Finding applications of artificial intelligence in the automotive industry requires only a scant reading of news headlines. From IBM Watson's partnership with the General Motors OnStar platform to Toyota's $1 billion investment in AI-based self-driving technology, the marriage of AI with automotive technology has surely been consummated. It seems that every stakeholder in the automotive industry is looking for a way to capitalize on recent advances in AI technology. While artificial intelligence automotive applications that involve driverless cars receive the most attention, this is only one of many uses for artificial intelligence in the car industry. In this article, we will look at artificial intelligence automotive industry trends to see what factors are driving this explosive new market. Although attempts to create driverless cars began in the 1970s, the lack of suitable technology kept autonomous vehicles a distant dream for decades.

In 1940, Henry Ford said: "Mark my words – a combination aeroplane and motor car is coming." With flying taxis apparently on the way, it looks like he was right, but what a wait. Eight decades years later, "dude, where's my flying car?" is shorthand for any stuff "they" promised us that we haven't got. We have always wanted to fly, so, as soon as cars came on to the scene, we wanted those to fly too. Early blueprints for the US interstate highway grid even had adjacent runways ready for flying cars.

AI knows when you're going to die. But unlike in sci-fi movies, that information could end up saving lives. A new paper published in Nature suggests that feeding electronic health record data to a deep learning model could substantially improve the accuracy of projected outcomes. In trials using data from two US hospitals, researchers were able to show that these algorithms could predict a patient's length of stay and time of discharge, but also the time of death. The neural network described in the study uses an immense amount of data, such as a patient's vitals and medical history, to make its predictions.

"Microsoft Monday" is a weekly column that focuses on all things Microsoft. This week "Microsoft Monday" features news about the Flipgrid acquisition, next-gen Xbox and Surface details leaking, a preview of Office 2019 for Mac, how AI is being used for determining which Windows 10 devices are ready to be updated and much more! Today Microsoft announced that it has acquired social education company Flipgrid. Microsoft is making Flipgrid free for all educators and is offering prorated refunds to everyone that bought a subscription in the last year. The terms of this deal were undisclosed.

To become wealthier, a country needs strong growth in productivity--the output of goods or services from given inputs of labor and capital. For most people, in theory at least, higher productivity means the expectation of rising wages and abundant job opportunities. Productivity growth in most of the world's rich countries has been dismal since around 2004. Especially vexing is the sluggish pace of what economists call total factor productivity--the part that accounts for the contributions of innovation and technology. In a time of Facebook, smartphones, self-driving cars, and computers that can beat a person at just about any board game, how can the key economic measure of technological progress be so pathetic?

By means of the CTBC systems, the exact position of a train is known more precisely than with the regular signaling systems. This results in a more efficient and safe way to manage the railway traffic. Metros and other railway systems are able to improve headways while maintaining or even improving safety. The main objective of the CTBC is to increase capacity by reducing the time interval (headway) between trains. Traditional signaling systems detect trains in discrete sections of the track called'blocks', each protected by signals that prevent a train from entering an occupied block.

I've always had a close relationship with robots -- even a fondness for them. When I was a kid I had a Baby Alive doll, mass-produced in the 1970s by Kenner. The baby ate through its battery-operated mouth and subsequently pooped, a ridiculously simple simulation of the real-life care of a newborn. In the wake of an avalanche of news on the ethical implications of technology -- whether that's Google bowing out of the military's Project Maven or a report questioning whether it's emotionally healthy to have sex with robots (it's probably not) -- I connected the trail of my own attachment with AIs and their physical counterparts, robots. Throughout my own career, I've thought a lot about robots replacing jobs.

Event detection is a critical feature in data-driven systems as it assists with the identification of nominal and anomalous behavior. Event detection is increasingly relevant in robotics as robots operate with greater autonomy in increasingly unstructured environments. In this work, we present an accurate, robust, fast, and versatile measure for skill and anomaly identification. A theoretical proof establishes the link between the derivative of the log-likelihood of the HMM filtered belief state and the latest emission probabilities. The key insight is the inverse relationship in which gradient analysis is used for skill and anomaly identification. Our measure showed better performance across all metrics than related state-of-the art works. The result is broadly applicable to domains that use HMMs for event detection.

Local surrogate models, to approximate the local decision boundary of a black-box classifier, constitute one approach to generate explanations for the rationale behind an individual prediction made by the back-box. This paper highlights the importance of defining the right locality, the neighborhood on which a local surrogate is trained, in order to approximate accurately the local black-box decision boundary. Unfortunately, as shown in this paper, this issue is not only a parameter or sampling distribution challenge and has a major impact on the relevance and quality of the approximation of the local black-box decision boundary and thus on the meaning and accuracy of the generated explanation. To overcome the identified problems, quantified with an adapted measure and procedure, we propose to generate surrogate-based explanations for individual predictions based on a sampling centered on particular place of the decision boundary, relevant for the prediction to be explained, rather than on the prediction itself as it is classically done. We evaluate the novel approach compared to state-of-the-art methods and a straightforward improvement thereof on four UCI datasets.

Incomplete MaxSAT solving aims to quickly find a solution that attempts to minimize the sum of the weights of the unsatisfied soft clauses without providing any optimality guarantees. In this paper, we propose two approximation strategies for improving incomplete MaxSAT solving. In one of the strategies, we cluster the weights and approximate them with a representative weight. In another strategy, we break up the problem of minimizing the sum of weights of unsatisfiable clauses into multiple minimization subproblems. Experimental results show that approximation strategies can be used to find better solutions than the best incomplete solvers in the MaxSAT Evaluation 2017.