If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Governor Andrew Cuomo of the State of New York declared last month that New York City will join 13 other states in testing self-driving cars: "Autonomous vehicles have the potential to save time and save lives, and we are proud to be working with GM and Cruise on the future of this exciting new technology." For General Motors, this represents a major milestone in the development of its Cruise software, since the the knowledge gained on Manhattan's busy streets will be invaluable in accelerating its deep learning technology. In the spirit of one-upmanship, Waymo went one step further by declaring this week that it will be the first car company in the world to ferry passengers completely autonomously (without human engineers safeguarding the wheel). As unmanned systems are speeding ahead toward consumer adoption, one challenge that Cruise, Waymo and others may counter within the busy canyons of urban centers is the loss of Global Positioning System (GPS) satellite data. Robots require a complex suite of coordinating data systems that bounce between orbiting satellites to provide positioning and communication links to accurately navigate our world.
Earlier this year, we open-sourced a research project called AirSim, a high-fidelity system for testing the safety of artificial intelligence systems. AirSim provides realistic environments, vehicle dynamics and sensing for research into how autonomous vehicles that use AI that can operate safely in the open world. Today, we are sharing an update to AirSim: We have extended the system to include car simulation, which will help advance the research and development of self-driving vehicles. The latest version is available now on GitHub as an open-source, cross-platform offering. The updated version of AirSim also includes many other features and enhancements, including additional tools for testing airborne vehicles.
Paint-on-the-floor pedestrian crossings don't cut it anymore. They are outdated, and the cause of 20 incidents a day in the UK. Architectural firm Umbrellium reckons it's got a solution: a sensor-packed digital crossing that responds to your movements. "We've been designing a pedestrian crossing for the 21st century," says Usman Haque, Umbrellium's founding partner. "Crossings that you know were designed in the 1950s, when there was a different type of city and interaction."
In March 2016, Google's Alphago artificial intelligence (AI) program stunned the world by beating the human world champion Go player in front of 200 million spectators. This was living proof of the potential in AI technology and the level of maturity reached by neural network and deep learning technologies. Those astounded by the success included quite a few engineers and managers who have been leading the AI revolution in the world in recent years. One of these was Intel VP Naveen Rao, general manager of the company's Artificial Intelligence Products Group, which was founded last year. "When I studied at college in the 1990s, we regarded artificial intelligence as'creative work'," Rao relates.
Continued from: "Advanced image sensors take automotive vision beyond 20/20." And there are many others now in the race to process all of that vehicle sensor data. Among them, Toshiba has been evolving its Visconti line of image recognition processors in parallel with increasingly demanding European New Car Assessment Programme (Euro NCAP) requirements. Starting in 2014, the Euro NCAP began rating vehicles based on active safety technologies such as lane departure warning (LDW), lane keep assist (LKA), and autonomous emergency braking (AEB). These requirements extended to daytime pedestrian AEB and speed assist systems (SAS) in 2016.
Recent Gartner estimations lead us to believe that up to 20 billion connected things will be in use by 2020. Data is the oil of our century -- but should we be concerned with a "data spill hazard"? Will artificial intelligence curb this threatening phenomenon, or rather, will it reveal the full potential of IoT data value? If my calculations are correct, when artificial intelligence hits the Internet of Things... you're gonna see some serious sh*t." The question is no longer whether companies should embrace big data analytics technologies.
This week at the Intel Shift Conference in New York, I had the opportunity to listen to my colleague Amir Khosrowshahi, CTO of the Intel AI Products group, speak to a gathering of business executives about the transformative impacts of AI. Amir explained how artificial intelligence (AI) can change what organizations do and how they do it, creating new business opportunities. Every company is in some phase of their AI adoption course: evaluating and understanding the opportunities, testing AI use cases and its outcome on their business, or fully integrating AI systems that are increasingly driving business metrics. AI concepts have been around for more than 60 years, but we now have the technology to make AI a reality. AI is predicated on the simple idea that with the right training a computer can simulate human decision making.
AI and computer learning is quickly gaining use, so what happens when AI becomes commonplace? Before we dive into this it is important to understand what AI can and can't do today and what aspect of it is already common. Computer learning is a subset of AI, but the two are often discussed together or interchanged. Computer learning is a method where a computer is trained on a set of data and then uses that training to learn a task. Facial feature recognition is a common computer learning task where the computer is trained to recognize the various features (eyes, lips, nose and mouth) of anyone's face.
Artificial intelligence, Machine Learning, and Deep Learning are more than futuristic concepts. These technologies are impacting the insurance industry in a significant way right now and this impact is likely to increase in the near future. The idea of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) may fascinate consumers who enjoy talking to their digital while admiring a Nest thermostat. But for the insurance industry, these terms are business-changers that affect products and services offered and interactions with consumers and other industry partners. The definitions of these terms may be a bit confusing to the uninitiated (see sidebar).
After announcing plans this month to supply self-driving vehicles for Lyft's ride-hailing network, the autonomous tech developer has scored financial backing from Southeast Asian rideshare powerhouse Grab and plans to expand into Singapore. Singapore office will study that market as a potential place to deploy vehicles equipped with its software and self-driving hardware kits in government and business fleets, Tandon said. Amid the rush by auto and tech firms to perfect robotic vehicles, Tandon and his co-founders, who were all researchers from Stanford University's Artificial Intelligence Lab, founded Drive.ai to specialize in deep learning-based driving software for business, government and shared vehicle fleets. Small relative to well-funded programs at Waymo, General Motors' Cruise, Uber's Advanced Technology Vehicle Group and Ford's Argo AI, Mountain View, California-based Drive.ai has made quick progress.