AI Superstar Andrew Ng Is Democratizing Deep Learning With A New Online Course

#AI hero @AndrewYNg is #democratizing #deeplearning w/ new online course
in @FastCompany

  • That’s the vision of Andrew Ng, a founder of the Google Brain deep learning project, and former head of AI at Baidu–a position he left in March–who is today announcing a set of five interconnected online courses on the subject.
  • “Today, if you want to learn deep learning, there are lots of people searching online, reading [dozens of] research papers, reading blog posts, and watching YouTube videos,” Ng tells Fast Company.
  • As Ng sees it, getting to an AI-powered economy is going to take the work of much more than any one, or even several companies.
  • “I hope we can build an AI-powered future that provides everyone affordable healthcare, accessible education, inexpensive and convenient transportation, and a chance for meaningful work for every man and woman,” Ng says in his announcement, which is the first from his newly created company, deeplearning.ai.
  • Ng is aware that many people are still confused by AI, often getting bogged down in the different subspecialties, and lingo that can easily be misused.

The founder of Google Brain and former head of Baidu’s AI efforts wants to train a giant new workforce to help make “AI the new electricity.”

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Teaching machines to predict the future

Teaching machines to predict the future

  • ” The second is to have humans label the scene for the computer in advance, which is impractical for being able to predict actions on a large scale.
  • Computer systems that predict actions would open up new possibilities ranging from robots that can better navigate human environments, to emergency response systems that predict falls, to Google Glass-style headsets that feed you suggestions for what to do in different situations.
  • In a second study, the algorithm was shown a frame from a video and asked to predict what object will appear five seconds later.
  • When shown a video of people who are one second away from performing one of the four actions, the algorithm correctly predicted the action more than 43 percent of the time, which compares to existing algorithms that could only do 36 percent of the time.
  • After training the algorithm on 600 hours of unlabeled video, the team tested it on new videos showing both actions and objects.

Deep-learning vision system from the Computer Science and Artificial Intelligence Lab anticipates human interactions using videos of TV shows.
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