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I've been around AI since the end of the last big hype in the late 80s. The recent leap in machine learning has felt rather hyped to me. I don't think AGI is ne

by sago 9y ago

I've been around AI since the end of the last big hype in the late 80s. The recent leap in machine learning has felt rather hyped to me. I don't think AGI is near. But I find myself agreeing with this article. Strongly. And I have long suspected that, we miss a lot of the significance and opportunities in AI, because we have only one exemplar of 'higher' intelligence: a human being. AI folk are so concerned with getting computers to do the things that humans are good at, I suspect most will miss / 'refute' / deride the inflection point, because the system can't wash the dishes (or some other form of embodied cognition), or write poetry humans would find beautiful, (or understand some other socially-conditioned cue). The superhuman fallacy really is the bane of AI.

SomeStupidPoint 9y ago
Can you define 'near' for me? I think AGI is likely closer to the present than 1987 was -- that is, I'd bet on having AGI by 2047. (Note: this is distinct from superhuman AGI.) Do you not agree? I think a lot of people underestimate NNs because they think of NNs in terms of the semantics of their history instead of all possible semantics that can be fit to tensor networks. We know [P] that NNs are a sufficient abstraction to model human intelligence if we had arbitrary compute -- the questions that remain are all about making the hardware faster enough and the estimators efficient enough (which may require moving off tensor networks, but it's still only a refinement of the mathematics used). Of course, one could argue that humans are caught in a "tensor trap", in that too much of our intellectual effort is now relying on estimators built out of networks of tensors. (I do.) But even then, AGI is likely to appear out of similar methods with new mathematical objects. [P] Proof NNs can compute human intelligence with arbitrary compute: You can embed the standard model as a NN by changing how you view the network of tensor equations. Human intelligence is (arguably) embeded in the standard model by modern science. So we can embed a model of human intelligence in a (large enough) NN. This isn't immediately computationally useful, but it shows that there's not a fundamental flaw in using an estimator built out of a DAG of calculations to model intelligence if we can find an appropriate estimator for our computational needs.
- Figs 9y ago
  > DAG Feedback and memory are really important features of GI that you will not get out of a DAG ever. You need loops for that.
  - SomeStupidPoint 9y ago
    > estimator built out of a DAG of calculations All loops can be modeled as a DAG and single attached piece of memory (of sufficient width) allowed to execute to a steady state; sorry if it wasn't clear that I was talking about things like NTMs too. (It's why I used 'tensor network' most places; also, in practice, we tend to let subgraphs reach a steady state independently where possible.) Your comment is also an excellent example of a strawman: you picked out the word 'DAG' to raise a technical argument when the usage of DAG versus general tensor networks clearly wasn't the main point (as some NNs have feedback and the standard model is posed as differential equations). It's more constructive to respond to the strongest point, not pick at technical details that can easily be rephrased.
- leereeves 9y ago
  The ability to compute intelligence given infinite time and compute power isn't proof that NNs are a useful approach. For that, a technique must be able to compute human intelligence at a useful speed on a constructable system.
  - SomeStupidPoint 9y ago
    Didn't the same argument apply in general to why NNs weren't particularly useful for anything ~30 years ago? Using a network of tensors to compute intelligence is incredibly old (I believe, dating back about 80 years), but has only recently become tractable to do for any complex tasks. However, in the past ~30 years, we've gone from "intractable for moderate problems" to "world champion at go", "able to detect cancer in images as well as experts", etc. My contention is that in another ~30 years, we'll see a step sufficient for "can do average at most intellectual activities", even if that's just having the storage to keep 10,000 task specific NNs (of AlphaGo sophistication) on hand to interpolate all actions as mixes of specialist tasks. Do you really not think there's a strong heuristic case for that? (I would contend that you should be able to point to a specific task you don't think it will be able to do on that timeline -- do you know of such a task?) The proof was merely that we're not barking up a theoretically dead tree -- we have to rely on heuristics for if it will eventually converge to tractable.
rl3 9y ago
