4 ms·

Some AI Systems May Be Impossible to Compute

18 points by niccl 5y ago

version_five 5y ago
I wasn't aware of this and I'm happy it was posted. However I have to point out it contains one of the most strained analogies I've seen recently: > We are saying that there might be a recipe for the cake, but regardless of the mixers you have available, you may not be able to make the desired cake. Moreover, when you try to make the cake with your mixer in the kitchen, you will end up with a completely different cake.” > In addition, to continue the analogy, “it can even be the case that you cannot tell whether the cake is incorrect until you try it, and then it is too late,” Colbrook says. “There are, however, certain cases when your mixer is sufficient to make the cake you want, or at least a good approximation of that cake.”
- lil_dispaches 5y ago
  It is terrible, they literally can't explain what they mean. They don't say what it means to "compute the A.I. network". Makes me think it is a bogus story, some academic runoff.
  - YeGoblynQueenne 5y ago
    To "compute a neural network" is a long-established way to say "train a neural network", which in turn is a long-established way to say "find a set of weights for the neural network that maximises its accuracy". The idea is that a neural net is a kind of data structure used in AI, like a decision tree or a decision list (like a decision tree but it's a list). There are different algorithms that can "compute", i.e. construct, a decision tree from data. In modern parlance we say that the decision tree is "trained". Same goes for neural nets, except the network itself is typically constructed beforehand, and manually (we refer to it as the "architecture" of the neural net) and only its weights need to be tweaked until it has a good accuracy- at which point we say the training algorithm has "converged". It's all a bit confusing because in common parlance there is little distinction made between a neural net's network (its architecture), the algorithm that trains the neural net by finding the weights that minimise its error (backpropagation) and the neural net with trained weights (the "model"). Sometimes I wonder if this distinction is clear in the minds of people who actually train those things. Btw, the study is solid and meaningful. It's a theoretical result. More of those are needed in machine learning, we got plenty of empirical results.
    - lil_dispaches 5y ago
      Thanks. What reason do they give that these network weights cannot be computed? I am pretty hip to modern AI workings, and that is what I assumed they meant by compute. What I know also tells me that "compute" as a concept is lacking for what we might later call holistic AI, and if, for instance, they are conjecturing strictly SGD proved networks, then I maintain my position that they are merely noodling, with AI and terminology alike. Intelligence is not entirely computable; nor is occurrence entirely probabilistic. Respectively, there is randomness at work, and dynamic systems that produce novelty.
kylehotchkiss 5y ago
I'm not going to pretend to be especially knowledgeable in AI/Machine Learning/Neural Nets but I read something a few years ago that helped my perception of the limits of that tech: AI is not capable of explaining why it makes a decision that it does. This was a big flag to me in how much I trust things like full self driving - a car is making decisions with a trained cloud of intelligence that can't explain itself. I'm happy to see scientists and mathematicians trying to find the limits of what the tech can do, it's interesting to model it after how a human mind works, but we don't even understand human minds well enough to try to replicate how they learn intelligence. The world is closer to continuous numbers not discrete numbers etc. AI is still interesting enough to keep building on, Github's Copilot has saved me time just yesterday with very helpful autocompletes on some refactoring I was doing. More of that please - but let me be in the captains seat, deciding whether I want to accept the AI's decision or not. I wish companies like Tesla would accept this and shift some of the self driving budget to better battery tech. That's the need of the hour with fuel prices rising like they are and global warming looming over us.
- jameshart 5y ago
  Humans are also not able to explain why they do what they do. Yet they are somehow able to drive cars.
  - debdut 5y ago
    The point is that they can explain why they are taking a left turn!
    - jameshart 5y ago
      Can they? How exactly does your brain do route planning? How do you pick which lane to turn down in the parking lot when looking for a space? Why did you get off at exit 14? Wait, this isn’t even your turn, why did you go left here? I mean, when you get down to it, why are you even driving to this deadend job? But yeah, sure, humans can ‘explain’ their behavior, which is why we can trust them.
josourcing 5y ago
>a slight alteration in the data they receive can lead to a wild change in outcomes. In my experience, it's not the alteration that creates an unpredictable outcome; it's the lack of nuance. Accuracy requires an insane amount of details, and it appears that some people might be waking up to that fact that the brain is better suited to handle those nuances. I work with NLP and have discovered advantages in leaving some decisions up to the person using AI rather than the computer. One advantage being accuracy :-).

