While video becomes more important as a digital media type, video data often remains dark to analytics. This webinar demonstrates how IBM Bluemix OpenWhisk together with Watson services begins to unlock the value of video data. Dark Vision is an application that uploads video files or streams to the cloud, transcodes video data, extracts and passes frames through the Watson Image Recognition and the Alchemy Face Recognition services, and generates meta-data to use in categorizing the video data for searchability. In the presentation, Andreas Nauerz introduces the basics of IBM Bluemix OpenWhisk and Frederic Lavigne demonstrates how to create OpenWhisk code to accomplish Dark Vision's application workflow. Maybe you want to do that because a particular team already has developed skills in this area, maybe because they do mobile development or have done mobile development in the past, and now want to outsource computer-intensive tasks.

The assumption that a college education should take four years is baked into American culture. Colleges in the colonial days were founded on the premise of a four-year degree, a concept imported from Europe. Harvard University experimented with a three-year degree when it was founded in 1636, but the test was short-lived, and the four-year degree has been the standard ever since. We expect students to enter college at 18 and leave when they turn 22, and we worry about those who take a more circuitous route to graduation. But we need to reconsider that long-established, one-size-fits all model.

Today on In Case You Missed It: Ford is testing self-driving cars on a completely dark night course, designed to be used without headlights. An automation company built a flying sphere that can pick up and deliver things with a very sphincter-like gripper system. And Schaft Inc. is showing off its new bipedal robot that can walk on rocky beaches easily. The physicist who is thrilled about his job specializing in snowflakes is a must-watch (post-Zombie fakeout, of course). As always, please share any great tech or science videos you find by using the #ICYMI hashtag on Twitter for @mskerryd.

The workshop is agnostic and features the best open source Python libraries (Pandas, scikit-learn, SKLL), APIs and ML-as-a-Service platforms (Microsoft Azure ML, Amazon ML, BigML) for developers getting started in Machine Learning. It focuses on only two learning techniques, which turn out to be the most commonly used in practice: decision trees and ensembles. Each workshop is 2 day long and comprises 8 modules of 3 blocks of 30' each--including time for questions. Blocks are either Theory or Exercise, with at least one Exercise per module. The goal is to make you operational with machine learning at the end of the workshop.

The 2016 winners were as follows: Tom Dietterich, AAAI President, for AAAI 2017 Awards, please Manuela Veloso, AAAI Past President contact Carol Hamilton at hamilton@aaai.org.

WWTS (What Would Turing Say?) Turing's Imitation Game was a brilliant Turing was heavily influenced by the World War II "game" If Turing were alive today, what sort of test might he propose? If a machine could fool interrogators as often as a typical man, then one would have to conclude that that machine, as programmed, was as intelligent as a person (well, as intelligent as men.) As Judy Genova (1994) puts it, Turing's originally proposed game involves not a question of species, but one of gender. The current version, where the interrogator is told he or she needs to distinguish a person from a machine, is (1) much more difficult to get a program to pass, and (2) almost all the added difficulties are largely irrelevant to intelligence! And it's possible to muddy the waters even more by some programs appearing to do well at it due to various tricks, such as having the interviewee program claim to be a 13-year-old Ukrainian who doesn't speak English well (University of Reading 2014), and hence having all its wrong or bizarre responses excused due to cultural, age, or language issues.

Description: The ATLAS conference is an interdisciplinary workshop on mathematical and algorithmimcal approaches for high dimensional problems in data sciences. This year's event is particularly dedicated to signal processing and applications in different fields as medical imaging, neurosciences, astrophysics.... with a particular emphasis on the use of innovative optimization methods. The workshop's program will feature plenary talks given by experts in the field, as well as short talk.

Tree based learning algorithms are considered to be one of the best and mostly used supervised learning methods. Tree based methods empower predictive models with high accuracy, stability and ease of interpretation. Unlike linear models, they map non-linear relationships quite well. They are adaptable at solving any kind of problem at hand (classification or regression). Methods like decision trees, random forest, gradient boosting are being popularly used in all kinds of data science problems. Hence, for every analyst (fresher also), it's important to learn these algorithms and use them for modeling. This tutorial is meant to help beginners learn tree based modeling from scratch. After the successful completion of this tutorial, one is expected to become proficient at using tree based algorithms and build predictive models. Note: This tutorial requires no prior knowledge of machine learning.

Probabilistic graphical models has been quite a roller coaster over the past few years. Social network analysis is growing less rapidly as well. While the results herein may not come as much surprise, it's fascinating to see the extent to which AI, machine learning, and especially deep learning has proliferated and grown among advanced course offerings. It is incredibly exciting to witness the opportunities for predictive insights & machine intelligence that are affecting every industry and business application, and reassuring that knowledge and understanding of these core areas is only going to increase -- and perhaps become core tools & techniques for an upcoming wave of engineers.

At this point, it's a bit of an open secret that Facebook will announce the arrival of chatbots for Messenger at F8, its annual developer conference. Well, the latest Messenger update all but confirms it. If you were to do a search in the latest version of Facebook's chat app, you'll find a new category heading called "Bots and Businesses." Prior to the update, this would simply read "Businesses," which was a listing of companies that you could message for customer support and general inquiries. Messaging businesses typically involves speaking to a human, however, while talking to chatbots would likely be a more automated experience -- sort of like chatting to the equivalent of a phone tree.