Apple's biggest move was to open up the talking iPhone assistant to third-party developers for inclusion in their apps, paving the way for users to hail a ride from Uber or send a message with Tencent's WeChat using voice commands. Experts in artificial intelligence applauded the move as an important step forward, in part because the more people use an artificial intelligence system, the better it becomes. But some wondered why Apple had not made Siri an open platform much sooner, noting that competing products including Amazon.com's Alexa, Microsoft's Cortana and the Google app are already open to developers. "Is it too little too late?" "Siri is five years old and still trying to learn how to play well with others."

The International Symposium on Biomedical Imaging set the challenge between October 2015 to April 2016 to encourage research into identifying breast cancer by computers rather than by pathologists. Since the nineteenth century, the primary tool used to identify cells has always been the microscope but the report, by the Harvard team, identified many problems with this system. These included a lack of standardization across the board, diagnosis errors and the time it takes for pathologists to manually load millions of slides each year. Utilisting deep learning, and feeding the machine hundreds of slides showing both cancerous and non-cancerous lymph nodes, scientists were able to train AI to pick out hazardous cells. Using this technique they were able to make the AI accurate in 92 per cent of diagnosis and decrease the human rate of error by 85 per cent.

Text Mining is a general catch-all for a range of techniques for extracting information from text strings. Being able to extract, clean and summarize text data is a key ability for any Data Scientist. The following blog aims to highlight some of the process steps I use to clean text data as well as some summarization methods. To illustrate some of the approaches to text mining I am going to use the full text of 1984 by George Orwell. This data was extracted from msxnet.org/orwell with analysis carried out in R.

Ability to drive technical development and prototyping in a fast-paced startup-like environment. Strong understanding and ability to apply advanced mathematical concepts to solve real world problems. The Machine Learning Data Scientist will come from a very strong background in mathematics, applied mathematics, statistics and computer science (at least MA/MSc level, Engineer school/PhD from university a plus). A post-graduate degree in Machine Learning, Artificial Intelligence or a related technical field is a strong plus (any ML background will be considered). Global Markets Labs is a spin-off quantitative research team which mandate is to build the next generation of Data Intelligence and language understanding products used in the Banks Global Markets. This small team works on projects using the latest techniques in Artificial Intelligence, Datamodelling and Natural Language Understanding.

Machine learning is infiltrating many industries. Marketers are using complex data algorithms to target customers based on their behaviours, while urban planning firms are creating better transport systems, and health organisations are detecting diseases earlier. Last year, Amazon professor of machine learning at the University of Washington, Carlos Guestrin, said that in the next five years, every successful breakthrough app will use these methods at its core. But in the highly complex field of cancer, it's a more laborious and challenging task, according to professor Mathukumalli Vidayasagar, a US-based control theorist who has been working with machine learning methods since the 1990s. Vidayasagar is a Fellow of the Royal Society at the University of Texas and keynote speaker at the University of Melbourne's'Thinking Machines in the Physical World' conference yesterday.

The Air Force by the end of 2016 will have a beta version of a website that serves as an artificial intelligence (AI)-driven contracting officer, according to a key official.Air Force Assistant Secretary for Acquisitions Camron Gorguinpour said Friday the project,… You must be logged in as a subscriber to view this page. Please log in below to access the content. If you are already a Defense Daily subscriber or registered user, login here.

That's not much of a surprise, since the online message board is all about the ins and outs of erotic dolls, as in the kind men have sex with. Some regulars use the site to trade tips on gel butt implants. Others complain about the pubic hair of one doll or the breasts of another. Nukeno, however, uses it to tell the crowd what makes him happy: Nele and Kiko, his two dolls. "Perhaps I have been alone for too long," writes the self-described 34-year-old from Germany.

The PyImageSearch blog has grown a lot since I published that first post back in January 2014. It's been an incredible journey and I have you, the reader, to thank for all the support over the past 2.5 years. However, due to the whirlwind growth of PyImageSearch, I now receive 100 emails per day -- and it's gotten to the point where I can't keep up with them all. Believe it or not, I have been the only employee of PyImageSearch for the past 2.5 years -- and I've personally answered each and every email I've received during that time. Interacting with you is honestly one of the highlights of my day.

Last week I discussed the format of the FUSE tech summit we'll be holding in Philly September 12-14. One key component of FUSE is the forward-looking general sessions, where media technology experts and peers at media and publishing companies will share high-level insights and practical use cases. Below are some highlights from the program. See if you qualify here (or pass along to a colleague you think should attend.) This keynote session will look at the state of the publishing industry and the many challenges CDOs, CMOs, and CTOs are facing. In this session, FUSE Conference Chair Jeffrey Litvack will review the market dynamics that are affecting our industry and the new types of investment that will be needed in marketing, content creation, and ad tech to provide growth.

Helen of Troy may have had the face that launched a thousand ships, but even the best facial recognition algorithms may have had trouble finding her face in a crowd of one million strangers. The first benchmark test based on one million faces has shown how facial recognition algorithms from Google and other research groups around the world can still fall short in accurately identifying and verifying faces. Facial recognition algorithms that had previously performed with more than 95 percent accuracy on a popular benchmark test involving 13,000 faces saw significant drops in accuracy when faced with the new MegaFace Challenge involving one million faces. The best performer on one test, Google's FaceNet algorithm, dropped from near-perfect accuracy on five-figure datasets to 75 percent on the million-face test. Other top algorithms dropped from above 90-percent accuracy on the small datasets to below 60 percent on the MegaFace Challenge.