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    #31
    Here's how Google is preparing Android for the AI-laden future

    The future of Android will be a lot smarter, thanks to new programming tools that Google unveiled on Wednesday. The company announced TensorFlow Lite, a version of its machine learning framework that’s designed to run on smartphones and other mobile devices, during the keynote address at its Google I/O developer conference.

    “TensorFlow Lite will leverage a new neural network API to tap into silicon-specific accelerators, and over time we expect to see [digital signal processing chips] specifically designed for neural network inference and training,” said Dave Burke, Google’s vice president of engineering for Android. “We think these new capabilities will help power a next generation of on-device speech processing, visual search, augmented reality, and more.”

    The Lite framework will be made a part of the open source TensorFlow project soon, and the neural network API will come to the next major release of Android later this year.
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      #32
      Google’s A.I. Program Rattles Chinese Go Master as It Wins Match

      HONG KONG — It’s all over for humanity — at least in the game of Go.

      For the second game in a row, a Google computer program called AlphaGo beat the world’s best player of what many consider the world’s most sophisticated board game. AlphaGo is scheduled to play its human opponent, the 19-year-old Chinese prodigy Ke Jie, one more time on Saturday in the best-of-three contest.

      But with a score of 2-0 heading into that final game, and earlier victories against other opponents already on the books, AlphaGo has proved its superiority.

      Discussing the contest afterward, Mr. Ke said a very human element got the better of him: his emotions. In the middle of the game, when he thought he might have had a chance at winning, he got too keyed up, he said.

      “I was very excited. I could feel my heart bumping,” Mr. Ke said after the contest, which took place in Wuzhen, near Shanghai. “Maybe because I was too excited I made some stupid moves.”
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        #33
        I have been seeing stories about this. Seems like AlphaGo wasn't too concerned about winning big, but more concerned about just winning. The style of play it used this time around was a lot different.

        Also heard that the livestream was cut off in China when it was clear that he was going to lose. If that's true, that's kind of a jerky thing to do.
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        “Just when you think humanity has found the limits of stupid, they go and ratchet up the standard by another notch.” - Bob

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          #34
          Apple 'Neural Engine' chip could power AI on iPhones

          Apple's focused on increasing the speed of every new mobile processor generation, most recently pairing its quad core A10 Fusion chips with its iPhone 7 and 7 Plus models last September. But to keep its devices competitive, Apple is building a secondary mobile processor dedicated to powering AI.

          Sources told Bloomberg that Apple is developing the chips to participate in two key areas of artificial intelligence: Augmented reality and self-driving cars. The tech titan's devices currently split AI tasks between two chips -- the main processor and a GPU -- but this new one, allegedly known internally as the Apple Neural Engine, has its own module dedicated to AI requests. Offloading those tasks should improve battery life, too.
          Unfortunately, it's unclear if the chip will come out this year. That puts Apple further behind Qualcomm's latest Snapdragon mobile chips, which already have a dedicated AI module, and Google's Tensor Processing Units available in its Cloud Platform to do AI heavy lifting.



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            #35
            SkyNet so close right now~♪

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              #36
              Originally posted by Gryphus View Post
              SkyNet so close right now~♪
              Quite.

              ARM's new mobile processors are built for AI on the go

              Of note to me:

              First up is the Cortex-A75 CPU core, which the company says can deliver laptop-level performance without burning through any more power than existing mobile processors. ARM is promising a 50 percent boost in performance compared to the older A73 core, which should lend itself well to machine learning processes that run right on your devices.

              Whoa...
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                #37
                The Benefits of Building an Artificial Brain

                In the mid-1940s, a few brilliant people drew up the basic blueprints of the computer age. They conceived a general-purpose machine based on a processing unit made up of specialized subunits and registers, which operated on stored instructions and data. Later inventions—transistors, integrated circuits, solid-state memory—would supercharge this concept into the greatest tool ever created by humankind.

                So here we are, with machines that can churn through tens of quadrillions of operations per second. We have voice-recognition-enabled assistants in our phones and homes. Computers routinely thrash us in our ancient games. And yet we still don’t have what we want: machines that can communicate easily with us, understand and anticipate our needs deeply and unerringly, and reliably navigate our world.

                Now, as Moore’s Law seems to be starting some sort of long goodbye, a couple of themes are dominating discussions of computing’s future. One centers on quantum computers and stupendous feats of decryption, genome analysis, and drug development. The other, more interesting vision is of machines that have something like human cognition. They will be our intellectual partners in solving some of the great medical, technical, and scientific problems confronting humanity. And their thinking may share some of the fantastic and maddening beauty, unpredictability, irrationality, intuition, obsessiveness, and creative ferment of our own.

                The next big leap in AI could come from warehouse robots

                Ask Geordie Rose and Suzanne Gildert, co-founders of the startup Kindred, about their company’s philosophy, and they’ll describe a bold vision of the future: machines with human-level intelligence. Rose says these will be perhaps the most transformative inventions in history — and they aren’t far away. More intriguing than this prediction is Kindred’s proposed path for achieving it. Unlike some of the most cash-flush corporations in Silicon Valley, Kindred is focusing not on chatbots or game-playing programs, but on automating physical robots.

