Fast Drawing for Everyone

🎉 Cool @Google creative lab project!

🎨 Fast Drawing for Everyone

  • If you’re interested in learning more about the magic behind AutoDraw, check out “Quick, Draw!”
  • (one of our A.I. Experiments).
  • AutoDraw’s suggestion tool uses the same technology to guess what you’re trying to draw.Big thanks to the artists, designers, illustrators and friends of Google who created original drawings for AutoDraw.HAWRAF, Design StudioErin Butner, DesignerJulia Melograna, IllustratorPei Liew, DesignerSimone Noronha, DesignerTori Hinn, DesignerSelman Design, Creative StudioIf you are interested in submitting your own drawings, you can do that here.
  • We hope that AutoDraw, our latest A.I. Experiment, will make drawing more accessible and fun for everyone.

AutoDraw is a new A.I. Experiment, built by Google Creative Lab, which uses machine learning and artists’ drawings, to help everyone create anything visual, fast.
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Auxiliary [Adventure]

Auxiliary (Artificial Intelligence) - Adventure Map -  - By MatPatCat

  • The story picks up where it was last left off and takes you on a new adventure where you attempt to escape the genius mind of an artificial super intelligence called IRIS.
  • You’ve escaped from IRIS – at least you thought so until very recently.
  • IRIS looks to has captured you again and this time you will be sent through another set of twisted experiments.
  • But this time you remember IRIS and you immediately attempt to escape.

Auxiliary is the long away sequel to Genisys which by the way is another highly recommended map to those of you who haven’t played it already. The story picks up where it was last left off and takes you on a new adventure where you attempt to escape the genius mind of an artificial super intelligence called IRIS.
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A Visual Introduction to Machine Learning

A Visual Introduction to #MachineLearning #abdsc

  • 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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Time to Accept Artificial Intelligence as Part of the Family?

Time to Accept #ArtificialIntelligence as Part of the Family? #AI #Fintech #Martech #tech

  • By now, many of us have heard about or might even own one of the popular, sleek multi-functional voice-first devices, such as the Amazon Echo, also known as “Alexa”, the name used when waking the device to give a verbal command.
  • This joke is terrible for many reasons, not the least of which is that I ended up anthropomorphized a digital device, which may be one of the biggest issues with this devices.
  • Related: There’s No Doubt That Amazon Alexa Is the Next Big Thing

    First, according to Voice Labs Voice Report for 2017, 6.5 million voice-first devices — defined as an always-on piece of hardware utilizing artificial intelligence (AI) with primarily a voice interface, both for input and output — were shipped in 2016.

  • While Amazon and Google (and Siri on our iPhones) have an early lead in this sector, there are sure to be new entrants.
  • Here are predictions for the strategies of just the big players:

    The crazy thing is that even with the potential for 24 million devices to be in our homes soon, the potential impact still remains remarkably unclear.

Millions of households are welcoming these new voice-first home assistant devices into and as part of their families — even with all the uncertainties and unintended consequences.
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You can use this machine learning demo to roll Keanu Reeves’ (or anyone’s) eyes

You can use this machine learning demo to roll Keanu Reeves’ (or anyone’s) eyes

  • This time it’s DeepWarp, a demo created by Yaroslav Ganin, Daniil Kononenko, Diana Sungatullina, and Victor Lempitsky, that uses deep architecture to move human eyeballs in a still image.
  • The authors of the demo acknowledge that similar projects already exist (like the smile-manipulator FaceApp), but without such a singular, detailed focus.
  • I tried this using images of Keanu Reeves and several dogs, but the demo didn’t work with the dogs.
  • “Our system is reasonably robust against different head poses and deals correctly with the situations where a person wears glasses,” the authors wrote in their study.
  • The authors say they plan to work on making the demo work more quickly in the future.

Another day, another fun internet thing that uses neural networks for facial manipulation. This time it’s DeepWarp, a demo created by Yaroslav Ganin, Daniil Kononenko, Diana Sungatullina, and…
Continue reading “You can use this machine learning demo to roll Keanu Reeves’ (or anyone’s) eyes”

A Visual Introduction to Machine Learning

A Visual Introduction to #MachineLearning #abdsc

  • 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…
Continue reading “A Visual Introduction to Machine Learning”