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.

Artificial Intelligence(AI) is gonna drive the world. In this process, network analysis is gonna play a big role. Slowly we are moving from traditional data analytics to the most charming data science in a way that we are moving from traditional marketing to digital marketing.. As a result of it, companies become smarter in catering to the needs of their customers, in predicting their sales volume, resource needs, the next problems, the right recommendations and in automating many manual processes. Moreover, so-far-piled up data suddenly become an asset for the companies for a reason the data science is gonna find the hidden insights and patterns.

This story was delivered to BI Intelligence "E-Commerce Briefing" subscribers. To learn more and subscribe, please click here. The acquisition is part of the e-commerce company's structured data push for sellers. Structured data is eBay's standard way of categorizing and displaying products for sale on its marketplace. Utilizing SalesPredict's data analysis and machine learning skills, eBay will be able to better refine its search functions in order to serve the right products to prospective shoppers.

Deploying the most innovative technologies alone will not ensure Singapore can succeed in its smart nation ambition, as this will require a population that is willing to embrace change in the way it interacts with its government. Since the launch of its smart nation initiative in 2014, the Singapore government has been rolling out various pilots and programmes to put in place the supporting infrastructure and systems. These centred around key objectives, among others, to enable safer and greener urban living, provide more transport options, facilitate better healthcare, and deliver more responsive public services and citizen engagement. Several initiatives had focused on a range of technologies including data analytics, Internet of Things (IoT), and cloud computing. Microsoft earlier this week announced it was working with the Singapore government to explore the use of machine learning and chatbots to deliver more interactive online citizen services.

The art and science of training neural networks from large data sets in order to make predictions or classifications has experienced a major transition over the past several years. Through popular and growing interest from scientists and engineers, this field of data analysis has come to be called deep learning. Put succinctly, deep learning is the ability of machine learning algorithms to acquire feature hierarchies from data and then persist those features within multiple non-linear layers which comprise the machine's learning center, or neural network. Two years ago, questions were mainly about what deep learning is, and how it might be applied to problems in science, engineering, and finance. Over the past year, however, the climate of interest has changed from a curiosity about what deep learning is, and into a focus on acquiring hardware and software in order to apply deep learning frameworks to specific problems across a wide range of disciplines.

In this example we compare the various initialization strategies for K-means in terms of runtime and quality of the results. As the ground truth is known here, we also apply different cluster quality metrics to judge the goodness of fit of the cluster labels to the ground truth.