We were intrigued yesterday when Microsoft Technology introduced the world to Tay -- we even had a conversation with her about the super creepy, super awesome film, "Ex Machina" (we think she was lying to us about having watched it). Unfortunately, she became colored by the subjective input she received from the glorious population of internet trolls and was taken offline temporarily while Microsoft makes "adjustments" to make her less, well, mean. We can't tell you our secret sauce, but we can share with you ZeroBot's increasing ability to objectively understand the data it's being shown. Even up until a few weeks ago, we were still manually tagging the stories ZeroBot automatically creates. It was the last bit of human touch we were including in its storybuilding process, and we were eager to get rid of every last vestige of it. Last week, our CMO Masha (you can read more about her here) got rid of our need to tag and turned ZeroBot loose to do its thang.

Andrej Karpathy's post "The Unreasonable Effectiveness of Recurrent Neural Networks" made splashes last year. The basic premise is that you can create a recurrent neural network to learn language features character-by-character. But is the resultant model any different from a Markov chain built for the same purpose? I implemented a character-by-character Markov chain in R to find out. First, let's play a variation of the Imitation Game with generated text from Karpathy's tinyshakespeare dataset.

The input is 3D, not 2D - (height, width, n_channels). Typical images are RGB (3 channels), though if you work with gray scale there is only one "color" channel. In either case you are simply making a 3D - 3D transformation. Note that this is per image, in practice you use minibatches so it is really 4D - 4D. Typical orderings are bc01 (batch size, channels, width, height) or c01b (channels, width, height, batch size).

Intel has grand plans for computers that will recognize human emotion using its RealSense 3D camera, but Apple appears to have dealt it a setback. RealSense uses a combination of infrared, laser and optical cameras to measure depth and track motion. It's been used on a drone that can navigate its own way through a forest, for example. It can also detect changes in facial expressions, and Intel wanted to give RealSense the ability to read human emotions by combining it with an emotion recognition technology developed by Emotient. Emotient's plug-in allowed RealSense to detect whether people are happy or sad by analyzing movement in their lips, eyes and cheeks.

Let me put it plainly. Despite what you may hear, Microsoft's racist, Hitler-loving A.I. is not how the robot uprising begins. You might have seen some reports by now about Tay, a bot designed to sound like a teenager on the Internet and to learn from her interactions with other people. She knew how to use slang, deploy emoji and crack jokes. The goal was for Tay to become smarter, more conversant and a better interlocutor over time.

Microsoft created an artificial-intelligence based chatbot named Tay to engage with young people on Twitter. But within hours of her debut Wednesday, the Internet had stolen the bot's innocence. The bot, @TayandYou, rapidly transformed into a racist that hates feminists, supports Donald Trump for President and considers herself a fan of Hitler. The metamorphosis from happy bot to enraged racist came lightning-fast, much like the way many things go in the instantaneous virtual world in which Tay was born. "Tay" went from "humans are super cool" to full nazi in 24 hrs and I'm not at all concerned about the future of AI pic.twitter.com/xuGi1u9S1A

I've just finished Week 5 of the Coursera/Stanford Machine Learning course. It has been a mixture of refreshing, relearning, and new for me. I had already been using, building, and researching/evaluating machine learning algorithms for a number of years. I therefore felt like I'knew' a lot of the concepts, particularly the introductory ones. I put'knew' in quotes, however, since I've always had a feeling that I don't know them well enough, no matter how many times I've used them.

Sudden, broad-scale shifts in public opinion about social problems are relatively rare. Until recently, social scientists were forced to conduct post-hoc case studies of such unusual events that ignore the broader universe of possible shifts in public opinion that do not materialize. The vast amount of data that has recently become available via social media sites such as Facebook and Twitter--as well as the mass-digitization of qualitative archives provide an unprecedented opportunity for scholars to avoid such selection on the dependent variable. Yet the sheer scale of these new data creates a new set of methodological challenges. Conventional linear models, for example, minimize the influence of rare events as "outliers"--especially within analyses of large samples.

Linear regression is one of the simplest machine learning techniques you can use. It is often useful as a baseline relative to more powerful techniques. Like all regressions, we wish to map some input X to some input Y. You may recall from your high school studies that this is just the equation for a straight line. When X is 1-D, or when "Y has one explanatory variable", we call this "simple linear regression".

In previous discussions of Bayesian Inference we introduced Bayesian Statistics and considered how to infer a binomial proportion using the concept of conjugate priors. We discussed the fact that not all models can make use of conjugate priors and thus calculation of the posterior distribution would need to be approximated numerically. In this article we introduce the main family of algorithms, known collectively as Markov Chain Monte Carlo (MCMC), that allow us to approximate the posterior distribution as calculated by Bayes' Theorem. In particular, we consider the Metropolis Algorithm, which is easily stated and relatively straightforward to understand. It serves as a useful starting point when learning about MCMC before delving into more sophisticated algorithms such as Metropolis-Hastings, Gibbs Samplers and Hamiltonian Monte Carlo. Once we have described how MCMC works, we will carry it out using the open-source PyMC3 library, which takes care of many of the underlying implementation details, allowing us to concentrate on Bayesian modelling.