Machine learning is still too hard for software engineers
"Machine Learning is too difficult" ... says company selling "ML platform [which] can be used by anyone and it only takes minutes to train your first model."<p>No, actually, you're just being dishonest. Even if you hide TensorFlow and the keras models behind a nice GUI, people still need that mathematics knowledge to succeed. And yes, pre-training is great. But you need a shitload of stochastic analysis to make sure that the pre-trained embedding won't distort your results.<p>"For a software engineer, the hardest thing about developing Machine Learning functionality should be finding clean and representative ground-truth data, but it often isn’t."<p>That is (in my opinion) an entirely bogus request. Machine learning is a mathematical / statistical tool for modeling large unknown functions. I feel like this sentence is akin in usefulness to:<p>"For a nuclear power plant, the hardest thing about building one should be to draw how the finished building will look like in the press release".<p>Someone "doing" machine learning without the requisite math knowledge is effectively driving blind. And worse than that, they don't even know what they don't see, because they lack the skills to identify their blind spots. That's how you end up with a "tank detection AI" that in reality just classifies the weather into bright vs. dark. [1]<p>Companies like this who promise advanced mathematical algorithms with no prior skill or knowledge are how we unleash a plague of buggy unverified automatons upon the world.<p>[1] <a href="https://www.lesswrong.com/posts/5o3CxyvZ2XKawRB5w/machine-learning-and-unintended-consequences" rel="nofollow">https://www.lesswrong.com/posts/5o3CxyvZ2XKawRB5w/machine-le...</a>