M. Shadows of Avenged Sevenfold Tries to Stump a Robot

  • Heavy metal band’s frontman plays a game with a robot to guess his identity

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Heavy metal band’s frontman plays a game with a robot to guess his identity Subscribe to Rolling Stone on YouTube: http://bit.ly/1uGwgGg Check out http://www…
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Google’s new machine learning API recognizes objects in videos

Google’s new machine learning API recognizes objects in videos

  • At its Cloud Next conference in San Francisco, Google today announced the launch of a new machine learning API for automatically recognizing objects in videos and making them searchable.
  • The new Video Intelligence API will allow developers to build applications that can automatically extract entities from a video.
  • Until now, most similar image recognition APIs available in the cloud only focused on doing this for still images, but with the help of this new API, developers will be able to build applications that let users search and discover information in videos.
  • Besides extracting metadata, the API allows you to tag scene changes in a video.

Google’s new machine learning API recognizes objects in videos
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The Deep End of Deep Learning

Very good overview of DeepLeaning 10 years history  #deeplearning #AI #digitaltransformation

  • Artificial Neural Networks are inspired by some of the “computations” that occur in human brains—real neural networks.
  • In the past 10 years, much progress has been made with Artificial Neural Networks and Deep Learning due to accelerated computer power (GPUs), Open Source coding libraries that are being leveraged, and in-the-moment debates and corroborations via social media.
  • Hugo Larochelle shares his observations of what’s been made possible with the underpinnings of Deep Learning.
  • Hugo Larochelle is a Research Scientist at Twitter and an Assistant Professor at the Université de Sherbrooke (UdeS).
  • His professional involvement includes associate editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), member of the editorial board of the Journal of Artificial Intelligence Research (JAIR) and program chair for the International Conference on Learning Representations (ICLR) of 2015, 2016 and 2017.

Artificial Neural Networks are inspired by some of the “computations” that occur in human brains—real neural networks. In the past 10 years, much progress ha…
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GitHub

A #DeepLearning approach to colorizing images  #AI #MachineLearning

  • Use convert_images.py to create resized and gray images for training.
  • The training attempts to obtain the resized color image when given the resized gray image.
  • -n –normalize [y/n normalize training images]
  • /images/train/ -n n to start training on the small amount of sample images.
  • You can use this with some sample training images provided in images/train .

Colorful-Image-Colorization – A deep learning approach to colorizing images
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Creating videos of the future

AI creates videos of the future using deep-learning:

  • Given a still image, CSAIL deep-learning system generates videos that predict what will happen next in a scene.
  • QS ranks MIT the world’s top university for 2016-17
  • Given a still image from a scene, the CSAIL team’s deep-learning algorithm can create a brief video that simulates the future of that scene.
  • ” the videos show us what computers think can happen in a scene,” says Vondrick.
  • A new algorithm generates videos using a series of neural networks.

Researchers at MIT’s Computer Science and Artificial Intelligence Lab have developed a deep-learning algorithm that, given a still image from a scene, can create a brief video that simulates the future of that scene.
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Spoiler Alert: Artificial Intelligence Can Predict How Scenes Will Play Out

Spoiler alert: Artificial Intelligence can predict how scenes will play out

  • A new artificial intelligence system can take still images and generate short videos that simulate what happens next similar to how humans can visually imagine how a scene will evolve, according to a new study.
  • The system is also able to learn unsupervised, the researchers said.
  • When the researchers asked workers on Amazon’s Mechanical Turk crowdsourcing platform to pick which videos were real, the users picked the machine-generated videos over genuine ones 20 percent of the time, the researchers said.
  • “Compared to GANs, VPN are easier to train, but take much longer to generate a video,” he told Live Science.
  • But the researchers said that the approach could eventually help robots and self-driving cars navigate dynamic environments and interact with humans, or let Facebook automatically tag videos with labels describing what is happening.

A new AI system generates short videos that are similar to how humans can visually imagine how a scene will evolve
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Creating videos of the future

VIDEO: Deep-learning AI creates videos of the future from a still image:

  • QS ranks MIT the world’s top university for 2016-17
  • Given a still image from a scene, the CSAIL team’s deep-learning algorithm can create a brief video that simulates the future of that scene.
  • At forum, MIT community tackles tough ethical questions of climate change
  • Given a still image, CSAIL deep-learning system generates videos that predict what will happen next in a scene.
  • A new algorithm generates videos using a series of neural networks.

Researchers at MIT’s Computer Science and Artificial Intelligence Lab have developed a deep-learning algorithm that, given a still image from a scene, can create a brief video that simulates the future of that scene.
Continue reading “Creating videos of the future”

Enhancing Your Mobile Application with Machine Learning

#XamarinEvolve session replay: Enhancing Your Mobile Application with Machine Learning,

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@VisualStudio: “#XamarinEvolve session replay: Enhancing Your Mobile Application with Machine Learning,”


Coupling Microsoft’s recently announced Cognitive Services and Machine Learning capabilities to Xamarin’s acclaimed development technologies opens a new world of possibilities for developers building


Enhancing Your Mobile Application with Machine Learning