Mitsubishi Electric, Zenrin and nine other automakers will start collecting high resolution 3D maps for self-driving cars to use, in preparation for autonomous car deployment in the country. The project is backed by the Cabinet Office's Cross-ministerial Strategic Innovation Promotion Program (quite a mouthful), which commissioned Dynamic Map Planning, a joint venture of the 11 companies, to build the 3D maps. Japan's government hopes that self-driving cars will be on the road before the start of the Tokyo Summer Olympics in 2020, according to Nikkei. Highly detailed 3D maps provide a ton of data for self-driving cars, which can be analyzed by machine learning systems and fed into an entire fleet of vehicles. Once an entire city is mapped, the self-driving car could, in theory, know every single traffic light, walkway, and intersection while driving less than a mile.

Shop Direct, the company that runs the Littlewoods.com The move has paid off. The company turned a profit of 150.4m, a 43 per cent increase year-on-year, and said that the focus on digital, and especially mobile, was core to this growth. Traffic to Shop Direct sites grew by 12 per cent to 1.2 million visits a day, while traffic from smartphones grew 42 per cent to a daily average of 638,000. This led to a 46 per cent increase year on year in orders made via smartphones, while the company's app has now been downloaded almost a million times across iOS and Android.

Blockchain technology brings security structures and incentives in line with the way we share information in the 21st century. Lloyd's of London knows a thing or two about business losses--for three centuries, the world's oldest insurance market has been paying out damages triggered by wars, natural disasters, and countless acts of human error and fraud. So, it's worth paying attention when Lloyds estimates that cybercrime caused businesses to lose 400 billion between stolen funds and disruption to their operations in 2015. If that number seems weighty--and it ought to--try this one for size: 2.1 trillion. That's Juniper Research's total cybercrime loss forecast for the even more digitally interconnected world projected for 2019. To put that figure in perspective, at current economic growth rates, it would represent more than 2% of total world GDP.

Companies have invested over 4 billion to create bots and analyze the resulting data, which is now being generated by millions of users around the globe. Far from being a specialized subset, bot data contains all of the hallmarks of user behavior data that's been collected since the dawn of the internet, but it's more than just conversions, demographics, and engagement. Read on to learn how much more value bot data can provide, beyond answering wtf is that? Bots provide data streams that are inherently social and optimized for feeding machine learning algorithms and still include the same invaluable information inherent to any digital user process. For example, bots on a platform like Facebook Messenger can capture user demographics, traffic rates, sales conversions, or any activity or API request.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.

Over the past 30 years, tools once reserved for presidents, spies, generals and media moguls have been made available cheaply to billions. Streaming live video to a global audience used to cost millions. Now, it's free and you could be doing it in five seconds if you wanted to. Every great leader, from presidents to CEOs, is surrounded by trusted advisors who guide and inform at every step. Successful leaders often succeed in part because they have better advice or better information.

While artificial intelligence is making waves globally, Cognitive Business Intelligence (BI) is the next stage of machine learning to design and analyze unstructured data, video, images and human language, say experts. According to them, we are generating data but is this data being analyzed to create insights which could help in running businesses more effectively is the real concern for businesses. "This potential can be leveraged using Business Intelligence. Using BI, we can understand what really the data means," said Nikhilesh Tiwari, Co-Founder, Helical Insight, the world's first open source Business Intelligence (BI) framework. At the moment, less than 0.5 percent of all data is ever analyzed and used globally.

Contrary to fictional portrayals of humans being herded like animals by Artificially Intelligent (AI) machines, realistic concerns about artificial intelligence are far more benign. Still they are no less important and conversations have to begin looking at perhaps setting some ground rules. That's why the big players are stepping up. Researchers and scientists from large tech companies Google, Amazon, Microsoft, IBM, and Facebook have been meeting and discussing the future implications of AI for humans. While no hard details on the group's policies, objectives, or even it's name have come out, insiders have stated the group's intentions: to ensure that A.I. research is focused on benefiting people, not hurting them.

All of the above is part of working with NLP and machine learning. If you don't mind these steps, then you're ready to get to work.

Using data analytics to understand hospital emergency department overcrowding and wait times, two researchers have developed a methodology to predict future ER demand. Hospitals that use their analysis could use the results to reduce wait times for patients by as much as 15 percent, the researchers contend. The methodology uses machine learning technology to assess data on known patterns of ER activity, say Carri Chan, associate professor of business at Columbia Business School, and Kuang Xu, an assistant professor at Stanford Graduate School of Business. Their approach takes into account factors such as time of day, general level of severity, holidays, weather patterns, bad air quality, flu season and special events, to predict how many walk-in patients will come during a certain time period. That data then can help providers determine when to begin diverting them to their primary care physician, an urgent care facility or another hospital, as well as when to start diverting ambulances to other facilities.