Your next iPhone will be even better at guessing what you want to type before you type it. Or so say the technologists at Apple. Let's say you use the word "play" in a text message. In the latest version of the iOS mobile operating system, "we can tell the difference between the Orioles who are playing in the playoffs and the children who are playing in the park, automatically," Apple senior vice president Craig Federighi said Monday morning during his keynote at the company's annual Worldwide Developer Conference. Like a lot of big tech companies, Apple is deploying deep neural networks, networks of hardware and software that can learn by analyzing vast amounts of data.

By the nature of artificial intelligence, Apple's virtual assistant Siri needs a lot more data and a lot more people using it to get dramatically smarter. That's what Apple is shooting for by bringing Siri to the Mac and opening it to third-party developers. With more people using the smart digital assistant, Siri could become the service that it was expected to be. With Apple pushing ahead with expanding Siri, industry analysts expect the increasing A.I.-focused competition among industry giants Google, Microsoft, Amazon and Apple should propel smart technologies to a whole new level in a few years. "A.I. means a lot to all four of these companies," said Patrick Moorhead, an analyst with Moor Insights & Strategy.

We've seen some incredible applications of Deep Learning with respect to image recognition and machine translation but this particular use case has to do with public safety; in particular, how Deep Learning can be used to fight crime in the forward-thinking cities of San Francisco and Chicago. The cool thing about these two cities (and many others!) is that they are both open data cities, which means anybody can access city data ranging from transportation information to building maintenance records. So, if you are a data scientist or thinking about becoming a data scientist, there are publicly available city-specific datasets you can play with. For this example, we looked at the historical crime data from both Chicago and San Francisco and joined this data with other external data, such as weather and socioeconomic factors, using Spark's SQL context. We do the data import, ad-hoc data munging (parsing the date column, for example), and joining of tables by leveraging the power of Spark and then publish the Spark RDD as an H2O Frame (Figure 1).

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

The concept of statistical learning started from the method of least squares in the early 1900s has led to the invention of linear regression method. Most of the concepts at those times were applied to astronomical science. The evolution of linear and multiple regression methods gave rise to quantitative statistical computing. Statistical computing divides the majority of the conundrums into two categories. Those are supervised and unsupervised learning categories.

Just when you thought you'd got your head around the whole Machine Learning thing…BAMN! There's a new tech buzzword in town rearing up to take it's place. And while it may seem like just another Silicon Valley buzzword that all the new startups will claim to be using, deep learning is actually already being used to make some really astounding advances. We caught up with deep learning expert, Andrew Ng, and asked him to explain what deep learning is and how we should expect to see it change the world in 2016. Deep learning is a subset of machine learning that essentially refers to trying to map neural networks (the same stuff that makes your brain work).

It looks like Apple is planning a huge upgrade for Siri, one that will see the voice assistant surge ahead of rivals Google Now, Microsoft's Cortana, and Alexa, the AI behind Amazon Echo. Apple acquired U.K.-based speech processing startup VocalIQ last year, and will be integrating the advances made by the company in processing natural language queries and machine learning into Siri. In fact, it was so impressive that Apple bought VocalIQ before the company could finish and release its smartphone app. After the acquisition, Apple kept most of the VocalIQ team and let them work out of their Cambridge office and integrate the product into Siri. Before Apple bought the company, VocalIQ tested its product against Siri, Google Now, and Cortana, and the results were impressive.

Artificial intelligence has a lot of prominent people shaken up. Elon Musk, Sam Altman and others worry AI programs and AI-enabled robots might replace humans in their jobs before the economy can adapt, or worse run amok in Terminator-like apocalyptic scenarios. Entrepreneur and Singularity University founder Peter Diamandis is worried about the opposite situation. He fears the field of artificial intelligence could be stifled by rules the way stem cell research was under Republican President George W. Bush, who in 2001 announced a block on federal funding for new stem lines. "It had the experience of really putting the kibosh on that kind of work," Diamandis tells Inc. "One of of the things I think is very true and important for people to realize is that you can't regulate against technologies. If an individual is working in AI or biotechnology or whatever the case might be, and you say'that's way too dangerous, we need to slow this down, we're going to put hurdles and regulations in front of it here in the United States'... All that means is that technologies leave the U.S.," Diamandis said in a phone interview.

How can Artificial Intelligence (AI) help companies operate in the 21st century? How might it impact organisations and employees? AI has been around for years, but now it seems that it is taking the business world by storm. According to software startup advisor Steve Ardire (pictured right), it will fundamentally reshape organisations. "Human capital will start to shift from mundane tasks and transactions to higher-order and creative work. Along the way, we will see massive businesses where the technology transforms specific job functions," he tells me.

A few years ago, Magnus Egerstedt was walking through the swarm robotics laboratory at the Georgia Institute of Technology, where he is associate director of research, feeling proud of the research spearheaded there, when a disturbing thought crossed his mind. "I began thinking about the robotics laboratories where people are doing things that matter. There's not even ten of them globally," Egerstedt says. "That's weird, because so many people are working on swarm robotics, but it takes money and people to drive research that matters. He immediately envisioned a way to give robotics researchers who aren't with those top labs access to top-lab capabilities. And he knew students at all levels, grade school to graduate school, could benefit as well. "I used as a model the Large Hadron Collider," Egerstedt says. "Physicists realized large particle colliders were too expensive to build separately, so they share.