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.

Humans may one day create superintelligence, artificially intelligent machines that surpass mankind's intellect. Would these artificial intelligences choose to play games with us, and if so, which games? We believe this question is relevant for the ethics of general AI, the current widespread integration of AI systems into daily life, and for game AI research. We present a catalog of scenarios, some good for humanity and some bad, in which various kinds of play might take place between humans and intelligent machines. We assume a superintelligence, because of its greater cognitive ability, would stand in a similar relation to us as an adult does to a child, an expert to a novice, or a human to an animal. We define friendly games, learning games, observational games, and domination games, and proceed to consider games adults play with children, experts play with novices, and humans play with animals. Reasoning by analogy, we imagine corresponding games that superintelligences might choose to play with us, finding that domination games would pose a significant risk to humanity.

A key front for ethical questions in artificial intelligence, and computer science more generally, is teaching students how to engage with the questions they will face in their professional careers based on the tools and technologies we teach them.  In past work (and current teaching) we have advocated for the use of science fiction as an appropriate tool which enables AI researchers to engage students and the public on the current state and potential impacts of AI. We present teaching suggestions for E.M. Forster's 1909 story, "The Machine Stops," to teach topics in computer ethics.  In particular, we use the story to examine ethical issues related to being constantly available for remote contact, physically isolated, and dependent on a machine --- all without mentioning computer games or other media to which students have strong emotional associations. We give a high-level view of common ethical theories and indicate how they inform the questions raised by the story and afford a structure for thinking about how to address them.