It was not long ago that Artificial Intelligence (AI) was only in the realm of science fiction. Today, it has become a reality and is only growing more prominent in many different industries every day. This includes the internet as AI in search engine technology has been around for a few years. The algorithms used to rank pages have been affected considerably by AI already and that trend will continue into the foreseeable future. Currently, Google's RankBrain, an AI process used help set search engine rankings, is having a major impact which is only expected to expand.

Perhaps the most photographed individual at the AI for Global Good Summit in Geneva, Switzerland, last week was not a human but a humanoid called Sophia. Cyber protection: Have you received the data call? "As I get smarter, I hope to understand people better -- help you, work with you as a friend, to imagine and build a better future for us all," Sophia, an uncannily human-like robot, said in a Facebook Live interview. In that interview and onstage at the summit, her eyebrows lifted, she smiled gently, and her eyes lit up as she answered questions from the audience, with moments where only the glimpse of cords behind her face revealed that she is a machine. Hanson Robotics developed Sophia as part of its mission to "create genius machines that live and love," and work together with humans to build a "a smarter and better future."

A new photonic technology has enabled a computer system to mimic the way human brains learn from accumulating experience. Researchers from the Massachusetts Institute of Technology has developed a new approach for the deep learning computation using light, instead of electricity. A deep learning computer is a way computer system accumulate experiences and data and recognized the pattern in the accumulative data. Unfortunately, even the most powerful computer is limited with its transistor capacity to perform such function. In order to improve the deep learning computer system, researchers from the Massachusetts Institute of Technology discovered that light is a much better answer to perform such function, instead of electricity.

But the computations these systems must carry out are highly complex and demanding, even for the most powerful computers. Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. Their results appear today in the journal Nature Photonics in a paper by MIT postdoc Yichen Shen, graduate student Nicholas Harris, professors Marin Soljacic and Dirk Englund, and eight others. Soljacic says that many researchers over the years have made claims about optics-based computers, but that "people dramatically over-promised, and it backfired." While many proposed uses of such photonic computers turned out not to be practical, a light-based neural-network system developed by this team "may be applicable for deep-learning for some applications," he says.

One traditional use of critical-path heuristic functions is as effective sufficient criteria for unsolvability. To employ this for dead-end detection, the heuristic function must be evaluated on every new state to be tested, incurring a substantial runtime overhead. We show herein that the exact same dead-end detector can be captured through a nogood, a formula phiOFF computed once prior to search. This is mostly of theoretical interest, as phiOFF is large. We obtain practical variants by instead incrementally generating a stronger nogood psi, that implies phiOFF, online during search, generalizing from already tested states to avoid future heuristic-function evaluations.

Peer effects, in which the behavior of an individual is affected by the behavior of their peers, are posited by multiple theories in the social sciences. Other processes can also produce behaviors that are correlated in networks and groups, thereby generating debate about the credibility of observational (i.e. nonexperimental) studies of peer effects. Randomized field experiments that identify peer effects, however, are often expensive or infeasible. Thus, many studies of peer effects use observational data, and prior evaluations of causal inference methods for adjusting observational data to estimate peer effects have lacked an experimental "gold standard" for comparison. Here we show, in the context of information and media diffusion on Facebook, that high-dimensional adjustment of a nonexperimental control group (677 million observations) using propensity score models produces estimates of peer effects statistically indistinguishable from those from using a large randomized experiment (220 million observations). Naive observational estimators overstate peer effects by 320% and commonly used variables (e.g., demographics) offer little bias reduction, but adjusting for a measure of prior behaviors closely related to the focal behavior reduces bias by 91%. High-dimensional models adjusting for over 3,700 past behaviors provide additional bias reduction, such that the full model reduces bias by over 97%. This experimental evaluation demonstrates that detailed records of individuals' past behavior can improve studies of social influence, information diffusion, and imitation; these results are encouraging for the credibility of some studies but also cautionary for studies of rare or new behaviors. More generally, these results show how large, high-dimensional data sets and statistical learning techniques can be used to improve causal inference in the behavioral sciences.

Once upon a time in the future you will have to choose between a human and an AI to lead a country. The idea may sound far-fetched but already some business leaders predicting that companies will have artificial intelligence (AI) in the boardroom, driving and making strategic decisions. Jack Ma, CEO of Alibaba, has said he expects to see an AI on the cover of TIME magazine as'CEO of the Year' by 2030. So, could you choose a robot over a human to lead, or would there be an immediate and unfounded irrational fear that would stop you? Why would we still find it hard to choose between a human or robot leader if big business will one day be run by an AI? Will AI be CEO of the Year by 2030?

A business school in Paris will soon begin using artificial intelligence and facial analysis to determine whether students are paying attention in class. The software, called Nestor, will be used two online classes at the ESG business school beginning in September. LCA Learning, the company that created Nestor, presented the technology at an event at the United Nations in New York last week. Source: This French school is using facial recognition to find out when students aren't paying attention – The Verge This system will be used during videos to create quizzes based on when a student isn't paying attention. I don't understand the purpose since if they aren't paying attention a quiz isn't going to help them learn the material.

What could your computer, phone, or other gadget do differently if it knew how you were feeling? Rana el Kaliouby, founder and CEO of Affectiva, is considering the possibilities of such a world. Speaking at the Computer History Museum last week, el Kaliouby said that she has been working to teach computers to read human faces since 2000 as a PhD student at Cambridge University. "I remember being stressed," she says. "I had a paper deadline, and "Clippy" [that's Microsoft's ill-fated computer assistant] would pop up and do a little twirl and say'It looks like you are writing a letter.' I would think, 'No I'm not!'"

The Robot Academy is a new learning resource from Professor Peter Corke and the Queensland University of Technology (QUT), the team behind the award-winning Introduction to Robotics and Robotic Vision courses. There are over 200 lessons available, all for free. The lessons were created in 2015 for the Introduction to Robotics and Robotic Vision courses. We describe our approach to creating the original courses in the article, An Innovative Educational Change: Massive Open Online Courses in Robotics and Robotic Vision. The courses were designed for university undergraduate students but many lessons are suitable for anybody, as you can easily see the difficulty rating for each lesson.