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"Some bright bulb at Microsoft Research had the clever idea of turning a machine learning program loose on Twitter yesterday to learn how humans interact with each other. Humans, predictably, interacted terribly."

The image of a robot stepping into the shoes of a human worker couldn't be more wrong. When it comes to technology's most significant applications, we are neither usurped or seduced - because the systems involved are nothing like us in either their function or faculties. As a species, we are not in competition with information technology at all: we are, rather, busily adapting the fabric of our world into something machines can comprehend.

Organisational Knowledge in the machine intelligence era

Predictive analytics uses many techniques from data mining, statistics, machine learning and AI.

In the US, the number of manufacturing jobs peaked in 1979 and has steadily decreased ever since. At the same time, manufacturing has steadily increased, with the US now producing more goods than any other country but China. Machines aren't just taking the place of humans on the assembly line. They're doing a better job.

In 2015, Davenport and co-author Julia Kirby published "Beyond Automation" in the Harvard Business Review, in which they laid out five practical steps workers may take to improve their employability against machines.

Amazon Web Services announced that it was selling to the public the same kind of software it uses to figure out what products Amazon puts in front of a shopper, when to stage a sale or who to target with an email offer. The techniques, called machine learning, are applicable for technology development, finance, bioscience or pretty much anything else that is getting counted and stored online these days. In other words, almost everything.

Social search - is Google missing a trick?

Steve, have you tried Greplin and if so what's your experience? Does it negate the need for other search tools (I don't want a proliferation of search tools)? Does the indexing slow up your machine?

Gary, it does a better search of your social networks than Google, probably because you're giving it permission to index them. You still need a general search engine (such as Google) for the broader internet content. Indexing has no impact on your machine. I haven't used it long enough to determine whether or not it's features are useful enough to make it my first choice search engine for social media/social network content.

Nowadays organizations have realized the importance of knowledge and knowledge management.  The organizations know that machines, equipments, and building cannot count as the most important properties of the organization. It is clear that the most important property of every organization is organizational knowledge and correct management of it will cause core competencies for the organization and also victory against the competitors. Of course knowledge and knowledge management both are important for an organization, but are all knowledge management efforts in the organizations successful? If knowledge management efforts fail in an organization, what are the main failure factors of this phenomenon? This paper attempts to answer this question by analyzing a failed case study in implementing a knowledge management system .

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?

"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."

"To help space travelers recover from the kinds of mishaps that can occur in the reaches of deep space-mishaps that can end missions and potentially end lives-Lockheed Martin is combining machine learning and artificial intelligence with augmented and virtual reality interfaces to provide a bit more self-reliance to manned space exploration missions at distances at which it could take more than 40 minutes to get a reply from mission control."

CIOs have a major role to play in preparing businesses for the impact that artificial intelligence (AI) will have on business strategy and human employment, according to Gartner, Inc. Gartner predicts that by 2022, smart machines and robots may replace highly trained professionals in tasks within medicine, law and IT.

"The ability for machines to see, understand and interact with the world is growing at a tremendous rate and is only increasing with the volume of data that helps them learn and understand even faster. Big data is the fuel that powers AI."

"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."

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.

The application of AI to the development of smarter robo-advisers offers a dichotomy of hope or fear that it could yield 'intelligent' and cost-effective investment management advice.

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.

"It is somewhat safe to predict that AI will continue to be at the top of the hype cycle in 2018. But the following 51 predictions also envision it becoming more practical and useful, automating some jobs and augmenting many others, combining machine learning and big data for fresh insights, with chatbots proliferating in the enterprise."