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

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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."


How Drive.ai Is Mastering Autonomous Driving With Deep Learning

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Among all of the self-driving startups working toward Level 4 autonomy (a self-driving system that doesn't require human intervention in most scenarios), Mountain View, Calif.-based Drive.ai's Drive sees deep learning as the only viable way to make a truly useful autonomous car in the near term, says Sameep Tandon, cofounder and CEO. "If you look at the long-term possibilities of these algorithms and how people are going to build [self-driving cars] in the future, having a learning system just makes the most sense. There's so much complication in driving, there are so many things that are nuanced and hard, that if you have to do this in ways that aren't learned, then you're never going to get these cars out there." It's only been about a year since Drive went public, but already, the company has a fleet of four vehicles navigating (mostly) autonomously around the San Francisco Bay Area--even in situations (such as darkness, rain, or hail) that are notoriously difficult for self-driving cars. Last month, we went out to California to take a ride in one of Drive's cars, and to find out how it's using deep learning to master autonomous driving.


How Drive.ai Is Mastering Autonomous Driving with Deep Learning

#artificialintelligence

Among all of the self-driving startups working towards Level 4 autonomy (a self-driving system that doesn't require human intervention in most scenarios), Mountain View, Calif.-based Drive.ai's Drive sees deep learning as the only viable way to make a truly useful autonomous car in the near term, says Sameep Tandon, cofounder and CEO. "If you look at the long-term possibilities of these algorithms and how people are going to build [self-driving cars] in the future, having a learning system just makes the most sense. There's so much complication in driving, there are so many things that are nuanced and hard, that if you have to do this in ways that aren't learned, then you're never going to get these cars out there." It's only been about a year since Drive went public, but already, the company has a fleet of four vehicles navigating (mostly) autonomously around the San Francisco Bay Area--even in situations (such as darkness, rain, or hail) that are notoriously difficult for self-driving cars. Last month, we went out to California to take a ride in one of Drive's cars, and to find out how they're using deep learning to master autonomous driving.


How deep learning will transform the future of the auto industry ZDNet

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One of CES' major trends over the last few years has been the connected car -- the concept of adding Internet connectivity and networking to our vehicles. Stealing the spotlight this year was Nvidia, which launched the Drive PX 2 -- an in-car artificial intelligence system. PX 2 is designed for automakers exploring autonomous driving and includes 360-degree situational awareness, deep learning and the processing power of 150 MacBook Pros. Deep learning -- an advanced type of artificial intelligence (AI) -- is driving significant change for autonomous vehicles and for the automotive and transportation industries in general, according to a new report from advisory firm KPMG. The study predicts that by 2030 a new mobility services segment linked to products and services related to autonomy, mobility, and connectivity will be worth more than $1 trillion worldwide.


How deep learning will transform the future of the auto industry

ZDNet

One of CES' major trends over the last few years has been the connected car -- the concept of adding Internet connectivity and networking to our vehicles. Stealing the spotlight this year was Nvidia, which launched the Drive PX 2 -- an in-car artificial intelligence system. PX 2 is designed for automakers exploring autonomous driving and includes 360-degree situational awareness, deep learning and the processing power of 150 MacBook Pros. Deep learning -- an advanced type of artificial intelligence (AI) -- is driving significant change for autonomous vehicles and for the automotive and transportation industries in general, according to a new report from advisory firm KPMG. The study predicts that by 2030 a new mobility services segment linked to products and services related to autonomy, mobility, and connectivity will be worth more than $1 trillion worldwide.


Robots and the Future of Jobs: The Economic Impact of Artificial Intelligence

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I want to make one point, that this is on the record. But we're going to have a great time discussing "Robots and the Future of Jobs: The Economic Impact of Artificial Intelligence." So I'll start with simple introductions, and then we'll lay out some definitions about the kinds of terms that will be involved in this conversation. So my name is John Paul Farmer. Very happy to be here with three experts on the topic. Next to me is Dr. James Manyika, who is a recovering roboticist. And his day job is at McKinsey, at the McKinsey Global Institute, where he's been focusing on the future of jobs and the future of work in this new era. In the middle, we have Dr. Daniela Rus. Dr. Rus is a professor and roboticist at MIT, and she is also the director of the Computer Science and Artificial Intelligence Lab there. And at the end, we have Edwin van Bommel. Edwin is formerly of McKinsey, but now he's the chief cognitive officer at IPsoft. So, with that, let me lay out some definitions that are going to be important, I think, to following this conversation. You may have read in Foreign Affairs and elsewhere about this fourth industrial revolution, the changes that are happening in our society today and many more that will be coming down the pike. So as we--as we talk about these things, one, we should all be on the same page in terms of what artificial intelligence is. What do we mean when we say AI? And the definition that many accept is it's the development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and even translation between languages. AI is sometimes humorously referred to as whatever computers can't do today. Machine learning is another term you're going to hear a lot, sometimes thought of as a rebranding of AI, of artificial intelligence. But there's one key difference, which is that it takes a much more probabilistic approach as opposed to deterministic. So it looks at not just yes or no; it looks at a 30 percent chance of X, a 10 percent chance of Y, and so on. Big data, a term that I think we've all heard. Data is the raw material. Some people call it the new oil for this new era.


FiveAI win equity funding to develop Level 5 vehicle autonomy - Artificial Intelligence Online

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A UK start-up developing artificial intelligence and machine learning for fully autonomous vehicles has received 2.7m in equity funding. The funding, led by Amadeus Capital Partners with Spring Partners and Notion Capital, will enable Bristol-based FiveAI to grow its team, step-up its development and begin simulator and road testing of its software. According to FiveAI, early approaches to autonomous vehicles have required accurate, 3D maps built using point cloud technology. In use, each vehicle then correlates against that map to work out where it is and establish a track to follow. The company is now planning a system using much stronger AI and ML to ensure that autonomous vehicles can safely and accurately navigate all environments, including complex urban ones, with simpler maps.


Drive.ai Brings Deep Learning to Self-Driving Cars

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Drive.ai is the 13th company to be granted a license to test autonomous vehicles on public roads in California. This is exciting news, especially because we had no idea that Drive.ai even existed until just last week. The company has been in stealth mode for the past year, working on applying deep learning techniques to self-driving cars. We spoke with two of Drive.ai's Its core team is made up of experts with a wealth of experience developing deep learning systems in all kinds of different domains, including natural language processing, computer vision, and (most recently) autonomous driving.


Trust in Sparse Supervisory Control

AAAI Conferences

Command and Control (C2) is the practice of directing teams of autonomous units, regardless if the units are directed by human decision making or are unmanned. Humans are adaptive and their behaviors are recognizable to their superiors who were educated in a similar manner. This paper describes the sparse supervisory control that must be exercised over teams of highly autonomous units, and considers what it means for a commander to supervise autonomous un-manned systems (AUS) that employ machine learning and cooperative autonomy. Commanders must decide whether to trust behaviors they have never seen before, and developing that trust may require several strategies. This paper describes some possible strategies in an effort to explain the challenges that must be solved.