As it pulls the plug on iPhone jacks, Apple is making what appears to be a concerted move into machine learning as it seeks to upgrade its next operating system to improve its Siri virtual assistant along with emerging machine learning applications expected to find their way into other Apple devices. Apple (NASDAQ:AAPL) watchers point to last month's acquisition of machine learning startup Turi for about 200 million as another sign that the consumer electronics giants is laying the groundwork for a machine learning push. The Seattle-based startup's machine learning platform focuses on rapid development of real-time services and applications based on embedded machine learning models. Turi is seen as a natural fit for Apple's machine learning push since its application toolkits based on the Python programming language are designed to simplify development of machine learning models that can be embedded into applications and quickly scaled.

Michael Hsieh is the president of Fung Capital USA. Sometimes one must look to the past to define the future. Once upon a time the milkman delivered milk directly to your home. The Avon and Tupperware ladies had private parties in the comfort of a neighbor's house. The Hoover salesperson demonstrated the vacuum cleaner on your living room carpet.

Mathematician and physicist Freeman Dyson has had a career as varied as it has been successful. A former professor of physics at Princeton's Institute for Advanced Study, he has worked on the unification of the three versions of quantum electrodynamics invented by Richard Feynman, nuclear reactors, solid-state physics, ferromagnetism, astrophysics, biology, and the application of useful and elegant math problems. One of his ideas, the Dyson Sphere, was featured in a "Star Trek" episode. Today, Dyson frequently writes about science and technology's relationship to ethics and social issues. Business Insider sat down with him and talked about math, war, the human brain, the education system, and the Orion Project. This interview has been edited for clarity and length. Elena Holodny: Who has most inspired you in either math or science? Dick Feynman ... he has now become famous, to my great joy, because when I knew him he was completely unknown. But I recognized him as being something special. He was only for a short time at Cornell when I was a student and he was a young professor. So I didn't work with him, but I just sat at his feet, literally, and listened to him talk. He was a clown, of course, and also a genius. It was a good combination.

When will iOS 10 be released in the UK? What are iOS 10's best new features, and what are the differences between iOS 10 and iOS 9? Which iPhones, iPads and iPod touch models can run iOS 10, and is it possible to install iOS 10 right now? It showcased the software as it had done at WWDC in June. We've got all the details about iOS 10 that you need to know: all of its best and most interesting features, how to install it, and a list of the iPads and iPhones that can run it. Since then Apple has released a series of beta versions of iOS 10, and the final public version will roll out on 13 September to iPhone 5 and later and the iPad mini 2, iPad 4, iPad Air and later generations. In this article you'll find: the 10 best new features in iOS 10; when iOS 10 will be released to the public, and how to download it ahead of the official launch; a list of iPads and iPhones that can run iOS 10; and everything else you need to know about iOS 10 and its imminent launch. Updated, 9 September 2016, with the news that the iOS 10 final release candidate is being rolled out; on 7 September, following 13 September release date; on 31 August with news of Apple's newly announced press event on 7 September; on 24 August with news of iOS 10 beta 7; and on 23 August with iOS 10 release date rumours courtesy of Barbra Streisand Update, 9 September 2016: Apple has notified all members of its iOS beta program that the final release candidate is being rolled out today. "We are pleased to give you access to the iOS 10 and macOS Sierra final release candidates," says Apple, in an email. "We will be shipping the final versions to customers worldwide in the coming weeks and appreciate you trying out the releases and providing feedback. You can install the iOS 10 GM candidate from Software Update."

Robots and drones have already started to quietly transform many aspects of agriculture. And now a new report is predicting the agricultural robotics industry, now serving a 3 billion market, will grow to 10 billion by 2022. The report, by IDTechEx Research in Britain, is called Agricultural Robots and Drones 2016-2026: Technologies, Markets, and Players. It analyses how robotic market and technology developments will change agriculture, enabling ultra-precision farming and helping address key global challenges. It describes how robotic technology will enter into different aspects of agriculture, how it will change the way farming is done and transform its value chain, how it becomes the future of agrochemicals business and modifies the way we design agricultural machinery.

Google's DeepMind unit, which is working to develop super-intelligent computers, has created a system for machine-generated speech that it says outperforms existing technology by 50 percent. U.K.-based DeepMind, which Google acquired for about 400 million pounds ( 533 million) in 2014, developed an artificial intelligence called WaveNet that can mimic human speech by learning how to form the individual sound waves a human voice creates, it said in a blog post Friday. In blind tests for U.S. English and Mandarin Chinese, human listeners found WaveNet-generated speech sounded more natural than that created with any of Google's existing text-to-speech programs, which are based on different technologies. WaveNet still underperformed recordings of actual human speech. Many computer-generated speech programs work by using a large data set of short recordings of a single human speaker and then combining these speech fragments to form new words.

Germany is gearing up to lay down the ethical foundations for self-driving cars, banning A.I. from making decisions that could harm one group of people over another. The country's transport minister has outlined the basis for future legal guidelines for driverless vehicles, rules that echo Isaac Asimov's three laws of robotics, which manufacturers will be expected to work towards ahead of formal legalization. Dobrindt has created an ethics commission to work out the specifics in terms of regulation, but the above rules will serve as a starting point for future laws. The third rule may seem to suggest that the manufacturer cannot depend on the driver stepping in during an emergency, but Dobrindt indicated that drivers will be expected to have a basic awareness at all times. In practice, this will likely mean sleeping at the wheel is forbidden, but reading a book is allowed.

This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.

We have covered Manopt before several times ( tag Manopt). It now comes to Python. From the paper: Further successful applications of optimization on manifolds include matrix completion tasks (Vandereycken, 2013; Boumal and Absil, 2015), robust PCA (Podosinnikova et al., 2014), dimension reduction for independent component analysis (ICA) (Theis et al., 2009), kernel ICA (Shen et al., 2007) and similarity learning (Shalit et al., 2012). Many more applications to machine learning and other elds exist. While a full survey on the usefulness of these methods is well beyond the scope of this manuscript, we highlight that at the time of writing, a search for the term \manifold optimization" on the IEEE Xplore Digital Library lists 1065 results; the Manopt toolbox itself is referenced in 90 papers indexed by Google Scholar.

Songbirds provide a model system that neuroscientists use to understand how the brain learns and controls speech and similar skills. Much like infants learning to speak from their parents, songbirds learn their song from a tutor and practice it millions of times before reaching maturity. Also like humans, songbirds have evolved special brain regions for learning and producing their vocalizations. These newly-evolved brain regions in songbirds, known as the song system, are found within broader brain areas shared by birds and humans across evolution. So by studying how the song system works, we can learn about our own brains.