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  • richardmitnick 11:04 am on May 11, 2019 Permalink | Reply
    Tags: AI, BlueData software platform, HPE Apollo systems,   

    From insideHPC: “HPE BlueData Appliance Accelerates AI and Data-Driven Innovation” 

    From insideHPC

    Today HPE announced a powerful new appliance for AI and data driven-innovation based on the HPE BlueData software platform and HPE Apollo systems.

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    “To stay a step ahead of the competition, enterprise organizations in every industry are embarking on AI-enabled and data-driven digital transformation initiatives,” said Milan Shetti, GM, HPE Storage. “Having BlueData as part of HPE’s portfolio extends our best-in-class solutions for these customers, enabling us to provide differentiated hybrid IT solutions for AI, machine learning, and advanced analytics.”

    The announcement is an important milestone in HPE’s ongoing strategy for the AI market. Acquired by HPE in 2018, BlueData is also now available as a standalone HPE software solution. Customers can continue to run BlueData software on any infrastructure, leveraging the portability of containers across on-premises, hybrid cloud, and multi-cloud environments.

    Enterprise adoption of AI is accelerating rapidly. The number of enterprises implementing AI grew 270% in the past four years, according to Gartner’s recent 2019 CIO Survey1. However, organizations face many challenges in executing a successful AI strategy, including deployment complexity for distributed AI and analytics as well as the shortage of skilled data scientists and AI/ML developers.

    The container-based BlueData software platform provides customers with simple, one-click automated deployment for their AI and analytics tools of choice – and ensures enterprise-grade security, scalability and performance. Data science teams and AI/ML developers can focus on what they do best, with greater productivity and efficiency. These teams can rapidly build models and develop data pipelines to drive business innovation – without worrying about the underlying infrastructure.

    Customers can leverage the following capabilities and services from HPE and BlueData to accelerate their AI initiatives:

    BlueData software subscriptions can be ordered together with HPE Apollo server and storage infrastructure. New bundled solutions with BlueData and HPE infrastructure will be made available in a variety of different configurations.
    New HPE Pointnext services offerings are now available for BlueData to streamline the software implementation and ongoing support operations, ensuring success and faster time-to-value for AI and data-driven initiatives.
    A new HPE reference configuration is now available, providing guidance and best practices for deploying BlueData software on the HPE Elastic Platform for Analytics (EPA). This reference configuration will provide customers with a clear template for leveraging modular building blocks from HPE for compute, storage and networking.
    HPE plans to deliver new BlueData-based reference architectures for a variety of different AI/ML and analytics use cases and ecosystem tools, including Apache Kafka, Apache Spark, Cloudera, H2O, and TensorFlow. These reference architectures include in-depth testing and validation from HPE engineering to determine the workload-optimized configuration for high performance and greater efficiency.
    The BlueData team is developing new innovations for the use of containers in large-scale AI and analytics deployments, with ongoing contributions to the Kubernetes open source community.

    See the full article here .

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    Founded on December 28, 2006, insideHPC is a blog that distills news and events in the world of HPC and presents them in bite-sized nuggets of helpfulness as a resource for supercomputing professionals. As one reader said, we’re sifting through all the news so you don’t have to!

    If you would like to contact me with suggestions, comments, corrections, errors or new company announcements, please send me an email at rich@insidehpc.com. Or you can send me mail at:

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  • richardmitnick 10:14 am on May 1, 2019 Permalink | Reply
    Tags: AI, , MIT's Sertac Karman and Vivienne Sze developed the new chip, New chips   

    From M.I.T. Technology Review: “This chip was demoed at Jeff Bezos’s secretive tech conference. It could be key to the future of AI.” 

    MIT Technology Review
    From M.I.T. Technology Review

    1
    Photographs by Tony Luong

    May 1, 2019
    Will Knight

    Artificial Intelligence

    On a dazzling morning in Palm Springs, California, recently, Vivienne Sze took to a small stage to deliver perhaps the most nerve-racking presentation of her career.

    2
    MIT’s Sertac Karman and Vivienne Sze developed the new chip

    She knew the subject matter inside-out. She was to tell the audience about the chips, being developed in her lab at MIT, that promise to bring powerful artificial intelligence to a multitude of devices where power is limited, beyond the reach of the vast data centers where most AI computations take place. However, the event—and the audience—gave Sze pause.

    The setting was MARS, an elite, invite-only conference where robots stroll (or fly) through a luxury resort, mingling with famous scientists and sci-fi authors. Just a few researchers are invited to give technical talks, and the sessions are meant to be both awe-inspiring and enlightening. The crowd, meanwhile, consisted of about 100 of the world’s most important researchers, CEOs, and entrepreneurs. MARS is hosted by none other than Amazon’s founder and chairman, Jeff Bezos, who sat in the front row.

    “It was, I guess you’d say, a pretty high-caliber audience,” Sze recalls with a laugh.

    Other MARS speakers would introduce a karate-chopping robot, drones that flap like large, eerily silent insects, and even optimistic blueprints for Martian colonies. Sze’s chips might seem more modest; to the naked eye, they’re indistinguishable from the chips you’d find inside any electronic device. But they are arguably a lot more important than anything else on show at the event.

