The technologies on Gartner Inc.'s Hype Cycle for Emerging Technologies, 2016 reveal three distinct technology trends that are poised to be of the highest priority for organizations facing rapidly accelerating digital business innovation. Transparently immersive experiences, the perceptual smart machine age, and the platform revolution are the three overarching technology trends that profoundly create new experiences with unrivaled intelligence and offer platforms that allow organizations to connect with new business ecosystems. The Hype Cycle for Emerging Technologies report is the longest-running annual Gartner Hype Cycle, providing a cross-industry perspective on the technologies and trends that business strategists, chief innovation officers, R&D leaders, entrepreneurs, global market developers and emerging-technology teams should consider in developing emerging-technology portfolios. "The Hype Cycle for Emerging Technologies is unique among most Hype Cycles because it distills insights from more than 2,000 technologies into a succinct set of must-know emerging technologies and trends that will have the single greatest impact on an organization's strategic planning," said Mike J. Walker, research director at Gartner. "This Hype Cycle specifically focuses on the set of technologies that is showing promise in delivering a high degree of competitive advantage over the next five to 10 years."

OpenML stands for Open Machine Learning and is an online platform, which aims at supporting collaborative machine learning online. It is an Open Science project that allows its users to share data, code and machine learning experiments. At the time of writing this blog post I am in Eindoven at an OpenML workshop, where developers and scientists meet to work on improving the project. Some of these people are R users and they (we) are developing an R package that communicates with the OpenML platform. The OpenML R package can list and download data sets and machine learning tasks (prediction challenges).

There's no doubt that every single one of us wants to live forever and there is no doubt that nobody lives forever. But Josh Bocanegra, CEO of Humai, a technology company based in Los Angeles is working on a chatbot that will hypothetically keep his mom alive – even after death. Yes, Bocanegra is developing a chatbot that will help him talk to his mother even after her final breath. To that end, he and his mother are recording tons of audio messages. Bocanegra didn't reveal many details about his chatbot project but we do know it is built around Artificial intelligence, (AI).

While traffic lights do use sensors to try and make slightly more intelligent decisions than perhaps they once did, they are still fairly dumb tools for regulating the flow of traffic. A recent Chinese study explores whether machine learning can do a better job. The Elephant & Castle roundabout near where I live is notorious for its complexity, with rush hour traffic bustling onto it from several directions. It's perhaps understandable therefore that humans struggle to program traffic lights to function effectively. Of course, automating the process is no mean feat either, both because it requires an accurate model of the traffic flow, and then the challenges inherent in optimizing the flow.

Isaac Asimov gave us the basic rules of good robot behaviour: don't harm humans, obey orders and protect yourself. Now the British Standards Institute has issued a more official version aimed at helping designers create ethically sound robots. The document, BS8611 Robots and robotic devices, is written in the dry language of a health and safety manual, but the undesirable scenarios it highlights could be taken directly from fiction. Robot deception, robot addiction and the possibility of self-learning systems exceeding their remits are all noted as hazards that manufacturers should consider. Welcoming the guidelines at the Social Robotics and AI conference in Oxford, Alan Winfield, a professor of robotics at the University of the West of England, said they represented "the first step towards embedding ethical values into robotics and AI".

Investors dropped 681 million into A.I.-centric startups in Silicon Valley last year. This year, the number will likely reach 1.2 billion. Five years ago, total A.I. investment spiked at roughly 150 million. This is how Silicon Valley works: When something new is hyped and seems to have investor trust, everybody jumps on the train without asking, "Where does this train go?" The truth is that artificial intelligence does not exist yet, and most companies claiming to have A.I. technology are arrogantly re-selling an old concept of machine learning -- a technology that was first introduced in 1959 but which truly started to take off in the 1990s.

Investors dropped 681 million into A.I.-centric startups in the Valley last year. This year the number would reach 1.2 billion. Five years ago total A.I. investment piked at roughly 150 million. This is how Silicon Valley works: when there is a new hype that seems to have investor trust everybody jumps on the train without asking any questions. No one asked: "Where does this train go?"

The paper "Model Compression" is available here: https://www.cs.cornell.edu/ We also thank Experiment for sponsoring our series. Subscribe if you would like to see more of these! - http://www.youtube.com/subscription_c... The thumbnail background image was created by John Lord - https://flic.kr/p/nVUaB

If I swap positive class and negative class, then train a model again (I tried decision tree, adaboost, svm from scikit-learn built-in package) for a two class classification problem. Sometimes, I can see AUC slightly change (around 1-2%). Anyone have any ideas why there are such changes? For ROC curve, x-axis is false positive rate, and y-axis and true positive rate. When prediction model gives prediction scores, we will order the scores from higher value to lower value, and then choose threshold according to the sorted values and calculate at the specific threshold point, what is the fpr and tpr.

Every organization becomes involved in the transition to the new cognitive computing technology epoch soon. This begs the question: "Does artificial intelligence need leadership?" We talked about future challenges in leadership and business with Udo Hornfeck, Vice President at LEONI, a tier-1-supplier in the automotive industry with approximately 70.000 employees worldwide and located in Kitzingen, Germany. Mr Hornfeck leads the Global Research & Development departments. Mark McGregor Leadership Center: Mr Hornfeck, how do you define leadership?