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I've heard of OpenCog before and it, along with the Singularity crowd gives me a weird amateur, bullshitty, vague, generalist feeling that Noam Chomsky does. Ba

I've heard of OpenCog before and it, along with the Singularity crowd gives me a weird amateur, bullshitty, vague, generalist feeling that Noam Chomsky does. Basically - where's the beef? What has been done by either crowd apart from taking credit from those who do things in the actual industry/real world? My fundamental aversion to both OpenCog and the entire Singularity crowd is a) their statements are so general as to the point of being useless and b) they don't do anything. Google makes search simple - go to google.com and find out. Google makes cars drive themselves - ask Nevada/California and if you're a member of the press - request a test drive today. IBM's Watson definitively beat world champions in front of everyone and before that they did it with Blue Gene. Everyone in the other communities fall under this category: All talk - no walk. The entirety of what I've gotten out of both groups is essentially little more than what religious people get out of going to a sermon at a church. The future will be grand, lots of bullshitty buzz words, lots of hand waving with huge claims - no hard calculations, no hard examples of what they've actually achieved. I'll stick with Norvig/Google and his/their demonstrated achievements and knowledge over the talk, hype and vaporware projects of groups that have yet to show any hard progress apart from a bunch of lectures to rich people with a lot of vague words. The SENS movement gives me the exact same feeling. All talk - no walk.

