A nightmare-inducing robot tarantula has been built using a 3D printer. The all-terrain machine, which weighs less than 5 kilograms (11lbs), can climb tricky surfaces like rocks and stairs with ease. It runs using 18 different motors that help it move smoothly over uneven surfaces. The motors give the robot exceptional flexibility and even appear to make it dance in a video of the strange machine. A hexapod robot is a mechanical vehicle that walks on six legs.

There's just one male northern white rhino left in the world, and he's now on the Tinder dating app as wildlife experts try to keep his species alive. The rhino, named Sudan, will appear as an ad on Tinder as you're swiping through potential suitors – and despite the burden of having to save his species from extinction, he has'no problems performing under pressure.' It's hoped that the'Most Eligible Bachelor in the World' campaign launched by Ol Pejeta Conservancy and Tinder will help raise $9 million towards breeding methods before it's too late. The rhino, named Sudan, will appear as an ad on Tinder as you're swiping through potential suitors – and despite the burden of having to save his species from extinction, he has'no problems performing under pressure' While swiping through Tinder, Sudan will eventually appear. If you swipe right on his profile, it will be a'match.'

Picking fruit and vegetables by hand is back breaking work, often at low pay. But a robot designed by an international team of scientists could make this a thing of the past within a decade. The device, dubbed Thorvald, is able to provide farmers with invaluable information about their land and produce. A nimble farming machine called Thorvald (pictured) could soon become a common sight on working farms. One of the UK's largest produce companies is reported to have invested funding to allow the robot to be mass produced Thorvald was built to to perform a wide variety of agricultural tasks. Its lightweight design makes manoeuvring over uneven ground no problem and it is agile enough to navigate between rows of crops without touching plants.

Researchers have known that "artificial neurons" could carry out logical functions--i.e., learn the way humans do--since 1943. The term "artificial intelligence" has been around since its introduction at a science conference at Dartmouth University in 1956. But only in the past several years have we started seeing theory put into practice the way those researchers imagined. We now have machines that can translate languages, compose music, write novels, and operate vehicles. So what might the implications of these developments be for educators and students? The primary goal of AI research may be to teach machines how to learn, thereby automating some of the tasks that complicate our everyday lives, but brain scientists are saying it goes both ways: We now know more about human learning as a result of machine learning, and it has some exciting implications for the classroom.

Nvidia is on a quest to find the most disruptive artificial intelligence startups. This quest is part of a larger contest, dubbed Nvidia Inception, which is screening more than 600 entrants to cull the best AI startups in three big categories. We wrote about the first four candidates for the hottest emerging startup on Friday. And now we're focusing on the next five candidates in the category dubbed the "most disruptive" startups. Jen-Hsun Huang, CEO of Nvidia, hosted a Shark Tank-style event this week as part of the search to find the best AI startups. Huang and a panel of judges listened to pitches from 14 AI startups across three categories.

Chris Skinner is surprised when he hears that he's popular among bankers. Surprised because the messages he delivers are often uncomfortable. Skinner intends to push bankers out of their comfort zone. While never a banker himself, Skinner has worked on banking projects for several technology companies before establishing his niche as a fintech blogger (TheFinanser.com) His specialty is discerning relevant chords amid all the noise regarding technology and its impact on financial services.

Research Scientist – Bayesian Machine Learning Location: Cambridge - United Kingdom The Schlumberger Gould Research Centre offers a stimulating research environment with real-world problems that push the limits of scientific knowledge. We are committed to be at the leading edge of science and to incorporate new emerging technologies in Schlumberger's activities. To achieve that we recruit the most talented scientists from a variety of scientific and engineering backgrounds, and give them opportunities to advance their fields of expertise as well as to develop solutions of significant industrial impact. The Schlumberger Gould Research Centre strongly encourages the self-development of its scientists, offers high-end experimental facilities and scientific resources, and maintains strong collaborations with academic and industrial research groups worldwide. Schlumberger is currently developing new drilling systems with a high degree of autonomy and intelligence which will change the way the industry drills wells.

The smokestack of a refinery stands next to the Mantaro River in La Oroya, Peru. One of the things that has intrigued me the most about deep learning image cataloging algorithms is their ability to watch the world go by at scale each day through the incredible volume of news and social media images that are generated from every corner of the world and essentially generate a live ground truthed catalog of what's happening moment by moment. Of particular interest for disaster response and environmental monitoring is the ability of such algorithms to recognize imagery of flooding, drought, smog, litter, destruction, violence and other indicators of ongoing ground and air pollution and sudden natural disasters. What might a system look like? Two years ago I met Kadi Kenk, Head of Partnerships for "Let's do it" which is a social good organization founded in Estonia in 2008 that bills itself as a "social movement against trash."

When robot Nao laughs, he does so with his whole body: slapping his knees, shaking his head. But the adorable android, made by SoftBank Robotics, is not merely good at expressing mirth; he can correctly identify as much as 65 percent of happy laughter outbursts in humans, according to a study presented in 2015 at a nonverbal language workshop in the Netherlands. Once robots like Nao master human laughter, they will make far more likable and realistic companions. Nao's creators and other scientists are studying the minutiae of human laughter--acoustics, breath, body movements and vibrations--to translate them into algorithms that robots and avatars can learn. And that includes learning how to be funny. In 2016 researchers in South Korea and Singapore showed that Nao is already quite good at telling jokes.

Igor Kononenko studied computer science at the University of Ljubliana, Slovenia, receiving his BSc in 1982, MSc in 1985 and PhD in 1990. He is now professor at the Faculty of Computer and Information Science there, teaching courses in Programming Languages, Algorithms and Data Structures; Introduction to Algorithms and Data Structures; Knowledge Engineering, Machine Learning and Knowledge Discovery in Databases. He is the head of the Laboratory for Cognitive Modelling and a member of the Artificial Intelligence Department at the same faculty. His research interests include artificial intelligence, machine learning, neural networks and cognitive modelling. He is the (co) author of 170 scientific papers in these fields and 10 textbooks.