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

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.

IBM's Watson - the future of machine learning

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.

In line with fears often read about in the media, both anti-killer robot activist Dr. Sharkey and Brandeis University's Dr. Michael Bukatin believe that autonomous machines, either superintelligences fighting themselves and obliterating us in the process or rampant autonomous armed conflict, pose a legitimate threat. Another thought is that AI aren't evil (and never will be); instead, it's the humans behind the AI that are unpredictable and often untrustworthy, with short-sighted aims such as financial and political gains. Dr. Michael Shermer sees the likeliest risk of near-future AI in the near future involving "evil humans manipulating AI toward their ends, not evil AI itself, as no such thing will develop."

"As machine learning becomes more powerful, the field's researchers increasingly find themselves unable to account for what their algorithms know - or how they know it."

"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."

Crafty engineers leverage machine learning, graph theory, and AI to build smart solutions for growth-focused companies who want to use tomorrow's AI.

"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."

Google DeepMind claims to have significantly improved computer-generated speech with its AI technology, paving the way forward for sophisticated talking machines like those seen in sci-fi films like "Her" and "Ex-Machina."

Turing said in 1947 that if a machine is expected to be infallible, it cannot also be intelligent.

Of all the fields that artificial intelligence will disrupt in the coming years, healthcare may see the greatest paradigm shift. AI's influence in the industry will be deep and broad. Image-recognition algorithms already help detect diseases at an astonishing rate. Now, a few startups are using intelligent machines to redesign the clinic, redefine the role of the practitioner, and reposition the patient in relation to her own health.

While automation will eliminate very few occupations entirely in the next decade, it will affect portions of almost all jobs to a greater or lesser degree, depending on the type of work they entail.

Corporate leadership is already struggling to keep up with the connected workforce and increasing speed and complexity in the digital economy. But looking ahead to the rise of algorithmic and human-machine co-working, the situation is even more worrying. A reboot is overdue.

Last year was huge for advancements in artificial intelligence and machine learning. But 2017 may well deliver even more. Here are five key things to look forward to.

A future in which human workers are replaced by machines is about to become a reality at an insurance firm in Japan, where more than 30 employees are being laid off and replaced with an artificial intelligence system that can calculate payouts to policyholders.

You may think you choose to read one story over another, or to watch a particular video rather than all the others clamouring for your attention. But in truth, you are probably manipulated into doing so by publishers using clever machine learning algorithms

The use of machine learning, expert systems and analytics in combination with big data, is the natural evolution of what has been two different disciplines. They are converging.