>The recent leap in machine learning has felt rather hyped to me. I don't think AGI is near. I've always thought it's never too early to start allocating significant resources to AGI research and safety given the potential impact. That said, I've up until very recently agreed with your take on the situation. What changed my mind was an article detailing the latest advances in silver nanowire mesh networks.[0] I knew neural computing was a thing, but not that we already had a computing substrate capable of self-organizing its own neural architecture based entirely on external input, with power requirements analogous to the human brain. No firmware or software required. One could say that human physiology remains far more complex just on the substrate front alone, what with the brain being an incredibly complex, delicate balance of chemicals and heterogeneous cells. However, this particular artificial substrate is already succeeding in basic learning tasks, despite the fact it's far simpler. I steongly suspect we've figured out at least one artificial computing substrate not only capable of, but perhaps well-suited towards producing AGI, and that it's just a matter of scaling it. Of course, once you scale the technology sufficiently, the question then becomes how to architect and train it into an AGI. You say as much above, but I suspect the architecture need not be human to be a threat, or to otherwise become extremely powerful. [0] https://www.quantamagazine.org/a-brain-built-from-atomic-switches-can-learn-20170920/ https://www.quantamagazine.org/a-brain-built-from-atomic-swi...
- mrfusion 9y ago
  Thanks for pointing me to that. Very fascinating! I’m wondering if it’s possible to code a simulation of what these wires are doing? Since most of us don’t have access to the nanoscale silver contraption we could still be studying the operation.
- phs318u 9y ago
  Fascinating! > the question then becomes how to architect and train it into an AGI. If that’s the question then why even bother with silver nanowire “brains”? Why not just grow a human brain out of some suitable stem cells and work with that? Leaving aside the massive creepiness factor.
  - rl3 9y ago
bitL 9y ago
AI is already superhuman, but not in those areas we associate as strongly human :)
deong 9y ago
The article makes a lot of good points, but for me, the critical error is in assuming that if short term prediction is hard, long term prediction must be massively harder. He asked a panel for the least impressive thing they did not believe would be possible within a few years. In other words, pick the point closest to the boundary of that classifier. Obviously my future knowledge is imperfect, and anything close to the boundary is subject to a lot of uncertainty. From that difficulty, he hand waves an argument that long term prediction of the unlikelihood of AGI is folly. The problem is that these aren't in the same class of predictions. One is detailed and precise; the other coarse and broad. Predicting that it will rain at 2:00 PM November 10, 2017 is much more difficult than predicting that the average summer of 2040-2060 will be hotter than the average from 1980-2000. Precise local predictions just arent the same thing as broad global predictions, and difficulty doesn't transfer, because I'm not bootstrapping my global prediction on the local one. I'm using different methods entirely. There's a similar thing with AI, I think. I can't confidently tell you what the big splash we'll see at NIPS next year or the year after. But I can look at the way we know how to do AI and say I don't think 30 years will see a machine that can make dinner by gathering ingredients from a supermarket, driving home, and preparing the meal.
- JabavuAdams 9y ago
  > I don't think 30 years will see a machine that can make dinner by gathering ingredients from a supermarket, driving home, and preparing the meal. Really? Why not? Once or twice, if we cherry-pick its performance, or reliably? This is really surprising to me.
  - stretchwithme 9y ago
    It won't be a single machine. It will be multiple systems. And it's not that far away. Probably only 15 years. And it will be a great boon. Quality of meals will go up and costs will go down. The restaurant market will shrink but not completely disappear. But McDonald's will certainly die, as there'll be no need to sacrifice quality and nutrition to get speed and convenience. In fact, a table at McDonald's will be an inconvenient booth.
Balgair 9y ago
> The superhuman fallacy really is the bane of AI Great point, I'll be stealing that one ;) One annoyance I have heard about SV is that all the companies are just trying to replace your Jewish Mom: Uber/Lyft is Mom's minivan, GrubHub/DoorDash/BlueKitchen is Mom's cooking, Google is Mom's encylopedia, Yelp is the synagogue's meeting hallway, Tinder is your Mom's yenta, etc. The examples abound in a non-B2B space. In that vein, then AGI is not just a Superman fallacy, but SuperMom too.
- ciphergoth 9y ago
  Bulverism is a poor substitute for actually thinking about the future. See Scott Alexander's excellent recent essay: http://slatestarcodex.com/2017/10/09/in-favor-of-futurism-being-about-the-future/ http://slatestarcodex.com/2017/10/09/in-favor-of-futurism-be...
  - Balgair 9y ago
    TLDR?
eli_gottlieb 9y ago
I'm fairly sure there isn't really such a thing as disembodied cognition. You have to build the fancy sciency stuff on top of the sensorimotor prediction-and-control stuff.

