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A 20-Year Community Roadmap for Artificial Intelligence Research in the US

arXiv.org Artificial Intelligence

Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.


25 Geniuses Who Are Creating the Future of Business

WIRED

Soon, software will know how you feel--and will use that data to sell you things. The gig economy will go global (but it's not Uber-take-all). The tech industry will finally be inclusive. AI will achieve something like common sense, and it will be open source too. But that future won't build itself. Actual people (at least for now) have to make these things happen, and they aren't the C-suite hotshots you always hear about. The 25 people in these pages are the unsung creative, technical, and social visionaries working to bring the incredible world of tomorrow to you today. Get to know them now. Welcome to our second annual Next List. Surveillance is about to get much harder for overly snoopy governments. In November 2014 the Facebook-owned messaging service WhatsApp made a big change to its Android app: It encrypted messages so that even Facebook can't descramble them, no matter how many court orders the company receives. But the crypto software wasn't written by a Facebook employee.


China's AI Industry Has Given Birth To 14 Unicorns: Is It A Bubble Waiting To Burst?

#artificialintelligence

A staff member displays a DJI Phantom 3 4K drone during CES (Consumer Electronics Show) in Las Vegas, Nevada. It may come as a surprising fact that there are now 14 Chinese AI companies valued at $1 billion or more. These unicorns are worth a combined $40.5 billion, according to a report China Money Network recently released during the World Economic Forum's Summer Davos gathering in Beijing. Just to put these numbers in perspective. Google bought DeepMind for over $500 million in 2014. Chinese voice recognition giant iFlytek Co. has a market capitalization of 63 billion yuan ($9.2 billion). Chinese AI startups raised $27.7 billion via 369 VC deals in 2017, according to a recent report from Tsinghua University. So naturally, it raises questions on if there is a bubble waiting to pop in the Chinese AI space. How could these companies, with an average age of less than five years, be worth so much money?


A Review of Tracking, Prediction and Decision Making Methods for Autonomous Driving

arXiv.org Machine Learning

The models are updated using a CNN, which ensures robustness to noise, scaling and minor variations of the targets' appearance. As with many other related approaches, an online implementation offloads most of the processing to an external server leaving the embedded device from the vehicle to carry out only minor and frequently-needed tasks. Since quick reactions of the system are crucial for proper and safe vehicle operation, performance and a rapid response of the underlying software is essential, which is why the online approach is popular in this field. Also in the context of ensuring robustness and stability, some authors apply fusion techniques to information extracted from CNN layers. It has been previously mentioned that important correlations can be drawn from deep and shallow layers which can be exploited together for identifying robust features in the data.