    New capabilities

    Newly designed chips, like the ones being developed in Sze’s lab, may be crucial to future progress in AI—including stuff like the drones and robots found at MARS. Until now, AI software has largely run on graphical chips, but new hardware could make AI algorithms more powerful, which would unlock new applications. New AI chips could make warehouse robots more common or let smartphones create photo-realistic augmented-reality scenery.

    Sze’s chips are both extremely efficient and flexible in their design, something that is crucial for a field that’s evolving incredibly quickly.

    The microchips are designed to squeeze more out of the “deep learning” AI algorithms that have already turned the world upside down. And in the process, they may inspire those algorithms themselves to evolve. “We need new hardware because Moore’s law has slowed down,” Sze says, referring to the axiom coined by Intel cofounder Gordon Moore that predicted that the number of transistors on a chip will double roughly every 18 months—leading to a commensurate performance boost in computer power.

    3

    This law is increasingly now running into the physical limits that come with engineering components at an atomic scale. And it is spurring new interest in alternative architectures and approaches to computing.

    The high stakes that come with investing in next-generation AI chips, and maintaining America’s dominance in chipmaking overall, aren’t lost on the US government. Sze’s microchips are being developed with funding from a Defense Advanced Research Projects Agency (DARPA) program meant to help develop new AI chip designs (see The out-there AI ideas designed to keep the US ahead of China).

    But innovation in chipmaking has been spurred mostly by the emergence of deep learning, a very powerful way for machines to learn to perform useful tasks. Instead of giving a computer a set of rules to follow, a machine basically programs itself. Training data is fed into a large, simulated artificial neural network, which is then tweaked so that it produces the desired result. With enough training, a deep-learning system can find subtle and abstract patterns in data. The technique is applied to an ever-growing array of practical tasks, from face recognition on smartphones to predicting disease from medical images.

    The new chip race

    Deep learning is not so reliant on Moore’s law. Neural nets run many mathematical computations in parallel, so they run far more effectively on the specialized video game graphics chips that perform parallel computations for rendering 3D imagery. But microchips designed specifically for the computations that underpin deep learning should be even more powerful.

    The potential for new chip architectures to improve AI has stirred up a level of entrepreneurial activity that the chip industry hasn’t seen in decades (see The race to power AI’s silicon brains and China has never had a real chip industry. AI may change that).

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    Big tech companies hoping to harness and commercialize AI including Google, Microsoft, and (yes) Amazon, are all working on their own deep learning chips. Many smaller companies are developing new chips, too. “It impossible to keep track of all the companies jumping into the AI-chip space,” says Mike Delmer, a microchip analyst at the Linley Group , an analyst firm. “I’m not joking that we learn about a new one nearly every week.”

    The real opportunity, says Sze, isn’t building the most powerful deep learning chips possible. Power efficiency is important because AI also needs to run beyond the reach of large datacenters and so can only rely on the power available on the device itself to run. This is known as operating on the “edge.”

    “AI will be everywhere—and figuring out ways to make things more energy efficient will be extremely important,” says Naveen Rao, vice president of the Artificial Intelligence group at Intel.

    For example, Sze’s hardware is more efficient partly because it physically reduces the bottleneck between where data is stored and where it’s analyzed, but also because it uses clever schemes for reusing data. Before joining MIT, Sze pioneered this approach for improving the efficiency of video compression while at Texas Instruments.

    See the full article here .


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  • richardmitnick 3:22 pm on March 13, 2019 Permalink | Reply
    Tags: "Quantum computing should supercharge this machine-learning technique", AI, Certain machine-learning tasks could be revolutionized by more powerful quantum computers., , ,   

    From M.I.T Technology Review: “Quantum computing should supercharge this machine-learning technique” 

    MIT Technology Review
    From M.I.T Technology Review

    March 13, 2019
    Will Knight

    1
    The machine-learning experiment was performed using this IBM Q quantum computer.

    Certain machine-learning tasks could be revolutionized by more powerful quantum computers.

    Quantum computing and artificial intelligence are both hyped ridiculously. But it seems a combination of the two may indeed combine to open up new possibilities.

    In a research paper published today in the journal Nature, researchers from IBM and MIT show how an IBM quantum computer can accelerate a specific type of machine-learning task called feature matching. The team says that future quantum computers should allow machine learning to hit new levels of complexity.

    As first imagined decades ago, quantum computers were seen as a different way to compute information. In principle, by exploiting the strange, probabilistic nature of physics at the quantum, or atomic, scale, these machines should be able to perform certain kinds of calculations at speeds far beyond those possible with any conventional computer (see “What is a quantum computer?”). There is a huge amount of excitement about their potential at the moment, as they are finally on the cusp of reaching a point where they will be practical.

    At the same time, because we don’t yet have large quantum computers, it isn’t entirely clear how they will outperform ordinary supercomputers—or, in other words, what they will actually do (see “Quantum computers are finally here. What will we do with them?”).

