6 ms·

I don't buy that. Spiders can't beat a human at Go. Sure, they weren't evolved to do that. But how many generations of selective breeding do you think it would

I don't buy that. Spiders can't beat a human at Go. Sure, they weren't evolved to do that. But how many generations of selective breeding do you think it would take to evolve a spider that could beat the best humans at go? Whereas if you made a spider hunting video game, I bet researchers could train AI's that could beat it within a few months. Video game playing has actually become a big area of research recently and we will soon see deep nets that can beat starcraft and other real time games that take planning and skill.

pmyjavec 10y ago
How intelligent is a spider, truly ? I mean, creating a machine that beats us at a game we invented ourselves is one thing, but how does that compare to a species that has survived much longer on this earth than us? I mean most people are more worried about iPhone 7 than climate change. I am not arguing that we are not smarter than spiders, but I'm also not going to argue we truly know that we are. It would be wise to keep our egos under control.
- kkarakk 10y ago
  you could wipe out that species with a focused effort on your part in a couple of years. does that mean you are more important than spiders? bringing unrealistic philosophical considerations into tangible concerns is a pet peeve of mine
Fede_V 10y ago
Depending on how you define 'soon' - it might take a little longer. A bunch of really good people at Facebook AI just worked on Starcraft using reinforcement learning, and even getting the micro down is really hard.
gambler 10y ago
Well, yes, if you could make a completely artificial, controlled environment that presents a problem well-suited for the current flavor of AI, the AI would probably be able to beat a random animal at solving that problem.
- xyzzy123 10y ago
  Spiders have much better hardware than the state of the art in low cost miniatuarised robotics. However I'm not convinced that if you trained a deep net and gave it access to a spider's sensor suite and effectors, that it would do worse than an actual spider would. A lot worse in terms of efficiency per watt, sure...
  - YeGoblynQueenne 10y ago
    How would you train that deep net? What data would you give it? What would you train it to do exactly?
    - xyzzy123 10y ago
      Survive. Reproduce. Just like the video games nets are already trained to play, there's a pretty clear scoring function. The inputs would be spider sensorium, the outputs would be spider effectors. I'm kind of waiting for someone to do this already with C elegans by excising their 300 or so neurons and replacing them with a GPU and laser (but I imagine the practical impediments are way higher than I handwavingly imagine).
      - YeGoblynQueenne 10y ago
        >> The inputs would be spider sensorium, the outputs would be spider effectors. That sounds great on paper. In practice, how do you even begin to collect "spider sensorium"? fMRI? As to "spider effectors" I'm pretty sure we don't have the tech to do that yet, at least not in spider-scale (excluding Giant Spiders of Doom). On paper, you can approximate any function with a multi-layer perceptron with enough layers. In practice, "enough" layers may be infinite and you'd need infinite amounts of data infinitely difficult to collect.
        xyzzy123 10y ago
        I think the most reasonable near-term approach "if you had to do it" would be reverse engineering and virtualities, but please bear in mind it was a hypothetical to begin with. I expect the number of inputs to spider sensorium would be low thousands of analog inputs. To be clear I can't cite a reference for this. Yes it seems intractibly difficult on paper but spider hardware can do it. It's hard to rate what we'll expect from self driving cars on the flatworm to spider spectrum (probably closer to the flatworm end, but the cameras add an interesting dimension), but clearly reasonable yet ambitious people believe these problems are tractable with sufficient effort.
YeGoblynQueenne 10y ago
>> Spiders can't beat a human at Go. Finding the best next move from a given board position at Go is a single cognitive task, whereas surviving in the natural world requires an intelligence that can excel at a possibly infinite number of such tasks. Portia spiders are particularly amazing in that they can exhibit such complex behaviours as ambushing or stalking prey. Like all insects they do this sort of thing with a tiny number of neurons. We have no idea how to do so well with such few resources.
- Houshalter 10y ago
