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  • #21

    Re: AI Megathread

    What's everyone's thoughts on how AI will impact the economy?
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    • #22

      Re: AI Megathread

      This is older, but still interesting.

      Quantifying and defining hard versus soft takeoff of AGI

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      • #23

        Re: AI Megathread

        Google's artificial intelligence arm breaks into healthcare

        DeepMind, Google's artificial intelligence arm, has plans to break into healthcare via a new partnership with Imperial College London and the Royal Free London National Health Services Foundation Trust, according to a BloombergBusiness report.


        The company's new division, DeepMind Health, includes physician staff members to help guide research and development for smart medical devices and other healthcare technology. One of those physicians is DominicKing, MD, co-founder of U.K.-based startup Hark, which was purchased by DeepMind.
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        • #24

          Re: AI Megathread

          Researchers Work to Make Programming Robots Less Complicated

          Researchers at Keio University in Japan, are working on a project called PRINTEPS, a framework for creating practical applications of artificial intelligence, and putting it to practical use in society. The project's head is Takahira Yamaguchi, in the Department of Administration Engineering.

          Recently, humanoid robots like Pepper have made their debut in society, but developing software for robots takes a lot of time. So, the aim of the project is to construct a platform that enables robot software development to be done easily.

          PRINTEPS (PRactical INTElligent aPplicationS) is a ROS-based total intelligent application development platform that integrates five types of sub systems (knowledge based reasoning systems, speech dialogue systems, human and environment sensing systems, and machine learning systems). PRINTEPS supports end users to participate in AI applications design (user participation design) and to develop applications easily (within hours to days) by combining software modules.
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          • #25

            Re: AI Megathread

            Originally posted by macrorufus View Post
            I worry we shall see more Luddite behaviour in the future as a result of the digitalisation of jobs (and also the use of robotics)
            I wouldn't be surprised. 12,000 years of agriculture, nearly half a millennium of Protestant work ethic... it's going to be a big shock to a lot of people.

            And... here I am replying to a year old comment.
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            • #26

              Re: AI Megathread

              With that said, a news dump.

              Zukaro might like that this first piece of news is being told. Not sure how many people will listen to it though...

              Artificial intelligence won't destroy the human race anytime soon

              The Allen Institute for Artificial Intelligence (AI2), funded by billionaire Paul Allen's, is developing projects like an AI-based search engine for scientific papers and a system to extract "visual knowledge" from images and videos. According to Scientific American, another goal of AI2 is "to counter messages perpetuated by Hollywood and even other researchers that AI could menace the human race." SciAm's Larry Greenemeier interviewed AI2 CEO and computer scientist Oren Etzioni:

              Consider the next story along with the possibility of better quantum computers sometime in the next 2-5 years. Better medicines, incredible new materials, faster advances in manufacturing technology, IMPROVING AI SYSTEMS THEMSELVES. Yeah...

              Iris AI drastically expedites research through the power of artificial intelligence

              There are more than 30 million research papers out there, and more than 3,000 papers are published every day. Put simply, you haven’t a chance in hell to read all of them. So what’s a poor researcher to do when set a challenge in a brand new field of research? Once the wave of blind panic and urge to drink copious amounts of gin has dissipated, you reach for a technology solution. Iris believes it has just the thing. The company launched a public betato show off its technology this week.


              “We’re of course really excited with the first results,” says Anita Schjøll Brede, the company’s CEO. “But to be honest we’re running a marathon here. Our ultimate goal is an AI Scientist and we’re not done until we get there!”

              And now for some deepmind only stuff.


              Google’s ‘DeepMind’ AI platform can now learn without human input

              DeepMind is now capable of teaching itself based on information it already possesses.


              In a significant step forward for artificial intelligence, Alphabet’s hybrid system — called a Differential Neural Computer (DNC) — uses the existing data storage capacity of conventional computers while pairing it with smart AI and a neural net capable of quickly parsing it.

              “These models can learn from examples like neural networks, but they can also store complex data like computers,” wrote DeepMind researchers Alexander Graves and Greg Wayne.

              Technological unemployment. Here we come?


              DeepMind boffins are trying to help robots escape The Matrix and learn for themselves in the real world

              Google DeepMind is trying to teach machines human-level motor control using progressive neural networks – so that the robots can learn new skills on-the-go in the real world.


              The idea is to build droids that can constantly learn and improve themselves, all by themselves, from their own surroundings rather than rely on lab-built AI models created in simulations. Wouldn't it be great to have machines that each learn as individuals rather than all have a common copy of an updated model uploaded to them from the lab?


              And now for something completely different (read: "mundane" )


              Five real-life use cases for Google DeepMind’s machine learning systems

              DeepMind has attracted mixed headlines since Google paid £400 million for the artificial intelligence (AI) startup in January 2014. When an AI system built by DeepMind won a series of Go matches against the world's best player of the Chinese board game, the possibilities of the technology inspired wonder. But that was tempered by criticisms of its controversial access to personal health records, with concerns raised over data privacy.


              From their lab in Kings Cross, London, 140 DeepMind researchers develop software systems capable of independent learning through AI algorithms. DeepMind is working with a small number of organisations to implement its technology so far, but there are already some intriguing real-life examples emerging.

              News seems to be coming in at a faster pace than ever. So now for bets: soft takeoff or hard takeoff?

              Just kidding... mostly...
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              • #27

                Re: AI Megathread

                Well, that's basically the story I'm working on right now.
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                • #28

                  Re: AI Megathread

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                  • #29

                    Re: AI Megathread

                    Software Dreams Up New Molecules in Quest for Wonder Drugs


                    What do you get if you cross aspirin with ibuprofen? Harvard chemistry professor Alán Aspuru-Guzik isn’t sure, but he’s trained software that could give him an answer by suggesting a molecular structure that combines properties of both drugs.

                    The AI program could help the search for new drug compounds. Pharmaceutical research tends to rely on software that exhaustively crawls through giant pools of candidate molecules using rules written by chemists, and simulations that try to identify or predict useful structures. The former relies on humans thinking of everything, while the latter is limited by the accuracy of simulations and the computing power required.

                    Aspuru-Guzik’s system can dream up structures more independently of humans and without lengthy simulations. It leverages its own experience, built up by training machine-learning algorithms with data on hundreds of thousands of drug-like molecules.

                    "It explores more intuitively, using chemical knowledge it learned, like a chemist would," says Aspuru-Guzik. "Humans could be better chemists with this kind of software as their assistant." Aspuru-Guzik was named to MIT Technology Review’s list of young innovators in 2010.
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                    • #30

                      Re: AI Megathread

                      When you cross aspirin and ibuprofen you get... heartburn.
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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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                            • #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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