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Douglas Hofstadter is important because most AI work right now focuses either on (1) big-data-style statistical analysis or (2) emulating brain anatomy. DH is

by drcode 13y ago

Douglas Hofstadter is important because most AI work right now focuses either on (1) big-data-style statistical analysis or (2) emulating brain anatomy. DH is the most well known guy of a small, stubborn group of AI developers who still believe that "human thought" can be reasoned about and can be understood in isolation, and that we can build intelligence without simply reducing it to statistics or to brain anatomy. I applaud his efforts, and find some of the programs he's written both creative and refreshing.

RogerL 13y ago
I'm nobody compared to the quoted people, but I use AI in computer vision, and while I love using Bayesian math for parts of it, I've found no substitute for pragmatically programming in knowledge about the system. Which is different from what the article is talking about; I'm aware. I'm just trying to make a system that works, not understand how the brain works. But I view those as somewhat orthogonal problems ; my computer is structured differently than a human brain, perhaps my algorithms should be different as well. Certainly I see no reason to view the brain as having the best algorithm - perhaps a very non-human brain type of algorithm would be superior. Evolution worked with what it had, and it is unlikely that the result is optimal.
- jostmey 13y ago
  I think you are absolutely right. Evolution selected for a form of intelligence that was highly calibrated and biased for operating in the wilderness of Nature. Our minds are not necessary correctly geared for abstract thinking, logic, and rational thought.
joshuaeckroth 13y ago
Members of what you might call that "stubborn group" have started a yearly conference: Advances in Cognitive Systems. See the top of their FAQ: http://cogsys.org/faq http://cogsys.org/faq
- joe_the_user 13y ago
  Nice group but they are definitely swimming against the currently standard approach. That said, I think one really interest article here is: "Human-Level Artificial Intelligence Must Be an Extraordinary Science" by Nicholas L. Cassimatis Brings a lot of challenges into focus. http://www.cogsys.org/pdf/paper-1-5.pdf http://www.cogsys.org/pdf/paper-1-5.pdf www.cogsys.org
- jmmcd 13y ago
  Also http://www.agi-conference.org/2013/ http://www.agi-conference.org/2013/ which is held in conjunction with IJCAI.
jokoon 13y ago
Is there anything this guy can teach people about intelligence which has nothing to do with computers or neuron networks ?
- cgh 13y ago
  Most of GEB doesn't concern either of those things. Why not read it and find out for yourself?
  - huxley 13y ago
    Definitely, also worth reading is "Mind's I" which he co-edited --and did commentary-- with Daniel Dennett.
  - giardini 13y ago
    GEB was a considerable waste of time and contributed nothing to my understanding of intelligence or AI. The time would have been be better spent elsewhere. If you want to understand Godel's proofs then I recommend the book "Godel's Proof" by Ernest Nagel and James R. Newman: http://www.amazon.com/Gödels-Proof-Ernest-Nagel/dp/0814758371/ref=sr_1_cc_1?s=aps&ie=UTF8&qid=1382636501&sr=1-1-catcorr&keywords=godel%27s+proof http://www.amazon.com/Gödels-Proof-Ernest-Nagel/dp/081475837... Instead of Hofstadter's GEB, read some of his papers, e.g., "Analogy as the Core of Cognition" http://prelectur.stanford.edu/lecturers/hofstadter/analogy.html http://prelectur.stanford.edu/lecturers/hofstadter/analogy.h... But there are others who have focused longer on analogy, e.g., George Lakoff: "Metaphors we Live by" http://www.amazon.com/Metaphors-We-Live-George-Lakoff/dp/0226468011/ref=sr_1_1?ie=UTF8&qid=1382636169&sr=8-1&keywords=by+george+lakoff http://www.amazon.com/Metaphors-We-Live-George-Lakoff/dp/022... "Where Mathematics Come From: How The Embodied Mind Brings Mathematics Into Being": http://www.amazon.com/Where-Mathematics-Come-Embodied-Brings/dp/0465037712/ref=sr_1_1?s=books&ie=UTF8&qid=1382636378&sr=1-1&keywords=Where+Mathematics+Come+From%3A+How+The+Embodied+Mind+Brings+Mathematics+Into+Being http://www.amazon.com/Where-Mathematics-Come-Embodied-Brings... "Women, Fire, and Dangerous Things" http://www.amazon.com/Women-Fire-Dangerous-Things-Lakoff/dp/B0044SLTZE/ref=sr_1_2?ie=UTF8&qid=1382636599&sr=8-2&keywords=Women%2C+Fire%2C+and+Dangerous+Things http://www.amazon.com/Women-Fire-Dangerous-Things-Lakoff/dp/...
upquark 13y ago
You cannot not reduce it to statistics, vast majority of your cognitive activity is statistical in nature, in the more general sense of the word. The actual high-level structured thoughts are a very thin layer on top of a huge statistical information processing powerhouse.
- drcode 13y ago
  Yes, everything can theoretically be reduced to statistics. Next time I build a web app, I'll first design it from the ground up using a Turing machine ticker tape, since all computation can theoretically be reduced to a Turing machine. What's the point of bothering with JavaScript?
  - upquark 13y ago
    You need to use the right level of abstraction for the problem at hand. For modeling intelligence, abstracting away the probabilistic/statistical aspects of cognition greatly limits your options. Continuing the web development analogy, abstracting away statistics in cognitive science is like abstracting away JavaScript/HTML and using WYSIWYG GUI tools. With the same effect of only being applicable in toy problems and not scaling to real world scenarios :)
    - hharrison 13y ago
      There's a huge middle ground between reducing cognition to statistics (as you seem to be proposing) and "abstracting away" the statistics. You seem to be accusing any approach that's not "statistics all the way down" of ignoring the statistical nature of the mind. When in fact, there are many approaches to cognition which make heavy uses of advances in statistical theory without reducing cognition to statistics. What you're proposing is an overly reductionist ontology, IMO, and yet I agree that statistics are important, if not critical. (Hint: not the boring linear kind of statistics that you so often see in Big Data)
      - upquark 13y ago
        No I'm not arguing for a statistics-only scheme, just that you can't ignore it / abstract it away, and that its role is crucial in cognition. I think we agree with each other.

