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) …
People can differ on their perceptions of "evil." People can also change their minds. Still, it's hard to wrap one's head around how Google, famous for its "don't be evil" company motto, dealt with a small Defense Department contract involving artificial intelligence. Facing a backlash from employees, including an open letter insisting the company "should not be in the business of war," Google in April grandly defended involvement in a project "intended to save lives and save people from having to do highly tedious work." Less than two months later, chief executive officer Sundar Pichai announced that the contract would not be renewed, writing equally grandly that Google would shun AI applications for "weapons or other technologies whose principal purpose or implementation is to cause or directly facilitate injury to people."
Episode summary: Unlike the field of self-driving cars, the fields of construction, mining, agriculture, and other classes of "heavy industry" involve a huge variety of equipment and use-cases that go beyond traveling from A to B. The heavy industry leaders of today are no farther behind automakers in their understanding that AI and automation will be essential for the future of their companies. In this episode, guest Dr. Sam Kherat discusses the applications of AI in heavy industry, including: What type of capabilities and functions are automate-able, and at what level? Dr. Kherat also shines a light on how AI might affect the future of the industry within the next 2-3 years, and in what ways we can expect large equipment to become more autonomous. We'd like to thank RE-WORK for introducing us to Dr. Kherat at their autonomous vehicles conference in San Francisco. Dr. Kherat has expertise in the fields of robotics, autonomous excavation, and mining systems.
The concept of self-driving cars has always intrigued many people. We've seen it on television shows like Knight Rider and in films including Batman, Minority Report, and Total Recall. But within the last couple years, the idea is quickly turning into reality. The reason for this is the Internet of Things. The Internet of Things (IoT) refers to the connectivity of multiple devices through the Internet.
The Model X electric, sport-utility vehicle accelerated in the final seconds to about 71 miles an hour before the March 23 crash in Mountain View, Calif., according to a preliminary report from the National Transportation Safety Board. The report comes as auto makers and Silicon Valley are testing technologies that allow for varying levels of automation behind the wheel. Those include systems with driver-assist features as well as those that enable fully self-driving vehicles. Fatal crashes have fueled concerns about whether driverless technology is ready for the real world. A Tesla spokeswoman pointed to a previous company blog post about the incident that touted the safety of its vehicles.
Automation has already transformed industries in which complexity and performance demands must meet the challenges of scarcer resources, narrower profit margins, and expanding product volumes. Now the state of the art is beginning to move to autonomous technologies: driverless vehicles, self-tuning databases, adaptive robots, and the like. While automation involves programming a system to perform specific tasks, autonomous systems are programmed to perform automated tasks, accommodate for variation, and self-correct or self-learn with little or no human intervention. Which industries are ahead of the autonomous curve? These five industries stand out.
The path carved by recent impacts in AI-development has made way for unprecedented growth in the fleet industry. Enterprises are aware of the multitude of pain-points associated with overseeing thousands of vehicles and drivers. Challenges range from small to disruptive on a macro-level. Some of these disruptions require unique solutions that aren't available in standard handbooks. These challenges include having outdated software, unused or unauthorized usages of assets, unpredictable fuel costs, and a need to effectively manage nationally-dispersed vehicles.
Artificial intelligence is quickly growing in importance in the'smart building' sector. Paul Skelton looks at the road ahead for a complex technology. When Mark Chung received an unexpectedly high $500 monthly electricity bill, he turned to his utility for help and answers. However, despite'smart' meters being installed in his home, they were no help. So Mark – an electrical engineer trained at Stanford University – took matters into his own hands.
Flying in dynamic, urban, highly-populated environments represents an open problem in robotics. State-of-the-art (SoA) autonomous Unmanned Aerial Vehicles (UAVs) employ advanced computer vision techniques based on computationally expensive algorithms, such as Simultaneous Localization and Mapping (SLAM) or Convolutional Neural Networks (CNNs) to navigate in such environments. In the Internet-of-Things (IoT) era, nano-size UAVs capable of autonomous navigation would be extremely desirable as self-aware mobile IoT nodes. However, autonomous flight is considered unaffordable in the context of nano-scale UAVs, where the ultra-constrained power envelopes of tiny rotor-crafts limit the on-board computational capabilities to low-power microcontrollers. In this work, we present the first vertically integrated system for fully autonomous deep neural network-based navigation on nano-size UAVs. Our system is based on GAP8, a novel parallel ultra-low-power computing platform, and deployed on a 27 g commercial, open-source CrazyFlie 2.0 nano-quadrotor. We discuss a methodology and software mapping tools that enable the SoA CNN presented in  to be fully executed on-board within a strict 12 fps real-time constraint with no compromise in terms of flight results, while all processing is done with only 94 mW on average - 1% of the power envelope of the deployed nano-aircraft.
Elon Musk's hubris has come back to bite him -- and Tesla (NASDAQ:TSLA) shareholders -- yet again. Less than three months ago, Musk devoted a substantial part of Tesla's Q4 earnings call to talk about plans for massive automation of the vehicle manufacturing process. Musk even said that Tesla's factory and production process, rather than its brand or vehicle designs, would be its long-term competitive advantage. This article originally appeared in the Motley Fool. It didn't take long for reality to set in.
Multiple automakers have in development or in production automated driving systems (ADS) that offer freeway-pilot functions. This type of ADS is typically limited to restricted-access freeways only, that is, the transition from manual to automated modes takes place only after the ramp merging process is completed manually. One major challenge to extend the automation to ramp merging is that the automated vehicle needs to incorporate and optimize long-term objectives (e.g. successful and smooth merge) when near-term actions must be safely executed. Moreover, the merging process involves interactions with other vehicles whose behaviors are sometimes hard to predict but may influence the merging vehicle optimal actions. To tackle such a complicated control problem, we propose to apply Deep Reinforcement Learning (DRL) techniques for finding an optimal driving policy by maximizing the long-term reward in an interactive environment. Specifically, we apply a Long Short-Term Memory (LSTM) architecture to model the interactive environment, from which an internal state containing historical driving information is conveyed to a Deep Q-Network (DQN). The DQN is used to approximate the Q-function, which takes the internal state as input and generates Q-values as output for action selection. With this DRL architecture, the historical impact of interactive environment on the long-term reward can be captured and taken into account for deciding the optimal control policy. The proposed architecture has the potential to be extended and applied to other autonomous driving scenarios such as driving through a complex intersection or changing lanes under varying traffic flow conditions.