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Why Did Google Pay $400 Million for DeepMind?

Why Did Google Pay $400 Million for DeepMind?
How much are a dozen deep-learning researchers worth? Apparently, more than $400 million. This week, Google reportedly paid that much to acquire DeepMind Technologies, a startup based in London that had one of the biggest concentrations of researchers anywhere working on deep learning, a relatively new field of artificial intelligence research that aims to achieve tasks like recognizing faces in video or words in human speech (see “Deep Learning”). The acquisition, aimed at adding skilled experts rather than specific products, marks an acceleration in efforts by Google, Facebook, and other Internet firms to monopolize the biggest brains in artificial intelligence research. Companies like Google expect deep learning to help them create new types of products that can understand and learn from the images, text, and video clogging the Web. As advanced machine learning transitions from a primarily scientific pursuit to one with high industrial importance, Google’s bench is probably deepest. Related:  Big Data and data miningScience, Technology and Innovation to April 2014docs à examiner

Is Data Complexity Blinding Your IT Decision-Making? CIO — Is the complexity of your company's data making it difficult to make effective IT decisions? If so, you're not alone. Keeping the lights on and systems running while still finding the resources to innovate is a challenge for most IT organizations, and the growing complexity of data about IT environments is making that challenge nearly insurmountable for many. According to a new study by Forrester Research, commissioned by Data as a Service (DaaS) company BDNA (creator of the Technopedia repository of information on enterprise hardware and software), 73 percent of high-level IT decision makers cite the complexity of data as the largest challenge in making effective IT decisions in the next 12 months. There are two main drivers of the accelerating challenges around data complexity says BDNA CMO Mahesh Kumar: innovation and the Internet of Things. "Several years ago, outsourcing was going to reduce all the money we spend in IT," Kumar says. Continue Reading

Drones - the next big thing in cycle safety, or a case of too much blue sky thinking? Cyclodrone (picture credit Frog Design Mind blog) The product design firm that brought the world the Sony Walkman has unveiled a conceptual design of a drone that it says could help improve the safety of lone bike riders. Drones have attracted a lot of attention due to their use by the military as well as strong rumours, neither confirmed nor denied by the Metropolitan Police, that they were deployed above London during the Olympic Games in 2012. They were back in the headlines last month as a result of the news that is considering using them for deliveries. That’s despite the fact that the unmanned aircraft have not yet having been approved for civilian use in the United States, although Amazon CEO Jeff Bezos says it may be five years before they come into use. But in a post on its Design Mind blog, the consultancy Frog highlights several potential civilian uses, included mock-ups of how they could look in action.

How An Arcane Coding Method From 1970s Banking Software Could Save The Sanity Of Web Developers Everywhere Today’s web programmers grapple with problems that people in the early days never had to deal with. They’re building complex UIs, juggling a bunch of APIs, and running multiple processes at the same time. All of these tasks require mastering the flow of data between application components in real-time, something which even the most advanced developers struggle with. Why can’t things be easier? Dan Tocchini, CEO of a startup called The Grid, is part of a new generation of programmers who grew up struggling with complex multithreaded programming, asynchronous I/O, and nearly unlimited sources of data from hundreds of modern APIs. The Solution Is Not “More Programmers” In the 1970s, a Canadian bank serving 5 million customers implemented a brand new computer system using a new and little heard-of software development paradigm called “flow-based programming” (FBP). One of the reasons programmers didn’t warm to FBP is that it requires a new way of thinking about development. J.

Demis Hassabis, Founder of DeepMind Technologies and Artificial-Intelligence Wunderkind at Google, Wants Machines to Think Like Us Demis Hassabis started playing chess at age four and soon blossomed into a child prodigy. At age eight, success on the chessboard led him to ponder two questions that have obsessed him ever since: first, how does the brain learn to master complex tasks; and second, could computers ever do the same? Now 38, Hassabis puzzles over those questions for Google, having sold his little-known London-based startup, DeepMind, to the search company earlier this year for a reported 400 million pounds ($650 million at the time). Google snapped up DeepMind shortly after it demonstrated software capable of teaching itself to play classic video games to a super-human level (see “Is Google Cornering the Market on Deep Learning?”). Researchers are already looking for ways that DeepMind technology could improve some of Google’s existing products, such as search. DeepMind seeks to build artificial intelligence software that can learn when faced with almost any problem. Renaissance Man High Score Company Man

