Connected cars and the Internet of Things go together like peanut butter and jelly. But realizing the future of autonomous vehicles will demand close attention to be paid to cybersecurity, functional-safety standards, and other critical factors. IoT will advance the era of self-driving cars, which currently is dominated by Tesla Motors. At the same time, it will change some of the dynamics in this market. On one hand, it will turn automotive manufacturers into technology companies, which could provide new revenue streams for carmakers. On the other hand, it will open the door for new players that have never had a viable entry point in the automotive market. Consider the case of Velodyne LiDAR, a Morgan Hill, Calif.-based company, which last month opened a factory in nearby San Jose to manufacture its LIDAR product.

As part of an ongoing effort within Microsoft to improve the accuracy of artificial intelligence (AI) systems, Microsoft Translator is publicly releasing a set of data that includes multiple conversations between bilingual speakers who are speaking French, German and English. This corpus, which was produced by Microsoft using bilingual speakers, aims to create a standard by which people can measure how well their conversational speech translation systems work. It can serve as a standardized data set for testing bilingual conversational speech translation systems such as the Microsoft Translator live feature and Skype Translator. Christian Federmann, a senior program manager working with the Microsoft Translator team, said there aren't as many standardized data sets for testing bilingual conversational speech translation systems. "You need high-quality data in order to have high-quality testing," Federmann said.

Facial recognition technology has come a long way since Facebook first asked you to tag yourself in your uploaded photos. Refined over the years, it now tags you and your friends automatically, and can identify you in photos even when your face is hidden. Face recognition technology has sat comfortably in pop culture for decades, when detectives on TV scanned databases on the hunt for suspects, the faces blending into each other until PING! It took reality a little longer to catch up to the foresight of Hollywood, and while we haven't reached the invasive face analysis-based advertising showcased in Spielberg's'Minority Report', we may be well on our way. As significant advancements are being made in available processing power and deep learning, face recognition tech has taken off.

Computer technology has witnessed a number of moments of transformation. The invention of the IBM PC, the rise of the Windows Operating System and Intel processors ushered in a new era of personal computing and ensured that every average home and office had the opportunity of tapping into the vast possibilities of the Personal Computer. As the world was trying to grasp the full potentials of these amazing innovations, Sir Tim Berners-Lee ushered in the most transformative innovation in the history of mankind with his invention of the World Wide Web. The Web with its endless possibilities and immeasurable potentials, created new industries, transformed existing ones and changed the way we work, play and connect. To complete the chain of technology as we have it today, were the invention of the iPhone, the rise of Cloud Computing, IoT and the rise of Holographic Computing.

Ever since the election, talking heads have discussed how "fake news" could have tipped the election to Donald Trump by disseminating false stories favorable to him or unfavorable to Hillary Clinton. Social media users can spread these fake new stories far and wide, creating an aura of credibility which can make fake stories hard to distinguish from the truth. Social media websites like Facebook have begun to cooperate with human fact checkers to track fake news websites and notify users that a story is likely false, but it is impossible to trace every single news story as they pop up. There is just too much information on the internet for any human mind to process. So maybe an artificial intelligence (AI) can perform better. Facebook along with researchers and hackers are examining whether artificial intelligence and big data can help track down fake news stories faster than humans can.

Public Sector Agencies Must Adopt Emerging Technologies Like Machine Learning and Artificial Intelligence to Effectively Compete for Talent, Accenture Report Finds ARLINGTON, Va.; Feb. 2, 2017 – Public sector agencies must adopt emerging technologies – including machine learning, artificial intelligence and biometrics – to attract and retain more technically adept employees. This approach is critical to addressing a widening skills gap and strong competition from a better financed private sector, a new report from Accenture (NYSE: ACN) shows. The report, Emerging Technologies in Public Service, examines the adoption of emerging technologies across agencies with the most direct interaction with citizens or the greatest responsibility for citizen-facing services: health and social services, policing/justice, revenue, border services, administration and pensions / social security. As part of the report, Accenture surveyed nearly 800 public service technology professionals across nine countries to identify emerging technologies being implemented or piloted. These technologies include advanced analytics/ predictive modeling, the Internet of Things, intelligent process automation, video analytics, biometrics/ identity analytics, machine learning, and natural language processing/ generation.

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.

In the past human work was preferred over a machine's work because a human was more accurate than a machine. After all a human could look at all angles and make an informed decision, and a machine could not. But enter machine learning today, and a machine might be more useful than a human in shaping customer experiences. Today machine learning can help brands scale their engagement operations and provide increasingly relevant experiences. And the good news is now you don't have to be a software expert to use machine learning.

At Whetlab, a startup acquired by Twitter in 2015, the technique provided a better way of--wait for it--designing neural networks. Designing a neural network is a task of extreme trial and error. It's a difficult and time-consuming undertaking, but GPs and Bayesian optimization can help automate the task. As WhetLab founder and Harvard computer scientist Ryan Adams has said, the startup used "machine learning to improve machine learning." Neural networks can suffer from the "confident error" problem, and in identifying uncertainty, this kind of optimization can help address that problem.