8 ms·

> PS. This makes me wonder whether and to what degree the structure of the brain's connectome is a necessary prior for AGI. Well, I wouldn't mix up AGI and AGI

by jmmcd 9y ago

> PS. This makes me wonder whether and to what degree the structure of the brain's connectome is a necessary prior for AGI. Well, I wouldn't mix up AGI and AGI by deep learning, and more important I would emphasise that this is a good prior for images. The fundamental insight in CNNs and eventually in this work is that there is a correlation between pairs of nearby pixels. We have something similar for video and audio processing, but nothing remotely similar for other more abstract intelligence tasks.

cs702 9y ago
Thanks. I'm not mixing them up! I'm just wondering whether and to what degree architecture, i.e., network structure, will prove important for other, more advanced AI tasks, including up to AGI.
- garmaine 9y ago
  Yes, AGI as a discipline distinguishes itself in large part from machine learning by its focus on architecture over training or data.
- EGreg 9y ago
  https://en.m.wikipedia.org/wiki/Language_acquisition_device https://en.m.wikipedia.org/wiki/Language_acquisition_device
- mjburgess 9y ago
  To throw a dissenting voice into the mix: I do not think intelligence is a consequence of structure. Transparency is a consequence of how light interacts with objects. There is no "transparent gold". And being transparent is not something we can program gold to do. Programming is the application of an electric field across a silicon surface: this can no more transmute silicon into gold as it can into a nervous system. Consciousness is a biological activity. It is not something wood, silicon, sand, metal, glass, water, etc. can do by virtue of moving around in an interesting pattern. To me, this is akin to believing magic, or to thinking that the issue with Bug's Bunny's consciousness is that the ink wasnt laid out in the right way. Rather, there isnt any. As scientists we should begin very clearly by identifying the target phenomenon. Line up everything in the known universe that is conscious and you will find that it engages in a highly specific biochemical activity that requires a whole set of highly specific chemical interactions. To believe these can be achieved by applying an electric field across some silicon seems like the victorian superstition that a person could be raised from the dead by doing likewise. Rather, all we are able to do is imitate in the weakest sense. People fooled by chatbots are no more enlightened than the early victorians who were shocked to watch the movie of a train coming at them. Fooling people is a very easy thing to do. And esp. in the area of consciousness when "alertness to things that might be conscious" seems a deep psychological preoccupation of almost every animal to the point of absurdity (eg. being scared at the wind).
  - sanxiyn 9y ago
    Who said anything about consciousness? Intelligence does not equal consciousness.
    - mjburgess 9y ago
      Line up all the things in the universe that are intelligent. They are all conscious. Unless you mean "imitating intelligence", but that's not what AGI (artificial general intelligence) is targeting. An AGI robot would be one to include in any future line-up of "all the things that are intelligent". My view is that unless an AGI robot has a nervous system, it will never belong in that category. It is fashionable in tech today to speak metaphorically with abandon and to call whatever we like "intelligent" (and to have, machine "learning", etc.). These systems are no more intelligent than an abacus: upon inventing a wood-to-lcd converter, we could run Win95 on a few million wooden beads. Metaphorically, these are intelligent. They fill the role of prior uses of genuine intelligence -- ie., they can help us calculate and therefore substitute our calculative conscious behaviours for non-conscious substitutes. They are tools. My hammar is no more intelligent than my laptop however. Neither possess thoughts, nor can conceptualize or understand anything. A concept is a (biochemical) technique an animal acquires through a biochemical interaction with the world: an electric field can not have concepts. The argument that machines-which-seem-intelligent are intelligent is only a fallacy of ambiguity. What we mean, of machines, is a tool-which-helps-conceptualizing. We we mean of general intelligence is conceptualizing. The former and the latter are not the same. In a universe only of tools, nothing thinks to use them.
      - ben_w 9y ago
