<story><title>The truth about deep learning</title><url>http://blog.claymcleod.io/2016/06/01/The-truth-about-Deep-Learning/</url></story><parent_chain></parent_chain><comment><author>vonnik</author><text>Anyone following DL news knows that DL alone will not lead to strong AI. The most impressive feats in the last year or so have come from combining deep artificial neural networks with other algorithms, just as DeepMind combined deep ConvNets with reinforcement learning and Monte Carlo Tree Search. There&amp;#x27;s not really an interesting conversation to be had about whether DL will get us to strong AI. It won&amp;#x27;t. It is just machine perception; that is, it classifies, clusters and makes predictions about data very well in many situations, but it&amp;#x27;s not going to solve goal-oriented learning. But it solves perception problems very well, often better than human experts. So in the not too distant future, as people wake up to its potential, we will use those infinitely replicable NNs to extract actionable knowledge from the raw data of the world. That is, the world will become more transparent. It will offer fewer surprises. We may not solve cancer with DL, but we will spot it in X-rays more consistently with image recognition, and save more lives.&lt;p&gt;Disclosure: I work on the open-source DL project Deeplearning4j: &lt;a href=&quot;http:&amp;#x2F;&amp;#x2F;deeplearning4j.org&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;deeplearning4j.org&amp;#x2F;&lt;/a&gt;</text></comment>
<story><title>The truth about deep learning</title><url>http://blog.claymcleod.io/2016/06/01/The-truth-about-Deep-Learning/</url></story><parent_chain></parent_chain><comment><author>AndrewKemendo</author><text>I understand and empathize with the skepticism or rather criticisms around hand wringing with respect to the implications of current deep learning methods.&lt;p&gt;However, as someone who builds them for vision applications I&amp;#x27;m increasingly convinced that some form of ANN will underlie AGI - what he calls a universal algorithm.&lt;p&gt;If we assume that general intelligence comes from highly trained, highly connected single processors (neurons) with a massive and complex sensor system, then replicating that neuron is step one - which arguably is what we are building, albeit comparatively crudely, with ANN&amp;#x27;s.&lt;p&gt;If you compare at a high level how infants learn and how we train RNN&amp;#x2F;CNNs they are remarkably similar.&lt;p&gt;I think where the author, and in general the ML crowd focuses too much is on unsupervised learning as being pivotal for AGI.&lt;p&gt;In fact if you look again at biological models the bulk of animal learning is supervised training in the strict technical sense. Just look at feral children studies as proof of this.&lt;p&gt;Where the author detours too much is assuming the academic world would prove a broader scope for ANN if it were there. In fact however research priorities are across the board not focused on general intelligence and most machine learning programs explicitly forbid this research for graduate students as it&amp;#x27;s not productive over the timeline of a program.&lt;p&gt;Bengio and others I think are on the right track, focusing on the question of ANN towards AGI  and I think it will start producing results as our training methods.</text></comment>