Answers.Body: <blockquote>
  <p>at this point in time, what would be the IQ of our most intelligent AI systems?</p>
  </blockquote>
  <h3>Zero.</h3>
  <p>There are many different kinds of IQ tests including written, visual, and verbal assessments, but the majority of questions are based on abstract-reasoning problems that involve creative thinking and true intelligence.</p>
  <p>In other words, the computer would have to exhibit something that does not yet exist… &quot;strong AI&quot;.</p>
  <p>The intelligent computers of science fiction do not exist. At all. We are not even close. We have absolutely NO IDEA how to bridge the gap between what we can do now and what is depicted in pop-culture films. Even with cars that drive themselves and computers that play 'Go' — an underachieving mosquito possesses more cognitive intelligence than all the world's super computers <em>…combined!</em></p>
  <h3>…or possibly &quot;disqualified&quot; for cheating.</h3>
  <p>Even if we could pre-format the questions in a style and delivery system it understands, what does memorization, attention, or speed mean in the context of a computer? I'm not even sure if a standardized IQ test makes sense in this context. It might be like asking how a computer would do in a spelling bee.</p>
  <p>In human terms, we're not <em>allowed</em> to bring along reference materials to look up an answer; but how do you rectify that when reference-lookup is innate to a computer's existence? How do you measure memory when storage is non-volatile? This gets into an existential question about the nature of learning and knowledge vs. just taking a lot of notes.</p>
  <p>Still, how do you even <em><strong>teach</strong></em> a computer what is meant by <em>&quot;which animal is least like the other four?&quot;</em> Did the computer really figure out what was being asked out of general intelligence, or is the computer simply designed to parse out IQ-style questions specifically? If you designed something with a foreknowledge of what would likely be asked, the computers of today <em>might</em> simply be able to &quot;recognize&quot; it as question-style 496.527b and plug in the variables.</p>
  <p>But that's not <em>general intelligence</em> by any definition we use or understand. It's just a specialized, slick interpreter designed to parse out a specific type of standardized question. Ask it a style of question which it is not expecting, and you'll see the computer is exhibiting <strong><em>no</em> innate intelligence</strong> at all.</p>
  <p><strong>Until we create <em>strong AI,</em> a computer has effectively <em>no</em> IQ.</strong></p>
  
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Answers.Text: "how do you even teach a computer what is meant by "which animals is least like the other four?"". Like this: http://www.foundalis.com/res/diss_research.html
Answers.UserDisplayName: NietzscheanAI
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Answers.Text: @NietzscheanAI Yes, but I'm talking about understanding the nature of the question itself. That's the kind kind of *general intelligence* I'm describing in the context of this post.
Answers.UserDisplayName: Robert Cartaino
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Answers.Text: Looking at our respective answers, we seem to be in agreement about the core issue: transfer learning is what's missing from AI. What I'm saying is that (of all current AI architectures) the 'Fluid Concepts' approach is closest in spirit to achieving that, by virtue of the flexibility with which it manipulates/analogises domain knowledge.
Answers.UserDisplayName: NietzscheanAI
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Answers.LastEditDate: 2020-06-17T09:57:20.710Z
Answers.UserDisplayName: Robert Cartaino
Answers.Body: <p>It depends on how the IQ test is presented:</p>
  
  <ol>
  <li><p>If as for humans (effectively, as a video of the book containing the
  test questions being opened etc), then <strong>all AI programs</strong> would <strong>score
  zero</strong>.</p></li>
  <li><p>If presented as the test set of a <strong>supervised learning</strong> problem (e.g. as for <a href="https://en.wikipedia.org/wiki/Bongard_problem">Bongard Problems</a>) then one might imagine that a number of ML <strong>rule induction techniques</strong> (e.g. Learning Classifier Systems, Genetic Programming) might achieve <strong>some limited success</strong>. </p></li>
  </ol>
  
  <p>So all current AI programs require the problem to be 'framed' in a suitable fashion. It doesn't take too much thought to see that removing the need for such 'framing' is actually <em>the</em> core problem in AI, and (despite some of the claims about Deep Learning), eliminating framing remains a distant goal.</p>
  
  <p>More generally (just as with the Turing test), in order for an IQ test to be a <em>really</em> meaningful test of intelligence, it should be possible as a <em>side effect</em> of the program's capabilities, and not the specific purpose for which humans have designed it.</p>
  
  <p>Interestingly, there is only one program that I'm aware of that sits between 1. and 2.: </p>
  
  <p><strong>Phaeaco</strong> (developed by <a href="http://www.foundalis.com/res/diss_research.html">Harry Foundalis</a> at Douglas Hofstadter's research group) takes <em>noisy photographic images of Bongard problems as input</em> and (using a variant of Hofstadter's <a href="http://dl.acm.org/citation.cfm?id=525377">'Fluid Concepts'</a> architecture) successfully deduces the required rule in many cases.</p>
  
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Answers.LastEditDate: 2016-09-14T17:11:37.400Z
Answers.UserDisplayName: NietzscheanAI
Body: <p>If IQ were used as a measure of the intelligence of machines, as in humans, at this point in time, what would be the IQ of our most intelligent AI systems?</p>
  <p>If not IQ, then how best to compare our intelligence to a machine, or one machine to another?</p>
  <p>This question is not asking if we can measure the IQ of a machine, but, if IQ is the most preferred, or general, method of measuring intelligence, then how does artificial intelligence compare to our most accepted method of measuring intelligence in humans.</p>
  <p>Many people may not understand the relevance of a Turing Test as to how intelligent their new car is, or other types of intelligent machines.</p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2016-09-14T15:23:54.217Z
Comments.Text: Possible duplicate of [Can the IQ of an AI program be measured?](http://ai.stackexchange.com/questions/41/can-the-iq-of-an-ai-program-be-measured)
Comments.UserDisplayName: Matthew Gray
Tags: <intelligence-testing><intelligence-quotient>
Title: If IQ is used as a measure of intelligence in humans, could it also be used as a measure of intelligence in machines?
UserDisplayName: D. Wade
ThreadText: # If IQ is used as a measure of intelligence in humans, could it also be used as a measure of intelligence in machines?
  
