Tay isn't the first chatbot in history but it became the most prominent because of its ties with Microsoft. Then it became one of the most notorious chatbot in less than 24 hours after it switched from well-meaning teen to offensive, pro-Nazi, anti-feminist rebel. Naturally, Microsoft shut it down, "putting it to sleep", so to speak. Now the company has come out with a statement clarifying that Tay's words do not reflect the company's principles and values at all. They do own up to the "slight" oversight in protecting Tay from attacks.

The details of the project are revealed in a research paper titled "A Neural Algorithm of Artistic Style." "Here we introduce an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality," reads the paper, penned by a group of researchers from the University of Tubingen in Germany. "The system uses neural representations to separate and recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images." Using an image of a street in Germany (above), the team demonstrated the ability to recreate the photo using the visual styles of Vincent van Gogh, Edvard Munch and Pablo Picasso. Each transformed street photo, at least upon casual inspection, looks like it was indeed painted by one of the masters. Over the last couple of days, Andrej Karpathy, a PhD student at Stanford studying Machine Learning, posted a few experiments on Twitter using the technique, and the results were incredibly accurate. Karpathy had since removed all examples of his experiments with the image transfer, but we managed to capture some screenshots of his tests, including one that turned a photo of Gandalf into a Picasso-style portrait. Does this mean that, with a little more code tweaking, we'll begin to see fake works (created using this technique) suddenly "discovered" in coming years?

Onalytica, a firm which specializes in social network analysis and influencer relationship software, recently released their latest Top 100 Artifical Intelligence and Machine Learning Influencers and Brands Report. The report lists the top 100 in each category, and goes on to graphically explore the relationships between them. The report's website shares a list of the top 50 in each category (influencers and brands), while the downloadable full report contains the top 100. Below is a graph of the relationships between the top influencers (a similar graph exists on Onalytica's website for top brands). Onalytica reached out to some of the top influencers for some insight into their views on the subjects of AI and machine learning, and included some of this insight on their post.

Many experts say combining Big Data and artificial intelligence will bring about the 4th Industrial Revolution. Park Se-young has the details. The Industrial Revolution brought huge changes to the economy through the mass production of manufactured goods. The second industrial revolution came about on the back of new innovations in steel production, petroleum and electricity …which led to the introduction of public transportation and airplanes. A third round of changes was driven by internet technology and renewable energy, creating a new industry for ICT and the energy sector.

In April last year, the Chinese scientist announced they have successfully used CRISPR/Cas9 technology to change human embryo's genome. It sparked a worldwide discussion on designer babies. A discussion, that is still going on. The crazy and ambitious guys at DARPA are thinking how to increase the neural plasticity of the brain to increase the rate of learning "beyond normal levels", reducing the time it is needed to master foreign languages or other skills. Nowadays, even not having one arm is not a good excuse to not hit the gym.

An important step in data analysis is data exploration and representation. In this tutorial we will see how by combining a technique called Principal Component Analysis (PCA) together with Cluster Analysis we can represent in a two-dimensional space data defined in a higher dimensional one while, at the same time, being able to group this data in similar groups or clusters and find hidden relationships in our data. More concretely, PCA reduces data dimensionality by finding principal components. These are the directions of maximum variation in a dataset. By reducing a dataset original features or variables to a reduced set of new ones based on the principal components, we end up with the minimum number of variables that keep the maximum amount of variation or information about how the data is distributed. If we end up with just two of these new variables, we will be able to represent each sample in our data in a two-dimensional chart (e.g. a scatterplot). As an unsupervised data analysis technique, clustering organises data samples by proximity based on its variables.

Payment fraud evolves in ways that are truly frightening -- and with haste. Fraudsters are continually scrambling to keep up with, and have one foot in front of, technology, with recent stumbling blocks in the form of EMV, in the United States, likely to give rise to greater card-not-present fraud. But the would-be payments criminals have potent weaponry at hand, including ever faster, ever more powerful and ever cheaper computing power, and they have been targeting, according to data science firm Feedzai, the weaker links that exist in the financial services chain. In a recent whitepaper titled "A Primer to Machine Learning for Fraud Management," the firm noted that, even as financial services evolve to embrace a digital world -- with, say, virtual goods in hand and even virtual cash -- the prospects for successful payments malfeasance grow in lockstep. In fact, said Feedzai, as many as 65 percent of firms with annual revenues of at least 1 billion were victims of payments fraud as recently as 2014.

Linear regression is a very simple method but has proven to be very useful for a large number of situations. In this post you will discover exactly how linear regression works step-by-step. This tutorial was written for developers and does not assume any prior background in mathematics or statistics. This tutorial was written with the intention that you will follow a long in your own spreadsheet, which will help to make the concepts stick. Simple Linear Regression Tutorial for Machine Learning Photo by Catface27, some rights reserved.

For the past few months, I've been working on a project that involved machine learning, a subject that I was previously quite unfamiliar with. Though I'm still very much a novice, the resources here helped give me an overview of the subject, or are ones that I plan on exploring further.

Today whole economy is changing into Digital economy and disrupting all the respective markets and industries. Businesses are shifting from selling physical things to digital things. Just to take an example of Music industry – in old days we had to go to store to buy LP records, then came cassettes and CDs which were then disrupted due to introduction of MP3 format in 1990s. Later came IPod/IPhone and people could carry their music with them. Spotify further changed this to streaming music, so now you don't have to download music at all.