Spotlight

5 essential #TEDtalks on the #FutureOfWork  
@KM_Spotlight #AI #Robot #KMspotlight

  • As inventors rush to create mind-blowing bits of kit that have the power to transform our daily lives forever, is there anyone out there pausing to think about the real impacts of technology on our lives, our psyches and our society?
  • In the following TED talks, you’ll be able to navigate all the possibilities, and equip yourself with wisdom that will carry you through the changes.
  • Apart from a paycheck, there are intangible values that, Barry Schwartz suggests, our current way of thinking about work simply ignores.
  • Lisa Gansky, author of “The Mesh,” talks about a future of business that’s about sharing all kinds of stuff, either via smart and tech-enabled rental or, more boldly, peer-to-peer.
  • In this talk about the future of work, economist David Autor addresses the question of why there are still so many jobs and comes up with a surprising, hopeful answer.

5 TED talks for anyone interested in the changing work landscape
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How Artificial Intelligence and the robotic revolution will change the workplace of tomorrow

How Artificial Intelligence and the robotic revolution will change the workplace of tomorrow

  • The workplace is going to look drastically different ten years from now.
  • So what do we need to learn today about the jobs of tomorrow?
  • The robots and computers of the future will be based on a degree of complexity that will be impossible to teach to the general population in a few short years of compulsory education.
  • And some of the most important skills people will need to work with robots will not be the things they learn in computing class.
  • However, it looks like human workers will not be replaced by automation, but rather workers will work alongside robots.

How Artificial Intelligence and the robotic revolution will change the workplace of tomorrow
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This cutting edge AI startup from Finland is challenging tech giants and universities alike

This cutting edge #AI startup from Finland is challenging tech giants and universities alike

  • Curious AI is a research-based company founded 2015 as a spin-off from Aalto’s Deep Learning Research Group.
  • Curious AI does cutting edge research, pushing the boundaries of the machine learning commonly called artificial intelligence today towards the illusive limit of true artificial intelligence.
  • Curious AI recently had a breakthrough, using unsupervised learning for object recognition in Google Street View.
  • he term artificial intelligence is thrown around a lot these days, but usually, when a startup says they’re applying AI to some problem, it just means they are using machine learning in varying degrees of sophistication.
  • A major obstacle to reaching artificial intelligence is solving unsupervised learning – and this is what Curious AI’s primary focus.

Curious AI is driving the development towards the next wave of advanced artificial intelligence technology.
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Google releases massive visual databases for machine learning

Google releases massive visual databases for #MachineLearning #abdsc

  • Richard’s been tech obsessed since first laying hands on an Atari joystick.
  • Millions of images and YouTube videos, linked and tagged to teach computers what a spoon is.
  • For researchers, that’s where two recently-released archives from Google will come in.
  • The Open Images set comes from a collaboration between Google, Carnegie Mellon and Cornell, with 9 million entries that were tagged by computers first before having those notes verified and corrected by humans.
  • The  Google Research team says  it has enough images to train a neural network “from scratch,” so if you’d like to try your hand at a  DeepDream -style project, better version of Google Photos or the next  Prisma

This article was written by Richard Lawler. Richard’s been tech obsessed since first laying hands on an Atari joystick. 
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Millions of images and YouTube video…
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AI learns to write its own code by stealing from other programs

#AI has to resort to copying and pasting from StackOverflow too. #programming

  • Ultimately, the approach could allow non-coders to simply describe an idea for a program and let the system build it, says Marc Brockschmidt, one of DeepCoder’s creators at Microsoft Research in Cambridge, UK.
  • DeepCoder uses a technique called program synthesis: creating new programs by piecing together lines of code taken from existing software – just like a programmer might.
  • “It could allow non-coders to simply describe an idea for a program and let the system build it”

    One advantage of letting an AI loose in this way is that it can search more thoroughly and widely than a human coder, so could piece together source code in a way humans may not have thought of.

  • DeepCoder created working programs in fractions of a second, whereas older systems take minutes to trial many different combinations of lines of code before piecing together something that can do the job.
  • Brockschmidt says that future versions could make it very easy to build routine programs that scrape information from websites, or automatically categorise Facebook photos, for example, without human coders having to lift a finger

    “The potential for automation that this kind of technology offers could really signify an enormous [reduction] in the amount of effort it takes to develop code,” says Solar-Lezama.

Software called DeepCoder has solved simple programming challenges by piecing together bits of borrowed code
Continue reading “AI learns to write its own code by stealing from other programs”

Google releases massive visual databases for machine learning

Google releases massive visual databases for #MachineLearning #abdsc

  • Richard’s been tech obsessed since first laying hands on an Atari joystick.
  • Millions of images and YouTube videos, linked and tagged to teach computers what a spoon is.
  • For researchers, that’s where two recently-released archives from Google will come in.
  • The Open Images set comes from a collaboration between Google, Carnegie Mellon and Cornell, with 9 million entries that were tagged by computers first before having those notes verified and corrected by humans.
  • The  Google Research team says  it has enough images to train a neural network “from scratch,” so if you’d like to try your hand at a  DeepDream -style project, better version of Google Photos or the next  Prisma

This article was written by Richard Lawler. Richard’s been tech obsessed since first laying hands on an Atari joystick. 
.
Millions of images and YouTube video…
Continue reading “Google releases massive visual databases for machine learning”