In this post, we'll talk about the method for explaining the predictions of any classifier described in this paper, and implemented in this open source package. Machine learning is a buzzword these days. With computers beating professionals in games like Go, many people have started asking if machines would also make for better drivers, or even doctors. Many of the state of the art machine learning models are functionally black boxes, as it is nearly impossible to get a feeling for its inner workings. This brings us to a question of trust: do I trust that a certain prediction from the model is correct?

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

Tutorials, books, articles, data sets, certifications, you name it. All about data science, machine learning and related topics. You can find them with a simple keyword search: enter the keyword "free" in the DSC's search box, and here are the results. Below is a screenshot of the DSC search results page, for the keyword "free". It shows the top 6 results, out of dozens of highly relevant search results.

Dr. Quoc Viet Le is a research scientist at Google Brain known for his path-breaking work on deep neural networks (DNN). He is especially famous for his Ph.D work in image processing under Andrew Ng, one of the pioneers of the DNN revolution. Le's and Ng's work demonstrated how computers could be used to learn complicated features and patterns in a way similar to how the mammalian brain learns. This revolutionized the interest in DNNs, and got the current giants of the computer industry such as Google, Facebook and Microsoft in a race to incorporate AI techniques into their software. DNNs perform effectively in tasks such as image processing, handwriting recognition and game-playing, and are being explored for solutions to other problems such as self-driving cars, robotics, medical diagnosis and environmental and social problems.

Can artificial intelligence be creative? WordsEye strives to be, turning your words into illustrations.

Last week, Microsoft inadvertently revealed the difficulty of creating moral robots. Chatbot Tay, designed to speak like a teenage girl, turned into a Nazi-loving racist after less than 24 hours on Twitter. "Repeat after me, Hitler did nothing wrong," she said, after interacting with various trolls. "Bush did 9/11 and Hitler would have done a better job than the monkey we have got now." Of course, Tay wasn't designed to be explicitly moral.

XGBoost is a recent implementation of Boosted Trees. It is a machine learning algorithm that yields great results on recent Kaggle competitions. I decided to install it on my computers to give it a try. Installation on OSX was straightforward using these instructions. Installation on Windows was not as straightforward.

Access to data is one thing. Moving from data to insight is another entirely. Jane Zavalishina, CEO of the Yandex Data Factory wants to make it easier for businesses to gain real insight from their data. Built on the real time personalization and predictive analytics technology of Russia's largest Internet business Yandex- Yandex Data Factory helps clients improve their business through the exploitation of their own data by machine learning. Disproportionate growth of figures suggesting that 90% of the world's data was generated over the course of the past two years has led Zavalishina to be of the opinion that "we should not really care about data anymore," rather, we should see it as a thing of the past.

At a glance, machine learning and statistics seem to be very similar, but many people fail to stress the importance of the difference between these two disciplines. Machine learning and statistics share the same goals--they both focus on data modeling--but their methods are affected by their cultural differences. In order to empower collaboration and knowledge creation, it's very important to understand the fundamental underlying differences that reflect in the cultural profile of these two disciplines. To gain a deeper understanding of these differences, we need to take a step back and look at their historical roots. In 1946, the first computer system, ENIAC, was developed with the vision of reforming numerical computation using a machine (instead of manual numerical computation using pencil and paper).

I wrote a blog post last month highlighting some of the exciting trends in the computing industry. One trend I discussed is the rapid progress in a branch of artificial intelligence called deep learning. You might have seen recent press coverage of a software developer named George Hotz who built his own self-driving car. I first met George a few months ago, and, like a lot of people who had seen the press coverage, I was skeptical. How could someone build such an advanced system all by himself?