Google is using machine learning to sort good apps from bad on the Play Store

Google is using machine learning to sort good apps from bad on the Play Store

  • Google is using machine learning to group apps by function and spot the bad apples.
  • Image: Google

    With machine learning, Google can use peer grouping to scan apps that are being loaded on to the Play Store en masse.

  • “We focus on signals that can negatively affect user privacy, such as permission requests that are not related to core app functionality, and the actual, observed behaviors,” explains Martin Pelikan of Google’s security and privacy team over email.
  • In its most recent annual Android security review, the percentage of users who had installed harmful apps from the official Play Store fell from 0.15 percent in 2015 to 0.05 percent in 2016.
  • Many users — particularly those in China — install Android apps from alternative app stores, which the company doesn’t have control over.

Security on Android has always been a challenge for Google due to the operating system’s open nature. But in recent years, the company has been gaining ground in its fight against malware and…
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Learning Deep Learning with Keras

Learning #DeepLearning with #Keras  #NeuralNetworks @pmigdal

  • For that reason, I suggest starting with image recognition tasks in Keras, a popular neural network library in Python.
  • Deep learning is a name for machine learning techniques using many-layered artificial neural networks.
  • See a plot of AUC score for logistic regression, random forest and deep learning on Higgs dataset (data points are in millions):

    In general there is no guarantee that, even with a lot of data, deep learning does better than other techniques, for example tree-based such as random forest or boosted trees.

  • Deep learning (that is – neural networks with many layers) uses mostly very simple mathematical operations – just many of them.
  • Its mathematics is simple to the point that a convolutional neural network for digit recognition can be implemented in a spreadsheet (with no macros), see: Deep Spreadsheets with ExcelNet.

I teach deep learning both for a living (as the main deepsense.io instructor, in a Kaggle-winning team1) and as a part of my volunteering with the Polish Chi…
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GitHub

Our implementation of graph auto-encoders (in TensorFlow) is now available on GitHub:

  • This is a TensorFlow implementation of the (Variational) Graph Auto-Encoder model as described in our paper:

    Graph Auto-Encoders (GAEs) are end-to-end trainable neural network models for unsupervised learning, clustering and link prediction on graphs.

  • GAEs have successfully been used for:

    GAEs are based on Graph Convolutional Networks (GCNs), a recent class of models for end-to-end (semi-)supervised learning on graphs:

    A high-level introduction is given in our blog post:

    In order to use your own data, you have to provide

    Have a look at the function in for an example.

  • In this example, we load citation network data (Cora, Citeseer or Pubmed).
  • The original datasets can be found here: and here (in a different format): can specify a dataset as follows:

    You can choose between the following models:

    Please cite our paper if you use this code in your own work:

gae – Implementation of Graph Auto-Encoders in TensorFlow
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Microsoft is using 400 million PCs to build antivirus protection

Microsoft is using 400 million PCs to build antivirus protection

  • To prevent the next global malware crisis, an upcoming update will rely on machine learning from more than 400 million computers running Windows 10, Microsoft said Tuesday.
  • In its Fall Creators Update, Microsoft will use a wide range of data coming from its cloud programs such as Azure, Endpoint and Office to create an artificial intelligence antivirus that can pick up on malware behavior, said Rob Lefferts, director of program management for Windows Enterprise and Security.
  • If new malware is detected on any computer running Windows 10 in the world, Microsoft said it will be able to develop a signature for it and protect all the other users worldwide.
  • Microsoft sees artificial intelligence as the next solution for security as attacks get more sophisticated.
  • Using cloud data from Microsoft Office to develop malware signatures is crucial, for example, because recent attacks relied on Word vulnerabilities.

An upcoming security update will incorporate machine learning from millions of computers fending off malware, the company says.
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Machine learning is driving growth at Airbnb

Machine learning is driving growth at Airbnb

  • This story was delivered to BI Intelligence “E-Commerce Briefing” subscribers.
  • Machine learning (ML) has had a “profound” effect on Airbnb’s business growth, according to the company’s VP of engineering, Mike Curtis.
  • Airbnb uses ML to optimize matching between guests and hosts, connecting millions of guests to hosts using personalized criteria:

    Optimizing matches between hosts and guests will be critical to Airbnb’s success as it continues to grow.

  • The variety in types of accommodations Airbnb has is an advantage, as long as it ensures guests can easily find a host that meets their criteria.
  • And as Airbnb adds to its 3 million current listings, ensuring both guests and hosts are satisfied will become more crucial.

This story was delivered to BI Intelligence…
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Artificial Intelligence Helps in Learning How Children Learn

Artificial intelligence helps in learning how children learn

  • Researchers in artificial intelligence and machine learning have started to design software that allows computers to learn about causes the way that scientists do.
  • In one experiment, we showed preschool children a simple machine with a switch on one side and two disks that spin on top.
  • Bayesian inference considers both the strength of new evidence and the strength of your existing hypotheses.
  • Both toddlers and scientists hold on to well-confirmed hypotheses, but eventually enough new evidence can overturn even the most cherished idea.
  • Several studies show that youngsters integrate existing knowledge and new evidence in this way.

Alison Gopnik, author of “ Making AI Human ” in Scientific American ’s June issue describes the use of Bayesian statistics to outline how youngsters infer the basics of cause and effect.
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TensorFlow Dev Summit 2017: Integrating Keras and TensorFlow

TensorFlow Dev Summit 2017: Integrating #Keras and #TensorFlow

  • My choice has fallen to a presentation by François Chollet of the deep learning library API Keras and its integration with TensorFlow.
  • As Dr. Chollet explains, Keras integrated with TensorFlow promises to streamline deep learning frameworks in ways that will be increasingly user-friendly, rendering the mass adoption of these software developments a more feasible reality:

    Dr. François Chollet is the primary author of Keras, developing this tool while at Research at Google.

  • For instance the way video with text data is processed with the Keras-TensorFlow integration is nicely described with the stack of CNNs, LSTMs and dense final layers with softmax being features explained by Dr. Chollet.
  • The best practises advised by Dr. Chollet about the initialization of recurrent weighs of  the neural network is worth to listen, even if the experienced practitioner feels bored.
  • A final note to the confirmation by Dr. Chollet of the capacity of TensorFlow to streamline  a CloudML or a hyperparameter tuning process with just a few lines of code, enabling a distributed training platform able to enhance big data computes with productivity gains.

I am briefly sharing a video from the last TensorFlow Dev Summit in February 2017. My choice has fallen to a presentation by François Chollet of the deep learning library API Keras and its integration with TensorFlow. As Dr. Chollet explains, Keras integrated with TensorFlow promises to streamline deep learning frameworks in ways that will…
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