A team of computer scientists may have developed a surprising way to curb wildlife poaching. Funded by the National Science Foundation (NSF), a team of computer scientists at the University of Southern California (USC) have developed a model for "green security games" that use game theory to protect wildlife from poachers. Game theory uses mathematical equations "to predict the behavior of adversaries and plan optimal approaches for containment," explains NSF, which would allow park rangers to patrol parks and wildlife sanctuaries more effectively. "In most parks, ranger patrols are poorly planned, reactive rather than pro-active and habitual," Fei Fang, a Ph.D. candidate in the computer science department at USC and a researcher involved with the project, tells NSF. "We need to provide actual patrol routes that can be practically followed."

As we go about our daily lives, we tend to assume that our perceptions--sights, sounds, textures, tastes--are an accurate portrayal of the real world. Sure, when we stop and think about it--or when we find ourselves fooled by a perceptual illusion--we realize with a jolt that what we perceive is never the world directly, but rather our brain's best guess at what that world is like, a kind of internal simulation of an external reality. Still, we bank on the fact that our simulation is a reasonably decent one. If it wasn't, wouldn't evolution have weeded us out by now? The true reality might be forever beyond our reach, but surely our senses give us at least an inkling of what it's really like.

We humans take for granted our remarkable ability to predict things that happen around us. For example, consider Rube Goldberg machines: One of the reasons we enjoy them is because we can watch a chain-reaction of objects fall, roll, slide and collide, and anticipate what happens next. But how do we do it? How do we effortlessly absorb enough information from the world to be able to react to our surroundings in real-time? And, as a computer scientist might then wonder, is this something that we can teach machines?

Tesla Motors Inc (NASDAQ:TSLA) is riding on a tidal wave of notoriety since the start of 2016. The firm is continuing to lead an industry in a transition away from internal combustion engines and toward all electric power. Most of the media attention lately comes from the introduction of the "affordable" Tesla Model 3. That car won't even begin delivery until late next year but over 400k people have paid 1000 each just to get in line to buy one. One of the features that will be available on the Model 3 is Autopilot. Tesla gets a lot of attention for its Autopilot feature that provides properly equipped Model S and Model X SUVs with self-driving abilities on highways.

The world's first cafe using the tiny domestic unmanned aircraft as servers has opened in a Dutch university. The pop-up drone cafe will be serving up all weekend as part of celebrations for the'Dream and Dare' festival marking the 60th anniversary of the Eindhoven University of Technology. The drone (pictured), nicknamed Blue Jay, resembles a small white flying saucer. It has a luminescent strip for eyes, flies to a table and hovers as it takes a client's order. The customer then points to the list to signal what they would like.

When it comes to new age tech segments -- machine learning, IoT, augmented/virtual reality, India still has very few startups. To boost innovation and encourage entrepreneurs working in these domains, a new startup fund called Ideaspring Capital worth 125 crores has been set up by a clutch of famed investors and businessmen, reports ET. The fund will focus on new-age tech companies working in areas such as Machine Learning and Deep Learning, Computer Vision and Image Processing, Big Data Analytics, Internet of things, Augmented and Virtual Reality, Internet Security,etc. We will invest in 4-5 firms annually. Initially, we will invest up to Rs 3 crore in the seed round that would last them for 12-18 months.

I'm often asked, as we move more and more to digital communications: what will happen to the people? What will happen to the people? As we move to robotics, automated agents, augmented and artificial intelligence, what will happen to the people. If we have no branches, no structures and no buildings that need humans, what will happen to the people. It's a question that always crops up as we move from one form of work to another.

Artificial intelligence and UAVs are two emerging technologies with big potential. Due to the continual breakthroughs in semiconductors, natural language processing, and other technologies, some scientists think that AI could eclipse human intelligence as soon as 2029. Given the improvements in weight reduction and battery technology, many investors believe UAVs could do everything from powering Internet connections to delivering products quickly over the next decade too. Given the two technologies' potential, it isn't surprising that Chevron (NYSE: CVX) has big plans for artificial intelligenceand UAV technology for its future. Let's explore in more detail.

For people living in a world without sound, sign language can make sure their points of view are heard. But outside of the deaf and hard-of-hearing communities, this gesture-based language can lose its meaning. Now a pair of entrepreneurial technology students in the US has designed a pair of gloves to break down the communication barriers, by translating hand gestures into speech. US inventors have designed a pair of gloves, called'SignAloud', which translate the gestures of sign language to spoken English. The gloves (pictured) use embedded sensors to monitor the position and movement of the user's hands, while a central computer analyses the data and converts gestures to speech Called'SignAloud', the gloves use embedded sensors to monitor the position and movement of the user's hands.

Machine learning has come of age in public health reporting according to researchers from the Regenstrief Institute and Indiana University School of Informatics and Computing at Indiana University-Purdue University Indianapolis. They have found that existing algorithms and open source machine learning tools were as good as, or better than, human reviewers in detecting cancer cases using data from free-text pathology reports. The computerized approach was also faster and less resource intensive in comparison to human counterparts. Every state in the United States requires cancer cases to be reported to statewide cancer registries for disease tracking, identification of at-risk populations, and recognition of unusual trends or clusters. Typically, however, busy health care providers submit cancer reports to equally busy public health departments months into the course of a patient's treatment rather than at the time of initial diagnosis.