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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.

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.

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.

"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."

We live in strange times. These are workers without a workplace, striking against a company that does not employ them. They are managed not by people but by an algorithm that communicates with them via their smartphones. And what they are rebelling against is an app update.

Researchers trained an algorithm using more than 1,500 photos of Chinese citizens, hundreds of them convicts.They said the program was then able to correctly identify criminals in further photos 89% of the time. But the research, which has not been peer reviewed, has been criticised by criminology experts who say the AI may reflect bias in the justice system. #

"Going green" has become a topic of increased attention lately, but it's nothing new to knowledge management. By its nature, knowledge management promotes efficiency and optimal use of resources, which often reduces the amount of energy required to achieve a given goal. What has changed is the heightened awareness of those benefits. That awareness is creating new interest in KM solutions that can improve business performance while reducing environmental effects. Knowledge management also plays a role in the software tools that help companies improve their energy management, embedding expertise in algorithms to optimize use of office equipment and energy in buildings.

Today, AI is reality, thanks to big data analysis, exa-scale storage, and cloud technologies that complement AI algorithms with highly scalable methods to quickly access and analyze massive data sets. #ai

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.

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

These scenarios describe two predictions in what will be an algorithmic and smart machine driven world where people and machines must define harmonious relationships.

Using sophisticated statistical and linguistic algorithms, it pinpoints the key concepts and extracts the most relevant sentences, resulting in a Web site or document summary that is a shorter, condensed version of the original text.

A great example of AI in action is on Wall Street, where traders and bankers are being slowly replaced with machines. Wall Street is now a lot more sedate and well behaved. Investment decisions and trades are based on algorithms, statistics and trends, rather than prevailing human emotion or gut instinct. Is this is a good thing or bad?

it took decades for saddle makers and carriage builders to adjust to the disruptive rise of the automobile. In contrast, travel agents had less than five years to rechart their careers after Expedia, Orbitz and other travel sites took hold. Financial planners? Mammography technicians? Once the software programmers get their algorithms right, these and many other jobs could disappear or change very in the relative blink of an eye.

"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."

"Augmented reality might not be able to cure cancer (yet), but when combined with a machine learning algorithm, it can help doctors diagnose the disease."

"Relying on counterfactual explanations as a means to help us act rather than merely to understand could help us gauge the scope and impact of automated decisions in our lives. They might also help bridge the gap between the interests of data subjects and data controllers, which might otherwise be a barrier to a legally binding right to explanation."

AI is (currently) very good at specialist/single tasks that require brute force computational effort or training algorithms, but we are a long way from developing generalised AI, that requires some form of unsupervised deep learning. There are many things that humans understand but are well beyond the reach of AI, and this will remain the case for many years to come - if it ever happens.

Artificial intelligence algorithms are increasingly influential in peoples' lives, but their inner workings are often opaque. We examine why, and explore what's being done about it.