The XGBoost library for gradient boosting uses is designed for efficient multi-core parallel processing. This allows it to efficiently use all of the CPU cores in your system when training. In this post you will discover the parallel processing capabilities of the XGBoost in Python. How to Best Tune Multithreading Support for XGBoost in Python Photo by Nicholas A. Tonelli, some rights reserved. XGBoost is the high performance implementation of gradient boosting that you can now access directly in Python.

Blaise Agüera y Arcas, principal scientist at Google, works with deep neural networks for machine perception and distributed learning. In this captivating demo, he shows how neural nets trained to recognize images can be run in reverse, to generate them. The results: spectacular, hallucinatory collages (and poems!) that defy categorization. "Perception and creativity are very intimately connected," Agüera y Arcas says. "Any creature, any being that is able to do perceptual acts is also able to create."

As someone who watches technology trends closely as part of my business, I have been thinking about the future impact of all the technology innovations and automation we are currently experiencing and on the cusp of achieving. Many of the headlines I read about these trends -- and even some I write -- predict some pretty negative consequences right along with the monumental achievements and improvements. While improvements in machine learning, artificial intelligence, big data, and robot automation could mean huge advances in medicine, science, commerce and human understanding, it's also undeniable that there will be consequences as well. These technological advances represent a significant challenge to capitalism. Together, they are poised to potentially create jobless growth and the paradox of an exponentially growing number of products, manufactured more and more efficiently, but with rising unemployment and underemployment, falling real wages and stagnant living standards.

For years, science-fiction moviemakers have been making us fear the bad things that artificially intelligent machines might do to their human creators. But for the next decade or two, our biggest concern is more likely to be that robots will take away our jobs or bump into us on the highway. Now five of the world's largest tech companies are trying to create a standard of ethics around the creation of artificial intelligence. While science fiction has focused on the existential threat of AI to humans, researchers at Google's parent company, Alphabet, and those from Amazon.com, Facebook, IBM and Microsoft have been meeting to discuss more tangible issues, such as the impact of AI on jobs, transportation and even warfare.

AI in the real world: Tech leaders consider practical issues.

Two cucumber farmers in Japan have received an unusual boost to their business after their son adapted Google's powerful artificial intelligence software to carry out the arduous task of sorting vegetables. Makoto Koike returned to his parent's cucumber farm in 2015 after working as a computer systems designer in the automobile industry. While helping out on the farm, Koike realized that one of the most time-consuming processes could be overcome through automation. Taking inspiration from Google's powerful artificial intelligence computer program AlphaGo, Koike set about designing a sorting system using a 35 Raspberry Pi 3 computer and Google's open source deep-learning platform TensorFlow. "Each cucumber has different color, shape, quality and freshness," Koike said.

Artificial intelligence (AI), deep learning, and neural networks represent incredibly exciting and powerful machine learning-based techniques used to solve many real-world problems. For a primer on machine learning, you may want to read this five-part series that I wrote. While human-like deductive reasoning, inference, and decision-making by a computer is still a long time away, there have been remarkable gains in the application of AI techniques and associated algorithms. The concepts discussed here are extremely technical, complex, and based on mathematics, statistics, probability theory, physics, signal processing, machine learning, computer science, psychology, linguistics, and neuroscience. That said, this article is not meant to provide such a technical treatment, but rather to explain these concepts at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.

Mark Zuckerberg made it his new year's goal to create Jarvis-like artificial intelligence for his home -- at this point it lets him manage his house and toasts his bread. But a German startup called BuddyGuard may have beaten him to the punch. The company recently launched an artificially intelligent security system called Flare, and brought it to IFA to show it off. Flare is part camera, part AI, part IoT accessory. BuddyGuard bills its product as "the first home security system powered by true artificial intelligence."

The voracious reader and self-proclaimed "learning nerd" is known to plow through at least one book every week, with a heavy emphasis on business, science and mathematics. And although the summer is (almost) over, his book menu offers plenty of brain fodder for book lovers to absorb in the fall and beyond. Some of Gates' favorite tomes range from the eclectic to the informative, including an 800-page science fiction novel and a 200-page nonfiction book on how moribund Japan can regain its economic mojo. Writing on his personal blog, gatesnotes, Gates said he hoped each selection would inspire readers to "go off the beaten path." "[These] books are simply ones that I loved, made me think in new ways and kept me up reading long past when I should have gone to sleep."

The odds that artificial intelligence will enslave or eliminate humankind within the next decade or so are thankfully slim. So concludes a major report from Stanford University on the social and economic implications of artificial intelligence. At the same time, however, the report concludes that AI looks certain to upend huge aspects of everyday life, from employment and education to transportation and entertainment. The analysis is significant because the public alarm over the impact of AI threatens to shape public policy and corporate decisions. It predicts that automated trucks, flying vehicles, and personal robots will be commonplace by 2030, but cautions that remaining technical obstacles will limit such technologies to certain niches.