Saturday, September 25, 2021

g-f(2)516 "g-f" fishing of golden knowledge (GK) of the fabulous treasure of the digital age, Artificial Intelligence (9/25/2021)

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"g-f" fishing of golden knowledge (GK) of the fabulous treasure of the digital ageArtificial Intelligence (9/25/2021)  g-f(2)426 


How Artificial Intelligence Completed Beethoven’s Unfinished Tenth Symphony, Smithsonian Magazine 
    • On October 9, the work will be performed in Bonn, Germany, and a recording will be released.
    • This project would not have been possible without the expertise of human historians and musicians. It took an immense amount of work - and, yes, creative thinking - to accomplish this goal.
    • At one point, one of the music experts on the team said that the A.I. reminded him of an eager music student who practices every day, learns, and becomes better and better.
    • Now that student, having taken the baton from Beethoven, is ready to present the Tenth Symphony to the world.
    • Thanks to the work of a team of music historians, musicologists, composers and computer scientists, Beethoven’s vision will come to life.
    • I presided over the artificial intelligence side of the project, leading a group of scientists at the creative A.I. startup Playform AI that taught a machine both Beethoven’s entire body of work and his creative process.
    • How Artificial Intelligence Completed Beethoven’s Unfinished Tenth Symphony, Ahmed Elgammal, The Conversation, September 24, Smithsonian Magazine.

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        Opportunity, Benchmark Shows AIs Are Getting SpeedierIEEE Spectrum 
          • MLPerf stats show some systems have doubled performance this year, competing benchmark coming.
          • This week, AI industry group MLCommons released a new set of results for AI performance. The new list, MLPerf Version 1.1, follows the first official set of benchmarks by five months and includes more than 1800 results from 20 organizations, with 350 measurements of energy efficiency. The majority of systems improved by between 5-30 percent from earlier this year, with some more than doubling their previous performance stats, according to MLCommons. The new results come on the heels of the announcement, last week, of a new machine-learning benchmark, called TCP-AIx.
          • In MLPerf's inferencing benchmarks, systems made up of combinations of CPUs and GPUs or other accelerator chips are tested on up to six neural networks performing a variety of common functions—image classification, object detection, speech recognition, 3D medical imaging, natural language processing, and recommendation.
          • For the results Nvidia submitted itself, the company used software improvements alone to eke out as much as a 50 percent performance improvement over the past year. 
          • Separately from the formal MLPerf benchmarks, Nvidia showed off a new software technique called multi-instance GPU (MiG), which allows a single GPU to act as if it's seven separate chips from the point of view of software. When the company ran all six benchmarks simultaneously plus an extra instance of object detection (just as a flex, I assume) the results were 95 percent of the single-instance value.
          • Benchmark Shows AIs Are Getting Speedier, Samuel K. Moore, September 24, 2021, IEEE Spectrum.

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          Opportunity, How Does Artificial Intelligence Compare to Augmented Intelligence?, Health IT Analytics 
            • Both artificial intelligence and augmented intelligence share the same goal but have different approaches to achieve it.
            • As providers strive to improve patient outcomes, the use of machine learning has become more integrated into the healthcare system. As this new form of technology continues to expand, it’s important to understand how it can be used and how it differs from augmented intelligence.
            • Although both techniques use machine learning capabilities, artificial intelligence takes a full device approach, while augmented intelligence maintains the human aspect.
            • While artificial intelligence has proven to create positive outcomes in the health realm, some argue that artificial intelligence relies too much on the machine’s ability to assess data and provide treatment plans. Because of the sensitivity of the healthcare industry, human intelligence cannot be replaced, some skeptics say.
            • While artificial intelligence and augmented intelligence offer different solutions to improve patient outcomes, both are effective methods in enhancing the quality of care and creating a more technology-based healthcare system.
            • How Does Artificial Intelligence Compare to Augmented Intelligence?, Erin McNemar, MPA, September 24, 2021, Health IT Analytics.

            Opportunity, Common sense is a huge blind spot for AI developers, TNW 
              • Recent advances in deep learning have rekindled interest in the imminence of machines that can think and act like humans, or artificial general intelligence. By following the path of building bigger and better neural networks, the thinking goes, we will be able to get closer and closer to creating a digital version of the human brain.
              • But this is a myth, argues computer scientist Erik Larson, and all evidence suggests that human and machine intelligence are radically different. Larson’s new book, The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do, discusses how widely publicized misconceptions about intelligence and inference have led AI research down narrow paths that are limiting innovation and scientific discoveries.
              • And unless scientists, researchers, and the organizations that support their work don’t change course, Larson warns, they will be doomed to “resignation to the creep of a machine-land, where genuine invention is sidelined in favor of futuristic talk advocating current approaches, often from entrenched interests.”
              • The monopolization of AI is in turn hampering scientific research. With big tech companies focusing on creating applications in which they can leverage their vast data resources to maintain the edge over their competitors, there’s little incentive to explore alternative approaches to AI. Work in the field starts to skew toward narrow and profitable applications at the expense of efforts that can lead to new inventions.
              • In his book, Larson warns about the current culture of AI, which “is squeezing profits out of low-hanging fruit, while continuing to spin AI mythology.” The illusion of progress on artificial general intelligence can lead to another AI winter, he writes.
              • In The Myth of Artificial Intelligence, Larson provides an inference framework that sheds light on the challenges that the field faces today and helps readers to see through the overblown claims about progress toward AGI or singularity.
              • “My hope is that non-specialists have some tools to combat this kind of inevitability thinking, which isn’t scientific, and that my colleagues and other AI scientists can view it as a wake-up call to get to work on the very real problems the field faces,” Larson said.
              • Common sense is a huge blind spot for AI developers, Ben Dickson, September 24, 2021, The Next Web, tnw.

              What the Future Holds for Artificial Intelligence | Interview with Erik Larson

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              “genioux facts”: The online programme on MASTERING “THE BIG PICTURE OF THE DIGITAL AGE”, g-f(2)516, Fernando Machuca, September 25, 2021,,, Corporation.

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