<story><title>Andrew Ng: Unbiggen AI</title><url>https://spectrum.ieee.org/andrew-ng-data-centric-ai</url></story><parent_chain><item><author>vasco</author><text>Interestingly, when you learn how to drive you need to see approximately one example and you&amp;#x27;re able to identify them all.</text></item><item><author>itissid</author><text>That is the problem with generalization and cop outs like these. It&amp;#x27;s no good to people in the field doing actual work where the devil is in the detail.&lt;p&gt;Big data is fairly important to a lot of things, for example I was listening to Tesla&amp;#x27;s use of Deep net models where they mentioned that there were literally so many variations of Stop Signs that they needed to learn what was really in the &amp;quot;tail&amp;quot; of the distribution of Stop Sign types to construct reliable AI</text></item></parent_chain><comment><author>teruakohatu</author><text>That is called transfer learning. You might only need to see one photo of a sign to identify it in real life (although arguably learner drivers take a while to notice signs) but that is only because you have been training on identifying generic objects since you left the womb.&lt;p&gt;You brain already knows how to select the most important features of a sign. The shape, the size and the color. You have also learned how to understand the text on the sign.&lt;p&gt;A new born baby does not have that ability.&lt;p&gt;This is applied in ANN as well. Transfer learning is using a pre-trained neural network, which has already learned identifying objects, and then using it to train on identifying a new, usually smaller, set of objects using, usually, a lot less training data. That is what Andrew is talking about in the article.</text></comment>
<story><title>Andrew Ng: Unbiggen AI</title><url>https://spectrum.ieee.org/andrew-ng-data-centric-ai</url></story><parent_chain><item><author>vasco</author><text>Interestingly, when you learn how to drive you need to see approximately one example and you&amp;#x27;re able to identify them all.</text></item><item><author>itissid</author><text>That is the problem with generalization and cop outs like these. It&amp;#x27;s no good to people in the field doing actual work where the devil is in the detail.&lt;p&gt;Big data is fairly important to a lot of things, for example I was listening to Tesla&amp;#x27;s use of Deep net models where they mentioned that there were literally so many variations of Stop Signs that they needed to learn what was really in the &amp;quot;tail&amp;quot; of the distribution of Stop Sign types to construct reliable AI</text></item></parent_chain><comment><author>itissid</author><text>It does feel though that a model like the human mind will be very fundamentally different from any of the models of today. No?&lt;p&gt;Like the NN State of the art models of today are so different from state of the art 12 or so years ago which was SVMs.</text></comment>