The logo of Microsoft is pictured in Issy-les-Moulineaux, France, August 8, 2016. New York: In a major breakthrough in the field of speech recognition, Microsoft researchers have created a technology that accurately recognises the words in a conversation like humans do. The team from Microsoft Artificial Intelligence and Research reported a speech recognition system that makes the same or fewer errors than professional transcriptionists. The researchers reported a word error rate (WER) of 5.9 percent, down from the 6.3 percent WER the team reported just last month. The 5.9 percent error rate is about equal to that of people who were asked to transcribe the same conversation, and it's the lowest ever recorded against the industry standard "Switchboard" speech recognition task.

In 360 BC, Plato wrote, "Whatever comes into existence, always comes as a whole." In 2016, scientists are using the same philosophy to develop a new automation model for robotic systems. "No phenomenon exists entirely alone. Each is a part of other phenomena," wrote Hua Chen, an associate professor at Hohai University, Chanzhou Campus, with YangQuan Chen, a professor at the School of Engineering, University of California, Merced. In a paper published in IEEE/CAA Journal of Automatica Sinica (JAS), Chen and Chen proposed a new model for designing control systems used in automation.

How will AI shape the average North American city by 2030? A panel of experts assembled as part of a century-long study into the impact of AI thinks its effects will be profound. The One Hundred Year Study on Artificial Intelligence is the brainchild of Eric Horvitz, a computer scientist, former president of the Association for the Advancement of Artificial Intelligence, and managing director of Microsoft Research's main Redmond lab. Every five years a panel of experts will assess the current state of AI and its future directions. The first panel, comprised of experts in AI, law, political science, policy, and economics, was launched last fall and decided to frame their report around the impact AI will have on the average American city.

If you're reading this in the United States, there's a 50 percent chance that a photo of your face is in at least one database used in police facial-recognition systems. Police departments in nearly half of U.S. states can use facial-recognition software to compare surveillance images with databases of ID photos or mugshots. Some departments only use facial-recognition to confirm the identity of a suspect who's been detained; others continuously analyze footage from surveillance cameras to determine exactly who is walking by at any particular moment. Altogether, more than 117 million American adults are subject to face-scanning systems. These findings were published Tuesday in a report from Georgetown Law's Center for Privacy and Technology.

Researchers at Houston Methodist have developed an artificial intelligence (AI) software that reliably interprets mammograms, assisting doctors with a quick and accurate prediction of breast cancer risk. According to a new study published online Aug. 29 in Cancer, the computer software intuitively translates patient charts into diagnostic information at 30 times human speed and with 99 percent accuracy. "This software intelligently reviews millions of records in a short amount of time, enabling us to determine breast cancer risk more efficiently using a patient's mammogram. This has the potential to decrease unnecessary biopsies," said Stephen T. Wong, Ph.D., P.E., chair of the Department of Systems Medicine and Bioengineering at Houston Methodist Research Institute. The team led by Wong and Jenny C. Chang, M.D., director of the Houston Methodist Cancer Center used the AI software to evaluate mammograms and pathology reports of 500 breast cancer patients.

Getting ready to step into the self-driving Ford Fusion in Dearborn, MI. These technological advances, which are dependent on the machine learning branch of AI, rely on the data collected by car companies--data from real miles driven, such as with Tesla's Autopilot, data from simulations of autonomous driving, and data from test situations, such as Uber's driverless fleet in Pittsburgh. Big data, said Michael Cavaretta, director of analytics infrastructure at Ford Motor Company, means data that is "too big to easily handle within your computational resources." It's about looking at datasets with "high velocity, high volume and high variety," he said. And as computers have become more powerful and storage is cheaper, grappling with this data has become more difficult.

Pisa, 11-16 July We are pleased to announce the launch of the first edition of the International Summer School on'The Regulation of Robotics in Europe: Ethical, Legal and Economic Implications', which will be held in Pisa (Tuscany, Italy) from 11th to 16th July, 2016. This unique initiative, funded by the European Commission through the Jean Monnet Program, stems from the successful experience of the RoboLaw project (www.robolaw.eu) For further information please click HERE Bruxelles, September 24th, 2014 RoboLaw enters the European Parliament RoboLaw project coordinator Prof. Erica Palmerini and RoboLaw researcher Dr. Andrea Bertolini have been invited to present a paper on the main findings of the RoboLaw project at the workshop'Upcoming issues of EU law', organised by the JURI Committee of the European Parliament. The paper presented, entitled'REGULATING ROBOTICS: A CHALLENGE FOR EUROPE' is available here Bruxelles, September 24th, 2014 RoboLaw meets NEERI at the European Commission Prof. Erica Plamerni and Dr. Andrea Bertolini will participate in a meeting with the coordinator and the researchers of NEERI - Neuro-Enhancement: Responsible Research and Innovation, an FP7 project about human enhancement. The objective of the meeting is to discuss possible future collaborations and networking.

NEW YORK: In a major breakthrough in the field of speech recognition, Microsoft researchers have created a technology that accurately recognises the words in a conversation like humans do. The team from Microsoft Artificial Intelligence and Research reported a speech recognition system that makes the same or fewer errors than professional transcriptionists. The researchers reported a word error rate (WER) of 5.9 percent, down from the 6.3 percent WER the team reported just last month. The 5.9 percent error rate is about equal to that of people who were asked to transcribe the same conversation, and it's the lowest ever recorded against the industry standard "Switchboard" speech recognition task. This is an historic achievement," said Xuedong Huang, the company's chief speech scientist in a Microsoft blog post. The milestone means that, for the first time, a computer can recognise the words in a conversation as well as a person would. In doing so, the team has beat a goal they set less than a year ago - and greatly exceeded everyone else's expectations as well. "Even five years ago, I wouldn't have thought we could have achieved this.

What is consciousness, and how does it emerge from inanimate matter? After all, the atoms that constitute the brain -- the birthplace of consciousness -- are the same as the atoms that make up the chair you are currently sitting on. Why are we sentient and self-aware even though a multitude of inanimate objects around us are not? At what point do physical entities -- neurons, in this case -- give rise to something as abstract as consciousness? These are the questions that have, for the better part of the last two decades, occupied some of the greatest minds in physics, cognitive science and neuroscience. A team of scientists in France and Canada has now come up with an intriguing solution to the problem of consciousness.

Microsoft on Tuesday said that its researchers have "made a major breakthrough in speech recognition." In a paper [PDF] published a day earlier, Microsoft machine learning researchers describe how they developed an automated system that can recognize recorded speech as well as a professional transcriptionist. Using the NIST 2000 dataset of recorded calls, Microsoft's software performed slightly (0.4 per cent) better than the error rate the company attributes to professional transcriptionists (5.9 per cent) for the Switchboard portion of the data, in which strangers discuss a specified topic. It saw a similarly narrow margin of success with the CallHome portion of the data – in which family members converse without guidelines – where the human transcription error rate was 11.3 per cent. A month ago, Microsoft's researchers reported that their software had achieved a 6.3 per cent word error rate.