ilaksh 14y ago
So I figured it out. Basically, they take the idea of AGI seriously, and actually consider and talk about the repercussions, and therefore you dismiss them and their ideas as fringe and not worth investigating. I know that, because if you had investigated at all, you would see that all of those projects had really interesting results and these people are not being vague and hand-waving. Not all of those projects I listed identify themselves as AGI. However, they should go in the same group. And anyway, all of those projects have demonstrated progress. If you looked into them at all then you would see that. Ben Goertzel is using some aspects of his AGI research in mainstream (narrow) AI projects. OpenCog has released a number of solid demonstrations of current features. And Goertzel isn't hand-waving or bullshitting in his numerous books and scientific papers, for example Probabilistic Logic Networks: A Comprehensive Framework for Uncertain Inference (336 pages). Hawkins has demonstrated very interesting progress with his software and has a commercial application https://www.numenta.com/grok_info.html https://www.numenta.com/grok_info.html Voss is using his system at Adaptive AI as a commercial enterprise. Qualcomm is funding Brain Corporation (Izhikevich et al) so obviously they are taking it seriously. A bakery in Tokyo has tested Brain Corporation's machine vision technology to power a semi-automated cashier system http://www.diginfo.tv/v/12-0145-r-en.php http://www.diginfo.tv/v/12-0145-r-en.php
- joe_the_user 14y ago
  I'm sympathetic to both Chomsky and Open Cog's aims. I know Chomsky is a serious scientist with considerable accomplishment. I have seen totally loony stuff in videos of AGI conferences (Tachyons and stuff). Open Cog may be better than that. But it hasn't proved that it is better than that. The 1970-80's AI involved the Chomskyan paradigm of "draw up a naive design of the mind and/or brain and implement it". That failed so badly that you need a really good argument why you can do things differently - at least to move into mainstream science. That is, Ben Goertzel seems nice, smart and enthusiastic but I can't see him bringing anything new to the "table". Jeff Hawkins had interesting ideas with his temporal paradigm but it seemed like the model he chose to instantiate wasn't all that different from that used by the statistical-brute-force crowd. And Numenta has had really few announcements for a six year old enterprise. And the companies paying for AI to be added to their systems. That happened from the start but it wasn't ever enough. What's different here from the stuff from twenty years ago?
  - bengoertzel 14y ago
    This is Ben Goertzel... AGI is mainstream science, these days. The keynote of the 2012 AAAI conference (the major mainstream AI research conference each year), by the President of AAAI, was largely about how the time has come for the AI field to refocus on human-level AI. He didn't use the term "AGI" but that was the crux of it. The "AI winter" is over. Maybe another will come, but I doubt it. What's different from 20 years ago? Hardware is way better. The Internet is way richer in data, and faster. Software libraries are way better. Our understanding of cognitive and neural science is way stronger. These factors conspire to make now a much better time to approach the AGI problem. As for my own AGI research lacking anything new, IMO you think this because you are looking for the wrong sort of new thing. You're looking for some funky new algorithm or knowledge structure or something like that. But what's most novel in OpenCog is the mode of organization and interaction of the components, and the emergent structures associated with them. I realize it's a stretch for most folks to realize that the novel ingredients needed to make AGI lie in the domain of systemic organizational principles and emergent networks rather than novel algorithms, data structures or circuits -- but so it goes. It wouldn't be the first time that the mass of people were looking for the wrong kind of innovation, hmm? Regarding tachyons in videos of AGI conferences, could you provide a reference? AGI conference talks are all based on refereed papers published by major scientific publishers. Some papers are stronger than others, but there's no quackery there.... (There have been "Future of AGI" workshops associated with the AGI conferences, which have had some freer-ranging speculative discussions in them; could you be referring to a comment an audience participant made in a discussion there?)
deleted 14y ago
[deleted]
corporalagumbo 14y ago
Chomsky's expertise is in linguistics and political analysis. Stephen Pinker's The Language Instinct is a good, readable introduction to some of Chomsky's work (and the wider field to which he is pivotal.) Chomsky's Manufacturing Consent is probably his classic work of political analysis. http://en.wikipedia.org/wiki/Noam_Chomsky_bibliography http://en.wikipedia.org/wiki/Noam_Chomsky_bibliography He's no quack.
- confluence 14y ago
  You know in the soft sciences everyone is a quack because fundamentally they don't practice - wait for it - science. Science stops false connections by correctly attributing cause to its respective effect. Social sciences do not. For all intents and purposes, the vast majority of social science is either unreproducible, vague, mixing correlation with causation, uses dependent variables, poorly reasoned, statistical quirks, pushed by agendas or fundamentally flawed. > are effective and powerful ideological institutions that carry out a system-supportive propaganda function by reliance on market forces, internalized assumptions, and self-censorship, and without overt coercion -- http://en.wikipedia.org/wiki/Manufacturing_Consent:_The_Political_Economy_of_the_Mass_Media http://en.wikipedia.org/wiki/Manufacturing_Consent:_The_Poli... That's pretty self-evident to the point of being, well, pointless - admen of the 60s made their bread using this, and the PR pioneers of the 30s were already experts. But please let's all listen to what he has to say next. Let me guess: killing people is bad, and not killing people is good. If you call that amazing thinking, I'd hate to see the idiotic version. Even better: > Geoffrey Sampson maintains that universal grammar theories are not falsifiable and are therefore pseudoscientific theory. He argues that the grammatical "rules" linguists posit are simply post-hoc observations about existing languages, rather than predictions about what is possible in a language. Similarly, Jeffrey Elman argues that the unlearnability of languages assumed by Universal Grammar is based on a too-strict, "worst-case" model of grammar, that is not in keeping with any actual grammar. In keeping with these points, James Hurford argues that the postulate of a language acquisition device (LAD) essentially amounts to the trivial claim that languages