  There is no mathematical distinction between different "tasks". Playing Go requires thousands or even millions of different heuristics and pieces of knowledge. Each of those is a "task" in and of itself. The same technology which beat Go could also, in principle, be used to play a real time video game that requires hunting and stalking things. Insects are indeed tiny. But they are highly specialized. They can't learn to do things differently than they are programmed. GPUs are extremely general purpose. Not only can neural nets running on them learn many tasks, but they can execute many algorithms which aren't neural networks at all.
  - YeGoblynQueenne 10y ago
    >> There is no mathematical distinction between different "tasks". Sure there is. For instance, the minimax algorithm is a mathematical model of optimal behaviour in a specific kind of game (two-player, zero-sum, complete information games). Performing minimax to dominate such a game is a very clearly delimited task and that's exactly why a machine can do it in the first place. More broadly, any "task" can be described mathematically by means of a program for a given machine. Saying there's no mathematical distinction between different tasks is like saying you can't write a program that does a specific thing. >> Insects are indeed tiny. But they are highly specialized. Agreed- see my comment above. However, that on its own doesn't say much. Sure, insects have highly specialised firmware-style programming. How did they come to have them and how can we reproduce them? We can't hope to create such programs "by hand" so we're currently trying to create algorithms that learn them. We're still far from achieving it though. That neural networks and other learning algorithms can learn all sorts of different things is true, however you have to train a new instance of an algorithm for every different thing you want it to learn (what I call a "task", so a new program). For each instance you need new data (and a lot of it) another huge chunk of processing power and a long time to train. Then you have to find a way to combine all the trained models into one ... thing that can operate as a coherent whole. We haven't even started on the last bit. Just having the technology to create pigments is far from being the same as creating a Mona Lisa.
singularity2001 10y ago
Build us a machine which can navigate the environment as swiftly as a spider, THEN general spider AI is reached. ( not talking about the hardware, talking about the smooth integration of image recognition, motor control, general learning abilities and whatnot)
YeGoblynQueenne 10y ago
>> how many generations of selective breeding do you think it would take to evolve a spider that could beat the best humans at go? That's a different issue. Spiders, as all insects, don't learn. It seems that insects have some kind of firmware-style programming that can't change, because they don't have the cognitive ability to change it. This firmware can be unbelievably complex, like in the case of Portia spiders stalking prey, but it can't develop any further, at least not on the individual level. Even this however is nothing short of amazing. The truth is that we haven't developed a single program that can do what a Portia spider can do, and in fact learning algorithms (or strategies) like reinforcement learning are our best bet to avoid having to explicitly write down the rules for this sort of behaviour. And we want to avoid having to do that because it's probably beyond our abilities. The learning algorithms we have right now however aren't a very good way to develop intelligence that can display such complex behaviours. Those algorithms are one-trick ponies, they learn to do one thing very well but if you want to do more than one thing you need to train more instances of the algorithm- and then you need to find a way to combine the trained instances in a cognitive architecture. And on that we haven't made much progress yet.
- Houshalter 10y ago
  That's exactly my point. Spiders can't learn. Maybe they are amazing for what they do, but the comparison is apples to oranges. They do fundamentally different things. Of course spiders are complex. Everything in biology is complex. It's been slowly refined over millions and millions of years. Countless trillions of tweaks to its DNA tested and rejected until it hit upon something that worked. No human could design something as complex as a spider. Our artificial neural networks are just as complex though. AlphaGo probably knows millions of facts and heuristics about Go. You would never be able to understand everything it knows. Even though we didn't create the complexity ourselves, we "evolve" it just like the spider, sometimes even with algorithms based on evolution. But we have much more efficient algorithms than that, like backprop.
