Relax, Google, the Robot Army Isn't Here Yet


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."

OracleVoice: Top 5 Industry Early Adopters Of Autonomous Systems

Forbes Technology

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.

Artificial Intelligence: Science fiction to science fact - Connected Magazine


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.

Ultra Low Power Deep-Learning-powered Autonomous Nano Drones Artificial Intelligence

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 [1] 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.

Formulation of Deep Reinforcement Learning Architecture Toward Autonomous Driving for On-Ramp Merge Artificial Intelligence

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.

A guide to AI image recognition


Artificial intelligence is becoming a centralised part of our everyday lives, even if we don't realise it. In fact, half of the people who encounter AI don't know they are doing so.

Machine Learning is the Solution to the Big Data Problem Caused by the IoT 7wData


Big Data has already made fundamental changes to the way businesses operate. There are huge advantages for companies who can derive value from their data, but these opportunities come with challenges, too. For some, this is the challenge of acquiring data from new sources. For others, it is the task of building a scalable infrastructure that can manage the data in aggregate. For a brave few, it means extracting value from the data by implementing advanced analytic techniques and tools.

How Automotive AI Is Going to Disrupt (Almost) Every Industry - DZone AI


With almost every automaker on the planet launching predictions about the arrival date of driverless vehicles, we here at Arcbees are willing to bet that you've given at least a little thought to what that utopian future would be like napping, watching a movie, or getting work done in the backseat while your car deals with traffic. And how exciting would it be never to have to parallel park again? Ford & Argo AI claim they'll be "fully autonomous" by 2021 Hyundai, which is prioritizing affordability, has announced its goals for autonomous freeway driving by 2020 and the more complex navigation of urban driving by 2030. Elon Musk of Tesla, on the other hand, is characteristically audacious and ambitious, already offering many driver-assist AI features and promising full automation, via a tweet, in "3 months maybe, 6 months definitely" -- meaning by the end of 2017. When it comes to AI-driven autonomous vehicles, however, it's important to understand the terminology.

Age of Machine Learning and Artificial Intelligence Citrix Blogs


Cognitive AI systems like IBM Watson are working to understand all forms of data, interact naturally with people, and learn and reason at scale. Intel's Movidius compute stick is capturing our imaginations with low power computer vision and object recognition. Google is investing heavily in the open source TensorFlow machine learning project and a computer farm of TPUs (tensor processing units) to run TensorFlow algorithms at scale. Google also recently acquired our friends at, a natural language conversation engine with search capabilities. Machine learning focuses on the development of computer programs that can access data and use it to automatically learn and improve from experience without being explicitly programmed.. Artificial Intelligence is the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

Artificial Intelligence vs. Machine Learning: What's the Difference


As the topic says lets us see the difference between artificial intelligence and machine learning, we need to understand first each separately. Let s start with artificial intelligence. Artificial intelligence term itself says that it is a kind of intelligence coming from artificial intelligence. So the question arises what intelligence is? Well of human beings intelligence is a capacity to understand and derive complex things much easily. Which is used by humans in day to day life?