Behind the scenes, Yelp is improving its restaurant listings using your food porn pictures. That photo you took of you eating soft serve at your local ice cream shop? From that photo alone, the AI can determine whether the ice cream shop has outdoor seating, the ambience, and whether it's for couples of families. The AI will eventually be able to identify dishes in the photos by analyzing the color, texture, and shapes of the images. The result is photos and restaurant profiles that will be auto-captioned with phrases like, "good for kids, casual and loud atmosphere," with a percentile accuracy rating.

Each fall, groggy teenagers resign themselves to another year of fighting their body clocks so they can get to class on time. It's well known that teens who don't get at least eight hours of sleep a night face a slew of problems. That's why both the American Academy of Pediatrics and the Centers for Disease Control recommend shifting middle- and high-school start times to 8:30 a.m. or later. Yet during the 2011-12 school year -- the most recent statistics available -- only 17.7 % of the nation's public middle, high and combined schools met the 8:30 a.m. In California, the average start time was 8:07 a.m.

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."

Machine learning and predictive analytics are becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. It is one of the fastest growing trends in modern computing, and everyone wants to get into the field of machine learning. In order to obtain sufficient recognition in this field, one must be able to understand and design a machine learning system that serves the needs of a project. The idea is to prepare a learning path that will help you to tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques. Also, it will give you a solid foundation in the machine learning design process, and enable you to build customized machine learning models to solve unique problems.

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?"