GitHub

  • This is the code for my article “Coloring B&W portraits with neural networks” – – Earlier this year, Amir Avni used neural networks to troll the subreddit /r/Colorization.
  • For those who are not familiar, this subreddit is focused in “hand” coloring historical images using Photoshop.
  • Coloring images with neural networks creates some very interesting results.
  • Read the article to understand the context of the code.

Coloring-greyscale-images-in-Keras – Coloring B&W portraits with neural networks.
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Artificial intelligence now powers all of Facebook’s translation

Artificial intelligence now powers all of Facebook’s translation

  • On Thursday, Facebook announced that all of its user translation services—those little magic tricks that happen when you click “see translation” beneath a post or comment—are now powered by neural networks, which are a form of artificial intelligence.
  • Back in May, the company’s artificial intelligence division, called Facebook AI Research, announced that they had developed a kind of neural network called a CNN (that stands for convolutional neural network, not the news organization where Wolf Blitzer works) that was a fast, accurate translator.
  • Now, Facebook says that they have incorporated that CNN tech into their translation system, as well as another type of neural network, called an RNN (the R is for recurrent).
  • Facebook says that the new AI-powered translation is 11 percent more accurate than the old-school approach, which is what they call a “phrase-based machine translation” technique that wasn’t powered by neural networks.
  • As an example of the difference between the two translation systems, Facebook demonstrated how the old approach would have translated a sentence from Turkish into English, and then showed how the new AI-powered system would do it.

On Thursday, Facebook announced that all of its user translation services—those little magic tricks that happen when you click “see translation” beneath a post or comment—are now powered by neural networks, which are a form of artificial intelligence.
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Using Artificial Intelligence to Search for Extraterrestrial Intelligence

Using Artificial Intelligence to Search for Extraterrestrial Intelligence

  • #AI The Machine Learning 4 SETI Code Challenge (ML4SETI), created by the SETI Institute and IBM, was completed on July 31st 2017.
  • The Machine Learning 4 SETI Code Challenge (ML4SETI), created by the SETI Institute and IBM, was completed on July 31st 2017.
  • The ML4SETI project challenged participants to build a machine-learning model to classify different signal types observed in radio-telescope data for the search for extra-terrestrial intelligence (SETI).
  • The models from the top teams, using deep learning techniques, attained nearly 95% accuracy in signals from the test set, which included some signals with very low amplitudes.
  • Deep learning models trained for signal classification may significantly impact how SETI research is conducted at the Allen Telescope Array, where the SETI Institute conducts its radio-signal search.

#AI The Machine Learning 4 SETI Code Challenge (ML4SETI), created by the SETI Institute and IBM, was completed on July 31st 2017. Nearly 75 participants, with a wide range of backgrounds from industry and academia, worked in teams on the project. The top team achieved a signal classification accuracy of 95%. The code challenge was sponsored by IBM, Nimbix Cloud, Skymind, Galvanize, and The SETI League. The Machine Learning 4 SETI Code Challenge (ML4SETI), created by the SETI Institute and IBM, was completed on July 31st 2017. Nearly 75 participants, with a wide range of backgrounds from industry and academia, worked in teams on the project. The top team achieved a signal classification accuracy of 95%. The code challenge was sponsored by IBM, Nimbix Cloud, Skymind, Galvanize, and The SETI League.
The ML4SETI project challenged participants to build a machine-learning model to classify different signal types observed in radio-telescope data for the search for extra-terrestrial intelligence (SETI). Seven classes of signals were simulated (and thus, labeled), with which citizen scientists trained their models. We then measured the performance of these models with tests sets in order to determine a winner of the code challenge. The results were remarkably accurate signal classification models. The models from the top teams, using deep learning techniques, attained nearly 95% accuracy in signals from the test set, which included some signals with very low amplitudes. These models may soon be used in daily SETI radio signal research.
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Artificial Intelligence

Forms of creativity will be unleashed that we can not even imagine.

  • In the latest installment of Singularity University’s web series, Future of Everything With Jason Silva, Silva takes a look at artificial intelligence.
  • Surely to transform the world and the human race in ways that we can barely wrap our heads around,” Silva says.
  • Forms of creativity will be unleashed that we can not even imagine, and we’re going to transcend what it means to be human.

In this episode of “Future of Everything With Jason Silva,” Silva takes a look at the transformational potential of artificial intelligence.
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Best practices of orchestrating Python and R code in ML projects

Best practices of orchestrating #Python and #rstats code in #MachineLearning projects

  • Instead of arguing about Python vs R I will examine the best practices of integrating both languages in one data science project.
  • Today, data scientists are generally divided among two languages — some prefer R, some prefer Python.
  • Usually algorithms used for classification or regression are implemented in both languages and some scientist are using R while some of them preferring Python.
  • Instead of using logistic regression in R we will write Python jobs in which we will try to use random forest as training model.
  • py is presented below: – – Also here we are adding code for download necessary R and Python codes from above (clone the Githubrepository): – – Our dependency graph of this data science project look like this: – – Now lets see how it is possible to speed up and simplify…


Instead of arguing about Python vs R I will examine the best practices of integrating both languages in one data science project.

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Microsoft sets up $3.5 million competition for artificial-intelligence startups

#AI: Microsoft sets up $3.5 million competition for artificial-intelligence startups #Tech

  • Microsoft Ventures will invest $3.5 million in artificial-intelligence startups after competitions for the money in North America, Europe and Israel.
  • The bake-off, dubbed Innovate.Ai, will see Microsoft Ventures award $1 million loans that convert to an equity stake to one startup each from North America, Europe and Israel.
  • A separate, $500,000 investment will go to a startup Microsoft judges to be building products designed to improve society.
  • Israel is a hotbed of cutting-edge software research; Microsoft’s startup investment arm has an office there, and the company has acquired a few Israeli startups in recent years.
  • Microsoft has leaned heavily on such freebies to get companies interested in its technology after Amazon Web Services built a wide lead in providing cloud-computing services to startups.

Microsoft Ventures will invest $3.5 million in artificial-intelligence startups after competitions for the money in North America, Europe and Israel. Plans for the Innovate.Ai awards come as Microsoft ramps up its corporate focus on software that uses machine learning.
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Five Hot AI Startups Step into Spotlight at GTC Europe Inception Awards

Five hot #AI startups step into the spotlight at the #GTC17EU Inception Awards:

  • Then we gave one of them — Gamaya, a 20-person startup harnessing deep learning to help farms improve their productivity and sustainability — a new DGX Station in front of a room packed with more than 160 investors, entrepreneurs and industry observers.
  • The event’s contenders were selected from among the 700 European startups participating in our Inception program, which accelerates the development of startups involved in AI and deep learning.
  • After looking at an initial round of 25 startups, our judges chose companies we believe to be the five hottest in Europe to tell their stories.
  • Besides our winner Gamaya, the startups included presentations from: – – The Inception Awards continue the series of events we’ve held in Silicon Valley and China in conjunction with our GPU Technology Conference world tour.
  • Our Inception virtual accelerator program supports more than 1,900 AI startups with GPUs, deep learning expertise and other resources to help them be successful.

We brought five of the hottest startups in Europe and put them in front of a panel of some of tech’s savviest players at GTC Europe in Munich Tuesday.
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