New applications are driving demand for faster and more efficient vision processing. The Hot Chips conference, now in its 28th year, is known for announcements of "big iron" such as the Power and SPARC chips behind some of the world's fastest systems. But these days the demand for processing power is coming from new places. One of the big ones is vision processing, driven by the proliferation of cameras; new applications in cars, phones and all sorts of "things;" and the rapid progress in neural networks for object recognition. All of this takes a lot of horsepower, and at this week's conference, several companies talked about different ways to tackle it.

When the Federal Aviation Association issued its first formal rules governing the use of commercial drones, it should have included some privacy regulations, the Electronic Privacy Information Center (EPIC) is arguing in a lawsuit against the agency. The FAA has so far left privacy matters up to the National Telecommunications and Information Administration (NTIA). However, EPIC argues that, since Congress directed the FAA to develop "comprehensive" rules that "safely" integrate drones into US airspace, it's obligated to consider privacy issues. The advocacy group is asking the DC Circuit Court of Appeals to overrule the drone regulations and compel the FAA to conduct further proceedings. Earlier in the year, the NTIA issued drone privacy guidelines, which were developed with the help of privacy groups and businesses. However, they're completely voluntary guidelines, and some have argued they're too narrow in scope and plagued by loopholes.

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.

Since I was a young girl studying computer science and engineering in India, I've always been interested in exploring how science and technology can help solve real-world problems. In my IBM Research career that spans 20 years and counting, I've indulged my passion for problem-solving by building technologies that help businesses and organizations of all shapes and sizes overcome their challenges. I try to instill this fascination for problem-solving in my 10-year-old daughter, who recently attended a robotics summer camp. When she came home frustrated from the camp one day, she asked me, "Why should I care about artificial intelligence? What does A.I. have to do with me?"