Ian H. Witten is a professor of computer science at the University of Waikato in New Zealand. He directs the New Zealand Digital Library research project. His research interests include information retrieval, machine learning, text compression, and programming by demonstration. He received an MA in Mathematics from Cambridge University, England; an MSc in Computer Science from the University of Calgary, Canada; and a PhD in Electrical Engineering from Essex University, England. He is a fellow of the ACM and of the Royal Society of New Zealand.

A week after Donald Trump's election, a thirty-year-old cognitive scientist named Maya Shankar purchased a plane ticket to Flint, Michigan. Shankar held one of the more unorthodox jobs in the Obama White House, running the Social and Behavioral Sciences Team, also known as the President's "nudge unit." When she launched the team, in early 2014, it felt, Shankar recalls, "like a startup in my parents' basement"--no budget, no mandate, no bona-fide employees. Within two years, the small group of scientists had become a staff of dozens--including an agricultural economist, an industrial psychologist, and "human-centered designers"--working with more than twenty federal agencies on seventy projects, from fixing gaps in veterans' health care to relieving student debt. Usually, the initiatives had, at their core, one question: Could the growing body of knowledge about the quirks of the human brain be used to improve public policy? For months, Shankar had been thinking about how to bring behavioral science to bear on the problems in Flint, where a crisis stemming from lead contamination of the drinking water had stretched on for almost two years. She wondered if lessons from the beleaguered city could inform the Administration's approach to the broader threat posed by lead across America--in pipes, in paint, in dust, and in soil. "Flint is not the only place poisoning kids," Shankar said. In recent years, behavioral science has become a voguish field. In 2002, the Israeli psychologist Daniel Kahneman won a Nobel Prize in Economic Sciences for his work with a colleague, Amos Tversky, exploring the peculiarities of human decision-making in the face of uncertainty. A basic premise of the discipline they'd helped to create was that people's cognition is bias-prone, and susceptible to the cognitive equivalent of optical illusions. As a result, small tweaks of presentation or circumstance could make a major difference: if a judge rendered a decision about granting parole just before a meal, the inmate's odds for a favorable outcome dipped to near zero; just after the judge ate, the chances rose to around sixty-five per cent. Grocers had learned that they could sell double the amount of soup if they placed a sign above their cans reading "limit of 12 per person." But, for all the field's potential, its advances seemed mostly to have served the private sector. A prominent exception was the "nudge," a notion advanced by the legal scholar Cass R. Sunstein, now at Harvard Law School, and the University of Chicago behavioral economist Richard Thaler, in their 2008 best-seller "Nudge: Improving Decisions About Health, Wealth, and Happiness."

A chatbot is an artificial person, animal or other creature which holds conversations with humans. This could be a text based (typed) conversation, a spoken conversation or even a non-verbal conversation. Chatbot can run on local computers and phones, though most of the time it is accessed through the internet. Chatbot is typically perceived as engaging software entity which humans can talk to. It can be interesting, inspiring and intriguing.

Today, we are honored to have Michael Wu, Principal Scientist at Lithium Technologies, participate in our social media Q&A. I was a pure academic track research scientist before I entered the industry. I did research in computational neuroscience and my thesis was on modeling visual processing in the human brain using mathematical approaches, such as statistics and machine learning. Because computer vision is nowhere near the performance of humans in terms of its ability to segment objects, recognize people and understand a scene, my goal was to understand the computations that occur in our brains so we can recreate them in a machine vision system. I joined the industry right after finishing my PhD.

Victor David Scheinman, a pioneer in industrial robotics and a longtime Woodside resident, died Tuesday, Sept. 20, of complications of heart disease. Mr. Scheinman, starting as a graduate student at Stanford University, developed a robotic arm that allowed the use of robotics in industry to leap forward. A version of the arm, called the Scheinman Arm, was used for research in dozens of research labs, inspiring a generation of robotics engineers. Stanford professor Bernie Roth, who was at first Mr. Scheinman's adviser at Stanford and later his close friend, said that Mr. Scheinman's robotic arm was unique because it included sensors that gave the feedback to the computer controlling it. Professor Roth said Mr. Scheinman was "tenacious and very active," always trying to figure out how things worked and fixing anything that was broken.

Fake News, Cont'd: During a TV interview last night, Trump adviser Kellyanne Conway attempted to defend her boss's travel ban by pointing to "the Bowling Green Massacre"--which never took place. Conway tweeted that she "meant to say'Bowling Green terrorists,'" but her gaffe falls into a larger pattern of the Trump administration's "alternative facts." One true fact about the travel ban is that it revoked 60,000 visas--though a DOJ attorney erroneously said 100,000 earlier today. That error was poorly timed, since there's been a recent increase in fake news aimed at the biases of Trump's detractors as well as his supporters. We talked to Brooke Binkowski of the rumor-debunking site Snopes about the rise of fake news among progressives and what to do about it.

We propose an alternative to the Turing test that removes the inherent asymmetry between humans and machines in Turing’s original imitation game. In this new test, both humans and machines judge each other. We argue that this makes the test more robust against simple deceptions. We also propose a small number of refinements to improve further the test. These refinements could be applied also to Turing’s original imitation game.

Pity the 34 staff of Fukoku Mutual Life Insurance in Japan, diligently calculating insurance payouts and brutally replaced by an AI system. If you believe the reports from January, the AI revolution is here. In my opinion, the goings-on in Japan cannot possibly qualify as AI, but, in order to explain why, I have to explain what I think AI means. In one way, this attempt will be doomed to failure because there is no unified definition of AI. But I can, hopefully, provide a framework of understanding about the topic that may help.

Automated machine learning has become a topic of considerable interest over the past several months. A recent KDnuggets blog competition focused on this topic, and generated a handful of interesting ideas and projects. Of note, our readers were introduced to Auto-sklearn, an automated machine learning pipeline generator, via the competition, and learned more about the project in a follow-up interview with its developers. Prior to that competition, however, KDnuggets readers were introduced to TPOT, "your data science assistant," an open source Python tool that intelligently automates the entire machine learning process. For scikit-learn-compatible datasets, TPOT can automatically optimize a series of feature preprocessors and machine learning models that maximize the dataset's cross-validation accuracy, and outputs the optimal model as Python code leveraging scikit-learn.

From left, Dr Michael Tompsett (UK), Professor Eric Fossum (USA) and Professor Nobukazu Teranishi (Japan) are announced as the winners of the 2017 Queen Elizabeth Prize for Engineering at Carlton House Terrace on Wednesday, Feb. 1, 2017 in London. Taking a selfie is one of the easiest and quickest things you can do on your smartphone. But as with any landmark invention, it took decades and plenty of graft to develop the camera technology that lives in your pocket. A trio of engineers behind the invention of the image-sensing technology found in billions of smartphones, camera phones, PCs and hospital scanning technology, won the £1 million ($1.3 million) Queen Elizabeth Prize for engineering on Wednesday, and spoke about where the image-sensor technology they developed should go in the future. "I feel gobsmacked and very thankful to the Queen Elizabeth prize for this honor," said one of the engineers, Eric Fossum.