  - Cybiote 10y ago
    The large majority of deployed machine learning algorithms actually can't learn. The bias is so large that most (all?) specialized hardware assumes that only inference will be done. In contrast, there is evidence for online adaptability in insects^ ^ http://www.annualreviews.org/eprint/5PHddkgYYKaduPp4CcN5/full/10.1146/annurev.ento.53.103106.093343 http://www.annualreviews.org/eprint/5PHddkgYYKaduPp4CcN5/ful...
Cybiote 10y ago
I think you're missing the point there. Portia is able to, using just a handful of neurons (~600K), accomplish sophisticated vision tasks, complex planning, online learning and scheduling. Portia and insects in general are much more flexible to changes than our Neural nets. Changing objectives or a slight modification in the rules will often require retraining and a new design but insects can adapt to various lighting, environmental, prey, predator and navigational concerns. From the wiki on portia: > Laboratory studies show that Portia learns very quickly how to overcome web-building spiders that neither it nor its ancestors would have met in the wild. We should strive for its kind of energy and hence, computational efficiency in our models. Think for example, what that would mean for prosthetics. I find the overzealous defense of the current state of the art just as frustrating as out of hand dismissals of AI which constantly move goal posts.
- dharma1 10y ago
  How many connections betweens those 600k neurons? That is one large difference between biological and artificial neural networks (though there are many others). Current DNN architectures won't be the final solutions, but they do the job much better than anything else we've tried before. That said there is ongoing work in more biologically inspired approaches that might produce better results for the things you mentioned in the future
- dTal 10y ago
  >Portia and insects in general Spiders are not insects!
  - Cybiote 10y ago
    You're right! and I've known this but that distinction didn't come to mind as I was writing the post. It's not really relevant though, as my point was about how adaptable and surprisingly intelligent behaviours even these tiny, tiny creatures with barely more than a milliwatt (I'm guessing) to their name, can produce. And you know, and can be used in a similar way to either set union or intersection in natural language :)
- Fordrus 10y ago
flexie 10y ago
I agree that mastering board games requires intelligence. I also agree that good chess players are usually intelligent, even quite smart. But it's not like people's abilities in chess are directly proportional to their intelligence, is it? I mean, you would probably have mediocre chess players that could beat Plato or Darwin or Einstein. Why is it that computers' abilities to win chess or Go are seen as the proxy for how far AI has come? As if the pinnacle of civilisation is Go or if Magnus Carlsen is the greatest mind of all times. Go and chess are sports. It's rugby for nerds. A computer beating Kasparov is impressive in its own right, but it's a far cry from the computer having his intelligence.
- Houshalter 10y ago
  There is no such thing as a universal intelligence test. I'm not claiming that winning at Go implies intelligence. I'm simply demonstrating that spiders aren't generally intelligent. They can't learn to do things other than what they were programmed to do. Whereas ANNs can do a wide variety of tasks, including tasks no animals are remotely capable of.
- kamaal 10y ago
  Actually bulk of what we call human intelligence is basically nothing more than a chain of facts we built up, each fact derived based on the truths we learned(or perceived) before. Intelligence therefore is that process where you get from A to B, with steps in between that require consciously investigating the already available knowledge to us. This is already happening with machines today. Someday a person will come up with a generic learning algorithm, which has a meta component to either search for answers in the space it already knows, or use external means to learn more about it. Please also note without means to move around, use your hand/legs/eyes et al to test around and build your experiences, we will likely have the same primitive intelligence. Only difference now is instead of giving the machines these sensory organs, we are only working with an algorithm with inputs and outputs and then have it evolve.

