Computer processor maker Advanced Micro Devices (AMD) announced that Google will start using its compute accelerators on its cloud platform. Google plans to start rolling out the AMD hardware in 2017. It will use AMD's single-precision dual GPU compute accelerators, Radeon-based AMD FirePro S9300 x2 Server GPUs, to help accelerate Google Compute Engine and Google Cloud Machine Learning services. The GPUs can handle highly parallel calculations, including complex medical and financial simulations, seismic and subsurface exploration, machine learning, video rendering and transcoding, and scientific analysis. "Google is building up its GPU-based infrastructure, and they want to ensure they offer AMD's architecture," said Raja Koduri, senior vice president and chief architect at AMD, in an interview with Forbes.

We propose a lifelong learning system that has the ability to reuse and transfer knowledge from one task to another while efficiently retaining the previously learned knowledgebase. Knowledge is transferred by learning reusable skills to solve tasks in Minecraft, a popular video game which is an unsolved and high-dimensional lifelong learning problem. These reusable skills, which we refer to as Deep Skill Networks, are then incorporated into our novel Hierarchical Deep Reinforcement Learning Network (H-DRLN) architecture using two techniques: (1) a deep skill array and (2) skill distillation, our novel variation of policy distillation (Rusu et al. 2015) for learning skills. Skill distillation enables the H-DRLN to efficiently retain knowledge and therefore scale in lifelong learning, by accumulating knowledge and encapsulating multiple reusable skills into a single distilled network. The H-DRLN exhibits superior performance and lower learning sample complexity compared to the regular Deep Q Network (Mnih et al. 2015) in sub-domains of Minecraft.

There are several ways to start a career in ML since it all depends on where you are right now: Are you a CS undergrad student just about to graduate? Do you have a PhD in some science (e.g. Have you been working as a software engineer or analyst for years? Where you are right now will determine what is your best path forward. Learn some basics of ML (see My answer to Machine Learning: How do I learn machine learning?

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These are the people who qualify for membership in Mensa, an exclusive international society open only to people who score at or above the 98th percentile on an IQ or other standardized intelligence test. Mensa's mission remains the same as when it was founded in Oxford, England, in 1946: To identify and nurture human intelligence for humanity's benefit, to foster research in the nature of intelligence, and to provide social and other opportunities for its members. Nautilus spoke with five present and former members of the society: Richard Hunter, a retired finance director at a drinks distributor; journalist Jack Williams; Bikram Rana, a director at a business consulting firm; LaRae Bakerink, a business consultant; and clinical hypnotist John Sheehan. Together, they reflect on the meaning of genius, whether it can be measured, and what IQ has to do with it. If you pass that test, all it proves is that you have a certain IQ. That is not the same as making you an intelligent person, never mind a genius.

The cars are old and beat up, there are no timers or cheering crowds, and the California sun is nowhere to be seen. Instead, a brown murkiness hangs over the entire world, lending it an eerie and oppressive quality. This is a driving game inspired, not by long pleasure drives along some Pacific highway, but by a childhood spent living in and eventually fleeing the Soviet bloc. He had come home from kindergarten and asked his mother if she would hang out the Soviet flag for Labour Day – a "tradition" enforced by the Communist Party of Czechoslovakia. "When she told me she would not, I told her I would have to report that to my kindergarten educators because they asked us to," Švadlena says.

Not a day goes by when I don't hear another artificial intelligence horror case. There's something called artificial intelligence coming up. This big unknown is personified with a picture of the Terminator. If evoking more of a modern and less of a killer machine image is desired, the protagonist in Ex Machina (although no less scary) is selected. The audience is really interested now.

We just finished our Lean LaunchPad class at UC Berkeley's engineering school where many of the teams embedded machine learning technology into their products. It struck me as I watched the teams try to find how their technology would solve real customer problems, is that machine learning is following a similar pattern of previous technical infrastructure innovations. Early entrants get sold to corporate acquirers at inflated prices for their teams, their technology, and their tools. Later entrants who miss that wave have to build real products that people want to buy. I've lived through several technology infrastructure waves; the Unix business, the first AI and VR waves in the 1980's, the workstation wave, multimedia wave, the first internet wave.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.

Write your first R code, and discover vectors, matrices, data frames and lists. Write your first R code, and discover vectors, matrices, data frames and lists. Anytime you see a program dominated by ANOVA, t-tests, linear regression, and generally speaking, stuff published in any statistics 101 textbook dating back to 1930 (when computers did not exist), you are not dealing with actual data science. While it is true that data science has many flavors and does involve a bit of old-fashioned statistical science, most of the statistical theory behind data science has been entirely rewritten in the last 10 years, and in many occasions, invented from scratch to solve big data problems. You can find the real stuff for instance in Dr. Granville's Wiley book and his upcoming Data Science 2.0 book (for free), as well as in DSC's data science research lab.