And you are here why?

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...

/via https://bit.ly/2F8W8oY You’ve seen the news about Deep Learning and Health Care, right? About how image classification using these algorithms is turning out to be better than doctors (•) in identifying issues in fields like   pathology ,   ophthalmology , and   radiology ? It does sound like good news from a diagnosis perspective, with more accurate results resulting in earlier intervention, better treatment options, improvement in health care, and so on. But, there are   humans   involved in this process, humans who are deeply embedded in the health-care industry — the same industry that make billions and billions of dollars in profits based on a crazy-quilt of complexity when it comes to providing services (in the US at least). And that’s   before   we even begin to think about fraud and abuse. Lets stick with image classification. We   know   that there is an   entire universe   of   adversarial attacks , at...