reordering in the channel--though there's actually some cool new research in MT to bring it closer in line with modern speech processing!). Yes, there are huge amounts of specialization and hacks for each of these fields, but they are (mostly) based around core good statistical ideas. In fact, some people are worried that the AI community is so focused on log-linear models and the like that we're in some kind of local minimum, and that we're unlikely to work our way out any time soon. That said, Kurzweil is still wildly deluded, as you suggest.

giardini 17y ago

You're saying a lot of things here but one of your questions is worth answering: >Do these things really directly descend from pure AI reasearch(sic)? With the exception of Google's search engine [with whose internals I am not familiar], I can answer an emphatic "Yes". And so would any knowledgeable current or past researcher in those fields. The early AI researchers did a hell of a lot of good work and much of it remains relevant. As you demonstrate, many if not most people have no idea of what was actually done back then, much less the lineage of their income-tax software or the control system for their digital camera. Despite funding cuts AI continued to be an interesting and productive field, and remains so today.

plinkplonk 17y ago

"pure AI reasearch? Or are they really the result of a bunch of clever, yet extremely specialized algorithms," This presumes a dichotomy between "pure AI research" (whatever that is) and "clever algorithms".

JulianMorrison 17y ago

A lot of "narrow AI" (what real AI researchers spend their time doing) has made its way into a lot of products. However I doubt this is what you were thinking of as pure AI. There's more, though. Neuroscience continues piecing together how brains work. I've heard that the brain's embodied algorithms are recognizable from eg computer vision research. This seems to imply that there is more of a natural ramp-up from "narrow AI" into "humanlike AI" than at first it would appear.