by drcode 13y ago

RogerL 13y ago

I'm nobody compared to the quoted people, but I use AI in computer vision, and while I love using Bayesian math for parts of it, I've found no substitute for pragmatically programming in knowledge about the system. Which is different from what the article is talking about; I'm aware. I'm just trying to make a system that works, not understand how the brain works. But I view those as somewhat orthogonal problems ; my computer is structured differently than a human brain, perhaps my algorithms should be different as well. Certainly I see no reason to view the brain as having the best algorithm - perhaps a very non-human brain type of algorithm would be superior. Evolution worked with what it had, and it is unlikely that the result is optimal.

jostmey 13y ago
I think you are absolutely right. Evolution selected for a form of intelligence that was highly calibrated and biased for operating in the wilderness of Nature. Our minds are not necessary correctly geared for abstract thinking, logic, and rational thought.

joshuaeckroth 13y ago

Members of what you might call that "stubborn group" have started a yearly conference: Advances in Cognitive Systems. See the top of their FAQ: http://cogsys.org/faq http://cogsys.org/faq

joe_the_user 13y ago
Nice group but they are definitely swimming against the currently standard approach. That said, I think one really interest article here is: "Human-Level Artificial Intelligence Must Be an Extraordinary Science" by Nicholas L. Cassimatis Brings a lot of challenges into focus. http://www.cogsys.org/pdf/paper-1-5.pdf http://www.cogsys.org/pdf/paper-1-5.pdf www.cogsys.org
jmmcd 13y ago
Also http://www.agi-conference.org/2013/ http://www.agi-conference.org/2013/ which is held in conjunction with IJCAI.

jokoon 13y ago

Is there anything this guy can teach people about intelligence which has nothing to do with computers or neuron networks ?

