Taxpayers spend 39 billion a year on jailing 2.3 million people, making the U.S. the country with the highest incarceration rates in the world. And while technology is radically reshaping every aspect of our economy and society, none of our advances in computing and data are helping to stem the tide of mass incarceration. At a workshop in the capital last Tuesday, White House senior adviser Lynn Overmann of the Office of Science and Technology Policy called on the technologists of the country to figure out how to use data and technology to end widespread incarceration, according to Government Technology. Overmann wants artificial intelligence and machine learning programs that improve screening processes, scan body camera footage for police misconduct and make sentencing more fair. "I represented a client who was looking at spending 40 years of his life in prison because he stole a lawnmower and a weed-eater from a shed in a backyard," she said.

On Monday, Apple exec Craig Federighi quickly mentioned how Apple is going to try and catch up to Google's smart Photo app: „on-device intelligence."

We are on the cusp of a major breakthrough in how organizations collect, analyze, and act on knowledge. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. Editor's Note: This is the second in a special series of commissioned essays MIT Sloan Management Review will publishing in Frontiers over the Spring and Summer of 2016. Each essay gives the author's response to this question: "Within the next five years, how will technology change the practice of management in a way we have not yet witnessed?" Artificial Intelligence is about to transform management from an art into a combination of art and science.

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.