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.

Deep learning sounds daunting, but it's fast becoming a necessary technology in today's contextual world. Whether you're building simple bots or larger neural networks, a good understanding of artificial intelligence will help you succeed in your endeavor. If you're not even sure what deep learning is, you're definitely not alone. At its core is the building and programming of neural networks that allow machines to decipher speech or text to suit various needs. The best expression of deep learning comes with digital assistants such as Siri or Google Now.

If you're a software engineer (or someone who's learning the craft), chances are that you've heard about deep learning (which we'll sometimes abbreviate as "DL"). It's an interesting and rapidly developing field of research that's now being used in industry to address a wide range of problems, from image classification and handwriting recognition, to machine translation and, infamously, beating the world champion Go player in four games out of five. A lot of people think you need a PhD or tons of experience to get a job in deep learning, but if you're already a decent engineer, you can pick up the requisite skills and techniques pretty quickly. Important point: You need to have motivation and be able to code and problem solve well. Here at Deepgram we're using deep learning to tackle the problem of speech search.

The IG Barometer methodology results in the computation of four scores for each IG Issue: Relevance, Specificity, Diversity, and Positivity, all expressed as percentile ranks - similar to standardized test results reporting. Each of these scores reflects the relative position of a particular IG Issue in respect to all other issues encompassed by the analysis. The computation of the scores is based upon the previously statistically modeled IGF Session Transcripts Text Corpus: a collection of hundreds manually tagged session transcripts from the Internet Governance Forum 2006-14, rich with meta-data, encompassing the codification of expert IG knowledge as represented in the various IGF sessions, workshops, and fora . All IG Barometer computations are supported by the IG Terminological Model, a hand-picked and manually tagged selection of approximately 5,000 most relevant IG keywords, terms and phrases. Hereby we describe conceptually the elements upon which the computation of the IG Barometer scores is founded; the interpretation of the four IG Barometer scores is provided immediately afterwards.