On Wednesday March 23, Microsoft unleashed its brand new AI on Twitter. Her name was Tay, and she was programmed to tweet like a teenage girl. Microsoft didn't intend for that to happen, of course. It wanted to test and improve its algorithm for conversational language. According to Microsoft, Tay was built by "mining relevant [anonymous] public data" which was "modeled, cleaned, and filtered" to create her personality.

When you heard about Tay, Microsoft's tweeting A.I., were you really surprised that a computer that learned about human nature from Twitter would become a raging racist in less than a day? Poor Tay started out all "hellooooooo w rld!!!" and quickly morphed into a Hitler-loving, genocide-encouraging piece of computer crap. Naturally, Microsoft apologized for the horrifying tweets by the chatbot with "zero chill." In that apology, the company stressed that the Chinese version of Tay, Xiaoice or Xiaolce, provides a very positive experience for users in stark contrast to this experiment gone so very wrong. "In China, our Xiaolce chatbot is being used by some 40 million people, delighting with its stories and conversations. The great experience with XiaoIce led us to wonder: Would an AI like this be just as captivating in a radically different cultural environment? Tay – a chatbot created for 18- to 24- year-olds in the U.S. for entertainment purposes – is our first attempt to answer this question."

Where it applies, it heatedly enables data-rich and knowledge-lean automation of valuable tasks of perception, classification and numeric prediction. Let's consider where learning or discovery best applies -- and why this matters for business. Years ago I was a machine-discovery researcher. Scholarly articles were published in the journal Machine Learning, and presentations were made at Machine Learning conferences, since it seemed that learning and discovery were similar human activities. As a (veteran) entrepreneur, I'm often asked whether a learning approach makes sense for automating some task, which motivated me to pen this piece.

Yesterday, news emerged that Google was using a machine-learning artificial intelligence system called "RankBrain" to help sort through its search results. Wondering how that works and fits in with Google's overall ranking system? Here's what we know about RankBrain. The information covered below comes from three sources. First, the Bloomberg story that broke the news about RankBrain yesterday (see also our write-up of it).

OK, it's not the HAL 9000 (yet) but all over the world, intelligent machines are replacing jobs at an alarming rate. And the smarter they get, the faster they're going to be replacing us. What I am talking about here is not a subtle shift to more automation but a real threat to society and the human race. From self-driving cars and robot waiters to robotic doctors and robot journalists, what seem like novelties today will soon be commonplace. As just one example take Foxconn, the largest private employer in China that contract manufactures of products such as the iPhone, Kindle, Playstation etc.

Google Translate is expected to become the latest product of Google that will benefit from the company's deep learning technology. Deep learning is based on intelligent training of neural networks to analyze lots of data and then make predictions on new data. Google has already used this technology to enhance Gmail, Google Maps, and Google Photos. According to a report, a senior Google official, who has led many high-profile projects and now heads the team involved in deep learning, said that his team is working in tandem with the Translation team to carry out experiments with translations on the basis of deep learning. Google Translate currently operates using different technologies, and this particular development could make it based entirely on neural networks, mainly, long short-term memory network (LSTM).

"Cognitive conversations" with artificial intelligence are "the future" of customer care, IBM's CTO of Watson Europe reckons. Speaking at Computing's Big Data and Analytics Summit 2016 last week, Duncan Anderson explained how AI like IBM Watson is "not Ex Machina" - referring to the 2015 film in which human-like AIs become self-aware - but is now at the point where it can soon make considerable changes to the daily lives of people. "The practical reality of where we are is not Ex Machina - we're not building beautiful computers and spoofing humans, [but] we're solving practical problems today," he said. Anderson said Watson's growing ability to process unstructured data - "text documents, images, voice - the novel data types" - is now bringing to an end the traditional approach of putting such data "in a database clock and [doing] nothing with it". While this is nothing new, Watson's improving effectiveness at communicating what it's learning back to a user in the form of "chat" is now becoming an increasingly viable frontline tool. "If you're five years old and go into a hospital it's a scary place - it's all white and doctors are big scary old people," said Anderson.

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The consequences of Korean player Lee Se-dol's historic defeat against a computer program in March 2016 will be both global and political. One reason is that the ancient and revered board-game Go (some claim it was invented by a Chinese emperor around 2300 BC) -- in its very essence, is a profound meditation on the art of war. There are only two types of stones in Go, black or white -- reminiscent of zeroes and ones in digital computers. Contrary to the hierarchical pawns, bishops and kings in chess, the pieces in Go are identical and theoretically equal in value, somewhat analogous to people in a communist regime. The aim is to capture territory and annihilate the enemy stones by surrounding them.

The World Economic Forum kicks off today, and the theme of this year's gathering of the world's leaders, celebrities, billionaires and the merely wealthy will be what it calls the "Fourth Industrial Revolution." That's its term for the accelerating pace of technological changes, especially those that are "blurring the lines between the physical, digital and biological spheres" -- the combination of things like artificial intelligence, robotics, nanotechnology and 3-D printing. To go with that theme, the WEF has released research looking at the effect all that change will have on jobs. It projects that by 2020, 7.1 million jobs are expected to be lost, and two million gained, with a net impact of five million jobs lost in the next half decade. "Davos Robot Eclipses Davos Man as Gloom Descends on World Elite," Bloomberg wrote in covering the news of this year's theme, which will be the topic of 20 sessions over the four-day conference.