Microsoft Research is more important to the tech titan than ever. Crucial futuristic products like the HoloLens holographic goggles and Skype Translate came straight out of the company's science labs. But research wasn't always this central to Microsoft -- in fact, there used to be a "barrier" between the more academia-like Microsoft Research and the rest of the company, recalls Microsoft researcher emeritus and Silicon Valley icon Gordon Bell. For Microsoft Research, it was considered "not appropriate to be a part of what Microsoft was selling," Bell tells Business Insider. Bell, who helped set up Microsoft Research in the early 1990's, says that the team did great work on the very cutting edge of computer science and interaction design.

The stage and grade of psoriasis severity is clinically relevant and important for dermatologists as it aids them lead to a reliable and an accurate decision making process for better therapy. This paper proposes a novel psoriasis risk assessment system (pRAS) for stratification of psoriasis severity from colored psoriasis skin images having Asian Indian ethnicity. Machine learning paradigm is adapted for risk stratification of psoriasis disease grades utilizing offline training and online testing images. It uses two kinds of classifiers (support vector machines (SVM) and decision tree (DT)) during training and testing phases and two kinds of feature selection criteria (Principal Component Analysis (PCA) and Fisher Discriminant Ratio (FDR)), thus, leading to an exhaustive comparison between these four systems. Our database consisted of 848 psoriasis images with five severity grades: healthy, mild, moderate, severe and very severe, consisting of 383, 47, 245, 145, and 28 images respectively.

Berg is taking its tech-enabled approach to drug development into the clinic. The biotech, which is known for making brash statements about its ability to slash preclinical timelines, has incorporated its machine-learning technology into a Phase II pancreatic cancer trial in an attempt to identify the patients who are most likely to respond to the treatment. In its early years, Berg, which was cofounded in 2006 by real estate billionaire Carl Berg, applied a combination of genomics, systems biology, computational modeling and artificial intelligence to the discovery of a pipeline of products. Now, with two candidates in the clinic in multiple indications, the biotech is aiming to use similar capabilities to improve its odds of success in human trials. A Phase II study combining Berg's lead candidate, BPM 31510, with gemcitabine is acting as an early testing ground for the concept.

Please make Pull Requests for good resources, or create Issues for any feedback! Seq2Seq solves the traditional fixed-size input problem thatEffective Approaches to Attention-based Neural Machine Translation prevents traditional DNNs from mastering sequence based tasks such as translation and question answering. It has been shown to have state of the art performances in English-French and English-German translations and in responding to short questions. Seq2Seq was first introduced in late 2014 by 2 papers (Sequence to Sequence Learning with Neural Networks and Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation) from Google Brain and Yoshua Bengio's group. The two papers took a similar approach in machine translation, in which Seq2Seq was developed upon.

In late 2015, our Chief Customer Officer, Joe Gagnon, and I, met with the IT Director, and the COO of Radisson Blu Edwardian in London. A long-term customer of Aspect's, we were there to share what we have been so diligently working on over the past year: our vision for re-imagining customer service that would combine the best of all forms of consumer interaction types, and the best of what we and the industry have been able to develop in next generation CX technology. In essence, the vision for re-imagining customer service has at its core how to use Interactive Text Response (ITR), or what is also known as "bots" to provide the ability to let customers self-serve on text channels at blazing-fast speeds with a User Interface that resembles that of a natural conversation with a person. Needless to say, it didn't require much convincing that this approach would provide an opportunity to "surprise and delight" in their prestigious hotel chain. With over 2 billion people using texting and messaging solutions extensively today, offering service over these channels just makes sense. Furthermore, it has the promise of saving cost through smart automation while providing a state-of-the-art customer experience, or in the words of the COO: "I want this by Monday."

Well; US DoE and EPA has already been using AI for a very, very long time in monitoring and proactively acting on any waste release. I was one of the lead architects and developers of the solution. As computers get smarter, scientists look at new ways to enlist them in environmental protection.

Although the game between AlphaGo AI and legendary chess master Lee Sedol ended over a month ago, the event's influence is opening doors for companies seeking to explain the significance of AI technology. Fu Sheng, CEO Cheetah Mobile who predicted Lee would win the game, is one of them. At the company's global media conference held Tuesday, Fu announced that Cheetah Mobile, the Beijing-based startup best known for its utility apps, is going to open a robotics division with a 50 million USD investment designated for artificial intelligence development. Fu says he is going to lead the team himself. "AI technology is maturing and there's no doubt that human labor is going to be replaced by robots", he said, adding that deep learning and AI will offer a chance to bypass competitors and disrupt the industry landscape.

Every year Sergey Brin and Larry Page write a letter to their stakeholders, giving an overview of the company and where it is headed. This year, post the re-organisation that made Sundar Pichai CEO of Google and Alphabet became the parent company, the founder's letter was written by Pichai. The letter has some clues to where Google services are headed. Search is going to get more contextual awareness, personal demographic factors, location characteristics and environmental variables will all start changing search results. Search will not just get more personal, but also consider the time and activity.

In the last part we introduced Classification, which is a supervised form of machine learning, and explained the K Nearest Neighbors algorithm intuition. In this tutorial, we're actually going to apply a simple example of the algorithm using Scikit-Learn, and then in the subsquent tutorials we'll build our own algorithm to learn more about how it works under the hood. To exemplify classification, we're going to use a Breast Cancer Dataset, which is a dataset donated to the University of California, Irvine (UCI) collection from the University of Wisconsin-Madison. UCI has a large Machine Learning Repository.

The question of human existence -whether as a species in general or just in the working world- has generated quite some concern around the world. Famous technologists and successful entrepreneurs such as Stephen Hawking, Elon Musk or Jaan Tallinn have responded with organisations like Future of Life Institute and OpenAI. They strive to protect human life from intelligent machines taking over the world. Thanks to Paul O'Connell, founder and organiser of the Uprise Festival in Amsterdam, I had the opportunity to dive a bit deeper into this issue. As part of the Festival -already in its 3rd edition and coming to Dublin later this year- I was able to bring to the audience a panel of guest speakers with the goal of answering one bold question: are humans still relevant?