cgh 13y ago
Most of GEB doesn't concern either of those things. Why not read it and find out for yourself?
- huxley 13y ago
  Definitely, also worth reading is "Mind's I" which he co-edited --and did commentary-- with Daniel Dennett.
- giardini 13y ago
  GEB was a considerable waste of time and contributed nothing to my understanding of intelligence or AI. The time would have been be better spent elsewhere. If you want to understand Godel's proofs then I recommend the book "Godel's Proof" by Ernest Nagel and James R. Newman: http://www.amazon.com/Gödels-Proof-Ernest-Nagel/dp/0814758371/ref=sr_1_cc_1?s=aps&ie=UTF8&qid=1382636501&sr=1-1-catcorr&keywords=godel%27s+proof http://www.amazon.com/Gödels-Proof-Ernest-Nagel/dp/081475837... Instead of Hofstadter's GEB, read some of his papers, e.g., "Analogy as the Core of Cognition" http://prelectur.stanford.edu/lecturers/hofstadter/analogy.html http://prelectur.stanford.edu/lecturers/hofstadter/analogy.h... But there are others who have focused longer on analogy, e.g., George Lakoff: "Metaphors we Live by" http://www.amazon.com/Metaphors-We-Live-George-Lakoff/dp/0226468011/ref=sr_1_1?ie=UTF8&qid=1382636169&sr=8-1&keywords=by+george+lakoff http://www.amazon.com/Metaphors-We-Live-George-Lakoff/dp/022... "Where Mathematics Come From: How The Embodied Mind Brings Mathematics Into Being": http://www.amazon.com/Where-Mathematics-Come-Embodied-Brings/dp/0465037712/ref=sr_1_1?s=books&ie=UTF8&qid=1382636378&sr=1-1&keywords=Where+Mathematics+Come+From%3A+How+The+Embodied+Mind+Brings+Mathematics+Into+Being http://www.amazon.com/Where-Mathematics-Come-Embodied-Brings... "Women, Fire, and Dangerous Things" http://www.amazon.com/Women-Fire-Dangerous-Things-Lakoff/dp/B0044SLTZE/ref=sr_1_2?ie=UTF8&qid=1382636599&sr=8-2&keywords=Women%2C+Fire%2C+and+Dangerous+Things http://www.amazon.com/Women-Fire-Dangerous-Things-Lakoff/dp/...

upquark 13y ago

You cannot not reduce it to statistics, vast majority of your cognitive activity is statistical in nature, in the more general sense of the word. The actual high-level structured thoughts are a very thin layer on top of a huge statistical information processing powerhouse.

drcode 13y ago
Yes, everything can theoretically be reduced to statistics. Next time I build a web app, I'll first design it from the ground up using a Turing machine ticker tape, since all computation can theoretically be reduced to a Turing machine. What's the point of bothering with JavaScript?
- upquark 13y ago
  You need to use the right level of abstraction for the problem at hand. For modeling intelligence, abstracting away the probabilistic/statistical aspects of cognition greatly limits your options. Continuing the web development analogy, abstracting away statistics in cognitive science is like abstracting away JavaScript/HTML and using WYSIWYG GUI tools. With the same effect of only being applicable in toy problems and not scaling to real world scenarios :)
  - hharrison 13y ago
    There's a huge middle ground between reducing cognition to statistics (as you seem to be proposing) and "abstracting away" the statistics. You seem to be accusing any approach that's not "statistics all the way down" of ignoring the statistical nature of the mind. When in fact, there are many approaches to cognition which make heavy uses of advances in statistical theory without reducing cognition to statistics. What you're proposing is an overly reductionist ontology, IMO, and yet I agree that statistics are important, if not critical. (Hint: not the boring linear kind of statistics that you so often see in Big Data)
    - upquark 13y ago
      No I'm not arguing for a statistics-only scheme, just that you can't ignore it / abstract it away, and that its role is crucial in cognition. I think we agree with each other.