You have been working on autonomous cars for 15 years now. What progress has been made over this period? Arnaud de La Fortelle -- Fifteen years before I started, there were already prototypes of smart cars: the first units dated back from the late 1980s. I am talking of autonomous vehicles driving at 130 km/h on French highways. Thirty years later, we are still at the same point! The greatest advances relate to computational power and sensors.

In terms of the future of smart machines, the Internet of Things (IoT), and artificial intelligence (AI), it more or less comes down to "Like It or Not, This Is Happening." The quoted statement is a section in the first chapter of the book What to Do When Machines Do Everything from Cognizant Technology Solutions. "What to Do When Machines Do Everything" offers deep insight on how emerging technologies like artificial intelligence and the Internet of Things will change our labor force and production industries. The authors do not sugar-coat the inevitable future. In chapter one, they state how this next stage of technology and business is the same as what we have experienced in the past.

The latest to succumb is Go's top-ranked player, Ke Jie, who lost 3-0 in a series hosted in China this week. The AI, developed by London-based DeepMind, which was acquired by Google for around $500 million in 2014, also overcome a team of five top players during a week of matches. AlphaGo first drew headlines last year when it beat former Go world champion Lee Sedol, and the China event took things to the next level with matches against 19-year-old Jie, and doubles with and against other top Go pros. Challengers defeated, AlphaGo has cast its last competitive stone, DeepMind CEO Demis Hassabis explained. This week's series of thrilling games with the world's best players, in the country where Go originated, has been the highest possible pinnacle for AlphaGo as a competitive program.

Bosch Putting Brains Behind Autonomous Driving Bosch AI onboard computer to guide autonomous cars through complex traffic situations, constantly learn new ones. BOXBERG, Germany – Robert Bosch recently sold its 10 millionth radar unit, a fundamental ingredient of autonomous and advanced driver-assistance systems, but that represents just the tip of the iceberg when it comes to the global supplier's ambitions for self-driving cars. Harald Kröger, president-Automotive Electronics, says the supplier intends to be a leader in the segment by creating a brain for autonomous vehicles capable of collecting and processing, in a split second, three times the information as the human brain through the use of artificial intelligence and deep learning. "A car equipped with artificial intelligence will not only react faster than any human, but also drive more defensively," Kröger says during remarks here ahead of a deep dive into the global supplier's vast automotive product portfolio that includes a growing suite of ADAS and autonomous technology. "It makes the roads in our urban areas safer – for pedestrians, cyclists, and, not least, for the occupants of vehicles. Our development goal is clear: Bosch wants to make cars smart," he says.

If you're deep into the world of artificial intelligence, you certainly know Kaggle, the Google Cloud-owned platform where AI coders compete on projects, often with financial rewards for the winning solutions. The platform recently passed 1 million members, a testament to what a hotbed the field of AI is. He's entered 39 competitions over the past year, recently placing second in a contest to develop an algorithm that can detect duplicate ads on the same platform. With his skill, enthusiasm, and cooperative attitude within the community, Mikel is very much the template of a rising star in the Kaggle and greater AI communities. Except for one thing: Mikel is just 16 years old.

A person needs breaks, but a robot can work forever. With this in mind, one of Sweden's biggest banks, has tapped a robot to be'always at work, 24/7, 365 days a year.' Now those who bank at SEB can bring their financial questions to Aida, an artificially intelligent customer service representative. 'There are some frequent, simple tasks that we need to deal with manually today, and in that effort we're looking into AI to see how we can deploy it, and Aida is one,' Johan Torgeby, the chief executive officer of SEB, told Bloomberg. Aida is a chatbot with vast amounts of individual client data, meaning she - Aida was designed to sound like a woman because of research suggesting customers feel more comfortable with female voices - can quickly handle straightforward customer needs.

Deep learning (DL) is a branch of machine learning that learns multiple levels of representations from data [1, 2]. DL has been successfully applied in pattern recognition and classification tasks such as image recognition and language processing. Recently, the application of DL to physics research is rapidly growing, such as in particle physics [3-7], nuclear physics [8], and condensed matter physics [9-14]. DL is shown to be very powerful in extracting pertinent features especially for complex nonlinear systems with high-order correlations that conventional techniques are unable to tackle. This suggests that it could be utilized to unveil hidden information from the highly implicit data of heavy-ion experiments.

Quick and accurate medical diagnosis is crucial for the successful treatment of a disease. Using machine learning algorithms, we have built two models to predict a hematologic disease, based on laboratory blood test results. In one predictive model, we used all available blood test parameters and in the other a reduced set, which is usually measured upon patient admittance. Both models produced good results, with a prediction accuracy of 0.88 and 0.86, when considering the list of five most probable diseases, and 0.59 and 0.57, when considering only the most probable disease. Models did not differ significantly from each other, which indicates that a reduced set of parameters contains a relevant fingerprint of a disease, expanding the utility of the model for general practitioner's use and indicating that there is more information in the blood test results than physicians recognize. In the clinical test we showed that the accuracy of our predictive models was on a par with the ability of hematology specialists. Our study is the first to show that a machine learning predictive model based on blood tests alone, can be successfully applied to predict hematologic diseases and could open up unprecedented possibilities in medical diagnosis.

The study of networks leads to a wide range of high dimensional inference problems. In most practical scenarios, one needs to draw inference from a small population of large networks. The present paper studies hypothesis testing of graphs in this high-dimensional regime. We consider the problem of testing between two populations of inhomogeneous random graphs defined on the same set of vertices. We propose tests based on estimates of the Frobenius and operator norms of the difference between the population adjacency matrices. We show that the tests are uniformly consistent in both the "large graph, small sample" and "small graph, large sample" regimes. We further derive lower bounds on the minimax separation rate for the associated testing problems, and show that the constructed tests are near optimal.

We provide a comprehensive study of the convergence of forward-backward algorithm under suitable geometric conditions leading to fast rates. We present several new results and collect in a unified view a variety of results scattered in the literature, often providing simplified proofs. Novel contributions include the analysis of infinite dimensional convex minimization problems, allowing the case where minimizers might not exist. Further, we analyze the relation between different geometric conditions, and discuss novel connections with a priori conditions in linear inverse problems, including source conditions, restricted isometry properties and partial smoothness.