    Feature matching is a technique that converts data into a mathematical representation that lends itself to machine-learning analysis. The resulting machine learning depends on the efficiency and quality of this process. Using a quantum computer, it should be possible to perform this on a scale that was hitherto impossible.

    The MIT-IBM researchers performed their simple calculation using a two-qubit quantum computer. Because the machine is so small, it doesn’t prove that bigger quantum computers will have a fundamental advantage over conventional ones, but it suggests that would be the case, The largest quantum computers available today have around 50 qubits, although not all of them can be used for computation because of the need to correct for errors that creep in as a result of the fragile nature of these quantum bits.

    “We are still far off from achieving quantum advantage for machine learning,” the IBM researchers, led by Jay Gambetta, write in a blog post. “Yet the feature-mapping methods we’re advancing could soon be able to classify far more complex data sets than anything a classical computer could handle. What we’ve shown is a promising path forward.”

    “We’re at stage where we don’t have applications next month or next year, but we are in a very good position to explore the possibilities,” says Xiaodi Wu, an assistant professor at the University of Maryland’s Joint Center for Quantum Information and Computer Science. Wu says he expects practical applications to be discovered within a year or two.

    Quantum computing and AI are hot right now. Just a few weeks ago, Xanadu, a quantum computing startup based in Toronto, came up with an almost identical approach to that of the MIT-IBM researchers, which the company posted online. Maria Schuld, a machine-learning researcher at Xanadu, says the recent work may be the start of a flurry of research papers that combine the buzzwords “quantum” and “AI.”

    “There is a huge potential,” she says.

    See the full article here .


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    The mission of MIT Technology Review is to equip its audiences with the intelligence to understand a world shaped by technology.

     
  • richardmitnick 2:06 pm on March 12, 2019 Permalink | Reply
    Tags: "Nine companies are steering the future of artificial intelligence", AI,   

    From Science News: “Nine companies are steering the future of artificial intelligence” 

    From Science News

    March 12, 2019
    Maria Temming

    1
    UNDUE INFLUENCE The Big Nine explores how a handful of tech corporations involved in developing artificial intelligence, including Apple, whose headquarters is shown above, play an outsized role in determining the future of society. Uladzik Kryhin/Shutterstock

    1
    The Big Nine
    Amy Webb
    PublicAffairs, $27

    The book highlights warning signs of what happens when we increasingly rely on technology created by corporations that prioritize commercial and political interests over the public. These red flags include mismanagement of users’ personal data (SN Online: 4/15/18) in the United States and a state-sanctioned “social credit” system that monitors people’s behavior in China. Webb generally holds the Big Nine accountable but occasionally pivots to defend the companies, which she believes are led by people with good intentions.

    Readers who aren’t as convinced of the Big Nine’s noble intentions may at least agree with Webb that great power begets great responsibility. The second half of the book details three possible futures through 2069, ranging from a best-case scenario where the Big Nine commit to making user interests the No. 1 priority to a worst-case scenario where the Big Nine continue business as usual.

    Webb’s assessments are based on analyses of patent filings, policy briefings, interviews and other sources. She paints vivid pictures of how AI could benefit the average person, via precision medicine or smarter dating apps, for example, though she primarily focuses on people in the United States. Her forecasts are provocative and unsettlingly plausible.

    Webb closes with a somewhat perfunctory call to action, including predictable steps like reading the G-MAFIA’s terms of service. Unfortunately, The Big Nine may leave many readers feeling less like empowered citizens and more like extras in a film where tech giants and world leaders play the protagonists. But for anyone who wants a preview of how a few tech firms could reshape society in relatively short order, Webb’s account is an accessible, intriguing read.

    See the full article here .


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    • Skyscapes for the Soul 5:13 pm on March 12, 2019 Permalink | Reply

      A couple years ago I met a guy who is the CEO of H2O.ai – which he said was an AI company. I’m surprised they weren’t on the list.

      Like

  • richardmitnick 11:43 am on November 5, 2018 Permalink | Reply
    Tags: AI, Toby Walsh Scientia Professor of Artificial Intelligence at UNSW Sydney considers 2062 the year that artificial intelligence will match human intelligence although a fundamental shift has already occu,   

    From University of New South Wales: “AI will match human intelligence by 2062 claims UNSW expert” 

    U NSW bloc

    From University of New South Wales

    05 Nov 2018
    Lori Youmshajekian

    Scientia Professor Toby Walsh tells the Festival of Dangerous Ideas that Artificial Intelligence is less than 50 years away from matching humans.

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    Toby Walsh, Scientia Professor of Artificial Intelligence at UNSW Sydney, delivers a talk at the Festival of Dangerous Ideas. Photo by Yaya Stempler

    The idea that Artificial Intelligence will learn unique human traits like adaptability, creativity and emotional intelligence is something that many in society consider to be an unlikely or distant possibility.

    But Toby Walsh, Scientia Professor of Artificial Intelligence at UNSW Sydney, has put a date on this looming reality. He considers 2062 the year that artificial intelligence will match human intelligence, although a fundamental shift has already occurred in the world as we know it.

