For roboticists working in the field of biomimetics, copying a bat's complex flight patterns has been a difficult problem to solve. Or, as Caltech professor and Jet Propulsion Laboratory researcher Soon-Jo Chung put it during a press conference, "bat flight is the holy grail of aerial robotics." And according to a new research paper published by Chung and his JPL colleagues in the journal Science Robotics this week, that holy grail has officially been discovered. Robotic birds and winged insects are relatively easy to create, but with over 40 joints in their wings, bats offer a new level of intricacy. By simplifying that wing structure into just nine key joints covered by a flexible membrane, however, the team successfully created the first Bat Bot.

A Northwestern University team developed a new computational model that performs at human levels on a standard intelligence test. This work is an important step toward making artificial intelligence systems that see and understand the world as humans do. "The model performs in the 75th percentile for American adults, making it better than average," said Northwestern Engineering's Ken Forbus. "The problems that are hard for people are also hard for the model, providing additional evidence that its operation is capturing some important properties of human cognition."The The platform has the ability to solve visual problems and understand sketches in order to give immediate, interactive feedback.

Mel Silverman walked over to a whiteboard and picked up a marker, listing all the academic disciplines that the band of renegade scientists asking him for money represented. Assembled there 12 years ago at Vancouver's Metropolitan Hotel was a group of about 15 people, ranging from computer scientists to biologists to experimental engineers. What united them was their interest in a concept that was, at the time, generally perceived as the domain of the lunatic fringe. They believed it was possible to teach a machine to learn the same way a child does, through artificial neural networks that mimic the function of the human brain. In the process of teaching a machine to learn like a human, they figured there was likely a lot to discover about how humans learn as well.

Summary: In this last article in our series on recommenders we look to the future to see how the rapidly emerging capabilities of Deep Learning can be used to enhance recommender performance. In our first article, "Understanding and Selecting Recommenders" we talked about the broader business considerations and issues for recommenders as a group. In our second article, "5 Types of Recommenders" we attempted to detail the most dominant styles of Recommenders. Our third article, "Recommenders: Packaged Solutions or Home Grown" focused on how to acquire different types of recommenders and how those sources differ. In this last article in our series on recommenders we look to the future to see how the rapidly emerging capabilities of Deep Learning can be used to enhance performance.

Don't know where or how to start learning? But learning more about artificial intelligence, and the myriad overlapping and related fields and application domains does not require a PhD. Getting started can be intimidating, but don't be discouraged; check out this motivating and inspirational post, the author of which went from little understanding of machine learning to actively and effectively utilizing techniques in their job within a year. With more and more institutes of higher learning today making the decision to allow course materials to be openly accessible to non-students via the magic of the web, all of a sudden a pseudo-university course experience can be had by almost anyone, anywhere. Have a look at the following free course materials, all of which are appropriate for an introductory level of AI understanding, some of which also cover niche application concepts and material.

Cyber threats are an ever-present danger to global economies and are projected to surpass the trillion dollar mark in damages within the next year. As a result, the cybersecurity industry is investing heavily in machine learning in hopes of providing a more dynamic deterrent. ABI Research forecasts machine learning in cybersecurity will boost big data, intelligence, and analytics spending to $96 billion by 2021. "We are in the midst of an artificial intelligence security revolution," says Dimitrios Pavlakis, Industry Analyst at ABI Research. "This will drive machine learning solutions to soon emerge as the new norm beyond Security Information and Event Management, or SIEM, and ultimately displace a large portion of traditional AV, heuristics, and signature-based systems within the next five years."

For people living with Asperger's syndrome, every social interaction can be a battle. While high-functioning in some aspects, those suffering from the form of autism often struggle to engage with other people and topics outside of their own spheres of interest. Keeping up with conversations can be especially challenging then, since difficulty interpreting the meaning of nonverbal communication (like gestures and facial expressions) and modulations in the speech patterns of others is one of the hallmarks of the condition. A pair of MIT researchers have set out to make these interactions less harrowing. Using wearable tech and AI deep-learning systems, they've developed a tool that could someday act as a real-time virtual social coach.

In December 2016 a new venue opened up in London, UK called the Realities Centre, focused on promoting virtual reality (VR), augmented reality (AR) and mixed reality (MR) technology. THe last couple of months have seen several conferences and hackathons taking place and now the venue has revealed its latest events. Taking place in March 2017, the Realities Centre will be hosting another conference and hackathon, this time dedicated to artificial intelligence (AI) and VR. Held on 16th March will be the AI VR Conference, featuring an evening of discussions exploring the two fields, namely how AI machine learning and natural language processing can be used in VR. Speaking at the event will be Rob Whitehead, co-founder and CTO of Improbable, whose talk will be on'A high level introduction of the AI and VR industry'.

I work in computational quantum condensed-matter physics: the study of matter, materials, and artificial quantum systems. Complex problems are our thing. Researchers in our field are working on hyper-powerful batteries, perfectly efficient power transmission, and ultra-strong materials--all important stuff to making the future a better place. To create these concepts, condensed-matter physics deals with the most complex concept in nature: the quantum wavefunction of a many-particle system. Think of the most complex thing you know, and this blows it out of the water: A computer that models the electron wavefunction of a nanometer-size chunk of dust would require a hard drive containing more magnetic bits than there are atoms in the universe.

Autonomous cars are here, but are we ready for them? As with any new technology, it'll take some time for society to let go of the wheel and settle into the passenger seat. But researchers at the Swiss Federal Institute of Technology have developed an algorithm that may make this process a little smoother. The idea is that all vehicles on the road, autonomous or not, will communicate with one another via WiFi. In addition, every car will be outfitted with sensors such as lasers, video cameras, and GPS. Essentially, cars will cooperate to gather contextual information, but will be able to independently adjust their speed and position to keep traffic traveling.