  (Asked by: username_0 on 2016-09-14)
  
  If IQ were used as a measure of the intelligence of machines, as in humans, at this point in time, what would be the IQ of our most intelligent AI systems?
  
  If not IQ, then how best to compare our intelligence to a machine, or one machine to another?
  
  This question is not asking if we can measure the IQ of a machine, but, if IQ is the most preferred, or general, method of measuring intelligence, then how does artificial intelligence compare to our most accepted method of measuring intelligence in humans.
  
  Many people may not understand the relevance of a Turing Test as to how intelligent their new car is, or other types of intelligent machines.
  
  **Tags:** intelligence-testing, intelligence-quotient
  
  > **Comments:**
  > **username_1**: Possible duplicate of [Can the IQ of an AI program be measured?](http://ai.stackexchange.com/questions/41/can-the-iq-of-an-ai-program-be-measured)  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_3 on 2016-09-14. Score: 7
  
  It depends on how the IQ test is presented:
  
  1. If as for humans (effectively, as a video of the book containing the
     test questions being opened etc), then **all AI programs** would **score
     zero**.
  2. If presented as the test set of a **supervised learning** problem (e.g. as for [Bongard Problems](https://en.wikipedia.org/wiki/Bongard_problem)) then one might imagine that a number of ML **rule induction techniques** (e.g. Learning Classifier Systems, Genetic Programming) might achieve **some limited success**.
  
  So all current AI programs require the problem to be 'framed' in a suitable fashion. It doesn't take too much thought to see that removing the need for such 'framing' is actually *the* core problem in AI, and (despite some of the claims about Deep Learning), eliminating framing remains a distant goal.
  
  More generally (just as with the Turing test), in order for an IQ test to be a *really* meaningful test of intelligence, it should be possible as a *side effect* of the program's capabilities, and not the specific purpose for which humans have designed it.
  
  Interestingly, there is only one program that I'm aware of that sits between 1. and 2.:
  
  **Phaeaco** (developed by [Harry Foundalis](http://www.foundalis.com/res/diss_research.html) at Douglas Hofstadter's research group) takes *noisy photographic images of Bongard problems as input* and (using a variant of Hofstadter's ['Fluid Concepts'](http://dl.acm.org/citation.cfm?id=525377) architecture) successfully deduces the required rule in many cases.
  
  
  ---
  
  ### Answer by username_2 on 2016-09-14. Score: 4
  
  > at this point in time, what would be the IQ of our most intelligent AI systems?
  
  ### Zero.
  
  There are many different kinds of IQ tests including written, visual, and verbal assessments, but the majority of questions are based on abstract-reasoning problems that involve creative thinking and true intelligence.
  
  In other words, the computer would have to exhibit something that does not yet exist… "strong AI".
  
  The intelligent computers of science fiction do not exist. At all. We are not even close. We have absolutely NO IDEA how to bridge the gap between what we can do now and what is depicted in pop-culture films. Even with cars that drive themselves and computers that play 'Go' — an underachieving mosquito possesses more cognitive intelligence than all the world's super computers *…combined!*
  
  ### …or possibly "disqualified" for cheating.
  
  Even if we could pre-format the questions in a style and delivery system it understands, what does memorization, attention, or speed mean in the context of a computer? I'm not even sure if a standardized IQ test makes sense in this context. It might be like asking how a computer would do in a spelling bee.
  
  In human terms, we're not *allowed* to bring along reference materials to look up an answer; but how do you rectify that when reference-lookup is innate to a computer's existence? How do you measure memory when storage is non-volatile? This gets into an existential question about the nature of learning and knowledge vs. just taking a lot of notes.
  
  Still, how do you even ***teach*** a computer what is meant by *"which animal is least like the other four?"* Did the computer really figure out what was being asked out of general intelligence, or is the computer simply designed to parse out IQ-style questions specifically? If you designed something with a foreknowledge of what would likely be asked, the computers of today *might* simply be able to "recognize" it as question-style 496.527b and plug in the variables.
  
  But that's not *general intelligence* by any definition we use or understand. It's just a specialized, slick interpreter designed to parse out a specific type of standardized question. Ask it a style of question which it is not expecting, and you'll see the computer is exhibiting ***no* innate intelligence** at all.
  
  **Until we create *strong AI,* a computer has effectively *no* IQ.**
  
  > **Comments:**
  > **username_3**: "how do you even teach a computer what is meant by "which animals is least like the other four?"". Like this: http://www.foundalis.com/res/diss_research.html  
  > **username_2**: @username_3 Yes, but I'm talking about understanding the nature of the question itself. That's the kind kind of *general intelligence* I'm describing in the context of this post.  
  > **username_3**: Looking at our respective answers, we seem to be in agreement about the core issue: transfer learning is what's missing from AI. What I'm saying is that (of all current AI architectures) the 'Fluid Concepts' approach is closest in spirit to achieving that, by virtue of the flexibility with which it manipulates/analogises domain knowledge.  
  
  ---
  
  