pmyjavec 10y ago

How intelligent is a spider, truly ? I mean, creating a machine that beats us at a game we invented ourselves is one thing, but how does that compare to a species that has survived much longer on this earth than us? I mean most people are more worried about iPhone 7 than climate change. I am not arguing that we are not smarter than spiders, but I'm also not going to argue we truly know that we are. It would be wise to keep our egos under control.

kkarakk 10y ago
you could wipe out that species with a focused effort on your part in a couple of years. does that mean you are more important than spiders? bringing unrealistic philosophical considerations into tangible concerns is a pet peeve of mine

Fede_V 10y ago

Depending on how you define 'soon' - it might take a little longer. A bunch of really good people at Facebook AI just worked on Starcraft using reinforcement learning, and even getting the micro down is really hard.

gambler 10y ago

Well, yes, if you could make a completely artificial, controlled environment that presents a problem well-suited for the current flavor of AI, the AI would probably be able to beat a random animal at solving that problem.

xyzzy123 10y ago
Spiders have much better hardware than the state of the art in low cost miniatuarised robotics. However I'm not convinced that if you trained a deep net and gave it access to a spider's sensor suite and effectors, that it would do worse than an actual spider would. A lot worse in terms of efficiency per watt, sure...
- YeGoblynQueenne 10y ago
  How would you train that deep net? What data would you give it? What would you train it to do exactly?
  - xyzzy123 10y ago
    Survive. Reproduce. Just like the video games nets are already trained to play, there's a pretty clear scoring function. The inputs would be spider sensorium, the outputs would be spider effectors. I'm kind of waiting for someone to do this already with C elegans by excising their 300 or so neurons and replacing them with a GPU and laser (but I imagine the practical impediments are way higher than I handwavingly imagine).
    - YeGoblynQueenne 10y ago
      >> The inputs would be spider sensorium, the outputs would be spider effectors. That sounds great on paper. In practice, how do you even begin to collect "spider sensorium"? fMRI? As to "spider effectors" I'm pretty sure we don't have the tech to do that yet, at least not in spider-scale (excluding Giant Spiders of Doom). On paper, you can approximate any function with a multi-layer perceptron with enough layers. In practice, "enough" layers may be infinite and you'd need infinite amounts of data infinitely difficult to collect.
      - xyzzy123 10y ago
        I think the most reasonable near-term approach "if you had to do it" would be reverse engineering and virtualities, but please bear in mind it was a hypothetical to begin with. I expect the number of inputs to spider sensorium would be low thousands of analog inputs. To be clear I can't cite a reference for this. Yes it seems intractibly difficult on paper but spider hardware can do it. It's hard to rate what we'll expect from self driving cars on the flatworm to spider spectrum (probably closer to the flatworm end, but the cameras add an interesting dimension), but clearly reasonable yet ambitious people believe these problems are tractable with sufficient effort.

YeGoblynQueenne 10y ago

>> Spiders can't beat a human at Go. Finding the best next move from a given board position at Go is a single cognitive task, whereas surviving in the natural world requires an intelligence that can excel at a possibly infinite number of such tasks. Portia spiders are particularly amazing in that they can exhibit such complex behaviours as ambushing or stalking prey. Like all insects they do this sort of thing with a tiny number of neurons. We have no idea how to do so well with such few resources.

Houshalter 10y ago
There is no mathematical distinction between different "tasks". Playing Go requires thousands or even millions of different heuristics and pieces of knowledge. Each of those is a "task" in and of itself. The same technology which beat Go could also, in principle, be used to play a real time video game that requires hunting and stalking things. Insects are indeed tiny. But they are highly specialized. They can't learn to do things differently than they are programmed. GPUs are extremely general purpose. Not only can neural nets running on them learn many tasks, but they can execute many algorithms which aren't neural networks at all.
- YeGoblynQueenne 10y ago
  >> There is no mathematical distinction between different "tasks". Sure there is. For instance, the minimax algorithm is a mathematical model of optimal behaviour in a specific kind of game (two-player, zero-sum, complete information games). Performing minimax to dominate such a game is a very clearly delimited task and that's exactly why a machine can do it in the first place. More broadly, any "task" can be described mathematically by means of a program for a given machine. Saying there's no mathematical distinction between different tasks is like saying you can't write a program that does a specific thing. >> Insects are indeed tiny. But they are highly specialized. Agreed- see my comment above. However, that on its own doesn't say much. Sure, insects have highly specialised firmware-style programming. How did they come to have them and how can we reproduce them? We can't hope to create such programs "by hand" so we're currently trying to create algorithms that learn them. We're still far from achieving it though. That neural networks and other learning algorithms can learn all sorts of different things is true, however you have to train a new instance of an algorithm for every different thing you want it to learn (what I call a "task", so a new program). For each instance you need new data (and a lot of it) another huge chunk of processing power and a long time to train. Then you have to find a way to combine all the trained models into one ... thing that can operate as a coherent whole. We haven't even started on the last bit. Just having the technology to create pigments is far from being the same as creating a Mona Lisa.

singularity2001 10y ago

Build us a machine which can navigate the environment as swiftly as a spider, THEN general spider AI is reached. ( not talking about the hardware, talking about the smooth integration of image recognition, motor control, general learning abilities and whatnot)

YeGoblynQueenne 10y ago

>> how many generations of selective breeding do you think it would take to evolve a spider that could beat the best humans at go? That's a different issue. Spiders, as all insects, don't learn. It seems that insects have some kind of firmware-style programming that can't change, because they don't have the cognitive ability to change it. This firmware can be unbelievably complex, like in the case of Portia spiders stalking prey, but it can't develop any further, at least not on the individual level. Even this however is nothing short of amazing. The truth is that we haven't developed a single program that can do what a Portia spider can do, and in fact learning algorithms (or strategies) like reinforcement learning are our best bet to avoid having to explicitly write down the rules for this sort of behaviour. And we want to avoid having to do that because it's probably beyond our abilities. The learning algorithms we have right now however aren't a very good way to develop intelligence that can display such complex behaviours. Those algorithms are one-trick ponies, they learn to do one thing very well but if you want to do more than one thing you need to train more instances of the algorithm- and then you need to find a way to combine the trained instances in a cognitive architecture. And on that we haven't made much progress yet.

