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.

With the continued rise of phenotypic- and genotypic-based screening projects, computational methods to analyze, process, and ultimately make predictions in this field take on growing importance. Here we show how automated machine learning workflows can produce models that are predictive of differential gene expression as a function of a compound structure using data from A673 cells as a proof of principle. In contrast to the usual in silico design paradigm, where one interrogates a particular target-based response, this work opens the opportunity for virtual screening and lead optimization for desired multitarget gene expression profiles.

The app boom is over. There are now more than 4.2 million apps available for Android and iOS, but three-quarters of American smartphone users download a grand total of zero new apps per month. They might be mostly free and easy to access, but apps are struggling to make it on to our phones and tablets. According to comScore, we spend the majority of our screen time using just three apps, with the average American spending almost half their time in just one. With the eyeballs of the world glued to WhatsApp, Facebook Messenger, WeChat and Skype, developers have started turning once-simple chat apps into complex ecosystems. And at the centre of this change is a horde of subservient bots. You've just walked into your kitchen after a long week. "Play Etta James," you say.

I would add HDT, Jackknife regression, density estimation, attribution modeling (to optimize marketing mix), linkage (in fraud detection), indexation (to create taxonomies or for clustering large data sets consisting of text), bucketisation, and time series algorithms.

Google and Microsoft redefined the definition of search through messaging technology like chatbots and apps for mobile and desktop by making a case that it can reach across a brand's or retailer's Web site to find and return information on consumer queries. Messaging creates a new form of search advertising simply by returning information based on chatbot queries. Last week at the Bing Ads Next event in Redmond, Washington, Microsoft demonstrated how companies like airline ticket site Skyscanner, as well as Delta Airlines in a demo, use a chatbot by pulling information from its Web site to answer questions. Microsoft also demonstrated a chatbot that serves up on bing.com in search results. A restaurant called Moksha is testing the technology in the Redmond, Washington area.

Campaign rhetoric will calm down. People will return to their daily lives and America will go back to focusing on its future. Trump's victory, largely led by the voices that got ignored by the previous administrations, provides a clear mandate for the Trump administration. The minor problem: when it comes to technology, the Trump mandate is mostly silent. The major problem:technology strategy by the Trump administration can be at odds with the low-to-medium skilled job creation promise on one hand or lead to decline in American competitiveness on the other hand.