Researchers at Georgia Tech believes Robots can learn to conform to human norms, the paper argues, through a method called "Quixote", which teaches artificial agents to read stories that demonstrate human values and then rewards them for "good" behaviour. The learning process starts when a story is passed along to Quixote by Scheherazade, it then converts and breaks the information into different actions, either reward or punishment signals. The teaching is done via programming. Mark Riedl and Brent Harrison, researchers at Georgia Tech's School of Interactive Computing, have developed software that allows robots to read fables and glean proper sequences of events. Then the technique assigns'rewards' (basically robotic gold stars) to artificial intelligences that then make decisions that align with the good behavior in stories.

But more importantly, a logic-based artificial intelligence allows humans to define the ethical code from which potential Superintelligences can act. "I look at the hypothetical possibility of AI doing drugs from the standpoint of the kind of rationality that I would like to give to a robot," Bringsjord says, "and since that does not emulate the human brain, which is irrational, then the machine is not going to take that drug." John Licato, a research assistant at Rensselaer, supports the theory. "It would be irrational to make choices that would take an AI away from the primary thing that the robot is programmed to do," Licato says. This might not be because an AI won't have the capability of addiction, however, but instead because we equivocate drug use with satisfying a short-term, lower-level bodily desire, like using ecstasy as an aphrodisiac or opiates as a means to escape.

Microsoft had to shutdown its AI-powered chat bot less than 24 hours after its launch. This "conversational understanding" experiment was promising. The more Twitter users would chat with Tay, the smarter it was supposed to get. In fact, Tay became more human, which is not always synonymous with smart. Internet users bombarded Tay with misogynist, racist and abusive tweets.

We look forward to your answer to this and many other questions, and now the results... More than half (53 percent) of Americans feel that our quest to advance the field of artificial intelligence is important. Computers already create complex financial algorithms for retirement planning, and help people pick schools and life partners with the help of statistical analysis but when it comes to decisions concerning end of life care, this may be the right place for humanity to draw a line. If they had their own robot, a majority of Americans (53 percent) would use it for doing day-to-day chores, 21 percent chose problem solving, 17 percent said protection and four percent picked companionship. Two out of three Americans think that human intelligence poses a greater threat to humanity and 30 percent think that Artificial Intelligence does.

Welcome to the 60 Minutes/Vanity Fair Poll for April 2016. This month's poll probes the world of artificial intelligence, a term said to be first used by pioneering Stanford professor John McCarthy more than 60 years ago. He described it as "the science and engineering of making intelligent machines." From those humble beginnings, machines, computers and robots have made extraordinary advances in their applications and capabilities. Whoever thought that IBM would build a computer that could defeat two of the best Jeopardy players in history?

Rabbits are r-strategists, designed to exploit free resources, like fields of grass. The five psychological traits inherent to the r-strategy are docility/conflict-avoidance, promiscuity/non-monogamy, single-mom'ing, early sexualization of young, and no loyalty to a competitive in-group. Wolves are K-strategists, designed for when resources are too limited for everyone to survive. The five traits of a K-strategist are competitiveness/ aggressiveness/protectiveness, competitive mate monopolization/ monogamy, high-investment two-parent rearing, only mating when mature, and high loyalty to one's competitive in-group. All these traits either help you win, or produce fitter offspring, so they will win.

Large-scale data has great advantages in terms of finding natural experiments (to take a trivial example, if you want to measure how a July 15 price change affected sales, it's much more powerful to have daily sales data than monthly sales data). But for the most part economists trying to estimate causal models on large-scale data are using traditional methods like fixed effects linear regression. Having some easy to use alternatives would probably make a significant difference in empirical research.

For those of us worried that robots are coming for our jobs, economist Dr. Erik Brynjolfsson offers words of comfort: Bots actually may create new employment opportunities. Brynjolfsson discussed how new technologies may influence future jobs in an interview (above) with Andrew McAfee, co-director of the MIT Initiative on the Digital Economy, during the World Economic Forum at Davos, Switzerland, earlier this year. McAfee and Brynjolfsson are co-authors of The Second Machine Age. "It's true that robots are taking away some jobs, but at the same time they're creating lots of new jobs," said Brynjolfsson, director of the Initiative. "The thing that's different is whether or not the new job creation is keeping in balance with the old job creation, and there's no economic law that says that's automatically going to happen."

SAN FRANCISCO: Google is putting its machine learning technology and services into the cloud with the unveiling of Cloud Machine Learning (CML). Jeff Dean, a senior fellow at Google Research, said at the Google Cloud Platform (GCP) Next event in San Francisco that the suite of machine learning tools is built on top of the firm's TensorFlow open source intelligent software library and offers the ability to tap into cloud-power translation, image recognition and data analysis services. CML is effectively a packaged suite of machine learning APIs that Google already uses in the Photos and Translate services, as well as voice recognition developed for Android, delivered as services through GCP. The tool is aimed at application makers looking to embed machine learning into apps and companies looking to apply machine learning analysis to datasets. It provides pre-built machine learning models and allows users to build their own versions for specific uses.

In this special guest feature, Assaf Baciu, co-founder and SVP Product and Engineering at Persado, brings a historical perspective to the discussion of advertising and how machine learning has revolutionized this field. Assaf is responsible for the progression and foresight of Persado's growing product portfolio and the management of all product advancements. Prior to joining Persado, Assaf was VP of Product for Upstream, where Persado's core technology originated. Assaf previously worked for speech and imaging solutions supplier Nuance Communications as a senior director of product strategy where he was responsible for developing on-demand and mobile solutions. Assaf joined Nuance from BeVocal, following its acquisition in 2007.