    Speaking at the Festival of Dangerous Ideas, Walsh argued that we are already experiencing the risks of artificial intelligence that seem to be so far in the future.

    “Even without machines that are very smart, I’m starting to get a little bit nervous about where it’s going and the important choices we should be making”, Walsh said.

    The key challenge will be to avoid the apocalyptic rhetoric of AI and to determine how to move forward in the new age of information.

    Weapons of mass persuasion

    Privacy concerns about the collection of personal data is nothing new. Citing the Cambridge Analytica scandal, Walsh argues that we should be more sceptical about how data is misused by tech companies.

    “A lot of the debate has focused on how personal information was stolen from people, and we should be rightly outraged by that,” Walsh said.

    “But there is another side to the story that I’m surprised hasn’t gotten as much attention from the media, which is that the information was used very actively to manipulate how people were going to vote.”

    Information is the currency of today’s tech giants, and there is a growing fear that many people are in denial, or even complicit, in just how much data is collected about themselves on a daily basis. According to Walsh, breaches of data privacy will occur more often and are becoming increasingly normalised.

    “Many of us have smartwatches that are monitoring our vital signs; our blood pressure, our heartbeat, and if you look at the terms of service, you don’t own that data,” Walsh explained.

    “We’re giving up our analogue privacy, the most personal things about us. Just think what you could do as an advertiser if you could tell how people really respond to your adverts.

    “You can lie about your digital preferences, but you can’t lie about your heartbeat.”

    The ethics of killer robots

    Untangling the ethics of machine accountability will be the second fundamental shift in the world as we know it, according to Walsh.

    “Fully autonomous machines will radically change the nature of warfare and will be the third revolution in warfare,” Walsh said.

    But using autonomous machines as weapons of war poses an ethical dilemma – can you hold a machine accountable for death?

    “Machines have no moral compass, they are not sentient, they don’t suffer pain and they can’t be punished,” Walsh added.

    “This takes us into interesting new legal territory of who should be held responsible, and there is no simple answer.”

    Artificial Intelligence is developed by learning from examples – therefore the key driver of its behaviour is the environment that it is exposed to, more so than the programmer.

    Walsh believes the issue is creating machines that are aligned with human values, which is currently a problem on other platforms driven by Artificial Intelligence.

    “Facebook is an example of the alignment problem, it is optimised for your attention, not for creating political debate or for making society a better place,” Walsh said.

    But it’s not all doom and gloom, according to Walsh. Artificial Intelligence isn’t necessarily heading towards an apocalyptic scenario.

    “The future is not fixed. There is this idea that technology is going to shape our future and that we are going to have to deal with it, but this is the wrong picture to think of because society gets to push back and change the technology,” he said.

    Instead of being proponents of technological determinism, Walsh argued that we need to push for societal determinism, ensuring that we build trustworthy systems with distinct lines of accountability.

    Toby Walsh’s new book, 2062: The World that AI Made, is now available.

    See the full article here .


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    U NSW Campus

    Welcome to UNSW Australia (The University of New South Wales), one of Australia’s leading research and teaching universities. At UNSW, we take pride in the broad range and high quality of our teaching programs. Our teaching gains strength and currency from our research activities, strong industry links and our international nature; UNSW has a strong regional and global engagement.

    In developing new ideas and promoting lasting knowledge we are creating an academic environment where outstanding students and scholars from around the world can be inspired to excel in their programs of study and research. Partnerships with both local and global communities allow UNSW to share knowledge, debate and research outcomes. UNSW’s public events include concert performances, open days and public forums on issues such as the environment, healthcare and global politics. We encourage you to explore the UNSW website so you can find out more about what we do.

     
  • richardmitnick 6:55 am on September 4, 2018 Permalink | Reply
    Tags: AI, ,   

    From École Polytechnique Fédérale de Lausanne: “Artificial intelligence helps create at the right time” 

    EPFL bloc

    From École Polytechnique Fédérale de Lausanne

    04.09.18
    Cécilia Carron

    1
    Ana Manasovska is working on improving the semantic recognition and recommendation platform for the inventors. © 2018 Alain Herzog

    Student project (9/9). By using artificial intelligence to comb through the vast array of published research and detect the findings most relevant for invention, engineers can magnify their creative ability and invent faster and more disruptively than has been previously possible. This is the approach that Ana Manasovska helped develop as a Master’s student at EPFL, and the one used by creative Artificial Intelligence firm Iprova, based at EPFL’s Innovation Park, to come up with a wide range of inventions. Manasovska, whose Master’s research involved testing different phrase recognition methods, now works for the firm.

    Inventions like sensors for self-driving cars that can monitor passengers’ health, a geolocation system that can help smooth out passenger traffic on public transportation, and a smartphone feature for virtually painting the light ambience of a room involve pulling together data from several research fields in an inventive way. The ever increasing amount of information in the world, spread across many different industries and markets, makes this an increasingly difficult task. To make it possible for inventors to sense the inventive signal in this ever increasing amount of noise, AI researchers and software developers at Iprova – the innovation creation firm that came up with the aforementioned inventions – have developed an artificial intelligence platform that includes sophisticated semantic analysis algorithms. Ana Manasovska helped create this program as part of her Master’s degree in computer science at EPFL, in association with the school’s Artificial Intelligence Laboratory. She now works for the company, which is based at EPFL’s Innovation Park, to further develop the software that makes it easier for engineers to invent faster and more disruptively..

