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"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."

"Morgan Stanley is about to augment its 16,000 financial advisers with machine-learning algorithms that suggest trades, take over routine tasks and send reminders when your birthday is near."

Algorithms making decisions in situations where the stakes are high need to be very accurate to make-up for high penalties when things go wrong. On the flipside, if the penalty from making an error is low, even inaccurate algorithms might be up to the task.

"For problems that are original, whose answers are not already known, and that require intuition, judgment and ingenuity, you need people. Smart people who not only know how to search, but who have the expertise and judgment to know whether what they've found is most relevant to solving the problem at hand."

"Big data's potential just keeps growing. Taking full advantage means companies must incorporate analytics into their strategic vision and use it to make better, faster decisions."

I want to tell you straight off what this story is about: Sometime in the next 40 years, robots are going to take your job. I don't care what your job is. If you dig ditches, a robot will dig them better. If you're a magazine writer, a robot will write your articles better. If you're a doctor, IBM's Watson will no longer "assist" you in finding the right diagnosis from its database of millions of case studies and journal articles. It will just be a better doctor than you.

Hidden biases may be written inadvertently into the algorithms used to decide who gets a job interview or who qualifies for a loan or for parole. If a data set considers the word "programmer" closer to the word "man" than "woman," or if you build a system that learns from Wikipedia, where only 17 per cent of profiles of notable people are women, these biases will be perpetuated in the machine.

As fears grow that artificial intelligence could wipe us out, one radical idea to stop it is that man and machine merge

Machines, even virtual ones, have biases. They are designed, necessarily, to favour some kinds of data over others. Unfortunately, we rarely question the judgments of mathematical models and, in many cases, their biases can pervade and distort operational reality, creating unintended consequences that are hard to undo.

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.

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

Workers in wholesale and retail sectors at highest risk from breakthroughs in robotics and artificial intelligence, PwC report finds.

Researchers at the MIT Media Lab, think that facial recognition software has problems recognizing black faces because its algorithms are usually written by white engineers who dominate the technology sector. These engineers build on pre-existing code libraries, typically written by other white engineers.

"A look at what the technological terrain will look like in 2018"

Chatbots Webinar for KIN and KCN by Steve Dale