Last week, Google released Parsey McParseface, a funny name for a state-of-the-art tool aimed at one of the most difficult problems in artificial intelligence. For all that computers have accomplished in the past five years, from winning on "Jeopardy!" to defeating a Go grandmaster, they are still terrible at figuring out what people are saying. Language is one of the most complex tasks that humans perform. That's why there has been such a hullaballo over McParseface, which is pretty much a glorified sentence diagrammer. McParseface does what most students learn to do in elementary school.

Artificial intelligence has bested humanity in almost every intellectual pursuit going. They've trounced us at chess, murdered us on the TV show Jeopardy! But when it comes to poetry, computers are worse than that beret-wearing kid from high school who always rhymed "love" with "above". A poetic Turing test was recently held at Dartmouth College and the results were pretty messy. In an attempt to test the artist skills of robots, researchers from Dartmouth College pitted an artificial intelligence program designed to produce sonnets against human poets in a genteel "rap" face-off.

As we saw in our first blog post on AI Everywhere and Nowhere, defining'Artificial Intelligence' is like trying to hit a disappearing target. As soon as any aspect of AI gains widespread adoption, people fail to distinguish it as an AI technology, and it dissolves into the sea of general technology. As a result, most detractors of AI, at least until recently, have questioned the real-world applications of AI. In turn, AI never gains the respect and recognition it needs to evolve and reach its full potential. The beauty (and bane) of AI is that it is everywhere and yet nowhere – it is becoming ubiquitous in all of our interactions (at least all of our'virtual interactions'), yet most people fail to recognize and respect it.

In most organizations, technology is neither properly understood nor managed. It is bought and it is used. Management expect it to do magical things like improve productivity without them having to be in any way involved in managing it. In fact, technology is so magical that it is expected to do the managing. Take Content Management Software as an example.

The world stands poised on the brink of a fourth industrial revolution. Rapid advances in a host of technologies, including artificial intelligence, 3-D printing, robotics, Big Data and data science, genetics, medical imaging and computer vision are combining to alter almost every profession and every industry in potentially radical ways. What sets this "industrial revolution" apart from its predecessors is both its speed and its breadth. Instead, this fourth industrial revolution is also transforming services and professions. Tasks that were once thought to be the exclusive province of the human mind -- from driving trucks on busy highways to drafting legal documents to analyzing medical research -- can now be carried out by software, thanks to artificial intelligence and big data analytics.

The past few years has seen a blur of software giants releasing AI and Machine Learning themed APIs and services. I was, frankly, surprised at how many options there currently are for developers and companies. I think it's a positive sign for the industry that there are multiple options from reputable brands when it comes to topics like visual and language recognition – these have almost become commodities. You also see strong consolidation into very typical categories like machine learning for building general predictive models, visual recognition, language and speech recognition, conversational bots and news analysis. Did I miss something from this list?

Balochistan, PAKISTAN/KABUL/WASHINGTON – Taliban supremo Mullah Akhtar Mansour was killed in a U.S. drone attack in Pakistan, senior militant sources told AFP Sunday, adding that an insurgent assembly was underway to decide on his successor. Saturday's bombing raid, the first known U.S. assault on a top Afghan Taliban leader on Pakistani soil, marks a major blow to the militant movement, which saw a new resurgence under Mansour. The elimination of Mansour, who rose to the rank of leader nine months earlier after a bitter internal leadership struggle, could also scupper any immediate prospect of peace talks. "I can say with good authority that Mullah Mansour is no more," a senior Taliban source told AFP. Mansour's death, which risks igniting new succession battles within the fractious group, was confirmed by two other senior figures who said its top leaders were gathering in Quetta to name their future chief.

This video will get you up and running with your first movie recommender system in just 10 lines of C . We train a neural network on a MovieLens dataset of movie ratings by different users to generate a top 10 recommendation list for the default user ID. Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now. Much more to come so please subscribe, like, and comment.

The growth of machine learning as a discipline is embarrassing. There's no doubt that machine learning has shown its potential to enhance search recommendations – by effective analysis of patterns. Day by day, applications of this field are increasing- including text processing, video analysis, voice recognition, email spam filtering, search recommendations, and more. But, a question is quite relevant here. Search recommendations are mandatory for an online business.

You might think literary criticism is no place for statistical analysis, but given digital versions of the text you can, for example, use sentiment analysis to infer the dramatic arc of an Oscar Wilde novel. Now you can apply similar techniques to the works of Jane Austen thanks to Julia Silge's R package janeaustenr (available on CRAN). The package includes the full text the 6 Austen novels, including Pride and Prejudice and Sense and Sensibility. With the novels' text in hand, Julia then applied Bing sentiment analysis (as implemented in R's syuzhet package), shown here with annotations marking the major dramatic turns in the book: There's quite a lot of noise in that chart, so Julia took the elegant step of using a low-pass fourier transform to smooth the sentiment for all six novels, which allows for a comparison of the dramatic arcs: This is super interesting to me. Emma and Northanger Abbey have the most similar plot trajectories, with their tales of immature women who come to understand their own folly and grow up a bit.