    Millions of publications sifted

    Millions of research, industry news and other articles are published around the world every year. One part of Iprova’s artificial intelligence platform works by performing a semantic analysis of the terms in published articles. Manasovska’s thesis on summarization methods contributed to this by testing various phrase recognition methods, which she did by representing individual phrases as vectors. If two phrases have a close virtual spatial location, then their meanings are probably similar. This technique can be used to generate better summaries by measuring phrases’ semantic similarity.

    What Manasovska found when comparing the different methods is that the more complicated architectures weren’t necessarily better suited to this task. “Even with a simple architecture, we got excellent results in identifying phrases with similar meanings,” she says. “We also learned that the best way to generate the kinds of summaries that Iprova needs is to approach them from an inventor’s perspective. Most conventional summary-generation methods don’t do that, which is why we wanted to develop our own,” she adds.

    Today Manasovska is working on further improving the semantic recognition and recommendation platform. She works closely with inventors, aiming to find out what kind of data they need and how they plan to use it. She has developed programs allowing engineers to create inventions using input data that they wouldn’t have been able to easily get otherwise. An example of this is the linking of information from inventively relevant, but otherwise disparate, research fields that open up entirely new invention opportunities. “What I really like about my work is that it lets me stay on top of the latest developments in machine learning and natural language processing (NLP) – two fields that are advancing rapidly. I have the opportunity to use the latest technology and the power of data to help people spot relevant new findings more efficiently,” she says.

    “Traditional inventors were scientists or engineers with a deep understanding of a specific technical field. This only gave the inventor access to a limited amount of research insight. Even collaborative inventing through teamwork only provides insight into a handful of additional fields, since it’s just a team of specialists. With such approaches to invention, researchers can only dig deeper into specific areas rather than offering genuine innovation by taking the field in a different direction.

    Iprova does this on a massive scale – in real-time – by using data from across the spectrum of human knowledge to make connections between ideas from different fields of study.”says Julian Nolan, CEO of Iprova. The company is combining AI with a team of creative scientific minds – the invention developers – to accelerate the development of tomorrow’s products and services. Its customers include some of the best known technology companies in Silicon Valley, Japan and Europe. Hundreds of patents have been filed based on its inventions, which are cited by companies including Google, Microsoft and Amazon.

    See the full article here .

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    EPFL campus

    EPFL is Europe’s most cosmopolitan technical university. It receives students, professors and staff from over 120 nationalities. With both a Swiss and international calling, it is therefore guided by a constant wish to open up; its missions of teaching, research and partnership impact various circles: universities and engineering schools, developing and emerging countries, secondary schools and gymnasiums, industry and economy, political circles and the general public.

     
  • richardmitnick 7:51 am on August 13, 2018 Permalink | Reply
    Tags: , AI, , Computers can’t have needs cravings or desires,   

    From aeon: “Robot says: Whatever” 

    1

    From aeon

    8.13.18
    Margaret Boden

    1
    Chief priest Bungen Oi holds a robot AIBO dog prior to its funeral ceremony at the Kofukuji temple in Isumi, Japan, on 26 April 2018. Photo by Nicolas Datiche /AFP/Getty
    https://www.headlines24.nl/nieuwsartikel/135259/201808/robot–whatever

    What stands in the way of all-powerful AI isn’t a lack of smarts: it’s that computers can’t have needs, cravings or desires.

    In Henry James’s intriguing novella The Beast in the Jungle (1903), a young man called John Marcher believes that he is marked out from everyone else in some prodigious way. The problem is that he can’t pinpoint the nature of this difference. Marcher doesn’t even know whether it is good or bad. Halfway through the story, his companion May Bartram – a wealthy, New-England WASP, naturally – realises the answer. But by now she is middle-aged and terminally ill, and doesn’t tell it to him. On the penultimate page, Marcher (and the reader) learns what it is. For all his years of helpfulness and dutiful consideration towards May, detailed at length in the foregoing pages, not even she had ever really mattered to him.

    That no one really mattered to Marcher does indeed mark him out from his fellow humans – but not from artificial intelligence (AI) systems, for which nothing matters. Yes, they can prioritise: one goal can be marked as more important or more urgent than another. In the 1990s, the computer scientists Aaron Sloman and Ian Wright even came up with a computer model of a nursemaid in charge of several unpredictable and demanding babies, in order to illustrate aspects of Sloman’s theory about anxiety in humans who must juggle multiple goals. But this wasn’t real anxiety: the computer couldn’t care less.

    See the full article here .


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    Since 2012, Aeon has established itself as a unique digital magazine, publishing some of the most profound and provocative thinking on the web. We ask the big questions and find the freshest, most original answers, provided by leading thinkers on science, philosophy, society and the arts.

