Would you know if one of your Teaching Assistants was a bot? Online learning is becoming the norm in universities across the globe, bringing sweeping changes to the way we learn. But earlier this year on online graduate class at Georgia Tech took things a stage further. "Our Teaching Assistants are getting bogged down answering routine questions," said Ashok Goel, who teaches a graduate science course. Students in the class typically post 10,000 messages a semester on the Piazza forum for the course, many of which are either variations on a theme or simple logistical questions.

Machine learning automatically recognizes complex, previously unknown, novel, and useful patterns and information in all types of data. Data driven algorithms are the wave of the future and their results improve as the amount of data increases. Machine learning algorithms are used in search engines, image analysis, multimedia database retrieval, bioinformatics, industrial automation, speech recognition, and many other fields. This survey course covers the concepts and principles of a large variety of data mining methods, equips you with a working knowledge of these techniques and prepares you to apply them to real problems. The statistical programming language R is used to implement machine learning algorithms.

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.

Many of you probably know that I have my own bot -- but one of the more useful features of my bot is its ability to field private messages on my behalf. These messages get sent to me via Messenger, and I respond from within that context. Unlike email which I can and tend to put off indefinitely (sorry!), messages that I receive via MessinaBot stack up quickly, and therefore need to be addressed serially, as they're received. This forces me to respond in a relatively timely and brief fashion. Sometimes though, I get tougher questions that require a bit more thought. Given the frequency of questions that I receive related to bots, I asked Anne Cathrine Saarem whether she'd mind if I published my responses to her questions publicly.

HOW HAS ARTIFICIAL intelligence, associated with hubris and disappointment since its earliest days, suddenly become the hottest field in technology? The term was coined in a research proposal written in 1956 which suggested that significant progress could be made in getting machines to "solve the kinds of problems now reserved for humans…if a carefully selected group of scientists work on it together for a summer". That proved to be wildly overoptimistic, to say the least, and despite occasional bursts of progress, AI became known for promising much more than it could deliver. Researchers mostly ended up avoiding the term, preferring to talk instead about "expert systems" or "neural networks". The rehabilitation of "AI", and the current excitement about the field, can be traced back to 2012 and an online contest called the ImageNet Challenge.

Marc Carrel-Billiard, Managing Director Global Technology R&D, AccentureBangalore: AI can double annual economic growth rates by 2035 by changing the nature of work and spawning a new relationship between man and machine, according to Accenture Research. The impact of AI technologies on business is projected to boost labour productivity by up to 40 percent by fundamentally changing the way work is done and reinforcing people's role to drive growth in business. In an interview with ETCIO, Accenture's Managing Director Global Technology R&D, Marc Carrel-Billiard talks about company's technology labs and its focus areas, new technologies and its impact and key tech trends that CIOs and businesses need to look for and much more. Marc is with Accenture for the past 18 years and currently oversees the Accenture Technology Labs, Accenture Open Innovation, Accenture's global technology R&D organization which explores new and emerging technologies, across seven locations around the world. Which are the key technology domains that you are trying to focus on?

What are the differences between data science, data mining, machine learning, statistics, operations research, and so on? Here I compare several analytic disciplines that overlap, to explain the differences and common denominators. Sometimes differences exist for nothing else other than historical reasons. Sometimes the differences are real and subtle. I also provide typical job titles, types of analyses, and industries traditionally attached to each discipline. Underlined domains are main sub-domains. It would be great if someone can add an historical perspective to my article. First, let's start by describing data science, the new discipline. Job titles include data scientist, chief scientist, senior analyst, director of analytics and many more.

In a major win for artificial intelligence, Google DeepMind's AlphaGo has beat European Go champion Fan Hui in the complex 2,500-year-old Chinese game of Go, touted the official Google blog. A victory in a Go game against a human champion has long been coveted among AI researchers, because the possible moves that a player can take can reach into the quadrillions and beyond. As a result, Go has proven a formidable challenge for artificial intelligence researchers. Microsoft and Facebook, for example, have been working on ways to win in the game over a human champion, but have had no luck to date, according to a BBC news report. Last October, Google DeepMind held a private, closed-door Go match in its London office between its AlphaGo system and Hui.

The omnipresence of deep learning architectures such as deep convolutional neural networks (CNN)s is fueled by the synergistic combination of ever-increasing labeled datasets and specialized hardware. Despite the indisputable success, the reliance on huge amounts of labeled data and specialized hardware can be a limiting factor when approaching new applications. To help alleviating these limitations, we propose an efficient learning strategy for layer-wise unsupervised training of deep CNNs on conventional hardware in acceptable time. Our proposed strategy consists of randomly convexifying the reconstruction contractive auto-encoding (RCAE) learning objective and solving the resulting large-scale convex minimization problem in the frequency domain via coordinate descent (CD). The main advantages of our proposed learning strategy are: (1) single tunable optimization parameter; (2) fast and guaranteed convergence; (3) possibilities for full parallelization. Numerical experiments show that our proposed learning strategy scales (in the worst case) linearly with image size, number of filters and filter size.

Developers of text-to-speech synthesizers (TTS) often make use of human raters to assess the quality of synthesized speech. We demonstrate that we can model human raters' mean opinion scores (MOS) of synthesized speech using a deep recurrent neural network whose inputs consist solely of a raw waveform. Our best models provide utterance-level estimates of MOS only moderately inferior to sampled human ratings, as shown by Pearson and Spearman correlations. When multiple utterances are scored and averaged, a scenario common in synthesizer quality assessment, AutoMOS achieves correlations approaching those of human raters. The AutoMOS model has a number of applications, such as the ability to explore the parameter space of a speech synthesizer without requiring a human-in-the-loop.