Twilight’s Kristen Stewart co-authored a paper on artificial intelligence

Kristen Stewart co-wrote an academic paper about artificial intelligence

  • Kristen Stewart â the actress best known for “Twilight” â has co-written a paper on machine learning.
  • Kristen Stewart – the actress best known…
  • According to the paper, the project was based on an impressionistic painting of Stewart’s, which shows a man waking up.
  • The response to the paper has been positive from the academic community, if slightly bemused.
  • The paper, first spotted by Quartz , is co-bylined with Adobe research engineer Bhautik J. Joshi and producer David Shapiro.

The actress and director outlined the use of neural style transfer in her film ‘Come Swim’.
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Meet ‘Peeqo,’ the DIY robot that communicates via GIF

Meet 'Peeqo,' the DIY robot that communicates via GIF

  • Better still, its developer wrote a Chrome extension that uses the device to keep him in check when he should be working instead of playing on social media.
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  • Peeqo uses voice recognition from Google Speech API and API.ai to listen in on your queries and provide the appropriate GIF-based response.
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Are You Ready for Robot Colleagues?

Ready?

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  • The article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management.
  • Schmitt comes at the questions not as a computer scientist but as a marketing expert.
  • What’s happening the week at the intersection of management and technology.

In the future workplace, humans may supplement the skills of machines — and not the other way around.

Continue reading “Are You Ready for Robot Colleagues?”

Are You Ready for Robot Colleagues?

Ready?

  • To enjoy more articles like this one sign in , or create an account .
  • sign up for a free account : comment on articles and get access to many more articles.
  • What’s happening the week at the intersection of management and technology.
  • The article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management.
  • Schmitt comes at the questions not as a computer scientist but as a marketing expert.

In the future workplace, humans may supplement the skills of machines — and not the other way around.

Continue reading “Are You Ready for Robot Colleagues?”

srez/README.md at master · david-gpu/srez · GitHub

This is CSI-level: facial reconstruction via machine learning DCGA networks
 HT @diogomonica

  • The generator network relies on ResNet modules as we’ve found them to train substantially faster than more old-fashioned architectures.
  • ‘s an random, non cherry-picked, example of what this network can do.
  • The adversarial term of the loss function ensures the generator produces plausible faces, while the L1 term ensures that those faces resemble the low-res input data.
  • In addition to that the loss function of the generator has a term that measures the L1 difference between the 16×16 input and downscaled version of the image produced by the generator.
  • Extract all images to a subfolder named dataset .

srez – Image super-resolution through deep learning
Continue reading “srez/README.md at master · david-gpu/srez · GitHub”

GitHub

Code for super-resolution of faces, model is DCGAN, implementation TensorFlow:

  • The generator network relies on ResNet modules as we’ve found them to train substantially faster than more old-fashioned architectures.
  • The resulting 64×64 images display sharp features that are plausible based on the dataset that was used to train the neural net.
  • We have found that this L1 term greatly accelerates the convergence of the network during the first batches and also appears to prevent the generator from getting stuck in a poor local solution.
  • ‘s an random, non cherry-picked, example of what this network can do.
  • Download zip file titled Align&Cropped Images and extract all images to a subfolder named dataset .

Read the full article, click here.


@Reza_Zadeh: “Code for super-resolution of faces, model is DCGAN, implementation TensorFlow:”


srez – Image super-resolution through deep learning


GitHub

Machine learning ‘poverty map’ could help aid get to the right places in Africa

#Machine learning ‘poverty map’ could help aid get to the right places in Africa

  • The task was to come up with a way to use these images to extract valuable insights.
  • The idea of using satellite images for the work came about as a way of dealing with these so-called “data gaps.”
  • Machine learning ‘poverty map’ could help aid get to the right places in Africa
  • Using a combination of satellite data and machine learning, they’ve developed a “poverty map” of Africa that could help direct aid to some of the world’s most deprived areas.
  • “One part of the problem when it comes to dealing with poverty is that we don’t have very good data,” Neal Jean, a Ph.D student in Machine Learning at Stanford, told Digital Trends. “

Read the full article, click here.


@DigitalTrends: “#Machine learning ‘poverty map’ could help aid get to the right places in Africa”


Researchers at Stanford University are using machine learning and satellite data to develop a detailed “poverty map” of Africa.


Machine learning ‘poverty map’ could help aid get to the right places in Africa