    Aeon has three channels, and all are completely free to enjoy:

    Essays – Longform explorations of deep issues written by serious and creative thinkers

    Ideas – Short provocations, maintaining Aeon’s high editorial standards but in a more nimble and immediate form. Our Ideas are published under a Creative Commons licence, making them available for republication.

    Video – A mixture of curated short documentaries and original Aeon productions

    Through our Partnership program, we publish pieces from university research groups, university presses and other selected cultural organisations.

    Aeon was founded in London by Paul and Brigid Hains. It now has offices in London, Melbourne and New York. We are a not-for-profit, registered charity operated by Aeon Media Group Ltd. Aeon is endorsed as a Deductible Gift Recipient (DGR) organisation in Australia and, through its affiliate Aeon America, registered as a 501(c)(3) charity in the US.

    We are committed to big ideas, serious enquiry and a humane worldview. That’s it.

     
  • richardmitnick 8:18 am on August 8, 2018 Permalink | Reply
    Tags: "2062: The World that AI Made", AI, Toby Walsh,   

    From University of New South Wales: “Humanity confronts a defining question: how will AI change us?” 

    U NSW bloc

    From University of New South Wales

    07 Aug 2018
    Penny Jones

    In his new book 2062: The World that AI Made, UNSW artificial intelligence expert Toby Walsh urges us to choose wisely as we define the effects on our lives of the Fourth Industrial Revolution.

    1
    Scientia Professor Toby Walsh with Baxter.

    What will happen when we’ve built machines as intelligent as us? According to the experts this incredible feat will be achieved in the year 2062 – a mere 44 years away – which certainly begs the question: what will the world, our jobs, the economy, politics, war, and everyday life and death, look like then?

    Fortunately, Toby Walsh, Scientia Professor of Artificial Intelligence (AI) at UNSW has done the research for us.

    An avid sci-fi fan from childhood, Walsh, who also leads the Algorithmic Decision Theory group at Data61 – Australia’s Centre of Excellence for ICT Research, has long been fascinated by robots, machines and the future. In 2017, he published his first book, It’s Alive!, in which he tells the story of AI and how it is already affecting our societies, economies and interactions.

    “After I published It’s Alive!, people started asking me lots of questions about the social impact of AI, in particular the increasing concerns about how it’s encroaching into our lives,” he says. “That’s why I wrote my second book, 2062: The World that AI Made, which ignores the technology, and focuses instead on the reality and impact of where AI is going to take us.”

    According to Walsh, (and, he says, the vast majority of his colleagues) this future looks less like the dystopian world of The Terminator and more like the sensitive world of Short Circuit.

    “Most of the movies from Hollywood featuring AI paint a very disturbing picture of the future. But there is one movie that seems to get it right,” Walsh continues.

    He is referring to the 2013 American sci-fi movie Her, where the protagonist falls in love with his intelligent computer operating system.

    “One thing Her does really well is to demonstrate our deepening relationship with machines,” Walsh explains.

    “As the Internet of Things gets more established and our devices become interconnected, things like your front door, washing machine, fridge and TV, will all be voice activated. You’ll just walk into a room and start talking and the room will obey your commands,” he says.

    While Walsh makes a series of predications based on the way the technology is heading, he is very careful to emphasise that the future isn’t fixed. There is no technological determinism and what happens next in AI is very much the product of the choices we make today. In other words, we must consciously choose the future we want.

    “We are at a critical junction in history where there’s a lot to play for. It’s rightly called the Fourth Industrial Revolution, and we need to start making choices so that it turns out to be a revolution that everyone can benefit from,” he says.

    The book covers topics including privacy, education, equality, politics, warfare and work and although Walsh says there is absolutely no need to be worried about a Hollywood version of the future where the robots rise up and take over the world, there are a few things we do need to urgently address.

    Jobs is one area that is already seeing major disruptions. From drivers to pilots and from medicine to journalism, AI is infiltrating every industry. Another concern is the impact AI will have on warfare.

    “Think about the implications of handing over the reins to decide who lives and who dies to machines,” says Walsh, who was one of more than 100 global tech leaders who signed an open letter in 2017 calling on the United Nations to ban killer robots.

    Another big concern, says Walsh, is that we may unconsciously build algorithms with many of the biases we are currently struggling with – racism, sexism, ageism, etc. “We have to be careful not to bake these into algorithms and take ourselves backwards,” he says.

    Walsh says he is a pessimist in the short term but an optimist in the long term. “I wrote 2062: The World that AI Made to stimulate the conversation I think the whole of society should be having. We are in for a period of struggle as the world changes but, ultimately, I think technology will deliver on things like climate change and the other crises we are experiencing,” he says.

    “If we make the right decisions now, we can build a future where we let the machines take the sweat and we can focus on the more important things in life, our families and art, for example. Just think about it. This could usher in the next Renaissance, a great flaring of creativity,” he says.

    2062: The World that AI Made by Toby Walsh was published this week and is available online and in all good book shops.

    Toby Walsh is also part of a panel discussion entitled “Good Robot/Bad Robot: Living with Intelligent Machines” at the Sydney Opera House on 12 August. Tickets are available here.