Big Data Security, Privacy Concerns Remain Unanswered Approaches to storing, managing, analyzing and mining Big Data are new, introducing security and privacy challenges within these processes. Big Data transmits and processes an individual's PII as part of a mass of data–millions to trillions of entries–flowing swiftly through new junctions, each with its own vulnerabilities. [Chomsky, Gellman talk Big Data at MIT conference] Deidentification masks PII, separating information that identifies someone from the rest of his or her data. Reidentification science, which pieces PII back together reattaching it to the individual thwarts deidentification approaches that would protect Big Data, making it unrealistic to believe that deidentification can really maintain the security and privacy of personal information in Big Data scenarios. Vulnerabilities, Exposure and Deidentification Enterprises manage Big Data using large, complex systems that must execute hand-offs from system to system. [Can we use Big Data to stop healthcare hacks?] Losing Faith

Glowing Plant | Seeds Does Your iPhone Have Free Will? — The Physics arXiv Blog If you’ve ever found your iPhone taking control of your life, there may be a good reason. It may think it has free will. That may not be quite as far-fetched as it sounds. Today, one leading scientist outlines a ‘Turing Test’ for free will and says that while simple devices such as thermostats cannot pass, more complex ones like iPhones might. The problem of free will is one of the great unsolved puzzles in science, not to mention philosophy, theology, jurisprudence and so on. This is not a question that is likely to be answered quickly or easily. There are two relatively new ideas that are particularly relevant. Today, we get an answer thanks to the work of Seth Lloyd, one of the world’s leading quantum mechanics and theorists, who is based at the Massachusetts Institute of Technology in Cambridge. He argues that there are clear mechanisms in computation that make the outcome of a given calculation unpredictable, especially to the person or object making it. Q1: Am I a decider?

Molecular Memory The brains of a computer is the semiconductor chip. Much of the progress over the last 35 years in making computers faster, smaller and cheaper has been a numbers game, squeezing ever more transistors and other electronic devices onto this postage-stamp-sized piece of silicon. Today’s PCs pack tens of millions of transistors onto a chip, each transistor as small as a few hundred nanometers (billionths of a meter). But continuing this miniaturization will not be easy-or cheap. So what if, instead of carving transistors and other microelectronic devices out of chunks of silicon, you used organic molecules? Nowhere have the advances been more impressive-or the ambitions greater-than at Hewlett-Packard Laboratories in Palo Alto, CA (see “Computing after Silicon,” TR September/October 1999). “We’re trying to reinvent the integrated circuit, with all its functions,” says Kuekes, a computer architect. The HP patent is one of the first to issue that covers a molecular electronic device.

Beyond Data Mining This article first appeared in IEEE Software magazine and is brought to you by InfoQ & IEEE Computer Society. The predictive modeling community applies data miners to artifacts from software projects. This work has been very successful-we now know how to build predictive models for software effects and defects and many other tasks such as learning 'developers' programming patterns (see the extended version of this article for more detail). That said, to truly impact the work of industrial practitioners, we need to change the predictive modeling community's focus. This article compares and contrasts the four kinds of miners shown in Figure 1: Algorithm miners explore tuning parameters in data mining algorithms. Note that algorithm and landscape mining are more research-focused activities that explore the miners' internal details. Algorithm Mining Sadly, that original aim seems to be forgotten. Landscape Mining Figure 1. W2 has two important features. Decision Mining Discussion Mining 1 K.

Glowing plants and DIY bio succeed on Kickstarter In the last week, over 3,000 people on Kickstarter ignored the fact it's next to impossible to keep a houseplant alive and backed the now fully-funded "Glowing Plants: Natural Lighting with no Electricity" campaign. The funds will be used to build upon existing technology and create a transgenic plant that has a soft blue-green glow to act as an electricity-free nightlight. Backer rewards, each glowing, include an arabidopsis plant, a rose plant, and arabidopsis seeds. We check in as the Glowing Plants team heads towards their first stretch goal and look at how this project is part of a bigger trend in DIY biology. But be warned: this is not your grandma's seed catalog. View all Arabidopsis thaliana is a small unassuming plant, but is as famous in science circles as any plant has a hope of achieving. The process is like building a custom hot rod. Some of the backer rewards also encourage this DIY biology ethic. Source: Glowing Plant, Kickstarter

The Man Who Would Teach Machines to Think - James Somers Douglas Hofstadter, the Pulitzer Prize–winning author of Gödel, Escher, Bach, thinks we've lost sight of what artificial intelligence really means. His stubborn quest to replicate the human mind. Greg Ruffing “It depends on what you mean by artificial intelligence.” Douglas Hofstadter is in a grocery store in Bloomington, Indiana, picking out salad ingredients. “If somebody meant by artificial intelligence the attempt to understand the mind, or to create something human-like, they might say—maybe they wouldn’t go this far—but they might say this is some of the only good work that’s ever been done.” Their operating premise is simple: the mind is a very unusual piece of software, and the best way to understand how a piece of software works is to write it yourself. The idea that changed Hofstadter’s existence, as he has explained over the years, came to him on the road, on a break from graduate school in particle physics. GEB, as the book became known, was a sensation. “I mean, who knows?”