And you are here why?

/via http://www.commitstrip.com/en/2018/09/24/trolling-the-ai/ This  particular episode of CommitStrip  pretty much represents the popular take on AI. Mind you, “popular take” is, as usual,  not  the same as “realistic”. After all,  when you’re designing robust systems, one of the very first things you’ll do is correct for signs that are missing, misplaced, or mis-identified , and if/when self-driving cars actually make their way into the real world, one of the things that they  will  be is robust against errors at multiple levels. Heck, that’s pretty much the way  humans  work — our vision is pretty seriously fallible, but we correlate a lot of  other  information to make sure that we don’t f**k up! Think about how you process visual feedback when driving. You are, unconsciously, running rules like these in your head all the time • A sign saying “60 mph” in a residential neighborhood is clearly wrong •  If you are wond...

As you’ve probably heard,  IBM is about to release a pretty monster dataset  — over a million images — along with tool, all with the aim of helping get rid of bias in facial analysis. The cool part for me is actually the announcement of a  second dataset — around 36,000 images — that are “ equally distributed across skin tones, genders, and ages ”. So, why does this matter? Before answering this, let’s first take a brief diversion. Let’s say you are doing something involving Machine Learning and facial recognition. You’d need a dataset to train your models against — think about how you would select your dataset. You’d probably take into consideration the specifics of the task (“ I need to know if the face is smiling or not ”), the details of the algorithm that you’re working on (“ Can I still tell it’s a smile if the background changes? ”) and such-like. You’d then go to one of the  handy-dandy collection of facial-recognition databases , and pick the most appropri...