                Gildert, a physicist who conceived Kindred in 2013 while working with Rose at quantum computing company D-Wave, thinks giving AI a physical body is the only way to make real progress toward a true thinking machine. “If you want to build intelligence that conceptually thinks in the same way a human does… it needs to have a similar sensory motor as humans do,” Gildert says. The trick to achieving this, she thinks, is to train robots by having them collaborate with humans in the physical world. Rose, who co-founded D-Wave in 1999, stepped back from his role as chief technology officer to work on Kindred with Gildert.
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                  #38
                  Machine intelligence hobby tutorial!

                  Experimenting With Machine Intelligence

                  Our objective is to build a robot that can learn on its own and constantly adapt to changes in its environment. The robot must have a well-defined goal so it can evaluate the effectiveness of its actions, thus providing a basis for modifying its own behavior.
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                    #39
                    A new machine learning tool could flag dangerous bacteria before they cause an outbreak

                    A new machine learning tool that can detect whether emerging strains of the bacterium, Salmonella are more likely to cause dangerous bloodstream infections rather than food poisoning has been developed. The tool, created by a scientist at the Wellcome Sanger Institute and her collaborators at the University of Otago, New Zealand and the Helmholtz Institute for RNA-based Infection Research, a site of the Helmholtz Centre for Infection Research, Germany, greatly speeds up the process for identifying the genetic changes underlying new invasive types of Salmonella that are of public health concern.

                    Reported today (8 May) in PLOS Genetics, the machine learning tool could be useful for flagging dangerous bacteria before they cause an outbreak, from hospital wards to a global scale.

                    As the cost of genomic sequencing falls, scientists around the world are using genetics to better understand the bacteria causing infections, how diseases spread, how bacteria gain resistance to drugs, and which strains of bacteria may cause outbreaks.
                    Meet TPU 3.0: Google teases world with latest math coprocessor for AI

                    Google IO The latest iteration of Google’s custom-designed number-crunching chip, version three of its Tensor Processing Unit (TPU), will dramatically cut the time needed to train machine learning systems, the Chocolate Factory has claimed.

                    Google CEO Sundar Pichai revealed the third version of the Google-crafted matrix math processor during his Google IO developer conference keynote, saying a pod of TPU 3.0s is eight times faster than a pod of its predecessor. In a separate session, Zak Stone, product manager for TensorFlow and Cloud TPUs, gave a slightly more deeper dive into the details.
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                      #40
                      DARPA has done interesting stuff before. Wonder what this will lead to...

                      On the threshold of a major jump in AI technology that can adapt to new circumstances

                      Machine learning (ML) and artificial intelligence (AI) systems have significantly advanced in recent years. However, they are currently limited to executing only those tasks they are specifically designed to perform and are unable to adapt when encountering situations outside their programming or training. DARPA’s Lifelong Learning Machines (L2M) program, drawing inspiration from biological systems, seeks to develop fundamentally new ML approaches that allow systems to adapt continually to new circumstances without forgetting previous learning.

                      The L2M research teams are now focusing their diverse expertise on understanding how a computational system can adapt to new circumstances in real time and without losing its previous knowledge. One group, the team at University of California, Irvine plans to study the dual memory architecture of the hippocampus and cortex. The team seeks to create an ML system capable of predicting potential outcomes by comparing inputs to existing memories, which should allow the system to become more adaptable while retaining previous learnings.
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                        #41
                        A few pieces here.

                        Intel's Loihi roadmap calls for its brain chips to be as 'smart' as a mouse by 2019

                        Intel said this week that a system based on its Loihi chip planned for 2019 will include the equivalent of 100 billion synapses, which is about the same brain complexity as a common mouse.

                        Last September, Intel introduced the world to Loihi, a chip designed for what Intel calls probabilistic computing. Intel sees probabilistic computing as an important step on the road to artificial intelligence.

                        Unlike a Core chip, which uses a sequential pipeline of instructions, Loihi is designed to mimic the way the brain works. The version of the Loihi chip that Intel introduced last year included 130,000 silicon “neurons” connected with 130 million “synapses,” the junctions that in humans connect the neurons within the brain.
                        And now... *whistles Twilight Zone theme*

                        Google’s AI is learning to navigate like humans

                        The company’s DeepMind artificial intelligence subsidiary has developed an AI that has learned how to navigate like a human being, the company announced in a blog post. Specifically, DeepMind’s AI has developed a system of spacial awareness that mimics human’s and other mammal’s grid cells–specific cells in the brain that allow for vector-based navigation, which allow us to calculate the direction and a distance to a location even if we’ve never traveled that route before. What’s most impressive about the AI’s mimicking of mammalian grid cells is that the AI did it on its own–it wasn’t programmed to mimic them.
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                          #42
                          I also saw that Google was able to call out objects for people with visual impairments. I am particularly interested in this as it's something I've wanted for years. Maybe with the DeepMind navigation, it might eventually be able to help me get places. It's easy to get paranoid about all of this stuff, but we can't forget that it can genuinely help people too.
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                          Commander Daryn SkyStrike
                          Ascenseon - The Avian Planet

                          “Just when you think humanity has found the limits of stupid, they go and ratchet up the standard by another notch.” - Bob

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