46 ms·

What do you mean it doesn't exist? I work in that field, it exists.

by myrryr 11y ago

blumkvist 11y ago
What do you mean it exists? How do you definite AI? Can your project reason with you? Or is it simply an Input-Output type of program? Just because you use natural language with it instead of punch cards doesn't mean it is intelligent. People tried to do AI in the 60s to 90s era. It is dubbed symbolic AI. It didn't work out. A good chance that it never will. Today machine learning algos and a bunch of automated statistics is called "Artificial Intelligence". It's not intelligence at all. Intelligence implies something more than I/O computation.
- one-more-minute 11y ago
  I tend to agree that AI today is a long way from what the founders of the subject imagined – it's become something more like "Applied Computer Science". But what's now called "Artificial General Intelligence" isn't dead, and people are still working on it. Also, it's more tricky than you'd think to narrow down what counts as intelligence. There aren't really any hard lines between an I/O program and an intelligent agent, even though they seem pretty far apart.
  - protonfish 11y ago
    Just because you don't see the hard lines doesn't mean they aren't there. We are deluding ourselves by avoiding a hard definition of intelligence so we can keep believing that we are creating AI when its really nothing of the sort.
    - one-more-minute 11y ago
      Just because you can't see unicorns doesn't mean they aren't there, but at some point you have to give up the search. It's fine to talk about how, broadly speaking, rats are more intelligent than ants, plants or microbes (which are basically I/O rules with a body), chimpanzees more so than rats, humans more so than than chimps etc. But in general there's a ton of overlap and the qualities we associate with intelligence – memory, planning, self-awareness, tool use, whatever – are only loosely correlated continua. There are a few more binary measures in intelligence research, such as the mirror test, but at best they're only a small piece of the puzzle. There's no sudden point where everything clicks into place. Of course, if you have such a good definition of intelligence, feel free to enlighten me.
      - protonfish 11y ago
        You are repeating the same mistake as before. The difference is significant between admitting we don't know what those lines are and claiming that they don't exist.
        one-more-minute 11y ago
        It's not a mistake – I see the difference and I am claiming that those lines don't exist. There's plenty of evidence for that, including the continuous range of intelligent behaviour from plants to humans. It's just an empirical fact that there's no hard line. Of course, that could be wrong – new evidence may come to light, after all. But even so, it doesn't make any sense to say that trying to understand and replicate intelligence is deluded, just because we don't know where that line is – because figuring out what intelligence is is exactly the problem that people are trying to solve. AI is one part of that, along with more empirical research in fields like cognitive science and biology. Are people researching quantum gravity deluding themselves because they don't yet have a hard definition of quantum gravity? Figuring that out is exactly the point!
        protonfish 11y ago
        If you are going in the wrong direction, you won't get to your destination by going faster. You should stop and rethink and you won't do this unless you can admit that you might be wrong. How much research had you done before you assertively proclaimed that those lines don't exist? Because it looks nothing like a smooth transition to me.
      - eli_gottlieb 11y ago
        Well I would say that "intelligence" is learning and inference with causal models rather than just predictive or correlative models. You can then cash it all out into a few different branches of cognition, like perception (distinguishing/classifying which available causal models best match the feature data under observation), learning (taking observed feature data and using it to refine causal models for greater accuracy), inference (using causal models to make predictions under counterfactual conditions, which can include planning as a special case), and the occasional act of conceptual refinement/reduction (in which a model is found of how one model can predict the free parameters of another).

What do you mean it doesn't exist? I work in that field, it exists.

by myrryr 11y ago

What do you mean it doesn't exist? I work in that field, it exists.

What do you mean it exists? How do you definite AI? Can your project reason with you? Or is it simply an Input-Output type of program? Just because you use natural language with it instead of punch cards doesn't mean it is intelligent. People tried to do AI in the 60s to 90s era. It is dubbed symbolic AI. It didn't work out. A good chance that it never will. Today machine learning algos and a bunch of automated statistics is called "Artificial Intelligence". It's not intelligence at all. Intelligence implies something more than I/O computation.

one-more-minute 11y ago
I tend to agree that AI today is a long way from what the founders of the subject imagined – it's become something more like "Applied Computer Science". But what's now called "Artificial General Intelligence" isn't dead, and people are still working on it. Also, it's more tricky than you'd think to narrow down what counts as intelligence. There aren't really any hard lines between an I/O program and an intelligent agent, even though they seem pretty far apart.
- protonfish 11y ago
  Just because you don't see the hard lines doesn't mean they aren't there. We are deluding ourselves by avoiding a hard definition of intelligence so we can keep believing that we are creating AI when its really nothing of the sort.
  - one-more-minute 11y ago
    Just because you can't see unicorns doesn't mean they aren't there, but at some point you have to give up the search. It's fine to talk about how, broadly speaking, rats are more intelligent than ants, plants or microbes (which are basically I/O rules with a body), chimpanzees more so than rats, humans more so than than chimps etc. But in general there's a ton of overlap and the qualities we associate with intelligence – memory, planning, self-awareness, tool use, whatever – are only loosely correlated continua. There are a few more binary measures in intelligence research, such as the mirror test, but at best they're only a small piece of the puzzle. There's no sudden point where everything clicks into place. Of course, if you have such a good definition of intelligence, feel free to enlighten me.
    - protonfish 11y ago
      You are repeating the same mistake as before. The difference is significant between admitting we don't know what those lines are and claiming that they don't exist.
      - one-more-minute 11y ago
        It's not a mistake – I see the difference and I am claiming that those lines don't exist. There's plenty of evidence for that, including the continuous range of intelligent behaviour from plants to humans. It's just an empirical fact that there's no hard line. Of course, that could be wrong – new evidence may come to light, after all. But even so, it doesn't make any sense to say that trying to understand and replicate intelligence is deluded, just because we don't know where that line is – because figuring out what intelligence is is exactly the problem that people are trying to solve. AI is one part of that, along with more empirical research in fields like cognitive science and biology. Are people researching quantum gravity deluding themselves because they don't yet have a hard definition of quantum gravity? Figuring that out is exactly the point!
        protonfish 11y ago
        If you are going in the wrong direction, you won't get to your destination by going faster. You should stop and rethink and you won't do this unless you can admit that you might be wrong. How much research had you done before you assertively proclaimed that those lines don't exist? Because it looks nothing like a smooth transition to me.
    - eli_gottlieb 11y ago
      Well I would say that "intelligence" is learning and inference with causal models rather than just predictive or correlative models. You can then cash it all out into a few different branches of cognition, like perception (distinguishing/classifying which available causal models best match the feature data under observation), learning (taking observed feature data and using it to refine causal models for greater accuracy), inference (using causal models to make predictions under counterfactual conditions, which can include planning as a special case), and the occasional act of conceptual refinement/reduction (in which a model is found of how one model can predict the free parameters of another).