Framework: The role of Humans in the Future of Work

Framework: The role of Humans in the Future of Work | #Analytics #Artificialintelligence #RT

  • For the last years in addressing the future of work I have often focused on the human capabilities that will drive value as machines become more capable and the work landscape is transformed.
  • To help define and clarify these capabilities I created a landscape on the role of Humans in the Future of Work, which I first shared publicly in my keynote yesterday.
  • This framework overlaps and builds on my Future of Work Framework, specifically building out the distinctive human capabilities that will be relevant and valued as the work landscape is transformed.
  • I have spoken and written before about the three fundamental human capabilities for the future of work: EXPERTISE, RELATIONSHIPS and CREATIVITY.
  • Recognizing these distinctive human capabilities allows us to design work, organizations and education to use and develop these capabilities to best effect.

For the last years in addressing the future of work I have often focused on the human capabilities that will drive value as machines become more capable and the work landscape is transformed.
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GitHub

  • Linux GPU: Python 2 ( build history ) / Python 3.4 ( build history ) / Python 3.5 ( build history )
  • Latest commit 55b0159 Jan 1, 2017 yifeif committed on GitHub Merge pull request #6588 from terrytangyuan/run_config_flag
  • TensorFlow is an open source software library for numerical computation using data flow graphs.
  • Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them.
  • TensorFlow also includes TensorBoard, a data visualization toolkit.

tensorflow – Computation using data flow graphs for scalable machine learning
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Must Know Tips/Tricks in Deep Neural Networks

Must Know Tips/Tricks in Deep Neural Networks:  #abdsc #MachineLearning #DeepLearning

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  • Must Know Tips/Tricks in Deep Neural Networks
  • Deep Neural Networks, especially Convolutional Neural Networks ( CNN ), allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction.
  • They collected and concluded many implementation details for DCNNs.

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@KirkDBorne: “Must Know Tips/Tricks in Deep Neural Networks: #abdsc #MachineLearning #DeepLearning”


This article was posted by Xiu-Shen Wei.  Xiu-Shen Wei is a 2nd-year Ph.D. candidate of Department of Computer Science and Technology in Nanjing University and…


Must Know Tips/Tricks in Deep Neural Networks