I don't want to tell data scientists and AI developers what to do in any given situation. I want to give scientists and engineers tools for thinking about problems. We surely can't predict all the problems and ethical issues in advance; we need to be the kind of people who can have effective discussions about these issues as we anticipate and discover them.

Experts from across the AI world came together in New York for the O'Reilly AI Conference in New York 2016. Below you'll find links to highlights from the event. Building reliable, robust software is hard, says Peter Norvig. It's even harder when we move from deterministic domains, such as balancing a checkbook, to uncertain domains, such as recognizing speech or objects in an image. Watch "Software engineering of systems that learn in uncertain domains."

Artificial intelligence is learning to take on an increasing number of sophisticated tasks. Google Deepmind's AI is now able to imitate human speech, and just this past August IBM's Watson successfully diagnosed a rare case of leukemia. Rather than viewing these advances as threats to job security, we can look at them as opportunities for AI to fill in critical gaps in existing service providers, such as mental healthcare professionals. In the US alone, nearly eight percent of the population suffers from depression (that's about one in every 13 American adults), and yet about 45 percent of this population does not seek professional care due to the costs. There are many barriers to getting quality mental healthcare, from searching for a provider who's within your insurance network to screening multiple potential therapists in order to find someone you feel comfortable speaking with.

Machine Learning Algorithms From Scratch was designed for you. The book that finally unlocks how machine learning algorithms work. You don't need the math. Everything is explained in simple words, and we work in the language you do know: code. You don't need to be a Python master.

In a 2013 report by IBM, the amount of data created everyday was estimated to be roughly 2,500,000TB. It very likely greatly exceeds this now, as wearables, AI, and connected devices have increasingly embedded themselves into society, gathering a veritable tidal wave of additional information for organisations to interrogate. This data comes in three forms: unstructured, semi-structured, and structured. Since the dawn of IT, structured data has been the main resource of analysts. Even today, this is the case.

Machine Learning and Haute Couture may not be what you expect to hear in the same sentence. But can Machine Learning help the creative side of fashion? There are plenty of examples where machine learning is applied within fashion retailing to do predictive stock assortments, trend forecasting and much more, but I was interested in the more creative side of fashion. One example is Google's Muze project. It hasn't exactly received universal acclaim and OMG! personally I would not be seen dead in it!

Many people new to data science might believe that this field is just about R, Python, Hadoop, SQL, and traditional machine learning techniques or statistical modeling. Below you will find fundamental articles that show how modern, broad and deep the field is. Some data scientists are actually doing none of the above. In my case, I don't even code, but instead, I make various applications talk to each other, in a machine-to-machine communication framework. It is true though that most data scientists use R, Python and Hadoop-related systems.

WIPO Director General Francis Gurry speaks about WIPO's ground-breaking new "artificial intelligence"-based translation tool for patent documents, a new service that hands innovators around the world the highest-quality service yet available for accessing information on new technologies. WIPO Translate now incorporates cutting-edge neural machine translation technology to render highly technical patent documents into a second language in a style and syntax that more closely mirror common usage, out-performing other translation tools built on previous technologies.

The task of removing eyeglasses from faces is not a new one, by far. A hefty amount of scientific literature documents a variety of image processing algorithms to remove eyeglasses, often for the goal of improving facial recognition technologies. Using some thoughtful math with features such as contrast, edges, and congruency, these techniques typically detect and subtract the image pixels containing the glasses and then synthesize the obfuscated facial region through smoothing or inference. Despite the ingenuity, these algorithms can fall short at the recognition of the glasses and/or the reconstruction of the face. They can also notably struggle with generalizing across different skin tones and correcting for shadows, magnification, and glare caused by the frames and lenses.

Some videos are available only by enrolling in a Coursera or EdX class. It is free to do so, but sometimes the classes are no longer in session so you have to wait a couple of months, so you have no access. I'm going to be adding more videos from public sources and replacing the online course videos over time. I like using university lectures.