are learnt by humans, and thus, that the LAD is less a theory than an explanandum looking for theories. Sampson, Roediger, Elman and Hurford are hardly alone in suggesting that several of the basic assumptions of Universal Grammar are unfounded. Indeed, a growing number of language acquisition researchers argue that the very idea of a strict rule-based grammar in any language flies in the face of what is known about how languages are spoken and how languages evolve over time. For instance, Morten Christiansen and Nick Chater have argued that the relatively fast-changing nature of language would prevent the slower-changing genetic structures from ever catching up, undermining the possibility of a genetically hard-wired universal grammar. In addition, it has been suggested that people learn about probabilistic patterns of word distributions in their language, rather than hard and fast rules (see the distributional hypothesis). It has also been proposed that the poverty of the stimulus problem can be largely avoided, if we assume that children employ similarity-based generalization strategies in language learning, generalizing about the usage of new words from similar words that they already know how to use. Another way of defusing the poverty of the stimulus argument is to assume that if language learners notice the absence of classes of expressions in the input, they will hypothesize a restriction (a solution closely related to Bayesian reasoning). In a similar vein, language acquisition researcher Michael Ramscar has suggested that when children erroneously expect an ungrammatical form that then never occurs, the repeated failure of expectation serves as a form of implicit negative feedback that allows them to correct their errors over time. This implies that word learning is a probabilistic, error-driven process, rather than a process of fast mapping, as many nativists assume. Finally, in the domain of field research, the Pirahã language is claimed to be a counterexample to the basic tenets of Universal Grammar. This research has been primarily led by Daniel Everett, a former Christian missionary. Among other things, this language is alleged to lack all evidence for recursion, including embedded clauses, as well as quantifiers and color terms. Some other linguists have argued, however, that some of these properties have been misanalyzed, and that others are actually expected under current theories of Universal Grammar. -- http://en.wikipedia.org/wiki/Universal_grammar#Criticisms http://en.wikipedia.org/wiki/Universal_grammar#Criticisms Looks like I'm not the only one that sees through bullshit. Let me repeat - just to imprint on people's minds: > This implies that word learning is a probabilistic, error-driven process, rather than a process of fast mapping, as many nativists assume. Chomsky's theories are, and always were, DOA.
xyzzy123 14y ago
I think AGI is an important field of study - but only from an ethical viewpoint! In terms of engineering yeah, the trend in AI at the moment is applied statistics for sure, and it wins hard.
mturmon 14y ago
As a point of reference, here's the agenda for the most recent AGI conference: http://agi-conf.org/2011/conference-schedule/ http://agi-conf.org/2011/conference-schedule/ And, just for comparison, here's the agenda for the most recent ICML conference: http://icml.cc/2012/schedule/ http://icml.cc/2012/schedule/ To me, the AGI conference seems to have a much higher ratio of "speculative ideas"/"technical results" talks. Also to me, this pretty much justifies the "all talk - no walk" assessment.
- bengoertzel 14y ago
  This is Ben Goertzel, chief founder of the AGI conference series. You are correct that the AGI conferences have a higher ratio of "speculative ideas"/"technical results" to ICML. This is intentional and I belief appropriate -- because AGI is at an earlier stage of development than machine learning, and because it's qualitatively different in character than machine learning. Machine learning (in the sense that the term is now typically used, i.e. supervised classification, clustering, data minign, etc.) can be approached mainly via a narrowly disciplinary approach. Some cross-disciplinary ideas have proved valuable, e.g. GAs and neural nets, but the cross-disciplinary ideas there have quickly been "computer science ized"... OTOH, I think AGI is inherently more complex and multifarious than ML as currently conceived, and hence requires more "out of the box" and freely multi-disciplinary thinking. I think that in 10-15 years, when the AGI field is much more mature, the conferences will seem a bit more like ML conferences in terms of the percentage of papers reporting strong technical results. BUT, they will never seem as narrowly disciplinary as ML conferences, because AGI is a different sort of pursuit...
  - mturmon 14y ago
    Thanks for the kind reply. I said ICML, but NIPS would have been a better point of reference -- since it was originally conceived as a cross-disciplinary enterprise. The NIPS TOC looks like this: http://nips.djvuzone.org/nipsxx-toc.html http://nips.djvuzone.org/nipsxx-toc.html which indicates it's possible to have a selection of papers both technically sharp and interdisciplinary. We should all be so lucky to attract such a set of papers. I'm reminded of the 1958 editorial by Peter Elias in the IEEE Information Theory Transactions ("Two Famous Papers"): http://oikosjournal.files.wordpress.com/2011/09/elias1958ire-editorial.pdf http://oikosjournal.files.wordpress.com/2011/09/elias1958ire... I sincerely wish you, your conference, and your research enterprise the best.
bengoertzel 14y ago
Hi, this is Ben Goertzel, the chief founder of the OpenCog AGI-focused software project and of the AGI conference series. Comparing Google Search and IBM Watson to OpenCog and other early-stage research efforts is silly. Google Search and IBM Watson have taken fairly mature technologies, pioneered by others over decades of research, and productized them fantastically. OpenCog is a research project and is aimed at breaking fundamentally new research ground, not at productizing and scaling-up technologies already basically described in the academic literature. Lecturing is a very small percentage of what those of us involved with OpenCog do. We are building complex software and developing associated theory. Indeed parts of our approach are speculative, and founded in intuition alongside math and empirics. That's how early-stage research often goes. Of course you can trash all early-stage research as not having results yet. And the majority of early-stage research will fail, probably making you tend to feel vindicated and high and mighty in your skepticism ;p .... But then, a certain percentage of early-stage research will succeed, because of researchers having the guts to follow their intuitions in spite of the ceaseless tedious sniping of folks like you ;p ... - Ben Goertzel