        > Line up all the things in the universe that are intelligent. They are all conscious. The words “intelligent” and “conscious” are not sufficiently well defined to make that claim. By “intelligence”, do you mean: 1) “the ability to learn or understand or deal with new or trying situations”? Even current AI can do that. 2) “the ability to apply knowledge to manipulate one’s environment”? That’s another rabbit hole itself. 3) “think abstractly as measured by [tests]”? I think current AI fails at this. 4) the thing which is separate to “body” in Cartesian Dualism? I don’t believe in that (i.e. souls) any more, so I can’t claim any AI would pass this test (nor any human). And “conscious”? Is that: 1) Opposite of unconscious? I’d say they are. 2) Opposite of subconscious? ‍️ 2)a) As in, not just autonomous functions like breathing or the equivalent for a robot? I’d say they are. 2)b) As in, “System 2 thinking”? ‍️I don’t know. 3) Critical self-evaluation? Generative adversarial networks pass, other than that I think AI fail this test. 4) Mirror test? Pass, but in a special case I don’t know the details of and which might well be newspaper fluff rather than proper AI.
- zgao 9y ago
  Although probably not sufficient for AGI, network architecture is essentially guaranteed to be important, because of both ample empirical evidence of the importance of architectures and ample reason, from facts about numerics, to believe that it is important. In the first category (empirical evidence), - The discrete leap from non-LSTM RNN to LSTM network performance on NLP was essentially due to a "better factoring of the problem": breaking out the primitive operations that equate to an RNN having "memory" had a substantial effect on how well it "remembered." - The leap in NMT from LSTM seq2seq to attention-based methods (the Transformer by Google) is another example. Long-distance correlations made yet another leap because they are simply modeled more directly by the architecture than in the LSTM. - The relation network by DeepMind is another excellent example of a drop-in, "pure" architectural intuition-motivated replacement that increased accuracy from the 66% range to the 90% range on various tasks. Again, this was through directly modeling and weight-tying relation vectors through the architecture of the network. - The capsule network for image recognition is yet another example. By shifting the focus of the architecture from arbitrarily guaranteeing only positional invariance to guaranteeing other sorts, the network was able to do much better at overlapping MNIST. Again, a better factoring of the problem. These developments all illustrate that picking the architecture and the numerical guarantees baked into the "factoring" of the architecture (for example, weight tying, orthogonality, invariance, etc.) can have and has had a profound effect on performance. There is no reason to believe this trend won't continue. In fact, there are some very interesting ways to think about the principles behind network structure -- I can't say for sure that it has any predictive power yet, but types are one intuitively appealing way to look at it: http://colah.github.io/posts/2015-09-NN-Types-FP/ http://colah.github.io/posts/2015-09-NN-Types-FP/
  - cs702 9y ago
    Thanks. I agree. The anecdotal evidence suggests that architecture is indeed important. This paper is the only direct evidence I've seen of it, though. Great work. Compelling.
deleted 9y ago
[deleted]
otakucode 9y ago
Well, there is correlation between 'nearby' things of all types. An apple is an 'apple' because we encountered the word and the thing in close proximity to one another temporally and perceptively many times during our life. All knowledge is this. Tremendously overlapping and filled with mistakes (spurious correlations induced by coincidence and happenstance), but fundamentally just that same 'insight'. Our brains are association machines. Once sufficient quantity of associations exist in a brain in a body living in a world we recognize, we call the property that results 'consciousness'. Unfortunately there is no reason to think that you can substract the 'in a body living in a world we recognize' part and end up with anything like the same property. All those associations come from perceptive inputs, and result in motive outputs, with the biofeedback that occurs being of profoundly fundamental importance (heck, put a pen in your mouth which causes your mouth to 'smile' and you will 'feel happier'). While of course there is definitely a possibility that a machine-based intelligence could be conscious, it would doubtless have to be very different from anything we could recognize, derived as it would be from its own input, output, and the feedback between the two.