    See the full article here .


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    Please help promote STEM in your local schools.

    Stem Education Coalition

    U NSW Campus

    Welcome to UNSW Australia (The University of New South Wales), one of Australia’s leading research and teaching universities. At UNSW, we take pride in the broad range and high quality of our teaching programs. Our teaching gains strength and currency from our research activities, strong industry links and our international nature; UNSW has a strong regional and global engagement.

    In developing new ideas and promoting lasting knowledge we are creating an academic environment where outstanding students and scholars from around the world can be inspired to excel in their programs of study and research. Partnerships with both local and global communities allow UNSW to share knowledge, debate and research outcomes. UNSW’s public events include concert performances, open days and public forums on issues such as the environment, healthcare and global politics. We encourage you to explore the UNSW website so you can find out more about what we do.

     
  • richardmitnick 7:08 am on July 21, 2018 Permalink | Reply
    Tags: AI, , ,   

    From Exascale Computing Project: “ECP Announces New Co-Design Center to Focus on Exascale Machine Learning Technologies” 

    From Exascale Computing Project

    07/20/18

    The Exascale Computing Project has initiated its sixth Co-Design Center, ExaLearn, to be led by Principal Investigator Francis J. Alexander, Deputy Director of the Computational Science Initiative at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory.

    1
    Francis J. Alexander. BNL


    ExaLearn is a co-design center for Exascale Machine Learning (ML) Technologies and is a collaboration initially consisting of experts from eight multipurpose DOE labs.

    Brookhaven National Laboratory (Francis J. Alexander)
    Argonne National Laboratory (Ian Foster)
    Lawrence Berkeley National Laboratory (Peter Nugent)
    Lawrence Livermore National Laboratory (Brian van Essen)
    Los Alamos National Laboratory (Aric Hagberg)
    Oak Ridge National Laboratory (David Womble)
    Pacific Northwest National Laboratory (James Ang)
    Sandia National Laboratories (Michael Wolf)

    Rapid growth in the amount of data and computational power is driving a revolution in machine learning (ML) and artificial intelligence (AI). Beyond the highly visible successes in machine-based natural language translation, these new ML technologies have profound implications for computational and experimental science and engineering and the exascale computing systems that DOE is deploying to support those disciplines.

    To address these challenges, the ExaLearn co-design center will provide exascale ML software for use by ECP Applications projects, other ECP Co-Design Centers and DOE experimental facilities and leadership class computing facilities. The ExaLearn Co-Design Center will also collaborate with ECP PathForward vendors on the development of exascale ML software.

    The timeliness of ExaLearn’s proposed work ties into the critical national need to enhance economic development through science and technology. It is increasingly clear that advances in learning technologies have profound societal implications and that continued U.S. economic leadership requires a focused effort, both to increase the performance of those technologies and to expand their applications. Linking exascale computing and learning technologies represents a timely opportunity to address those goals.

    The practical end product will be a scalable and sustainable ML software framework that allows application scientists and the applied mathematics and computer science communities to engage in co-design for learning. The new knowledge and services to be provided by ExaLearn are imperative for the nation to remain competitive in computational science and engineering by making effective use of future exascale systems.

    “Our multi-laboratory team is very excited to have the opportunity to tackle some of the most important challenges in machine learning at the exascale,” Alexander said. “There is, of course, already a considerable investment by the private sector in machine learning. However, there is still much more to be done in order to enable advances in very important scientific and national security work we do at the Department of Energy. I am very happy to lead this effort on behalf of our collaborative team.”

    See the full article here.

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    Please help promote STEM in your local schools.

    Stem Education Coalition

    About ECP

    The ECP is a collaborative effort of two DOE organizations – the Office of Science and the National Nuclear Security Administration. As part of the National Strategic Computing initiative, ECP was established to accelerate delivery of a capable exascale ecosystem, encompassing applications, system software, hardware technologies and architectures, and workforce development to meet the scientific and national security mission needs of DOE in the early-2020s time frame.

    About the Office of Science

    DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, please visit https://science.energy.gov/.

    About NNSA

    Established by Congress in 2000, NNSA is a semi-autonomous agency within the DOE responsible for enhancing national security through the military application of nuclear science. NNSA maintains and enhances the safety, security, and effectiveness of the U.S. nuclear weapons stockpile without nuclear explosive testing; works to reduce the global danger from weapons of mass destruction; provides the U.S. Navy with safe and effective nuclear propulsion; and responds to nuclear and radiological emergencies in the United States and abroad. https://nnsa.energy.gov

    The Goal of ECP’s Application Development focus area is to deliver a broad array of comprehensive science-based computational applications that effectively utilize exascale HPC technology to provide breakthrough simulation and data analytic solutions for scientific discovery, energy assurance, economic competitiveness, health enhancement, and national security.

    Awareness of ECP and its mission is growing and resonating—and for good reason. ECP is an incredible effort focused on advancing areas of key importance to our country: economic competiveness, breakthrough science and technology, and national security. And, fortunately, ECP has a foundation that bodes extremely well for the prospects of its success, with the demonstrably strong commitment of the US Department of Energy (DOE) and the talent of some of America’s best and brightest researchers.

