NVIDIA gives automakers, tier-1 suppliers, automotive research institutions, and start-ups the power and flexibility to develop and deploy breakthrough artificial intelligence (AI) systems for self-driving vehicles. NVIDIA's unified AI computing architecture enables training deep neural networks in the data center on the NVIDIA DGX-1, and then seamlessly runs them on NVIDIA DRIVE PX 2 inside the vehicle. This end-to-end approach leverages NVIDIA DriveWorks software and allows cars to receive over-the-air updates to add new features and capabilities throughout the life of a vehicle.

Over the last decade, the "data revolution" has touched every aspect of our work and personal lives. Today's business challenges have never been more complex, and the critical insights that can address these challenges are often buried in an avalanche of data. In today's marketplace, the business that wins, is the business that "thinks." The viability of a company in the marketplace now depends on its ability to use data and analytics to fuel a thinking business. Companies in industries as diverse as healthcare, retail, banking and manufacturing are already using cognitive technologies to reshape business and do things faster and more efficiently than ever before.

I started my career in 1997 with the advent of modern eDiscovery. In fact, it was not even called eDiscovery when I developed my first applications for processing data in the context of eDiscovery. I founded Advocate Solutions, Inc around that same time and we developed Discovery Cracker - one of the first eDiscovery processing applications. Producing documents was a different game back then as the price for processing was incredibly high. I Joined kCura, known for its legal database application Relativity, in 2010 and saw firsthand how fast the amount of data involved eDiscovery was rising.

Initially based in Peru, Laboratoria has expanded into Mexico and Chile.

Sophia, a young animator is offered what should be her dream job. But when she discovers the truth of the modern "creative" process, she must make a hard choice about her passion for film. Set in an unsettling tomorrow, REAL ARTISTS, is the new sci-fi short film from award-winning director/screenwriter Cameo Wood (DUKHA IN SUMMER) and based on the short story by Hugo/Nebula/World Fantasy winning author Ken Liu (THE GRACE OF KINGS) and stars renowned actress Tamlyn Tomita (FOUR ROOMS, JOY LUCK CLUB, THE DAY AFTER TOMORROW) and marks the debut of Tiffany Hines (BONES) in a sci-fi indie role. Help us bring this story to life. We need 1000 followers on Seed&Spark in order to be eligible for distribution on Netflix, Hulu, iTunes, and Amazon - If we get 1000 followers, we also get a grant of filmmaking products and services worth 8,500.

AI is the technology that will have the single biggest impact on cities over the next decade. At Urban.Us, we already meet teams using very large datasets to train algorithms to drive cars, water yards efficiently, guide drones to survey construction sites and route first responders to the people who need them most, and others who use bots to provide legal guidance to people with parking fines. These startups are benefiting from an explosion of data generated by human activities and sensors. Ironically, while the flood of data is difficult for people to understand, it's great for teaching machines. Thanks to cheaper storage and processing to train new algorithms, we've seen a surge in AI deep-learning techniques.

When the writer Rebecca Forster first heard how Google was using her work, it felt like she was trapped in a science fiction novel. "Is this any different than someone using one of my books to start a fire? I have no idea," she says. "I have no idea what their objective is. Certainly it is not to bring me readers."

Throughout time, humans have been fascinated by the idea of machines "thinking" like humans. In Eagle Eye we became acquainted with ARIIA (an Autonomous Reconnaissance Intelligence Integration Analyst), in Her, we were introduced to Samantha (a highly capable computer program that doubled as a personal assistant), in Ex Machina we fell for Ava (a humanoid robot with genuine human traits) and in I, Robot, we befriended Sonny (a friendly robot). Concepts like machine learning, robotics, deep learning and artificial intelligence, have been thrown around, here and there, spiking our interest and commercializing these once scientific concepts. But what exactly is artificial intelligence, and what are its implications? John McCarthy, an American computer scientist and cognitive scientist coined the term "artificial intelligence" in 1955 describing it as "every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to stimulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves".

LESLIE FAUGHNAN finds that while automation, augmented intelligence and artificial intelligence are developing at a furious pace in today's enterprise, we are a long way from replacing people and the things they are good at One of the many dictionary definitions of the word'smart' includes the phrase "quick-witted intelligence", which seems as good a way as any of describing what we currently mean by a smart enterprise. Definition is a challenge yet to be met because there is no tech spec for smart. But both the IT industry and its corporate clients share a broad vision of what we are aiming for -- an organisation that is fully joined up digitally and capable of realising the benefits of that synergy. That in turn means corporate agility based on the ability to utilise all of its collected data, complemented by relevant external data, in real time. A useful term in the ether now is'Augmented Intelligence', which will help us somewhat limited humans to make better decisions.

When it comes to understanding intelligence, the greatest challenge out there is not a Rubik's Cube, or chess, or even Go. These games are difficult in the sense that there are often many options, but they are still transparent: nothing is hidden; every bit of information is in front of you. The main obstacle is converting this perfect information into a strategy. There is a fixed set of rules out there, and if a computer can find them, it will achieve the optimal result in every game. When Garry Kasparov lost to IBM's Deep Blue chess computer in 1997, he lamented this approach.