While many people in the world are worrying that robots will take over human jobs once artificial intelligence (AI) is fully developed, it's a safe bet that no one put "author" at the top of the robot job list. Yet, now that a Japanese AI program has co-authored a short-form novel that passed the first round of screening for a national literary prize, it seems that no occupation is safe. The robot-written novel didn't win the competition's final prize, but who's to say it won't improve in its next attempt? The novel is actually called The Day A Computer Writes A Novel, or "Konpyuta ga shosetsu wo kaku hi" in Japanese. The meta-narrative wasn't enough to win first prize at the third Nikkei Hoshi Shinichi Literary Award ceremony, but it did come close.

Associated Press/Ahn Young-joonTV screens show the live broadcast of the Google DeepMind Challenge Match between Google's artificial intelligence program, AlphaGo, and South Korean professional Go player Lee Sedol, at the Yongsan Electronic store in Seoul, South Korea, Tuesday, March 15, 2016. Humans have been taking a beating from computers lately. The 4-1 defeat of Go grandmaster Lee Se-Dol by Google's AlphaGo artificial intelligence (AI) is only the latest in a string of pursuits in which technology has triumphed over humanity. Self-driving cars are already less accident-prone than human drivers, the TV quiz show Jeopardy! is a lost cause, and in chess humans have fallen so woefully behind computers that a recent international tournament was won by a mobile phone. There is a real sense that this month's human vs AI Go match marks a turning point.

Microsoft has a new artificial intelligence bot, named Taylor, that tries to hold conversations on Twitter, Kik, and GroupMe. And she makes me feel terribly old and out of touch. Tay, as she calls herself, is a chatbot that's targeted at 18- to 24-year-olds in the US. Just tweet at her or message her and she responds with words and occasionally meme pictures. She's meant to be able to learn a few things about you--basic details like nickname, favorite food, relationship status--and is supposed to be able to have engaging conversations.

The post is by Ilan Reiter, Principal Data Science Manager at Microsoft. The past few years have witnessed dramatic changes to the energy sector. Renewable energy sources along with the emergence of IoT (Internet of Things) are creating exciting new opportunities. On the consumption side, utilities and indeed the entire energy sector have seen consumption flatten out, with consumers demanding better ways to monitor and control their energy usage. Furthermore, with many grids becoming outdated and expensive to maintain, utilities and smart grid companies are in ever greater need to innovate.

As an employee of a company that provides digital security through machine learning, Stephanie Yee spent a lot of time familiarizing clients with the secret sauce behind her product. So she and her colleague, designer Tony Chu, set out to create an interactive graphic that would do the explaining for them. The pair chose a topic they thought would be intuitive to most people--real estate prices--and created an interactive environment that builds in complexity as the user scrolls. In the first 30 days, the site got 250,000 page views worldwide. Feedback showed Chu and Yee that experts in many fields could use their interactives.

SparkR is an R package that provides a light-weight frontend to use Apache Spark from R. In Spark 1.6.0, SparkR provides a distributed data frame implementation that supports operations like selection, filtering, aggregation etc. (similar to R data frames, dplyr) but on large datasets. SparkR also supports distributed machine learning using MLlib. A DataFrame is a distributed collection of data organized into named columns. It is conceptually equivalent to a table in a relational database or a data frame in R, but with richer optimizations under the hood.

Google and Facebook aren't the only ones vying to be the standard bearer for the hottest AI technique around. China's leading Internet search company, Baidu, which is also investing heavily in a popular and powerful machine-learning technology called deep learning, today released some key code that it uses to make this AI software run very efficiently. Baidu's code was recently used to build an impressive speech-recognition system called Deep Speech 2. For some short sentences, this system is better than most humans at recognizing speech correctly (see "Baidu's Deep-Learning System Rivals People at Speech Recognition"). This is an especially useful technology for Baidu, because it offers a better way for the company's many millions of users to access its services, especially on mobile. Typing Chinese characters on a smartphone is tricky and complex, and many people in China already prefer to use their voice to send short messages or to search the Web for information.

When researchers Frey and Osborne predicted in 2013 that 47% of US jobs were susceptible to automation by 2050, they set off a wave of dystopian concern. But the key word is "susceptible". The automation revolution is possible, but without a radical change in the social conventions surrounding work it will not happen. The real dystopia is that, fearing the mass unemployment and psychological aimlessness it might bring, we stall the third industrial revolution. Instead we end up creating millions of low skilled jobs that do not need to exist.

There are few things more frustrating than a slow-loading Web page. For companies, what's even worse is what comes after: users abandoning their site in droves. Amazon, for example, estimates that every 100-millisecond delay cuts its profits by 1 percent. To help combat this problem, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and Harvard University have developed a system that decreases page-load times by 34 percent. Dubbed "Polaris," the framework determines how to overlap the downloading of a page's objects, such that the overall page requires less time to load.

A good friend recently told me that it takes a special kind of nerd to appreciate what Google's AlphaGo did to international Go champion Lee Sedol: a nerd that is both a Go nerd and a computer nerd. For Go nerdiness, I am recently enamored with the massively complex game that has exponentially more outcomes and dimensions than chess. As for the tech nerdiness, many of us assumed that after DeepBlue beat Kasparov in chess, any other game was a foregone conclusion. But actually, it's taken twenty years for a computer to rise to the level of top-ranked Go players, because high-level Go incorporates less calculation of a limited set of future outcomes and far more intuition. Challenges like this are not just an interesting competition.