18 points by niccl 5y ago

I wasn't aware of this and I'm happy it was posted. However I have to point out it contains one of the most strained analogies I've seen recently: > We are saying that there might be a recipe for the cake, but regardless of the mixers you have available, you may not be able to make the desired cake. Moreover, when you try to make the cake with your mixer in the kitchen, you will end up with a completely different cake.” > In addition, to continue the analogy, “it can even be the case that you cannot tell whether the cake is incorrect until you try it, and then it is too late,” Colbrook says. “There are, however, certain cases when your mixer is sufficient to make the cake you want, or at least a good approximation of that cake.”

lil_dispaches 5y ago
It is terrible, they literally can't explain what they mean. They don't say what it means to "compute the A.I. network". Makes me think it is a bogus story, some academic runoff.
- YeGoblynQueenne 5y ago
  To "compute a neural network" is a long-established way to say "train a neural network", which in turn is a long-established way to say "find a set of weights for the neural network that maximises its accuracy". The idea is that a neural net is a kind of data structure used in AI, like a decision tree or a decision list (like a decision tree but it's a list). There are different algorithms that can "compute", i.e. construct, a decision tree from data. In modern parlance we say that the decision tree is "trained". Same goes for neural nets, except the network itself is typically constructed beforehand, and manually (we refer to it as the "architecture" of the neural net) and only its weights need to be tweaked until it has a good accuracy- at which point we say the training algorithm has "converged". It's all a bit confusing because in common parlance there is little distinction made between a neural net's network (its architecture), the algorithm that trains the neural net by finding the weights that minimise its error (backpropagation) and the neural net with trained weights (the "model"). Sometimes I wonder if this distinction is clear in the minds of people who actually train those things. Btw, the study is solid and meaningful. It's a theoretical result. More of those are needed in machine learning, we got plenty of empirical results.
  - lil_dispaches 5y ago
    Thanks. What reason do they give that these network weights cannot be computed? I am pretty hip to modern AI workings, and that is what I assumed they meant by compute. What I know also tells me that "compute" as a concept is lacking for what we might later call holistic AI, and if, for instance, they are conjecturing strictly SGD proved networks, then I maintain my position that they are merely noodling, with AI and terminology alike. Intelligence is not entirely computable; nor is occurrence entirely probabilistic. Respectively, there is randomness at work, and dynamic systems that produce novelty.

kylehotchkiss 5y ago

I'm not going to pretend to be especially knowledgeable in AI/Machine Learning/Neural Nets but I read something a few years ago that helped my perception of the limits of that tech: AI is not capable of explaining why it makes a decision that it does. This was a big flag to me in how much I trust things like full self driving - a car is making decisions with a trained cloud of intelligence that can't explain itself. I'm happy to see scientists and mathematicians trying to find the limits of what the tech can do, it's interesting to model it after how a human mind works, but we don't even understand human minds well enough to try to replicate how they learn intelligence. The world is closer to continuous numbers not discrete numbers etc. AI is still interesting enough to keep building on, Github's Copilot has saved me time just yesterday with very helpful autocompletes on some refactoring I was doing. More of that please - but let me be in the captains seat, deciding whether I want to accept the AI's decision or not. I wish companies like Tesla would accept this and shift some of the self driving budget to better battery tech. That's the need of the hour with fuel prices rising like they are and global warming looming over us.

jameshart 5y ago
Humans are also not able to explain why they do what they do. Yet they are somehow able to drive cars.
- debdut 5y ago
  The point is that they can explain why they are taking a left turn!
  - jameshart 5y ago
    Can they? How exactly does your brain do route planning? How do you pick which lane to turn down in the parking lot when looking for a space? Why did you get off at exit 14? Wait, this isn’t even your turn, why did you go left here? I mean, when you get down to it, why are you even driving to this deadend job? But yeah, sure, humans can ‘explain’ their behavior, which is why we can trust them.

josourcing 5y ago

>a slight alteration in the data they receive can lead to a wild change in outcomes. In my experience, it's not the alteration that creates an unpredictable outcome; it's the lack of nuance. Accuracy requires an insane amount of details, and it appears that some people might be waking up to that fact that the brain is better suited to handle those nuances. I work with NLP and have discovered advantages in leaving some decisions up to the person using AI rather than the computer. One advantage being accuracy :-).