ilaksh 14y ago

So I figured it out. Basically, they take the idea of AGI seriously, and actually consider and talk about the repercussions, and therefore you dismiss them and their ideas as fringe and not worth investigating. I know that, because if you had investigated at all, you would see that all of those projects had really interesting results and these people are not being vague and hand-waving. Not all of those projects I listed identify themselves as AGI. However, they should go in the same group. And anyway, all of those projects have demonstrated progress. If you looked into them at all then you would see that. Ben Goertzel is using some aspects of his AGI research in mainstream (narrow) AI projects. OpenCog has released a number of solid demonstrations of current features. And Goertzel isn't hand-waving or bullshitting in his numerous books and scientific papers, for example Probabilistic Logic Networks: A Comprehensive Framework for Uncertain Inference (336 pages). Hawkins has demonstrated very interesting progress with his software and has a commercial application https://www.numenta.com/grok_info.html https://www.numenta.com/grok_info.html Voss is using his system at Adaptive AI as a commercial enterprise. Qualcomm is funding Brain Corporation (Izhikevich et al) so obviously they are taking it seriously. A bakery in Tokyo has tested Brain Corporation's machine vision technology to power a semi-automated cashier system http://www.diginfo.tv/v/12-0145-r-en.php http://www.diginfo.tv/v/12-0145-r-en.php

joe_the_user 14y ago
I'm sympathetic to both Chomsky and Open Cog's aims. I know Chomsky is a serious scientist with considerable accomplishment. I have seen totally loony stuff in videos of AGI conferences (Tachyons and stuff). Open Cog may be better than that. But it hasn't proved that it is better than that. The 1970-80's AI involved the Chomskyan paradigm of "draw up a naive design of the mind and/or brain and implement it". That failed so badly that you need a really good argument why you can do things differently - at least to move into mainstream science. That is, Ben Goertzel seems nice, smart and enthusiastic but I can't see him bringing anything new to the "table". Jeff Hawkins had interesting ideas with his temporal paradigm but it seemed like the model he chose to instantiate wasn't all that different from that used by the statistical-brute-force crowd. And Numenta has had really few announcements for a six year old enterprise. And the companies paying for AI to be added to their systems. That happened from the start but it wasn't ever enough. What's different here from the stuff from twenty years ago?
- bengoertzel 14y ago
  This is Ben Goertzel... AGI is mainstream science, these days. The keynote of the 2012 AAAI conference (the major mainstream AI research conference each year), by the President of AAAI, was largely about how the time has come for the AI field to refocus on human-level AI. He didn't use the term "AGI" but that was the crux of it. The "AI winter" is over. Maybe another will come, but I doubt it. What's different from 20 years ago? Hardware is way better. The Internet is way richer in data, and faster. Software libraries are way better. Our understanding of cognitive and neural science is way stronger. These factors conspire to make now a much better time to approach the AGI problem. As for my own AGI research lacking anything new, IMO you think this because you are looking for the wrong sort of new thing. You're looking for some funky new algorithm or knowledge structure or something like that. But what's most novel in OpenCog is the mode of organization and interaction of the components, and the emergent structures associated with them. I realize it's a stretch for most folks to realize that the novel ingredients needed to make AGI lie in the domain of systemic organizational principles and emergent networks rather than novel algorithms, data structures or circuits -- but so it goes. It wouldn't be the first time that the mass of people were looking for the wrong kind of innovation, hmm? Regarding tachyons in videos of AGI conferences, could you provide a reference? AGI conference talks are all based on refereed papers published by major scientific publishers. Some papers are stronger than others, but there's no quackery there.... (There have been "Future of AGI" workshops associated with the AGI conferences, which have had some freer-ranging speculative discussions in them; could you be referring to a comment an audience participant made in a discussion there?)

