As a charitable service-based nonprofit organization (NPO) coordinating individuals, businesses, academia and governments with interests in High Technology, Big Data and Cybersecurity, we bridge the global digital divide by providing supercomputing access, applied research, training, tools and other digital incentives "to empower the underserved and disadvantaged."

AI will kill jobs in India, Are government policies a viable solution? Dissident artist Ai Weiwei says work was pulled from Yinchuan Biennale due to'political sensitivity' Apple claims there's a lot of "hidden" AI already in iOS

The acquisition this month by ride-hailing company Didi Chuxing Technology of Uber Technologies' China operation was a reminder of how strong Chinese tech companies have become domestically at outmaneuvering foreign rivals.

Jefferson Graham weighs in on the five years of the Tim Cook era at Apple, and how they compare to the previous 5 under the late Steve Jobs on #TalkingTech. LOS ANGELES -- It's been five years since Tim Cook took over as Apple CEO from a gravely ill Steve Jobs. His report card is a contradiction of Apple's still-impressive financial strength with signs innovation has slowed. Since Cook was named CEO on Aug. 24, 2011, Apple's stock price and revenue have doubled. Its net income has surged 84%.

While much of the work at Baidu we have focused on this year has centered on the Chinese search giant's deep learning initiatives, many other critical, albeit less bleeding edge applications present true big data challenges. As Baidu's Jian Ouyang detailed this week at the Hot Chips conference, Baidu sits on over an exabyte of data, processes around 100 petabytes per day, updates 10 billion webpages daily, and handles over a petabyte of log updates every 24 hours. These numbers are on par with Google and as one might imagine, it takes a Google-like approach to problem solving at scale to get around potential bottlenecks. Just as we have described Google looking for any way possible to beat Moore's Law, Baidu is on the same quest. While the exciting, sexy machine learning work is fascinating, acceleration of the core mission-critical elements of the business is as well--because it has to be.

This is a blog series in several parts -- where I describe my experiences and go deep into the reasons behind my choices. In Part 1, I discussed the pros and cons of different symbolic frameworks, and my reasons for choosing Theano (with Lasagne) as my platform of choice. Part 2 of this blog series is based on my upcoming talk at The Data Science Conference, 2016. Here in Part 2, I describe Deep Convolutional Neural Networks (DCNNs) and how Transfer learning and Fine-tuning helps better the training process for domain specific images. Please feel free to email me at [email protected] if you have questions.

Cellular Neural Networks (CNN) [wikipedia] [paper] are a parallel computing paradigm that was first proposed in 1988. Cellular neural networks are similar to neural networks, with the difference that communication is allowed only between neighboring units. Image Processing is one of its applications. CNN processors were designed to perform image processing; specifically, the original application of CNN processors was to perform real-time ultra-high frame-rate ( 10,000 frame/s) processing unachievable by digital processors. This python library is the implementation of CNN for the application of Image Processing.

Steven Levy has published an in-depth article about Apple's artificial intelligence and machine learning efforts, after meeting with senior executives Craig Federighi, Eddy Cue, Phil Schiller, and two Siri scientists at the company's headquarters. Apple provided Levy with a closer look at how machine learning is deeply integrated into Apple software and services, led by Siri, which the article reveals has been powered by a neural-net based system since 2014. Apple said the backend change greatly improved the personal assistant's accuracy. "This was one of those things where the jump was so significant that you do the test again to make sure that somebody didn't drop a decimal place," says Eddy Cue, Apple's senior vice president of internet software and services.Alex Acero, who leads the Siri speech team at Apple, said Siri's error rate has been lowered by more than a factor of two in many cases. "The error rate has been cut by a factor of two in all the languages, more than a factor of two in many cases," says Acero. "That's mostly due to deep learning and the way we have optimized it -- not just the algorithm itself but in the context of the whole end-to-end product."Acero

Select the Categorical option to specify that the values in the selected columns should be treated as categories, not as results, scores, labels, or other values. As a result, the actual data values will not be changed, but machine learning algorithms will handle the data differently. For example, suppose you have a column containing values of 0 and 1. If you know that those numbers actually represent the coding for some binary variable (such as Smoker vs. Nonsmoker), you should flag them as Categorical. As a result, other values in the table could then be categorized by using the binary variable.

Computers are core to modern life. They encrypt our digital information; they help control our cars and planes; they allow people to collaborate across the globe nearly instantly. Computer science explores the theory behind these mechanisms and the practical details needed to build them.