by jmmcd 9y ago

cs702 9y ago

Thanks. I'm not mixing them up! I'm just wondering whether and to what degree architecture, i.e., network structure, will prove important for other, more advanced AI tasks, including up to AGI.

garmaine 9y ago
Yes, AGI as a discipline distinguishes itself in large part from machine learning by its focus on architecture over training or data.
EGreg 9y ago
https://en.m.wikipedia.org/wiki/Language_acquisition_device https://en.m.wikipedia.org/wiki/Language_acquisition_device
mjburgess 9y ago
To throw a dissenting voice into the mix: I do not think intelligence is a consequence of structure. Transparency is a consequence of how light interacts with objects. There is no "transparent gold". And being transparent is not something we can program gold to do. Programming is the application of an electric field across a silicon surface: this can no more transmute silicon into gold as it can into a nervous system. Consciousness is a biological activity. It is not something wood, silicon, sand, metal, glass, water, etc. can do by virtue of moving around in an interesting pattern. To me, this is akin to believing magic, or to thinking that the issue with Bug's Bunny's consciousness is that the ink wasnt laid out in the right way. Rather, there isnt any. As scientists we should begin very clearly by identifying the target phenomenon. Line up everything in the known universe that is conscious and you will find that it engages in a highly specific biochemical activity that requires a whole set of highly specific chemical interactions. To believe these can be achieved by applying an electric field across some silicon seems like the victorian superstition that a person could be raised from the dead by doing likewise. Rather, all we are able to do is imitate in the weakest sense. People fooled by chatbots are no more enlightened than the early victorians who were shocked to watch the movie of a train coming at them. Fooling people is a very easy thing to do. And esp. in the area of consciousness when "alertness to things that might be conscious" seems a deep psychological preoccupation of almost every animal to the point of absurdity (eg. being scared at the wind).
- sanxiyn 9y ago
  Who said anything about consciousness? Intelligence does not equal consciousness.
  - mjburgess 9y ago
    Line up all the things in the universe that are intelligent. They are all conscious. Unless you mean "imitating intelligence", but that's not what AGI (artificial general intelligence) is targeting. An AGI robot would be one to include in any future line-up of "all the things that are intelligent". My view is that unless an AGI robot has a nervous system, it will never belong in that category. It is fashionable in tech today to speak metaphorically with abandon and to call whatever we like "intelligent" (and to have, machine "learning", etc.). These systems are no more intelligent than an abacus: upon inventing a wood-to-lcd converter, we could run Win95 on a few million wooden beads. Metaphorically, these are intelligent. They fill the role of prior uses of genuine intelligence -- ie., they can help us calculate and therefore substitute our calculative conscious behaviours for non-conscious substitutes. They are tools. My hammar is no more intelligent than my laptop however. Neither possess thoughts, nor can conceptualize or understand anything. A concept is a (biochemical) technique an animal acquires through a biochemical interaction with the world: an electric field can not have concepts. The argument that machines-which-seem-intelligent are intelligent is only a fallacy of ambiguity. What we mean, of machines, is a tool-which-helps-conceptualizing. We we mean of general intelligence is conceptualizing. The former and the latter are not the same. In a universe only of tools, nothing thinks to use them.
    - ben_w 9y ago
      > Line up all the things in the universe that are intelligent. They are all conscious. The words “intelligent” and “conscious” are not sufficiently well defined to make that claim. By “intelligence”, do you mean: 1) “the ability to learn or understand or deal with new or trying situations”? Even current AI can do that. 2) “the ability to apply knowledge to manipulate one’s environment”? That’s another rabbit hole itself. 3) “think abstractly as measured by [tests]”? I think current AI fails at this. 4) the thing which is separate to “body” in Cartesian Dualism? I don’t believe in that (i.e. souls) any more, so I can’t claim any AI would pass this test (nor any human). And “conscious”? Is that: 1) Opposite of unconscious? I’d say they are. 2) Opposite of subconscious? ‍️ 2)a) As in, not just autonomous functions like breathing or the equivalent for a robot? I’d say they are. 2)b) As in, “System 2 thinking”? ‍️I don’t know. 3) Critical self-evaluation? Generative adversarial networks pass, other than that I think AI fail this test. 4) Mirror test? Pass, but in a special case I don’t know the details of and which might well be newspaper fluff rather than proper AI.
zgao 9y ago
Although probably not sufficient for AGI, network architecture is essentially guaranteed to be important, because of both ample empirical evidence of the importance of architectures and ample reason, from facts about numerics, to believe that it is important. In the first category (empirical evidence), - The discrete leap from non-LSTM RNN to LSTM network performance on NLP was essentially due to a "better factoring of the problem": breaking out the primitive operations that equate to an RNN having "memory" had a substantial effect on how well it "remembered." - The leap in NMT from LSTM seq2seq to attention-based methods (the Transformer by Google) is another example. Long-distance correlations made yet another leap because they are simply modeled more directly by the architecture than in the LSTM. - The relation network by DeepMind is another excellent example of a drop-in, "pure" architectural intuition-motivated replacement that increased accuracy from the 66% range to the 90% range on various tasks. Again, this was through directly modeling and weight-tying relation vectors through the architecture of the network. - The capsule network for image recognition is yet another example. By shifting the focus of the architecture from arbitrarily guaranteeing only positional invariance to guaranteeing other sorts, the network was able to do much better at overlapping MNIST. Again, a better factoring of the problem. These developments all illustrate that picking the architecture and the numerical guarantees baked into the "factoring" of the architecture (for example, weight tying, orthogonality, invariance, etc.) can have and has had a profound effect on performance. There is no reason to believe this trend won't continue. In fact, there are some very interesting ways to think about the principles behind network structure -- I can't say for sure that it has any predictive power yet, but types are one intuitively appealing way to look at it: http://colah.github.io/posts/2015-09-NN-Types-FP/ http://colah.github.io/posts/2015-09-NN-Types-FP/
- cs702 9y ago
  Thanks. I agree. The anecdotal evidence suggests that architecture is indeed important. This paper is the only direct evidence I've seen of it, though. Great work. Compelling.

deleted 9y ago

[deleted]

otakucode 9y ago

Well, there is correlation between 'nearby' things of all types. An apple is an 'apple' because we encountered the word and the thing in close proximity to one another temporally and perceptively many times during our life. All knowledge is this. Tremendously overlapping and filled with mistakes (spurious correlations induced by coincidence and happenstance), but fundamentally just that same 'insight'. Our brains are association machines. Once sufficient quantity of associations exist in a brain in a body living in a world we recognize, we call the property that results 'consciousness'. Unfortunately there is no reason to think that you can substract the 'in a body living in a world we recognize' part and end up with anything like the same property. All those associations come from perceptive inputs, and result in motive outputs, with the biofeedback that occurs being of profoundly fundamental importance (heck, put a pen in your mouth which causes your mouth to 'smile' and you will 'feel happier'). While of course there is definitely a possibility that a machine-based intelligence could be conscious, it would doubtless have to be very different from anything we could recognize, derived as it would be from its own input, output, and the feedback between the two.