by sago 9y ago

SomeStupidPoint 9y ago

Can you define 'near' for me? I think AGI is likely closer to the present than 1987 was -- that is, I'd bet on having AGI by 2047. (Note: this is distinct from superhuman AGI.) Do you not agree? I think a lot of people underestimate NNs because they think of NNs in terms of the semantics of their history instead of all possible semantics that can be fit to tensor networks. We know [P] that NNs are a sufficient abstraction to model human intelligence if we had arbitrary compute -- the questions that remain are all about making the hardware faster enough and the estimators efficient enough (which may require moving off tensor networks, but it's still only a refinement of the mathematics used). Of course, one could argue that humans are caught in a "tensor trap", in that too much of our intellectual effort is now relying on estimators built out of networks of tensors. (I do.) But even then, AGI is likely to appear out of similar methods with new mathematical objects. [P] Proof NNs can compute human intelligence with arbitrary compute: You can embed the standard model as a NN by changing how you view the network of tensor equations. Human intelligence is (arguably) embeded in the standard model by modern science. So we can embed a model of human intelligence in a (large enough) NN. This isn't immediately computationally useful, but it shows that there's not a fundamental flaw in using an estimator built out of a DAG of calculations to model intelligence if we can find an appropriate estimator for our computational needs.

Figs 9y ago
> DAG Feedback and memory are really important features of GI that you will not get out of a DAG ever. You need loops for that.
- SomeStupidPoint 9y ago
  > estimator built out of a DAG of calculations All loops can be modeled as a DAG and single attached piece of memory (of sufficient width) allowed to execute to a steady state; sorry if it wasn't clear that I was talking about things like NTMs too. (It's why I used 'tensor network' most places; also, in practice, we tend to let subgraphs reach a steady state independently where possible.) Your comment is also an excellent example of a strawman: you picked out the word 'DAG' to raise a technical argument when the usage of DAG versus general tensor networks clearly wasn't the main point (as some NNs have feedback and the standard model is posed as differential equations). It's more constructive to respond to the strongest point, not pick at technical details that can easily be rephrased.
leereeves 9y ago
The ability to compute intelligence given infinite time and compute power isn't proof that NNs are a useful approach. For that, a technique must be able to compute human intelligence at a useful speed on a constructable system.
- SomeStupidPoint 9y ago
  Didn't the same argument apply in general to why NNs weren't particularly useful for anything ~30 years ago? Using a network of tensors to compute intelligence is incredibly old (I believe, dating back about 80 years), but has only recently become tractable to do for any complex tasks. However, in the past ~30 years, we've gone from "intractable for moderate problems" to "world champion at go", "able to detect cancer in images as well as experts", etc. My contention is that in another ~30 years, we'll see a step sufficient for "can do average at most intellectual activities", even if that's just having the storage to keep 10,000 task specific NNs (of AlphaGo sophistication) on hand to interpolate all actions as mixes of specialist tasks. Do you really not think there's a strong heuristic case for that? (I would contend that you should be able to point to a specific task you don't think it will be able to do on that timeline -- do you know of such a task?) The proof was merely that we're not barking up a theoretically dead tree -- we have to rely on heuristics for if it will eventually converge to tractable.