deleted 14y ago

[deleted]

corporalagumbo 14y ago

Chomsky's expertise is in linguistics and political analysis. Stephen Pinker's The Language Instinct is a good, readable introduction to some of Chomsky's work (and the wider field to which he is pivotal.) Chomsky's Manufacturing Consent is probably his classic work of political analysis. http://en.wikipedia.org/wiki/Noam_Chomsky_bibliography http://en.wikipedia.org/wiki/Noam_Chomsky_bibliography He's no quack.

confluence 14y ago
You know in the soft sciences everyone is a quack because fundamentally they don't practice - wait for it - science. Science stops false connections by correctly attributing cause to its respective effect. Social sciences do not. For all intents and purposes, the vast majority of social science is either unreproducible, vague, mixing correlation with causation, uses dependent variables, poorly reasoned, statistical quirks, pushed by agendas or fundamentally flawed. > are effective and powerful ideological institutions that carry out a system-supportive propaganda function by reliance on market forces, internalized assumptions, and self-censorship, and without overt coercion -- http://en.wikipedia.org/wiki/Manufacturing_Consent:_The_Political_Economy_of_the_Mass_Media http://en.wikipedia.org/wiki/Manufacturing_Consent:_The_Poli... That's pretty self-evident to the point of being, well, pointless - admen of the 60s made their bread using this, and the PR pioneers of the 30s were already experts. But please let's all listen to what he has to say next. Let me guess: killing people is bad, and not killing people is good. If you call that amazing thinking, I'd hate to see the idiotic version. Even better: > Geoffrey Sampson maintains that universal grammar theories are not falsifiable and are therefore pseudoscientific theory. He argues that the grammatical "rules" linguists posit are simply post-hoc observations about existing languages, rather than predictions about what is possible in a language. Similarly, Jeffrey Elman argues that the unlearnability of languages assumed by Universal Grammar is based on a too-strict, "worst-case" model of grammar, that is not in keeping with any actual grammar. In keeping with these points, James Hurford argues that the postulate of a language acquisition device (LAD) essentially amounts to the trivial claim that languages are learnt by humans, and thus, that the LAD is less a theory than an explanandum looking for theories. Sampson, Roediger, Elman and Hurford are hardly alone in suggesting that several of the basic assumptions of Universal Grammar are unfounded. Indeed, a growing number of language acquisition researchers argue that the very idea of a strict rule-based grammar in any language flies in the face of what is known about how languages are spoken and how languages evolve over time. For instance, Morten Christiansen and Nick Chater have argued that the relatively fast-changing nature of language would prevent the slower-changing genetic structures from ever catching up, undermining the possibility of a genetically hard-wired universal grammar. In addition, it has been suggested that people learn about probabilistic patterns of word distributions in their language, rather than hard and fast rules (see the distributional hypothesis). It has also been proposed that the poverty of the stimulus problem can be largely avoided, if we assume that children employ similarity-based generalization strategies in language learning, generalizing about the usage of new words from similar words that they already know how to use. Another way of defusing the poverty of the stimulus argument is to assume that if language learners notice the absence of classes of expressions in the input, they will hypothesize a restriction (a solution closely related to Bayesian reasoning). In a similar vein, language acquisition researcher Michael Ramscar has suggested that when children erroneously expect an ungrammatical form that then never occurs, the repeated failure of expectation serves as a form of implicit negative feedback that allows them to correct their errors over time. This implies that word learning is a probabilistic, error-driven process, rather than a process of fast mapping, as many nativists assume. Finally, in the domain of field research, the Pirahã language is claimed to be a counterexample to the basic tenets of Universal Grammar. This research has been primarily led by Daniel Everett, a former Christian missionary. Among other things, this language is alleged to lack all evidence for recursion, including embedded clauses, as well as quantifiers and color terms. Some other linguists have argued, however, that some of these properties have been misanalyzed, and that others are actually expected under current theories of Universal Grammar. -- http://en.wikipedia.org/wiki/Universal_grammar#Criticisms http://en.wikipedia.org/wiki/Universal_grammar#Criticisms Looks like I'm not the only one that sees through bullshit. Let me repeat - just to imprint on people's minds: > This implies that word learning is a probabilistic, error-driven process, rather than a process of fast mapping, as many nativists assume. Chomsky's theories are, and always were, DOA.

