Artificial intelligence is changing the world. At least, the White House thinks so. Last week, the Obama administration released a 60-page report titled Preparing for the Future of Artificial Intelligence. It paints with a broad stroke the current state of A.I. in several different fields -- health, education, the environment -- and proposes ways in which industry and government can work together to advance the public good. It's a remarkable document, if only for the fact that it's being issued by an outgoing administration in its final months in office.

The company's total income has increased from Rs 13,198.6 crore for the quarter ended September 30, 2015 to Rs 14,407.3 The company has posted a net profit after taxes, minority interest and share of profit of associates of Rs 2070 crore for the quarter ended September 30, 2016 as compared to Rs 2241 crore for the quarter ended September 30, 2015. Total Income has increased from Rs 13198.6 crore for the quarter ended September 30, 2015 to Rs 14407.3 On a standalone bais, the company has posted a net profit of Rs 1932 crore for the quarter ended September 30, 2016 as compared to Rs 2153 crore for the quarter ended September 30, 2015. Total Income has increased from Rs 11725 crore for the quarter ended September 30, 2015 to Rs. 12101 crore for the quarter ended September 30, 2016.

Mphasis, a leading IT services and solutions provider, today announced the launch of DigiOps driven by InfraGenie, an intelligent automation platform (IAP), powered by Arago, a pioneer in artificial intelligence (AI) and leader in intelligent IT automation. DigiOps delivers solutions by reducing manual effort across IT functions. InfraGenie intelligently predicts incidents before they arise so that companies have a reliable and consistent way of solving errors in their industry-specific IT operations. This smart infrastructure solution unites the proficiencies of advanced analytics ("prescriptive") and artificial intelligence based automation to offer resolutions for all types of infrastructure related events. Through InfraGenie, Mphasis will bring both automation and analytics together to reliably and consistently identify, predict and resolve the infrastructure problems of today's complex hybrid IT environment.

To cite this article: Gina Neff. The biggest challenge for the use of "big data" in health care is social, not technical. Data-intensive approaches to medicine based on predictive modeling hold enormous potential for solving some of the biggest and most intractable problems of health care. The challenge now is figuring out how people, both patients and providers, will actually use data in practice. "I FOUND THE BUZZ AS FEVERISHLY LOUD AROUND HEALTH INFORMATION INNOVATION AS IT WAS DURING MY RESEARCH ON THE FIRST DOT-COM BOOM." To understand how data-intensive solutions could have an impact on health care, our research team talked to frontline providers in impoverished and rural areas, technology enthusiasts in mobile health and health IT startups, clinicians and researchers in major research hospitals, Quantified Self members at data-driven meetup presentations of massive amounts of tracking data, and attendees at the growing number of conferences for health technology and innovation up and down both coasts. I found the buzz as feverishly loud around health information innovation as it was during my research on the first dot-com boom. One of our findings from this research seems at first blush so obvious that it is hard to believe it has been overlooked in the design and implementation of health-care innovation technologies.

In this article, I will use Data Science / Machine Learning methodologies to break down the real factors separating the playoff from non-playoff teams. In particular, I used the data from Basketball-Reference.com to associate 44 predictor variables which each team: "FG" "FGA" "FG." "X3P" "X3PA" "X3P." Using principal components analysis (PCA), I was able to project this 44-dimensional data set to a 5-D dimensional data set. That is, the first 5 principal components were found to explain 85% of the variance. In these plots, the teams are grouped according to whether they made the playoffs or not.

Machine learning, Tableau, and user experience design represented the fastest growing skills on freelancing platform Upwork during the third quarter of the year, a finding that makes sense in the context of the accelerating collection of data and the need to present it. "Companies are harvesting explosive amounts of data, and demand for machine learning specialists who can build adaptive algorithms and extract the value of this new data is increasing in turn," Upwork said in a statement. According to research firm CB Insights, over 200 companies focused on AI-related technology raised almost 1.5bn in funding in the first half of the year. And major tech companies like Apple, Facebook, Google, IBM, Microsoft, Oracle, and Salesforce made public commitments to AI-oriented products and services. Earlier this month, Upwork reported that the freelance workforce in the US grew from 53 million in 2014 to 55 million in 2016.

With mobile devices today having powerful processors and with broadband Internet becoming commonplace, the stage is set for a wave of surprisingly smart and always connected devices. So to help clear the air on what Artificial Intelligence really means, here's a handy guide: A field of software--the stuff that powers everything from your smartphone to your favourite chat app--that aims to make your digital experience'smarter', almost human-like The enabler of a whole new era of devices and services that will work with us humans more seamlessly--think smarter smartphones, intelligent homes that recognize our presence.

We may have just seen a major player in the drive towards autonomous cars apply screeching brakes. Apple has reportedly abandoned its plans to build its own self-driving electric vehicle and is instead going to focus on the underlying autonomous software. A similar initiative to produce a fully autonomous car by Google also appeared to run out of steam. Building self-driving cars clearly poses a challenge that even the world's top technology giants can't yet meet. So what is it about building autonomous cars that is proving to be such a challenge? The high-value consumer electronics and software industry is used to very different margins than the cut-throat automotive sector, which has tough market entry conditions and tribal supply-chain relationships.

Artificial Intelligence (AI) is the skill of a computer program or a machine to think and learn, in other words mimic human's learning capability. Most of the older AI techniques are just boosted programming tactics nowadays. The major difference between normal'computer program' and AI is, in first case programmer is the one providing all the smartness to the system by programming it in as a World Model, and in lateral case the system learns by itself by referring past experience and training. In earlier AI techniques that were built in the beginning of AI evolvement, 'Real world modeling' approach was preferred. "Do not model the Real World; Instead, Model the Mind."

I had the honor to be one of the warmup acts for President Obama at the White House Frontiers Conference at Carnegie Mellon University in Pittsburgh. Here is the prepared text and slides from the talk I delivered there. As you'll see if you watch the video, what I ended up saying isn't exactly what I had written out in advance, but it is reasonably close. Hearing that Bob Dylan just won the Nobel Prize for Literature, how could I not begin this talk with his famous line, "Something is happening here, but you don't know what it is, do you, Mr. Jones?" The future is full of amazing things.