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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Tencent Finally Makes a Big Bet on Artificial Intelligence — The Information

Tencent Finally Makes a Big Bet on #ArtificialIntelligence  #ai

  • Tencent, the company behind China’s largest social network and the world’s biggest online games business, always gets props for its products—but not necessarily for its cutting-edge tech.
  • It will be run by former Microsoft principal researcher Yu Dong, who announced he was joining Tencent at an artificial intelligence conference in March.
  • Mr. Yu is a leading speech recognition expert who spent nearly two decades at Microsoft.

Tencent, the company behind China’s largest social network and the world’s biggest online games business, always gets props for its products—but not necessarily for its cutting-edge tech. Now the juggernaut is taking steps to change that perception with a big push into AI.The owner of the WeChat …
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Try Deep Learning in Python now with a fully pre-configured VM

Try #DeepLearning in #Python now with a fully pre-configured VM

  • Try Deep Learning in Python now with a fully pre-configured VMI love to write about face recognition, image recognition and all the other cool things you can build with machine learning.
  • If you aren’t a long-time Linux user, it can be really hard to figure out how to get a system fully configured with all the required machine learning libraries and tools like TensorFlow, Theano, Keras, OpenCV, and dlib.
  • To make it simple for anyone to play around with machine learning, I’ve put together a simple virtual machine image that you can download and run without any complicated installation steps.The virtual machine image has Ubuntu Linux Desktop 16.04 LTS 64-bit pre-installed with the following machine learning tools:Python 3.5OpenCV 3.2 with Python 3 bindingsdlib 19.4 with Python 3 bindingsTensorFlow 1.0 for Python 3Keras 2.0 for Python 3Theanoface_recognition for Python 3 (for playing around with face recognition)PyCharm Community Edition already set up and ready to go for all these librariesConvenient code examples ready to run, right on the desktop!Even the webcam is preconfigured to work inside the Linux VM for OpenCV / face_recognition examples (as long as you set up your webcam to be accessible in the VMware settings).
  • So don’t the VirtualBox version unless you don’t have any other choice.You need VMware to run this virtual machine image.
  • Right-click on the code window and choose “Run” to run the current file in PyCharm.If you configure your webcam in VMware settings, you can access your webcam from inside the Linux virtual machine!

I love to write about face recognition, image recognition and all the other cool things you can build with machine learning. Whenever possible, I try to include code examples or even write libraries…
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We shouldn’t approach technology from a place of fear

We shouldn't approach #technology from a place of fear  #AI #Robotics

  • Millions of people will lose their jobs.
  • These fears have been felt with each new technological development throughout human history – the invention of the car, automation in factories, computers small and powerful enough to be used in the workplace.
  • Even someone rooted in technology – Bill Gates – mused recently about introducing a tax on robots to compensate for the loss to human jobs by the rise in artificial intelligence (AI).
  • Of course, we can’t overlook the impact of AI on jobs.
  • The World Economic Forum (WEF) forecasts five million jobs will be lost before 2020 as AI, robotics, nanotechnology and other socioeconomic factors replace the need for human jobs.

Millions of people will lose their jobs.
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New Machine Learning Cheat Sheet by Emily Barry

New #MachineLearning Cheat Sheet by Emily Barry #abdsc

  • This blog about machine learning was written by Emily Barry.
  • Emily is a Data Scientist in San Francisco, California.
  • The more she learns about machine learning algorithms, the more challenging it is to keep these subjects organized in her brain to recall at a later time.
  • This is by no means a comprehensive guide to machine learning, but rather a study in the basics for herself and the likely small overlap of people who like machine learning and love emoji as much as she do.
  • For more articles about machine learning, click here.

This blog about machine learning was written by Emily Barry. Emily is a Data Scientist in San Francisco, California. She really loves emoji. Another thing she…
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A Visual Introduction to Machine Learning

A Visual Introduction to Machine Learning | #DataScience #MachineLearning #RT

  • Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.
  • Let’s say you had to determine whether a home is in San Francisco or in New York.
  • In machine learning terms, categorizing data points is a classification task.Since San Francisco is relatively hilly, the elevation of a home may be a good way to distinguish the two cities.
  • Based on the home-elevation data to the right, you could argue that a home above 240 ft should be classified as one in San Francisco.
  • The data suggests that, among homes at or below 240 ft, those that cost more than $1776 per square foot are in New York City.