xyzzy123 14y ago

I think AGI is an important field of study - but only from an ethical viewpoint! In terms of engineering yeah, the trend in AI at the moment is applied statistics for sure, and it wins hard.

mturmon 14y ago

As a point of reference, here's the agenda for the most recent AGI conference: http://agi-conf.org/2011/conference-schedule/ http://agi-conf.org/2011/conference-schedule/ And, just for comparison, here's the agenda for the most recent ICML conference: http://icml.cc/2012/schedule/ http://icml.cc/2012/schedule/ To me, the AGI conference seems to have a much higher ratio of "speculative ideas"/"technical results" talks. Also to me, this pretty much justifies the "all talk - no walk" assessment.

bengoertzel 14y ago
This is Ben Goertzel, chief founder of the AGI conference series. You are correct that the AGI conferences have a higher ratio of "speculative ideas"/"technical results" to ICML. This is intentional and I belief appropriate -- because AGI is at an earlier stage of development than machine learning, and because it's qualitatively different in character than machine learning. Machine learning (in the sense that the term is now typically used, i.e. supervised classification, clustering, data minign, etc.) can be approached mainly via a narrowly disciplinary approach. Some cross-disciplinary ideas have proved valuable, e.g. GAs and neural nets, but the cross-disciplinary ideas there have quickly been "computer science ized"... OTOH, I think AGI is inherently more complex and multifarious than ML as currently conceived, and hence requires more "out of the box" and freely multi-disciplinary thinking. I think that in 10-15 years, when the AGI field is much more mature, the conferences will seem a bit more like ML conferences in terms of the percentage of papers reporting strong technical results. BUT, they will never seem as narrowly disciplinary as ML conferences, because AGI is a different sort of pursuit...
- mturmon 14y ago
  Thanks for the kind reply. I said ICML, but NIPS would have been a better point of reference -- since it was originally conceived as a cross-disciplinary enterprise. The NIPS TOC looks like this: http://nips.djvuzone.org/nipsxx-toc.html http://nips.djvuzone.org/nipsxx-toc.html which indicates it's possible to have a selection of papers both technically sharp and interdisciplinary. We should all be so lucky to attract such a set of papers. I'm reminded of the 1958 editorial by Peter Elias in the IEEE Information Theory Transactions ("Two Famous Papers"): http://oikosjournal.files.wordpress.com/2011/09/elias1958ire-editorial.pdf http://oikosjournal.files.wordpress.com/2011/09/elias1958ire... I sincerely wish you, your conference, and your research enterprise the best.

bengoertzel 14y ago

Hi, this is Ben Goertzel, the chief founder of the OpenCog AGI-focused software project and of the AGI conference series. Comparing Google Search and IBM Watson to OpenCog and other early-stage research efforts is silly. Google Search and IBM Watson have taken fairly mature technologies, pioneered by others over decades of research, and productized them fantastically. OpenCog is a research project and is aimed at breaking fundamentally new research ground, not at productizing and scaling-up technologies already basically described in the academic literature. Lecturing is a very small percentage of what those of us involved with OpenCog do. We are building complex software and developing associated theory. Indeed parts of our approach are speculative, and founded in intuition alongside math and empirics. That's how early-stage research often goes. Of course you can trash all early-stage research as not having results yet. And the majority of early-stage research will fail, probably making you tend to feel vindicated and high and mighty in your skepticism ;p .... But then, a certain percentage of early-stage research will succeed, because of researchers having the guts to follow their intuitions in spite of the ceaseless tedious sniping of folks like you ;p ... - Ben Goertzel