<story><title>Do neural networks dream of electric sheep?</title><url>http://aiweirdness.com/post/171451900302/do-neural-nets-dream-of-electric-sheep</url></story><parent_chain></parent_chain><comment><author>pitchups</author><text>This example, and others like it point to the central weakness of neural networks for image recognition: 
No matter how much data you feed it, they never really develop concepts or abstractions of what the objects it is classifying really represent or mean.  The weight and biases that get fine tuned by gradient descent, are no more than a highly complex function mapping the input pixels to discrete classes. While this may well represent how the visual cortex works at the lowest level, what appears to be missing are higher levels of abstraction and meaning.  Perhaps machine learning needs to be coupled with some of the older paradigms of AI which included modeling, logic, reasoning, to achieve understanding. As of right now, a well trained convolutional neural network is no more than a mechanical pattern matching algorithm on steroids.</text></comment>
<story><title>Do neural networks dream of electric sheep?</title><url>http://aiweirdness.com/post/171451900302/do-neural-nets-dream-of-electric-sheep</url></story><parent_chain></parent_chain><comment><author>GuiA</author><text>It&amp;#x27;s okay, humans make these kind of mistakes too.&lt;p&gt;A friend of mine has a young son (~2-3 years old), and a cat named Mono.&lt;p&gt;Her son knows the cat is named Mono - he plays with her everyday.&lt;p&gt;But when they go out for a walk, any 4 legged animal he sees is also a &amp;quot;Mono&amp;quot;.&lt;p&gt;Fortunately for her son, his developing, extremely plastic brain will soon know how to differentiate Mono the cat from a random dog on the street (unlike neural networks which we will need to entirely redesign, rebuild, and retrain to get similar progress).</text></comment>