My other car drives itself

If you are feeling lonely and looking for someone to talk with, try Ruuh, the new AI Chatbot. After testing it for more than a month, Microsoft has finally launched a new AI chatbot'Ruuh'. Microsoft has designed it specifically for the Indian users, and the name'Ruuh' stipulates the same. However, it supports English language only for now. Ruuh is a sheer entertainment program and will focus on chatting, humor, Bollywood, Music and other entertainment modes.

Recently, I finished binge-watching the first season of HBO's popular series, Westworld. The show sheds light on machines coming of age and the extent humans can go to exploit technology. From Matrix to Ex Machina, all these sci-fi movies reveal a darker side of not only humans but also the machines. I dread the idea of machines becoming smarter than us or a day when machines lead our lifestyles. But that might or might not happen in the next century, most probably I will not live to see that time.

The research, which includes new analysis of CB Insights data on 450 insurtech deals over the last three years, appears in a new Accenture report titled "The Rise of InsurTech." The report was released today in conjunction with Accenture's Fintech Innovation Lab in London, which for the first time includes a dedicated insurtech stream comprising leading industry startups. According to the report, the combined number of deals across AI (including automation) and the IoT (including connected insurance) increased 79 percent in 2016. Even though the two technologies represented only one-quarter (24 percent) of the 216 insurtech deals globally last year, they accounted for 44 percent or US$711 million of total insurtech investment -- compared with just 10 percent of global insurtech investment in 2015. "We've seen a rapid acceleration of investment into and deal activity around intelligent automation and IoT start-ups over the last 12 months," said Roy Jubraj, a co-author of the report and Accenture's Digital & Innovation lead in the company's Financial Services practice in the U.K. and Ireland.

America's biggest tech companies are remaking the internet through artificial intelligence. And more than ever, these companies are looking north to Canada for the ideas that will advance AI itself. This morning, Google announced it's starting an AI lab in Toronto. At the same time, it's helping to fund a public-private partnership with the University of Toronto to develop and commercialize AI talent and ideas. In November, the company made a similar move in Montreal--a city that has also attracted Microsoft's attention.

Need a teacher, airport guide, care taker, and customs control officer all in one? Standing at just under 3 feet tall with big cartoon eyes and flipper arms, Sanbot looks more helpless than helpful, but don't let that cute appearance fool you. Sanbot is packed with features to help "her" get the job done. Sanbot has 14 of them scattered throughout its three foot frame. Many of these are touch sensitive, allowing the robotic lady to respond with human-like emotion to a particular tap.

AI applications have been deployed and used for industrial, government, and consumer purposes for many years. The experiences have been documented in IAAI conference proceedings since 1989. Over the years, the breadth of applications has expanded many times over and AI systems have become more commonplace. Indeed, AI has recently become a focal point in the industrial and consumer consciousness. This article focuses on changes in the world of computing over the last three decades that made building AI applications more feasible. We then examine lessons learned during this time and distill these lessons into succinct advice for future application builders.

This issue features expanded versions of articles selected from the 2016 AAAI Conference on Innovative Applications of Artificial Intelligence held in Phoenix, Arizona. We present a selection of three articles that describe deployed applications, two articles that discuss work on emerging applications, and an article based on the 2016 Robert S. Engelmore Memorial Lecture.

How to Cut Cake Fairly.

Objective: In this work, we perform margin assessment of human breast tissue from optical coherence tomography (OCT) images using deep neural networks (DNNs). This work simulates an intraoperative setting for breast cancer lumpectomy. Methods: To train the DNNs, we use both the state-of-the-art methods (Weight Decay and DropOut) and a newly introduced regularization method based on function norms. Commonly used methods can fail when only a small database is available. The use of a function norm introduces a direct control over the complexity of the function with the aim of diminishing the risk of overfitting. Results: As neither the code nor the data of previous results are publicly available, the obtained results are compared with reported results in the literature for a conservative comparison. Moreover, our method is applied to locally collected data on several data configurations. The reported results are the average over the different trials. Conclusion: The experimental results show that the use of DNNs yields significantly better results than other techniques when evaluated in terms of sensitivity, specificity, F1 score, G-mean and Matthews correlation coefficient. Function norm regularization yielded higher and more robust results than competing methods. Significance: We have demonstrated a system that shows high promise for (partially) automated margin assessment of human breast tissue, Equal error rate (EER) is reduced from approximately 12\% (the lowest reported in the literature) to 5\%\,--\,a 58\% reduction. The method is computationally feasible for intraoperative application (less than 2 seconds per image).