Fast Neural Style

TensorFire runs neural networks in the browser using WebGL  #MachineLearning

  • TensorFire is a library that runs neural networks in the browser.
  • TensorFire mobile is not quite ready for prime-time, but here’s a video of how this demo would look:

    If possible please open this page in a desktop browser!

This is a demo app showing off TensorFire’s ability to run the style-transfer neural network in your browser as fast as CPU TensorFlow on a desktop.
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Development of AI and its future state

Development of #AI and its future state

  • Below is an extract from a 36-page report entitled “Technology and Innovation for the Future of Production: Accelerating Value Creation”, available for free here, and produced by the World Economic Forum.
  • The extract below, about the future of AI, is figure 7 at page 13.
  • This long report also discusses other interested topics and is peppered with many useful charts and illustrations.
  • The following picture (figure 4 in the report) illustrating IoT is also interesting:

Below is an extract from a 36-page report entitled Technology and Innovation for the Future of Production: Accelerating Value Creation , available for free he…
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Code redWhy China’s AI push is worrying

  • For sheer volume of research on AI, if not quality, Chinese academics surpass their American peers; AI-related patent submissions in China almost tripled between 2010 and 2014 compared with the previous five years.
  • No other country could generate such a volume of data to enable machines to learn patterns indicative of rare diseases, for example.
  • A cyber-security law that came into force in June requires foreign firms to store data they collect on Chinese customers within the country’s borders; outsiders cannot use Chinese data to offer services to third parties.
  • If it happens at all, the equivalent Chinese discussion about the limits of ethical AI research is far more opaque.
  • AI techniques are perfect for finding patterns in the massive amounts of data that Chinese censors must handle in order to maintain a grip on the citizenry.

IMAGINE the perfect environment for developing artificial intelligence (AI). The ingredients would include masses of processing power, lots of computer-science boffins, a torrent of capital—and abundant data with which to train machines to recognise and respond to patterns.
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PredictN: Power your digital business actions using predictive machine learning – BetaList

PredictN: Power your digital business actions using predictive machine learning

  • PredictN is a Prediction SaaS platform which automates all your business prediction requirements and helps improve efficiencies on your marketing and product development activities.
  • Connect your data sources (GA 360, Adobe, BigQuery, CRM) and PredictN generates predictions directly to your marketing platforms (AdWords, DoubleClick, MailChimp).

Power your digital business actions using predictive machine learning
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AI la modeCan data predict fashion trends?

Can data predict fashion trends?
#AI #machinelearning #ML #Digital #retail #tech

  • The business came into its own in Paris in the 1960s when agencies began releasing “trend books”, collections of fabrics and design ideas.
  • In response, forecasting agencies are making use of data collated from retailers’ IT systems and have added short-term predictions to their portfolio of services.
  • EDITED, a competing service, provides “solid metrics” in fashion, claiming to use machine learning, an AI technique, in order to predict short-term sales trends.
  • It releases a regular “Fashion Trends Report” based on the firm’s vast trove of search data.
  • Whether AI will ever truly replace the woolly methods of fashion forecasting remains to be seen.

IN THE film “The Devil Wears Prada”, the character of Miranda Priestly, whose role is based on a feared Vogue editor, scolds her new assistant for not understanding fashion. Fashion, she tells her, is whatever a select group of designers and critics says it is.
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Receptive Field Calculator

We built a tool for calculating the receptive field of convolutional filters:  #deeplearning

  • A convolutional layer operates over a local region of the input to that layer with the size of this local region usually specified directly.
  • You can also compute the effective receptive field of a convolutional layer which is the size of the input region to the network that contributes to a layers’ activations.
  • For example, if the first convolutional layer has a receptive field of 3×3 then it’s effective receptive field is also 3×3 since it operates directly on the input.
  • However if the second layer of a convolutional network also has a 3×3 filter, then it’s (local) receptive field is 3×3, but it’s effective receptive field is 5×5.

A convolutional layer operates over a local region of the input to that layer with the size of this local region usually specified directly. You can also compute the effective receptive field of a convolutional layer which is the size of the input region to the network that contributes to a layers’ activations. For example, if the first convolutional layer has a receptive field of 3×3 then it’s effective receptive field is also 3×3 since it operates directly on the input.
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Minds and machines: can we work together in the digital age?

Minds and machines: can we work together in the digital age? @iansample   #AI #ML #industry40

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    In 2016 Klaus Schwab, founder and chairman of the World Economic Forum, wrote: “We stand on the brink of a technological revolution that will fundamentally alter the way we live, work, and relate to one another.”

  • This fourth Industrial Revolution, he said, will fuse the physical, digital and biological worlds, and affect all corners of society – even challenging ideas of what it means to be human.
  • Andrew McAfee and Erik Brynjolfsson of the the Massachusetts Institute of Technology think not.
  • And in their latest book Machine, Platform, Crowd, they tell us why.
  • Joining Ian Sample in the studio, Andrew and Erik lay out their blueprint for the future of the workplace, including the role big data will play, how some processes involving decision-making could be automated, and how minds and machines can come together to cancel out each other’s errors.

Ian Sample sits down with Andrew McAfee and Erik Brynjolfsson to discuss the future of the workplace and the role artificial intelligence will play
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