SEO is moving away from general optimization practices and focusing on user engagement data. That doesn't mean that we shouldn't be optimizing websites with the traditional tactics, but we need to start thinking about the bigger picture. At the end of the day, make your website serve the purpose of your customers. Create an experience that will bring them back by creating a fluid user experience and content that is readable. After all, Google wants what is best for the user, not something that is optimized.

Argentinian goalkeeper Sergio Goycochea used to urinate on the pitch, certain French rugby players like to headbutt each other and Serena Williams apparently never changes her socks during a tournament--but as athletic rituals go, listening to music composed by a robot has got to be up there. In an effort to get one up on their rivals, the U.S. Olympic wrestling team has been listening to songs written by artificial intelligence to prepare for matches. The music, created by Brain.fm, Speaking to Newsweek, team coach Matt Lindland says he came across Brain.fm after actively seeking a sleep-based solution for his athletes. Lindland approached science journalist Steven Kotler, who recommended the AI-composed music as a safe and healthy performance-enhancing technique.

There is no question that artificial intelligence (AI) and automation are entering the workplace in many graduate level jobs, and this trend is likely to continue and quicken. Times Higher Education recently asked whether universities needed to rethink what they do and how they do it, given that artificial intelligence is beginning to take over many post-university careers. The implications and – most importantly – the potential benefits for education are significant, and perhaps not yet appreciated by higher education leaders. With that in mind, here are four examples of how AI can benefit universities. First, there is a new role for higher education, which is to equip graduates to work effectively alongside artificially intelligent systems.

Artificial intelligence (AI) has been around for decades, but only recently has it begun to emerge as a viable field with massive commercial opportunity. Data is the fuel that feeds AI and is opening opportunities that we can only have imagined until now. And, large enterprises are waking up to this opportunity. Findings from our newly published research report, "Outlook on Artificial Intelligence in the Enterprise 2016" indicate major changes as to the perception and use of AI-powered solutions. The survey results clearly speak to the power of the partnership.

Apple has acquired a machine learning and artificial intelligence (AI) startup called Turi, which is based in Seattle, according to reports. Apple on its part has not commented on the deal, and its statement only said the company does make these acquisitions from time to time, which is the standard Cupertino-line on such report. The deal was first reported by GeekWire, which said Apple is paying close to 200 million for the company. According to a report on ReCode, Turi was earlier known as both GraphLab and Dato, and is used by companies like Pandora used to power recommendations. The company was founded was Carlos Guestrin, a University of Washington professor, but he too refused to comment on the deal.

DESCRIPTION Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon's customers? Do you want to build advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify?

Last week, deep learning research leader Yann LeCun took part in a Quora Session, during which he answered questions from community members on a wide variety of (mostly machine/deep learning) topics. When will we see a theoretical background and mathematical foundation for deep learning? The answer turned into a very eloquent overview of three particular thoughts on why deep learning works so well. Here is a quick overview. LeCun's first point of explanation, which maps to a good reason why deep learning works so well, is as follows: One theoretical puzzle is why the type of non-convex optimization that needs to be done when training deep neural nets seems to work reliably.

You know what I love on a Monday? Coming into work to be handed a lengthy machine learning paper by my editor and asked to write up its findings by lunchtime. Any human reader out there can probably identify hints of sarcasm in these sentences (as it happens, this is a double sarcasm bluff: the paper's actually pretty darn interesting). A computer, however, takes things literally -- which is exactly the problem. "The goal of my present work is sarcasm detection," Silvio Amir at the University of Lisbon, Portugal, told Digital Trends.

This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. Four lessons from IoT early adopters: To paraphrase the late Roy Scheider in one of the greatest of all summer movies, you're gonna need a bigger router. In 2025, Machina Research predicts, the Internet of Things is going to be a 3 trillion market of 27 billion devices generating more than 2 zettabytes of data. Two zettabytes of data is something like twice the total global IP traffic we'll generate this year, according to Cisco. The IoT data deluge is, by the way, the first of four lessons drawn from early IoT adopters by contributing writer Howard Baldwin for his article in Computerworld.

Nils Lenke is the senior director of corporate research at Nuance Communications. There is an array of neural net machine learning approaches that are simply more than just "deep." In a time when neural networks are increasingly popular for advancing voice technologies and AI, it's interesting that many of the current approaches were originally developed for image or video processing. One of those methods, convolutional neural networks (CNNs), makes it easy to see why image-processing neural nets are strikingly similar to the way our brains process audio stimuli. CNNs, therefore, nicely illuminate that our audio and visual processes are connected in more ways than one.