Detecting anomalies is critical in conducting surveillance, countering credit-card fraud, protecting against network hacking, combating insurance fraud, and many more applications in government, business and healthcare. Sometimes, the analyst has a set of known anomalies, and identifying similar anomalies in the future can be handled as a supervised learning task (a classification model). More often, though, little or no such "training" data are available. In such cases, the goal is to identify cases that are very different from the norm. Some techniques (clustering, nearest neighbors) may be familiar to you, others less so (e.g. based on information theory or spectral techniques).

Even a decade ago, talking to your computer was probably a sign that you'd been working too hard and could do with a lie down. Today, no such stigma applies. That's because chatbots -- the conversational agents capable of simulating intelligent conversations with human users -- have made some massive leaps forward. From changing the way kids learn in schools to picking you out the perfect meal this evening, here are the seven of the most interesting chatbots doing the rounds at the moment. From MOOCs (Massive open online courses) to the use of iPads in schools, there's no doubt that technology is changing the way that we learn.

This online machine learning course is perfect for those who have a solid basis in R and statistics, but are complete beginners with machine learning. The rest of the course is dedicated to a first reconnaissance with three of the most basic machine learning tasks: classification, regression and clustering. In this first chapter, you get your first intro to machine learning. After learning the true fundamentals of machine learning, you'll experiment with the techniques that are explained in more detail in future chapters. You'll learn how to assess the performance of both supervised and unsupervised learning algorithms.

"Intelligent system designers do have ethical responsibilities." I have interviewed John Markoff, technology writer at The New York Times. In 2013 he was awarded a Pulitzer Prize. The interview is related to his recent book "Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots, published in August of 2015 by HarperCollins Ecco. Do you share the concerns of prominent technology leaders such as Tesla's chief executive, Elon Musk, who suggested we might need to regulate the development of artificial intelligence?

In September, San Diego robotics startup Brain Corporation will introduce artificial intelligence software that allows giant commercial floor-cleaning machines to navigate autonomously. The follow-up offering it wants to develop may be even more forward-looking: A training and certification program for janitors to operate the machines. The program, still in early stages of planning, is aimed at helping janitors maximize efficiency and establishing standards and best practices for the use of robots in janitorial work, according to Brain Corporation. The company says it is not aware any other such training program exists. There's additional incentive for Brain Corp. to offer training options.

There are currently over 100 million students waiting to become the next generation of engineers, rocket scientists and astrophysicists to get humans from Earth to Mars and beyond, but they may not be able to fulfill their potential simply due to a lack of access to a quality education. According to a study from UNESCO, more than 100 million young people worldwide, 62 to 66 million of whom are girls, are not attending school of any kind. Hundreds of millions more are unable to afford good-quality or safe schools. However, groups like OneWeb and ONE are aiming to provide universal Internet access worldwide by 2020, greatly expanding the ability to use educational resources online. Nevertheless, Internet access does not guarantee a quality education.

Asking about the benefits of artificial intelligence and machine learning reminds me a little of the transition to suitcases with wheels. Do you remember lugging around those old suitcases? If not, good for you -- this original advertisement from US Luggage will take you back! Thank Bernard Sadow for persistence with his idea to add wheels, because when he pitched his idea people thought he was crazy. Surely no one would want to pull their own suitcase?

The hire marks the start of the Alphabet ( GOOG)-owned, Art Levinson-helmed biotech's drive to build out a computational biology and machine learning team. Calico will tap into that expertise--and that of the machine learning team it has tasked Koller with building--to advance its drive to understand aging and, in doing so, enable people to live longer, healthier lives. "Daphne and her team will work in close collaboration with the basic and translational scientists at Calico and partner with other machine learning experts, including the team at Google, to derive novel insights and effective interventions," Calico R&D President Hal Barron said. The plan now is to add more staff to work with Koller, including a machine learning engineer and someone capable of developing algorithms to analyze biological images and videos.

The hire marks the start of the Alphabet ( GOOG)-owned, Art Levinson-helmed biotech's drive to build out a computational biology and machine learning team. Calico will tap into that expertise--and that of the machine learning team it has tasked Koller with building--to advance its drive to understand aging and, in doing so, enable people to live longer, healthier lives. "Daphne and her team will work in close collaboration with the basic and translational scientists at Calico and partner with other machine learning experts, including the team at Google, to derive novel insights and effective interventions," Calico R&D President Hal Barron said. The plan now is to add more staff to work with Koller, including a machine learning engineer and someone capable of developing algorithms to analyze biological images and videos.

On the first day of "camp," two dozen rising high school sophomores arrive at the Stanford Artificial Intelligence Laboratory's Outreach Summer program (SAILORS) giddy and ready to get started. The rigorous, two-week program is designed to encourage young women from underrepresented populations to get more involved in the field of science. High school students Ishla Zareef-Mustafa and Genaro Pamatz participate in an anatomy lab as part of the Stanford Medical Youth Science Program. On the last day of the summer residential Stanford Medical Youth Science Program (SMYSP), 24 high school students, surrounded by family members, friends and mentors, present the research they have been working on during the five-week summer program.These programs, which fall under the umbrella of Stanford Pre-Collegiate Studies, are designed to provide teenagers from underrepresented populations with an opportunity to explore careers in science, but also to build new relationships, while taking what they've learned back to their home communities. The SAILORS curriculum includes lectures, hands-on research projects and mentoring activities that are intended to educate and excite young women about artificial intelligence.