<story><title>Moravec&apos;s paradox</title><url>https://en.wikipedia.org/wiki/Moravec%27s_paradox</url></story><parent_chain></parent_chain><comment><author>tgflynn</author><text>It&apos;s an interesting observation and certainly something one needs to be aware of in thinking about intelligent systems but I&apos;m not convinced that the notion that perception is intrinsically harder than logic is quite true.  I think the difficulty of a problem is highly dependent on the representations and models used.  Computers were developed based on logical and mathematical principles so it makes sense that they are, in some sense, &quot;good&quot; at these kinds of problems.  On the other hand traditional logic is incredibly inefficient at dealing with probabilistic and perceptual problems.&lt;p&gt;It&apos;s also not clear that there&apos;s really a valid comparison here.  In order for computers to recognize objects we need to program them to learn recognition on their own (because programming them explicitly to do it would be far too hard).  When we program a computer to solve a logic problem the computer isn&apos;t learning to solve that problem, it&apos;s the programmer, not the program that &quot;knows&quot; how to solve it.&lt;p&gt;Trying to teach a neural network to play chess is probably much harder than teaching it to recognize images (at least my very limited experiments suggest this to be true).</text></comment>
<story><title>Moravec&apos;s paradox</title><url>https://en.wikipedia.org/wiki/Moravec%27s_paradox</url></story><parent_chain></parent_chain><comment><author>saulrh</author><text>Roboticist here. This is actually a huge problem in robotics and causes all sorts of problems for real robots. For example, my current project is to teach robots how levers work.</text></comment>