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In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. These techniques can be used to make highly accurate predictions. This animated presentation explains machine learning in simple to follow graphics.

What's keeping leaders from adopting machine learning? Well, tools are still evolving, practitioners are scarce, and the technology is a bit inscrutable for comfort. But five vectors of progress are making it easier, faster, and cheaper to deploy machine learning and could bring it into the mainstream.

Google Sheets is getting smarter. After adding the machine learning-powered "Explore" feature last year, which lets you ask natural language questions about your data, it's now expanding this feature to also automatically build charts for you. This means you can now simply ask Sheets to give you a "bar chart for fidget spinner sales" and it will automatically build one for you.

How do we learn the meaning of text from data? In other words, how can a machine truly understand the phrases that human beings blab into its search fields and microphone? The researchers at Google and elsewhere have settled on an answer to that question: machine learning; specifically, a form of artificial intelligence called neural networks-self-organising systems modelled on the way the brain works.

An exclusive inside look at how artificial intelligence and machine learning work at Apple

During the past few years, the terms artificial intelligence and machine learning have begun showing up frequently in technology news and websites. Often the two are used as synonyms, but many experts argue that they have subtle but real differences.

Google Search itself provides one of the most familiar examples of predictive intelligence. When you enter keywords in the search box, Google predicts what you are interested in and then presents you with results that match that intent. Since it released the first version of its Prediction API in 2010, Google has made some of these methods available to developers. Adoption among developers has not been high because machine learning requires a lot of infrastructure and validation to produce accurate results. Developers have also reported discomfort with basing products on black box APIs.

Use of machine learning and physical modelling for adaptive vehicle control. Data-driven decision making.

As automation technologies such as machine learning and robotics play an increasingly great role in everyday life, their potential effect on the workplace has, unsurprisingly, become a major focus of research and public concern. The discussion tends toward a Manichean guessing game: which jobs will or won't be replaced by machines?

What if, rather than asking the traditional question-What tasks currently performed by humans will soon be done more cheaply and rapidly by machines?-we ask a new one: What new feats might people achieve if they had better thinking machines to assist them?

Humans are biased, and the biases we encode into machines are then scaled and automated. This is not inherently bad (or good), but it raises the question: how do we operate in a world increasingly consumed with "personal analytics" that can predict race, religion, gender, age, sexual orientation, health status and much more.

"Continual learning is integral to the human experience. People who can learn faster and better than others tend to do well in life. The same is true for successful organizations."

A quick trip through time to examine the origins of machine learning as well as the most recent milestones.

The average piece of user-generated content (UGC) is awful. It's actually way worse than you think. It can be rife with misspellings, vulgarity or flat-out wrong information. But by identifying the best and worst UGC, machine-learning models can filter out the bad and bubble up the good without needing a real person to tag each piece of content.

"How the Quest for the Ultimate Learning Machine Will Remake Our World"

"Whether you're just learning to code or you're a seasoned machine learning practitioner, you'll find information and exercises in this resource center to help you develop your skills and advance your projects."

IBM's Watson - the future of machine learning

British professor Geoffrey Hinton says that humans should not fear the growing intelligence of machines, but the development of military robots should be a concern.

There is quite a lot of confusion about the difference between AI and machine learning. While many big companies use them interchangeably, they are not the same thing. Related, sure, but different.

McKinsey have developed an approach to retention: to detect previously unobserved behavioural patterns, they combine various data sources with machine-learning algorithms. Workshops and interviews are used to generate ideas and a set of hypotheses. Over time they collected hundreds of data points to test. Then ran different algorithms to get insights at a broad organisational level, to identify specific employee clusters, and to make individual predictions. Finally they held a series of workshops and focus groups to validate the insights from our models and to develop a series of concrete interventions. The insights were surprising and at times counterintuitive. They expected factors such as an individual's performance rating or compensation to be the top predictors of unwanted attrition. But analysis revealed that a lack of mentoring and coaching and of "affiliation" with people who have similar interests were actually top of list. More specifically, "flight risk" across the firm fell by 20 to 40 percent when coaching and mentoring were deemed satisfying.

McKinsey have developed an approach to retention: to detect previously unobserved behavioural patterns, they combine various data sources with machine-learning algorithms. Workshops and interviews are used to generate ideas and a set of hypotheses. Over time they collected hundreds of data points to test. Then ran different algorithms to get insights at a broad organisational level, to identify specific employee clusters, and to make individual predictions. Finally they held a series of workshops and focus groups to validate the insights from our models and to develop a series of concrete interventions. The insights were surprising and at times counterintuitive. They expected factors such as an individual's performance rating or compensation to be the top predictors of unwanted attrition. But analysis revealed that a lack of mentoring and coaching and of "affiliation" with people who have similar interests were actually top of list. More specifically, "flight risk" across the firm fell by 20 to 40 percent when coaching and mentoring were deemed satisfying.