Apple’s Secrecy Hurts Its AI Software Development
Let's propose a hypothetical scenario. Imagine an investigator in a large research lab somewhere makes a revolutionary AI breakthrough. Not just an incremental step, but one that achieves 99.9999% success in image object recognition (current state-of-the-art using GPU-accelerated Sparse ConvNets puts Univ. of Warwick's Dr. Ben Graham's team in the lead at about 95%[1]).<p>What would be the incentive to make the result public? Certainly if the discoverer were a grad student at Berkeley they may throw a brief summary onto Arxiv the next day to get some peer validation. But if it were the ML team at Uber's new secretive lab in Pittsburgh? Would they not have a compelling interest in keeping it under the kimono?<p>There are several possible outcomes for this game to play out. But for right now let's bask in the warmth of our open source learning toolkit abundance and concentrate on the real question of the day: now that we can teach machines, are there better ways to use their time then ascertaining what makes a top selfie?<p>[1] <a href="https://www.kaggle.com/c/cifar-10/leaderboard/public" rel="nofollow">https://www.kaggle.com/c/cifar-10/leaderboard/public</a>