Greedy, Brittle, Opaque, and Shallow: The Downsides to Deep Learning
Brittle and opaque are real problems. The brittleness seems to be associated with systems which put decision surfaces too close to points in some dimension. That&#x27;s what makes strange classifier errors possible.[1]  (This is also why using raw machine learning for automatic driving is a terrible idea. It will do well, until it does something totally wrong for no clear reason.)<p>Opacity comes from what you get after training - a big matrix of weights. Now what? &quot;Deep Dream&quot; was an attempt to visualize what a neural net used for image classification was doing, by generating high-scoring images from the net.  That helped some. Not enough.<p>The ceiling for machine learning may be in sight, though. Each generation of AI goes through this. There&#x27;s a new big idea, it works on some problems, enthusiasts are saying &quot;strong AI real soon now&quot;, and then it hits a ceiling. We&#x27;ve been through that with search, the General Problem Solver, perceptrons, hill-climbing, and expert systems. Each hit a ceiling after a few years. (I went through Stanford just as the expert system boom hit its rather low ceiling. Not a happy time there.)<p>The difference this time is that machine learning works well enough to power major industries.  So AI now has products, people, and money.  The previous generations of AI never got beyond a few small research groups in and around major universities. With more people working on it, the time to the next big idea should be shorter.<p>[1] <a href="https:&#x2F;&#x2F;blog.openai.com&#x2F;adversarial-example-research&#x2F;" rel="nofollow">https:&#x2F;&#x2F;blog.openai.com&#x2F;adversarial-example-research&#x2F;</a>