    ECP is composed of about 100 small teams of domain, computer, and computational scientists, and mathematicians from DOE labs, universities, and industry. We are tasked with building applications that will execute well on exascale systems, enabled by a robust exascale software stack, and supporting necessary vendor R&D to ensure the compute nodes and hardware infrastructure are adept and able to do the science that needs to be done with the first exascale platforms.

     
  • richardmitnick 9:22 am on June 28, 2018 Permalink | Reply
    Tags: AI, NASA FDL - Frontier DEvelopment Lab,   

    From SETI Institute: “NASA FDL Leverages Public/Private Partnership to Push New Boundaries of Space Science with Artificial Intelligence” 

    SETI Logo new
    From SETI Institute

    Jun 26, 2018

    James Parr
    FDL Director
    Frontier Development Lab
    james@frontierdevelopmentlab.org
    http://www.frontierdevelopmentlab.org

    Rebecca McDonald
    Director of Communications
    SETI Institute
    189 Bernardo Ave., Suite 200
    Mountain View, CA 94043
    rmcdonald@seti.org
    http://www.seti.org

    1

    AI Accelerator to Focus on Key Challenges in Space Resources, Astrobiology, Space Weather, And Exoplanets to Benefit the Space Program and Humanity.

    The NASA Frontier Development Lab (FDL) has announced it will apply artificial intelligence (AI) to four key space challenges. FDL is an AI/machine learning research accelerator powered by a public/private partnership between NASA, the SETI Institute, commercial leaders in AI, and pioneers in the private space industry.

    Entering its third year, FDL is building on a successful track record by expanding its focus to four key research areas: Space Resources, Astrobiology, Exoplanets, and Solar Weather. The final results of FDL 2018 will be presented at Intel in Santa Clara on August 16th.

    “This year, we have 50 phenomenally talented researchers and mentors from AI and the space sciences tackling these critical challenges to the space program,” said Dr. Dan Rasky, Chief, Space Portal, NASA’s Ames Research Center in Silicon Valley. “We’re excited that NASA is able to convene a group like this. AI is a game changer for space exploration and we’re looking forward to some fascinating results.”

    There is mounting excitement around the potential for great progress within each of the designated challenge areas. According to Bill Diamond, President and CEO at the SETI Institute, “The NASA FDL researchers are taking on some fascinating challenges. For example, can we find a way to predict solar weather? Can we get better at discovering new exoplanets and possibly even new forms of life? Can we enable multiple rovers to work together to effectively explore for resources like water on the moon?” Diamond continued, “These are great questions. We are excited to see some answers begin to emerge in this year’s program.”

    NASA FDL is a compelling example of how public/private partnerships can yield significant results. Hosted at the SETI Institute, the NASA FDL program pairs researchers from the space sciences with data scientists for an intense eight-week concentrated sprint, supported by leaders in AI, such as Intel, Google, Kx Systems, IBM and NVIDIA and key players in private space such as SpaceResourcesLu, Lockheed Martin, KBRWyle and XPRIZE.

    NASA FDL has consistently demonstrated the potential of applied AI to create useful results in accelerated time periods. According FDL Director James Parr, “We are proud of our achievements to date, and we expect even more from this year’s challenges. NASA FDL researchers have shared their results at numerous professional conferences, in both the AI and space science domains, and papers from 2016 and 2017 are being published in peer-reviewed journals. Moreover, functioning AI workflows from FDL are being deployed on NASA funded activities – including detecting long period comets.”

    The Frontier Development Lab is the latest NASA sponsored activity to push the boundaries of state-of-the-art in computing – specifically applied artificial intelligence – to assist in solving knowledge gaps in space science and exploration relevant to NASA and humankind.

    The space program and computing have a long history of advancement for mutual benefit. The push for miniaturization in the late 60’s, helped catalyze the development of the microprocessor which took humanity to the Moon. The Apollo Moonshots were also responsible for error-free software architectures which made computers reliable in deep space, but also credible everyday tools. More recently, NASA originated ‘camera-on-a-chip’ technology resides inside many smartphones and now, self-driving cars.

    To learn more about FDL, the 2018 challenge questions and to follow our 2018 program please visit the FDL website at http://www.frontierdevelopmentlab.org.

    About the NASA Frontier Development Lab (FDL)

    Hosted in Silicon Valley by the SETI Institute, the NASA FDL is an applied artificial intelligence research accelerator developed in partnership with NASA’s Ames Research Center. Founded in 2016, the NASA FDL aims to apply AI technologies to challenges in space exploration by pairing machine learning expertise with space science and exploration researchers from academia and industry. These interdisciplinary teams address tightly defined problems and the format encourages rapid iteration and prototyping to create outputs with meaningful application to the space program and humanity.

    See the full article here .


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    Please help promote STEM in your local schools.

    Stem Education Coalition

    SETI Institute – 189 Bernardo Ave., Suite 100
    Mountain View, CA 94043
    Phone 650.961.6633 – Fax 650-961-7099
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