Answers.Body: <p>This is about <em>hard AI</em> and <em>soft AI</em>: proponents of hard AI work on systems that simulate the way human cognition works, with the eventual (hypothetical) goal of replicating it. This presupposes that you know how cognition works, and presumably you will learn about it as you attempt to replicate it.</p>
  
  <p>Soft AI, on the other hand, tries to emulate the outcomes only. For example, Weizenbaum's <a href="https://en.wikipedia.org/wiki/ELIZA" rel="nofollow noreferrer"><em>ELIZA</em></a> is clearly on this side, as it uses simple pattern matching, and does not 'understand' anything about the conversations it is having.</p>
  
  <p>Obviously, we don't even know fully what it means to 'understand' something, and building working systems is not really possible with a hard approach. Hence, soft AI is more common, as researchers are usually measured by their outcomes rather than their ideas. As far as I am aware, the hard AI approach has been all but abandoned long ago.</p>
  
  <p>As current AI seems to be dominated by statistical approaches, I doubt that we can find out many useful things about cognition this way.</p>
  
  <p>One interesting side-note: it seems to me that the capabilities of modern AI systems have developed away from human capabilities. A three-year-old can do some things that a sophisticated AI system cannot do, but in some areas (chess, translation, ...) the capabilities of AI systems surpass what humans are capable of. Maybe imitation is indeed not the right way to approach AI.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-03-29T09:01:31.190Z
Answers.LastActivityDate: 2019-03-29T09:01:31.190Z
Answers.UserDisplayName: Oliver Mason
Body: <p>Can AI be used as a tool to investigate our minds? </p>
  
  <p>To be more precise, what am I specifically asking for here are examples of discoveries on artificial intelligence (so algorithms, programs and computers that try to implement intelligent systems) that brought to light facts about intelligence and cognition <em>in general</em>. Have this ever happened? Is it frequent? How influential and important were these discoveries, if any?</p>
  
  <p>A possible example of what I mean could be the <a href="https://en.wikipedia.org/wiki/Physical_symbol_system" rel="nofollow noreferrer">PSSH</a>, which states that a formal system is sufficient to simulate general intelligent behaviour. I believe that this is relevant to Cognitive Science in general because it entails our understanding of this phenomena. (Of course, this is just an hypotesis, but I believe that its importance in the AI debate makes it a really compelling result).</p>
  
Tags: <philosophy><cognitive-science>
Title: Can AI research lead to new findings in general cognitive science?
UserDisplayName: olinarr
ThreadText: # Can AI research lead to new findings in general cognitive science?
  
  (Asked by: username_0 on 2019-03-28)
  
  Can AI be used as a tool to investigate our minds?
  
  To be more precise, what am I specifically asking for here are examples of discoveries on artificial intelligence (so algorithms, programs and computers that try to implement intelligent systems) that brought to light facts about intelligence and cognition *in general*. Have this ever happened? Is it frequent? How influential and important were these discoveries, if any?
  
  A possible example of what I mean could be the [PSSH](https://en.wikipedia.org/wiki/Physical_symbol_system), which states that a formal system is sufficient to simulate general intelligent behaviour. I believe that this is relevant to Cognitive Science in general because it entails our understanding of this phenomena. (Of course, this is just an hypotesis, but I believe that its importance in the AI debate makes it a really compelling result).
  
  **Tags:** philosophy, cognitive-science
  
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  ## 1 answers
  
  ### Answer by username_1 on 2019-03-29. Score: 1
  
  This is about *hard AI* and *soft AI*: proponents of hard AI work on systems that simulate the way human cognition works, with the eventual (hypothetical) goal of replicating it. This presupposes that you know how cognition works, and presumably you will learn about it as you attempt to replicate it.
  
  Soft AI, on the other hand, tries to emulate the outcomes only. For example, Weizenbaum's [*ELIZA*](https://en.wikipedia.org/wiki/ELIZA) is clearly on this side, as it uses simple pattern matching, and does not 'understand' anything about the conversations it is having.
  
  Obviously, we don't even know fully what it means to 'understand' something, and building working systems is not really possible with a hard approach. Hence, soft AI is more common, as researchers are usually measured by their outcomes rather than their ideas. As far as I am aware, the hard AI approach has been all but abandoned long ago.
  
  As current AI seems to be dominated by statistical approaches, I doubt that we can find out many useful things about cognition this way.
  
  One interesting side-note: it seems to me that the capabilities of modern AI systems have developed away from human capabilities. A three-year-old can do some things that a sophisticated AI system cannot do, but in some areas (chess, translation, ...) the capabilities of AI systems surpass what humans are capable of. Maybe imitation is indeed not the right way to approach AI.
  
  
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