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:* What algorithm(s) are you using? Nobody should be able to brag publicly about their AI without simply naming the algorithms.* 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?* 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?We have a series of questions we suggest that people ask themselves when approaching a machine- or deep learning problem:http://deeplearning4j.org/questions.htmlI 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.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.https://twitter.com/fchollet/status/751483046436548608