Houshalter 10y ago
That's exactly my point. Spiders can't learn. Maybe they are amazing for what they do, but the comparison is apples to oranges. They do fundamentally different things. Of course spiders are complex. Everything in biology is complex. It's been slowly refined over millions and millions of years. Countless trillions of tweaks to its DNA tested and rejected until it hit upon something that worked. No human could design something as complex as a spider. Our artificial neural networks are just as complex though. AlphaGo probably knows millions of facts and heuristics about Go. You would never be able to understand everything it knows. Even though we didn't create the complexity ourselves, we "evolve" it just like the spider, sometimes even with algorithms based on evolution. But we have much more efficient algorithms than that, like backprop.
- Cybiote 10y ago
  The large majority of deployed machine learning algorithms actually can't learn. The bias is so large that most (all?) specialized hardware assumes that only inference will be done. In contrast, there is evidence for online adaptability in insects^ ^ http://www.annualreviews.org/eprint/5PHddkgYYKaduPp4CcN5/full/10.1146/annurev.ento.53.103106.093343 http://www.annualreviews.org/eprint/5PHddkgYYKaduPp4CcN5/ful...

Cybiote 10y ago

I think you're missing the point there. Portia is able to, using just a handful of neurons (~600K), accomplish sophisticated vision tasks, complex planning, online learning and scheduling. Portia and insects in general are much more flexible to changes than our Neural nets. Changing objectives or a slight modification in the rules will often require retraining and a new design but insects can adapt to various lighting, environmental, prey, predator and navigational concerns. From the wiki on portia: > Laboratory studies show that Portia learns very quickly how to overcome web-building spiders that neither it nor its ancestors would have met in the wild. We should strive for its kind of energy and hence, computational efficiency in our models. Think for example, what that would mean for prosthetics. I find the overzealous defense of the current state of the art just as frustrating as out of hand dismissals of AI which constantly move goal posts.

dharma1 10y ago
How many connections betweens those 600k neurons? That is one large difference between biological and artificial neural networks (though there are many others). Current DNN architectures won't be the final solutions, but they do the job much better than anything else we've tried before. That said there is ongoing work in more biologically inspired approaches that might produce better results for the things you mentioned in the future
dTal 10y ago
>Portia and insects in general Spiders are not insects!
- Cybiote 10y ago
  You're right! and I've known this but that distinction didn't come to mind as I was writing the post. It's not really relevant though, as my point was about how adaptable and surprisingly intelligent behaviours even these tiny, tiny creatures with barely more than a milliwatt (I'm guessing) to their name, can produce. And you know, and can be used in a similar way to either set union or intersection in natural language :)
Fordrus 10y ago

flexie 10y ago

I agree that mastering board games requires intelligence. I also agree that good chess players are usually intelligent, even quite smart. But it's not like people's abilities in chess are directly proportional to their intelligence, is it? I mean, you would probably have mediocre chess players that could beat Plato or Darwin or Einstein. Why is it that computers' abilities to win chess or Go are seen as the proxy for how far AI has come? As if the pinnacle of civilisation is Go or if Magnus Carlsen is the greatest mind of all times. Go and chess are sports. It's rugby for nerds. A computer beating Kasparov is impressive in its own right, but it's a far cry from the computer having his intelligence.

Houshalter 10y ago
There is no such thing as a universal intelligence test. I'm not claiming that winning at Go implies intelligence. I'm simply demonstrating that spiders aren't generally intelligent. They can't learn to do things other than what they were programmed to do. Whereas ANNs can do a wide variety of tasks, including tasks no animals are remotely capable of.
kamaal 10y ago
Actually bulk of what we call human intelligence is basically nothing more than a chain of facts we built up, each fact derived based on the truths we learned(or perceived) before. Intelligence therefore is that process where you get from A to B, with steps in between that require consciously investigating the already available knowledge to us. This is already happening with machines today. Someday a person will come up with a generic learning algorithm, which has a meta component to either search for answers in the space it already knows, or use external means to learn more about it. Please also note without means to move around, use your hand/legs/eyes et al to test around and build your experiences, we will likely have the same primitive intelligence. Only difference now is instead of giving the machines these sensory organs, we are only working with an algorithm with inputs and outputs and then have it evolve.