rl3 9y ago

>The recent leap in machine learning has felt rather hyped to me. I don't think AGI is near. I've always thought it's never too early to start allocating significant resources to AGI research and safety given the potential impact. That said, I've up until very recently agreed with your take on the situation. What changed my mind was an article detailing the latest advances in silver nanowire mesh networks.[0] I knew neural computing was a thing, but not that we already had a computing substrate capable of self-organizing its own neural architecture based entirely on external input, with power requirements analogous to the human brain. No firmware or software required. One could say that human physiology remains far more complex just on the substrate front alone, what with the brain being an incredibly complex, delicate balance of chemicals and heterogeneous cells. However, this particular artificial substrate is already succeeding in basic learning tasks, despite the fact it's far simpler. I steongly suspect we've figured out at least one artificial computing substrate not only capable of, but perhaps well-suited towards producing AGI, and that it's just a matter of scaling it. Of course, once you scale the technology sufficiently, the question then becomes how to architect and train it into an AGI. You say as much above, but I suspect the architecture need not be human to be a threat, or to otherwise become extremely powerful. [0] https://www.quantamagazine.org/a-brain-built-from-atomic-switches-can-learn-20170920/ https://www.quantamagazine.org/a-brain-built-from-atomic-swi...

mrfusion 9y ago
Thanks for pointing me to that. Very fascinating! I’m wondering if it’s possible to code a simulation of what these wires are doing? Since most of us don’t have access to the nanoscale silver contraption we could still be studying the operation.
phs318u 9y ago
Fascinating! > the question then becomes how to architect and train it into an AGI. If that’s the question then why even bother with silver nanowire “brains”? Why not just grow a human brain out of some suitable stem cells and work with that? Leaving aside the massive creepiness factor.
- rl3 9y ago

bitL 9y ago

AI is already superhuman, but not in those areas we associate as strongly human :)

deong 9y ago

The article makes a lot of good points, but for me, the critical error is in assuming that if short term prediction is hard, long term prediction must be massively harder. He asked a panel for the least impressive thing they did not believe would be possible within a few years. In other words, pick the point closest to the boundary of that classifier. Obviously my future knowledge is imperfect, and anything close to the boundary is subject to a lot of uncertainty. From that difficulty, he hand waves an argument that long term prediction of the unlikelihood of AGI is folly. The problem is that these aren't in the same class of predictions. One is detailed and precise; the other coarse and broad. Predicting that it will rain at 2:00 PM November 10, 2017 is much more difficult than predicting that the average summer of 2040-2060 will be hotter than the average from 1980-2000. Precise local predictions just arent the same thing as broad global predictions, and difficulty doesn't transfer, because I'm not bootstrapping my global prediction on the local one. I'm using different methods entirely. There's a similar thing with AI, I think. I can't confidently tell you what the big splash we'll see at NIPS next year or the year after. But I can look at the way we know how to do AI and say I don't think 30 years will see a machine that can make dinner by gathering ingredients from a supermarket, driving home, and preparing the meal.

JabavuAdams 9y ago
> I don't think 30 years will see a machine that can make dinner by gathering ingredients from a supermarket, driving home, and preparing the meal. Really? Why not? Once or twice, if we cherry-pick its performance, or reliably? This is really surprising to me.
- stretchwithme 9y ago
  It won't be a single machine. It will be multiple systems. And it's not that far away. Probably only 15 years. And it will be a great boon. Quality of meals will go up and costs will go down. The restaurant market will shrink but not completely disappear. But McDonald's will certainly die, as there'll be no need to sacrifice quality and nutrition to get speed and convenience. In fact, a table at McDonald's will be an inconvenient booth.

Balgair 9y ago

> The superhuman fallacy really is the bane of AI Great point, I'll be stealing that one ;) One annoyance I have heard about SV is that all the companies are just trying to replace your Jewish Mom: Uber/Lyft is Mom's minivan, GrubHub/DoorDash/BlueKitchen is Mom's cooking, Google is Mom's encylopedia, Yelp is the synagogue's meeting hallway, Tinder is your Mom's yenta, etc. The examples abound in a non-B2B space. In that vein, then AGI is not just a Superman fallacy, but SuperMom too.

ciphergoth 9y ago
Bulverism is a poor substitute for actually thinking about the future. See Scott Alexander's excellent recent essay: http://slatestarcodex.com/2017/10/09/in-favor-of-futurism-being-about-the-future/ http://slatestarcodex.com/2017/10/09/in-favor-of-futurism-be...
- Balgair 9y ago
  TLDR?

eli_gottlieb 9y ago

I'm fairly sure there isn't really such a thing as disembodied cognition. You have to build the fancy sciency stuff on top of the sensorimotor prediction-and-control stuff.