Top ten news you should know about A.I. before DLD

Here 10 news to read before heading to the DLD Conference, where we will be hosting a panel on Fixing Education for the A.I. age

  • 0-gL7PXXrkbTmqmOP6Half of the A.I. community believes computers will be as smart as humans by 2040.
    The survey asked for an optimistic year (one in which they believe there’s a 10% chance we’ll have AGI), a realistic guess (a year they believe there’s a 50% chance of AGI ), and a safe guess (the earliest year by which they can say with 90% certainty we’ll have AGI). The results are:
    Median optimistic year (10% likelihood) → 2022
    Median realistic year (50% likelihood) → 2040
    Median pessimistic year (90% likelihood) → 2075
  • Amazon Echo sales reportedly reached 5.2m, a quarter of a million of those people have apparently got down on one knee and proposed to their Alexa.
    In late 2015, Amazon released statistics saying that half a million people had told Alexa, “I love you.” It seems half of those people are ready to take the relationship to the next level. Alexa offered practical reasons for why it would never work. “We’re at pretty different places in our lives. I mean, literally — you are on earth and I am in the cloud.”
  • DeepMind’s new targets are the NHS and StarCraft II.
    DeepMind is on a scientific mission to push the boundaries of A.I., and games are the perfect environment in which to do this, allowing them to develop and test smarter, more flexible A.I. algorithms quickly and efficiently, and also providing instant feedback on how they’re doing through scores. StarCraft is an interesting testing environment for current A.I. research because it provides a useful bridge to the messiness of the real-world.
  • Universal Basic Income is back in fashion as Forrester estimates that by 2025 automation will displace 22.7 million US jobs.
    Concerns about job losses from automation have entered the mainstream, with headlines referencing the ‘rise of the robots’ and even an online tool by the BBC that allows users to find out the likelihood of their job disappearing. At the same time, many commentators argue that such projections are overblown and that the actual impact of automation will be far less dramatic.

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  • DARPA is looking to use ML to discover models of data to allow automated systems to query “What if?” scenarios.
    The goal was to model traffic flows as a function of time, weather and location for each block in downtown Manhattan, and then use that model to conduct “what-if” simulations of various ride-sharing scenarios and project the likely effects of those ride-sharing variants on congestion. The team managed to make the model, but it required about 30 person-months of NYU data scientists’ time and more than 60 person-months of preparatory effort to explore, clean and regularise several urban data sets, including statistics about local crime, schools, subway systems, parks, noise, taxis, and restaurants.
  • The large tech giants are all racing to open-source their ML libraries; Google -> TensorFlow, Microsoft -> CNTK.
    Google has not been the only company open sourcing their ML software though, many followed lead. Microsoft open sourced CNTK, Baidu announced the release of PaddlePaddle, and Amazon just recently announced that they will back MXNet in their new AWS ML platform.
  • Post the launch of OpenAI, other ethics groups are being created – today Reid Hoffman announces $27m fund – Ethics and Governance of Artificial Intelligence.
    The John S. and James L. Knight Foundation, Omidyar Network, LinkedIn founder Reid Hoffman, and others have formed a $27 million fund to apply the humanities, the social sciences and other disciplines to the development of AI.
    The MIT Media Lab and the Berkman Klein Center for Internet & Society at Harvard University will serve as founding academic institutions for the initiative, which will be named the Ethics and Governance of Artificial Intelligence Fund. The Fund will support a cross-section of A.I. ethics and governance projects and activities, both in the United States and internationally.
  • An A.I. did actually correctly predict Trump’s win – even though the pollsters failed.
  • The Prognos Registry of 5 billion clinical records (100m patients in 30 disease) launched to enable earlier identification of patients who can benefit from enhanced medical treatments.
    During its research and development phase as Medivo, Prognos invested in innovations exploring the potential of clinical diagnostic assets and is now realising the full value of those assets through A.I. and HIPAA-compliant advanced analytics with over 500 proprietary and learning clinical algorithms.
  • IBM filed another 8,000 patents in 2016, 2,700 related to Artificial Intelligence.
    Next on the list was Samsung with 5,518 patents, Canon with 3,665, Qualcomm with 2,897 and Google with 2,835. In total, the USPTO granted 304,126 patents in 2016, 10 percent more than the year before, IFI Claims said.
  • CB Insights top 100 startups have raised $3.8B in aggregate funding across 263 deals since 2012.
    Nearly 140 private companies working to advance artificial intelligence technologies have been acquired since 2011, with over 40 acquisitions taking place in 2016 alone.
  • Nick Bostrom’s book got us all obsessed with predicting if GAI will be “the oracle”, “a genie” or “a sovereign.”
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