AI for security: Microsoft Security Risk Detection makes debut

Full details at: –

Microsoft is making a cloud service that uses artificial intelligence to track down bugs in software generally available, and it will begin offering a preview version of the tool for Linux users as well.

Microsoft Security Risk Detection, previously known as Project Springfield, is a cloud-based tool that developers can use to look for bugs and other security vulnerabilities in the software they are preparing to release or use. The tool is designed to catch the vulnerabilities before the software goes out the door, saving companies the heartache of having to patch a bug, deal with crashes or respond to an attack after it has been released.

Free course on Deep Learning for Self-Driving Cars

self drive cars.pngA free course and introduction to deep learning through the applied task of building a self-driving car. Taught by Lex Fridman.

Visit for full details of “MIT 6.S094: Deep Learning for Self-Driving Cars“.

Data Science: Performance of Python vs Pandas vs Numpy

Re-post from

Speed and time is a key factor for any Data Scientist. In business, you do not usually work with toy datasets having thousands of samples. It is more likely that your datasets will contain millions or hundreds of millions samples. Customer orders, web logs, billing events, stock prices – datasets now are huge.

I assume you do not want to spend hours or days, waiting for your data processing to complete. The biggest dataset I worked with so far contained over 30 million of records. When I run my data processing script the first time for this dataset, estimated time to complete was around 4 days! I do not have very powerful machine (Macbook Air with i5 and 4 GB of RAM), but the most I could accept was running the script over one night, not multiple days.

Thanks to some clever tricks, I was able to decrease this running time to a few hours. This post will explain the first step to achieve good data processing performance – choosing right library/framework for your dataset.

The graph below shows result of my experiment (details below), calculated as processing speed measured against processing speed of pure Python.

Python vs Numpy vs Pandas

As you can see, Numpy performance is several times bigger than Pandas performance. I personally love Pandas for simplifying many tedious data science tasks, and I use it wherever I can. But if the expected processing time spans for more than many hours, then, with regret, I change Pandas to Numpy.

I am very aware that the actual performance may vary significantly, depending on a task and type of processing. So please, treat these result as indicative only. There is no single test that can shown “overall” comparison of performance for any set of software tools.

Posted on July 15, 2017 by

see full post @



Microsoft’s new iPhone app narrates the world for blind people

Microsoft Seeing AI.jpg

“Microsoft has released Seeing AI — a smartphone app that uses computer vision to describe the world for the visually impaired. With the app downloaded, the users can point their phone’s camera at a person and it’ll say who they are and how they’re feeling. They can also point it at a product and it’ll tell them what it is. All of this is done using artificial intelligence that runs locally on their phone”…

Webinar: Parallelize R Code Using Apache® Spark™ on August 15th, 2017

R is the latest language added to Apache Spark, and the SparkR API is slightly different from PySpark. SparkR’s evolving interface to Apache Spark offers a wide range of APIs and capabilities to Data Scientists and Statisticians. With the release of Spark 2.0, and subsequent releases, the R API officially supports executing user code on distributed data. This is done primarily through a family of apply() functions.

In this Data Science Central webinar, we will explore the following:
• Provide an overview of this new functionality in SparkR
• Show how to use this API with some changes to regular code with apply()
• Focus on how to correctly use this API to parallelize existing R packages
• Consider performance and examine correctness when using the apply() family of functions in SparkR

Hossein Falaki, Software Engineer — Databricks Inc.

Hosted by: Bill Vorhies, Editorial Director — Data Science Central
Title: Parallelize R Code Using Apache® Spark™
Date: Tuesday, August 15th, 2017
Time: 9:00 AM – 10:00 AM PDT

Introducing Microsoft R Server 9.1 release

“Expert data scientists are adopting Advanced Analytics (AA) and Machine Learning (ML) at a rapid pace. This pace can be significantly increased when enterprise-grade AA and ML are available within environments where the customers’ data is, infusing intelligence into mission-critical applications is made much easier and, enterprises can turn to a single vendor to make the world of AA and ML synthesized and supported with the SLAs they have come to expect. At Microsoft, our mission has been to make this vision of ambient intelligence a reality for our customers. We took the first step with Microsoft R Server 9.0 (, and this follow on release includes significant innovations such as:

• New machine learning enhancements and inclusion of pre-trained cognitive models such as sentiment analysis & image featurizers
• SQL Server Machine Learning Services with integrated Python in Preview
• Enterprise grade operationalization with real-time scoring and dynamic scaling of VMs
• Deep customer & ISV partnerships to deliver the right solutions to customers
• A panoply of sources to help you get started with ease

You can immediately download Microsoft R Server 9.1 from MSDN ( and Visual Studio Dev Essentials ( It comes packed with tons of value built on top of the latest open source R engine that makes R enterprise-class. Also check out R Client for Windows ( and R Client for Linux (”…


Creating R visuals in the Power BI service

Recently announced “The Power BI service supports viewing and interacting with visuals created with R scripts. Visuals created with R scripts, commonly called R visuals, can present advanced data shaping and analytics such as forecasting, using the rich analytics and visualization power of R.”

See for full details.

Pi – The Personal Assistant – Speech Recognition (Raspberry Pi + IBM® Watson)

Not as nice as having AI run independently on a Pi (like the Microsoft example shared yesterday), but this is another example of ML being used on a Pi…

… this example for Speech recognition requires Watson cloud services and subscription, but thought I would share in case anyone is interested.

Pi – The Personal Assistant – Speech Recognition (Raspberry Pi + IBM® Watson) – Prototype for a voice enabled personal assistant built on Raspberry Pi using IBM Watson services
Learn how to build your own voice enabled Personal Assistant that listens, understands & responds with your Raspberry Pi

… same would be just as easily achieved using Cortana ( or Google (

Microsoft made its AI work on a $10 Raspberry Pi


When you’re far from a cell tower and need to figure out if that bluebird is Sialia sialis or Sialia mexicana, no cloud server is going to help you. That’s why companies are squeezing AI onto portable devices, and Microsoft has just taken that to a new extreme by putting deep learning algorithms onto a Raspberry Pi…

…The idea came about from Microsoft Labs teams in Redmond and Bangalore, India. Ofer Dekel, who manages an AI optimization group at the Redmond Lab, was trying to figure out a way to stop squirrels from eating flower bulbs and seeds from his bird feeder. As one does, he trained a computer vision system to spot squirrels, and installed the code on a $35 Raspberry Pi 3. Now, it triggers the sprinkler system whenever the rodents pop up, chasing them away.”