Unless you've recently graduated from one of the new Data Science courses that have been popping up online and in various universities around the world, then becoming a Data Scientist was most likely slightly accidental and was more about the journey than the destination. I started out as a physicist and had a strong mathematical grounding, but I had a passion for medicine. After completing my bachelor's degree I took a master's degree in medical physics. This is where I gained an appreciation for the importance of image analysis and the role that data plays in medicine. I created a virtual model of a human torso by segmenting images from the Visible Human Project.

I received my copy today and instantly decided I would be using it in several of the courses I teach for pre-service teachers. They have been asking for a book like this for years, but the existing books were aimed at the wrong audience, had content that was oversimplified (or overly complex), or failed to incorporate important current research. Dr. Fredricks' book masterfully incorporates the most relevant research with perfect tone and an engaging narrative. There's no way I'll be assigning a 100 textbook when this new book does so much more at less than a third the price.

This complimentary business course is designed to help demystify artificial intelligence, provide an overview of a wide range of cognitive technologies, and offer a framework to help you understand the business implications of AI and cognitive technologies.

Even in Top IT companies, recruitment/hiring process is the least technology driven activity. Either companies are still living in stone-age and maintaining applicant records in excel; or they are forced to use custom tools/interfaces which somehow get integrated with their existing IT infrastructure. Although job seeking has evolved in last few years. Job seeker can use online professional networks, job portals, placement agencies, assessment companies and/or training institutes. But every single channel has its own path.

EdTech runs four or five years behind TechCrunch headlines. Accordingly, hot topics in tech this year–AI, robotics, VR, wearables–will become widespread in education in a few years. Makerspaces and online learning are both expected to be widely adopted by schools in one year's time or less to encourage students to take ownership of their education by creating and provide them with ubiquitous access to digital tools, discussion forums, rich media and more. The time to adopt for robotics and virtual reality are estimated within two to three years, while artificial intelligence and wearable technology are expected to be mainstream in schools within four to five years. Horizon Report is published by CoSN, the association of district EdTech professionals, and the New Media Consortium.

Humans have been interested in building intelligent computer systems since the beginning of computing technology. There's something both interesting and strange about mimicking the function of a human brain that we feel the need to explore -- how crazy is it to build a machine that can beat out the human brain in a game of chess? Of course, as applied to today's chatbots, artificial intelligence is still in its infancy. But don't take that the wrong way: Today's bots can still offer a great deal of utility for businesses and consumers. Messaging platforms like Facebook Messenger, WeChat, and Kik are making it easier than ever for brands and businesses to have a presence where millions of users are already spending time.

KEVIN C. DESOUZA is Research Associate with the Center for Research in Information Management, University of Illinois, Chicago. He lectures widely on topics in e-commerce, data warehousing, data management, and computer-based training, and has served in various managerial and technical capacities with organizations worldwide.

Faith Hedgepeth was bludgeoned to death four years ago inside her off-campus apartment at the University of North Carolina Chapel Hill in a case that remains unsolved. The killer left behind a chilling note and traces of DNA -- which a forensic technology company has now used to create a 3-D sketch of what the suspect might look like. A "snapshot tool" developed by Parabon NanoLabs has created a 3-D image of the killer based on DNA traits, and authorities are hopeful the sketch could lead to a break in the case. The Reston, Va.-based Parabon Nanolabs, with funding from the Department of Defense, debuted the breakthrough type of analysis called DNA phenotyping in 2015 which the company said can predict a person's physical appearance from the tiniest DNA samples, like a speck of blood or strand of hair. The DNA phenotyping service, commercially known as "Snapshot," could put a face on millions of unsolved cases, and generate investigative leads when the trail has gone cold.

Last time we concluded by noticing that minimizing the empirical risk (or the training error) is not in itself a solution to the learning problem, it could only be considered a solution if we can guarantee that the difference between the training error and the generalization error (which is also called the generalization gap) is small enough. That is if this probability is small, we can guarantee that the difference between the errors is not much, and hence the learning problem can be solved. In this part we'll start investigating that probability at depth and see if it indeed can be small, but before starting you should note that I skipped a lot of the mathematical proofs here. You'll often see phrases like "It can be proved that …", "One can prove …", "It can be shown that …", … etc without giving the actual proof. This is to make the post easier to read and to focus all the effort on the conceptual understanding of the subject. In case you wish to get your hands dirty with proofs, you can find all of them in the additional readings, or on the Internet of course!

LONDON--(BUSINESS WIRE)--According to the latest market study released by Technavio, the global artificial intelligence in education market is expected to grow at a CAGR of more than 39% during the forecast period. This research report titled'Global Artificial Intelligence in Education Market 2016-2020' provides an in-depth analysis of the market in terms of revenue and emerging market trends. This market research report also includes an up to date analysis and forecasts for various market segments and all geographical regions. "High student dropout rates coupled with macro-economic pressure to strengthen the general education levels is pushing educational institutions to adopt mechanisms to improve learning quality. Institutions are investing in artificial intelligence technologies, such as augmented reality and virtual reality, to positively impact the education industry," says Jhansi Mary, a lead analyst at Technavio for education technology research.