Artificial intelligence (A.I. as many refer to it) is quickly becoming our reality. And even though its technology is all around us, many of us don't understand what that technology is. The "misconception" about artificial intelligence, is that it's a "robot," says Tim Urban, whose stick-figure-filled explainer on the technology has been read by more than 4 million people on his website, Wait But Why. The robot, however, is merely the "container" for the artificial intelligence, Urban told Olivia Stern for "Sunday TODAY with Willie Geist." "The A.I. is the software inside the container. The A.I. is, in particular, software that can make decisions."

Much of our conception of Artificial Intelligence or AI has been shaped by popular movies: from the homicidal Red Queen of "Resident Evil", to the J.A.R.V.I.S. operating system / virtual butler that provides logistical support to "Iron Man".

Automation and robotisation are becoming more prominent in the media, although the uninitiated might be forgiven for still being in the dark about them. Machine learning is happening right now. Artificial intelligence and machine learning applied to massive amounts of data are being harnessed to automate more and more of the things that, hitherto, we have had to do manually. Right now most of this data is centralized and in the hands of a few key players in Silicon Valley. And this centralization of data, the fuel of machine learning, has also attracted an astounding number of the world's leading machine learning researchers -- the industry's true superstars -- who, in recent years, have been recruited from universities by companies like Facebook, Google and Microsoft.

Funding dollars to Internet of Things startups jumped 31% in the first quarter of this year, compared to Q4'15. That was the second-highest total yet in a quarter. As industrial and business processes are more frequently mediated by the IoT, and IoT-driven analytics solidifies as a category, startups are leveraging machine learning and deep learning to provide predictive insights based on the data collected from wearables and sensors. Just in the last quarter, companies like Tellmeplus, Neura, and Sentenai raised funds to integrate machine learning algorithms with different IoT functions, including data collection, sorting, analyzing, insights, and automation. "A good IoT solution gives you an extraordinarily useful view of your organization's data; a great one allows you to build algorithms that can predict what's next…" – Microsoft IoT blog.

In this special guest feature, Karthik Krishnan, Vice President of Product Management at Niara, discusses how machine learning is an option that's gaining popularity to detect cyber attacks given its effectiveness in classifying and clustering attack activity, even within large event data streams. Karthik is responsible for driving product strategy and direction as well as customer engagements. He helped to drive the initial framework for the Niara Security Analytics Platform. Before joining Niara, he served as vice president of product management at Embrane, acquired by Cisco in 2015, and senior director of product management at PGP, acquired by Symantec in 2010. Karthik also spent five years at Juniper as the director of product management, driving product strategy, sales and customer engagements for Juniper's Access Control products.

Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interesting or useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many.

This session took place in February 2016. Part 4 of 7 - Speaker: John Overington, Director Bioinformatics, Stratified Medical Data mining, machine learning and artificial intelligence are becoming the most talk-about topics in digital health. With vast volumes of medical data available, exploiting these techniques to derive valuable insights may both challenge and reshape certain elements of our healthcare system. These new approaches are leading to redefining drug discovery, assisting and automating diagnoses and helping predict and prevent diseases using health record data – or even our digital footprint. But there remains much hype, confusion and misunderstanding in the field.

PRINCETON, NEW JERSEY – Last month, AlphaGo, a computer program specially designed to play the game go, caused shock waves among aficionados when it defeated Lee Sidol, one of the world's top-ranked professional players, winning a five-game tournament by a score of 4-1. Why, you may ask, is that news? Twenty years have passed since the IBM computer Deep Blue defeated world chess champion Garry Kasparov, and we all know computers have improved since then. But Deep Blue won through sheer computing power, using its ability to calculate the outcomes of more moves to a deeper level than even a world champion can. Go is played on a far larger board (19 by 19 squares, compared to eight by eight for chess) and has more possible moves than there are atoms in the universe, so raw computing power was unlikely to beat a human with a strong intuitive sense of the best moves.

They heyday of the app is not coming to an end any time soon. But the next evolution of the apps economy is starting to take shape. And it looks nothing like what we think about as "apps." But that does not mean that the structure of software development and distribution will fundamentally be altered. The app store model of creating software, uploading it to a platform and wirelessly transmitting it to people on demand--a system which dramatically changed the nature of software distribution--will remain intact.

It has been a nightmare of a PR week for Microsoft. It started with the head of Microsoft's Xbox division, Phil Spencer, having to apologise for having scantily clad female dancers dressed as school girls at a party thrown by Microsoft at the Game Developers Conference (GDC). He said that having the dancers at this event "was absolutely not consistent or aligned to our values. That was unequivocally wrong and will not be tolerated". The matter was being dealt with internally and so we don't know who would have been responsible and why they might have thought this was going to be a good idea.