This article was written by Stephanie and Tony on R2D3. 
In machine learning, computers apply statistical learning techniques to automatically identify pattern…
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This interactive map uses machine learning to arrange visually similar fonts

This interactive map uses machine learning to arrange visually similar fonts

  • Typography enthusiasts likely already know how to identify fonts by name, but it’s always useful to explore visually similar fonts when you feel like changing up your options.
  • Design consultant firm IDEO’s Font Map helps you do exactly that, with an interactive tool that lets you browse through fonts by clicking on them and seeing ones nearby that look similar, or by specifically searching for fonts by name.
  • IDEO software designer Kevin Ho built the map using a machine learning algorithm that can sort fonts by visual characteristics, like weight, serif or san-serif, and cursive or non-cursive.
  • “Designers need an easier way to discover alternative fonts with the same aesthetic — so I decided to see if a machine learning algorithm could sort fonts by visual characteristics, and enabling designers to explore type in a new way,” he wrote in a blog post.
  • Services that compare and suggest visually similar fonts already exist, like Identifont and the blog Typewolf, but IDEO’s tool makes it easy to quickly browse and at the very least, appreciate all the options out there that help make the web more beautiful.

Typography enthusiasts likely already know how to identify fonts by name, but it’s always useful to explore visually similar fonts when you feel like changing up your options. Design consultant…
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How to Build a Recurrent Neural Network in TensorFlow

How to Build a Recurrent #NeuralNetwork in TensorFlow

  • The input to the RNN at every time-step is the current value as well as a state vector which represent what the network has “seen” at time-steps before.
  • The weights and biases of the network are declared as TensorFlow variables, which makes them persistent across runs and enables them to be updated incrementally for each batch.
  • Now it’s time to build the part of the graph that resembles the actual RNN computation, first we want to split the batch data into adjacent time-steps.
  • This is the final part of the graph, a fully connected softmax layer from the state to the output that will make the classes one-hot encoded, and then calculating the loss of the batch.
  • It will plot the loss over the time, show training input, training output and the current predictions by the network on different sample series in a training batch.


This is a no-nonsense overview of implementing a recurrent neural network (RNN) in TensorFlow. Both theory and practice are covered concisely, and the end result is running TensorFlow RNN code.

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Designing networks for IoT sensors can be a learning process

Designing networks for IoT sensors can be a learning process | #MachineLearning #IoT #RT

  • These are just a few examples of the various Internet of Things (IoT) sensors and other connected devices in Boulder, where electrical, solar and HVAC systems are also tied into IP networks.
  • Designing a wireless network to support these applications was a learning process for the city’s IT department, says Benjamin Edelen, a senior system administrator there.
  • Aimee Schumm, e-services manager at the Boulder Public Library, notes that staff members made sure to tuck access points in places where they couldn’t be reached easily — such as inside ceiling tiles or on the ceiling itself — so they won’t be tampered with.
  • Boulder built out its wireless network with more bandwidth than it needs currently, with the expectation that it will expand its use of IoT sensors and similar technologies in the future.
  • Once the IoT sensors were in place, the various city departments generally took ownership of the data, Edelen says.

As the city of Boulder optimized its wireless network to better support IoT sensors, the city’s IT pros found it had a “significant learning curve.”
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The 5 Forces Of Artificial Intelligence In B2B Sales

The Power Of #AI In Sales Is Astounding! Are You Up To Date With The Times? >>>>>>>

  • Well, almost…

    “80% Of Marketing Executives Predict Artificial Intelligence Will Revolutionize Marketing by 2020…Yet, Only 10% Are Currently Using It”

    Instead of fearing the likelihood that Terminator may happen in the coming years, I’m going to uncover the specific advantages that AI has the potential to bring to your B2B sales team…right now.

  • Strong AI, Super Intelligence, Narrow AI, Machine Learning and Deep Learning are terms that often get confused.
  • Strong AI is a ‘machine’ that demonstrates behaviour indistinguishable from that of a human being.
  • If Strong AI is human-like, Artificial Super Intelligence (ASI) is The Terminator.
  • With all variations defined, here are 5 forces of AI to transform your B2B sales methods:

80% Of Marketing Executives Predict Artificial Intelligence Will Revolutionize Marketing by 2020…Yet, Only 10% Are Currently Using It.
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