Simple questions to help reduce AI hype
Stephen makes a lot of good points. And the list of questions he says people should ask is also useful. I have a few to add:<p>* What algorithm(s) are you using? Nobody should be able to brag publicly about their AI without simply naming the algorithms.<p>* What is your objective function? That is, what are you trying to optimize; what value you are you trying to maximize or minimize? Is it classification error? Is it measuring similarities on unstructured and unlabeled data through reconstruction?<p>* Do you already have the data that you can train on to minimize that error? If not, do you have a realistic plan to gather it? Have you thought about how you'll store it and make it accessible to your algorithms?<p>We have a series of questions we suggest that people ask themselves when approaching a machine- or deep learning problem:<p><a href="http://deeplearning4j.org/questions.html" rel="nofollow">http://deeplearning4j.org/questions.html</a><p>I talk to a lot of startups making claims about their "AI", and I can't stop them from jumping on the bandwagon, but not all have the ability to build a machine-learning system and gather the data it needs.<p>Every good thing gets hyped, but that doesn't make it less good. It just means readers have more work to do.... Like Francois Chollet, I happen to believe that AI is even bigger than the hype, but in ways that the hype can't imagine yet.<p><a href="https://twitter.com/fchollet/status/751483046436548608" rel="nofollow">https://twitter.com/fchollet/status/751483046436548608</a>