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Now that's what I call Knowledge Transfer

In September, the Wikimedia Foundation won a $250,000 grant from the Knight Foundation to start building the "Wikimedia Knowledge Engine," a "system for discovering reliable and trustworthy public information on the internet," according to grant documents, which were released late last week. That the Knowledge Engine, now known as "Wikimedia Discovery," even existed was news to the Wikipedia editors community, who say the project's secretive nature and very existence are fundamentally at odds with Wikimedia's transparent ethos.

A computer program has beaten the world champion of one of civilisation's oldest board games for the first time in history.  Lee Se-dol, a 33-year-old South Korean, resigned the first of five matches of the fiendishly complex strategy game against the AlphaGo program, which is built by the Google-owned British company DeepMind.

In the cycle of accelerating computing power, we've gone from the slate to the paper, from the paper to mechanical systems, mechanical systems to the vacuum tube, vacuum tubes to silicon, and now we are moving to neurons.

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."

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.

It's not humans who are the recipients and decision makers of data and analysis, it's machines. Machines are making all or most of the decisions in areas like programmatic advertising, search engine optimization, credit approval, insurance underwriting, Internet of Things applications, and many more.

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

Though US-centric, this Pew Report "Public Predictions for the Future of Workforce Automation" gives some insight to workforce attitudes to automation. Despite their expectations that technology will encroach on human employment in general, most workers think that their own jobs or professions will still exist in 50 years. Heads in the sand, or justifiable pessimism?

It was hailed as the most significant test of machine intelligence since Deep Blue defeated Garry Kasparov in chess nearly 20 years ago. Google's AlphaGo has won two of the first three games against grandmaster Lee Sedol in a Go tournament, showing the dramatic extent to which AI has improved over the years. That fateful day when machines finally become smarter than humans has never appeared closer-yet we seem no closer in grasping the implications of this epochal event.

Do you broadcast on Periscope? Want to use it to connect with and grow your audience? Podcast.

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.

IBM has created a 'Visual Recognition Demo' to showcase Watson's latest trick, which allows users to feed Watson an image before it tells you what it believes it sees.