autonomous system


Autonomous systems - what kind of potential do they hold?

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And what kind of future will they bring with them? A future that is more efficient. A future that is safer. A future that is full of low-emission and energy-efficient solutions. A future that is abundant with this kind of business.


Robotic fighter jets could soon join military pilots on combat missions

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Military pilots may soon have a new kind of wingman to depend upon: not flesh-and-blood pilots but fast-flying, sensor-studded aerial drones that fly into combat to scout enemy targets and draw enemy fire that otherwise would be directed at human-piloted aircraft. War planners see these robotic wingmen as a way to amplify air power while sparing pilots' lives and preventing the loss of sophisticated fighter jets, which can cost more than $100 million apiece. "These drone aircraft are a way to get at that in a more cost-effective manner, which I think is really a game-changer for the Air Force," says Paul Scharre, director of the technology and national security program at the Center for a New American Security, a think tank in Washington, D.C. Unlike slow-moving drones such as the Reaper and the Global Hawk, which are flown remotely by pilots on the ground, the new combat drones would be able to operate with minimal input from human pilots. To do that, they'd be equipped with artificial intelligence systems that give them the ability not only to fly but also to learn from and respond to the needs of the pilots they fly alongside. "The term we use in the Air Force is quarterbacking," says Will Roper, assistant secretary of the U.S. Air Force for acquisition, technology and logistics and one of the experts working to develop the AI wingmen.


Researchers develop 'neural lander' to land drones smoothly

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The system was created by Caltech's Center for Autonomous Systems and Technologies (CAST) in a collaboration between artificial intelligence (AI) and control experts. The "neural lander", is a learning-based controller which tracks the position and speed of the drone, and modifies its landing trajectory and rotor speed accordingly to achieve the smoothest possible landing. "This project has the potential to help drones fly more smoothly and safely, especially in the presence of unpredictable wind gusts, and eat up less battery power as drones can land more quickly," said Soon-Jo Chung, a professor of Aerospace at the institute. For many experts developing unmanned aerial vehicles, landing multi-rotor drones smoothly remains a challenge. This is due to complex turbulence being created by the airflow from each rotor bouncing off the ground as the ground grows ever closer during a descent.


Standardizing Ethical Design For Artificial Intelligence And Autonomous Systems - Liwaiwai

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While there is a wide expanse of applications artificial intelligence (AI) brings to the table, some are still concerned about how the technology can be an equally powerful tool to cause harm. In line with this, the Institute of Electrical and Electronics Engineers (IEEE) Society Standards and Activities Board proposed some standards for the use and development of AI. The implications of the said standardization were intensively discussed in the paper, "Standardizing Ethical Design for Artificial Intelligence and Autonomous Systems" by Joanna Bryson and Alan Winfield. A common fear about AI is that the technology will be advanced enough to transcend the ability of humans. This will then enable them to predate the human race to extinction.


Podcast #31: Ethically Aligned Design in Autonomous Systems with John C. Havens

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One might easily say about the notion of the ethics of disruptive technology–much like Mark Twain's misattributed missive about the weather–that "everybody talks about it, but nobody does anything." But IEEE, the Institute of Electrical and Electronic Engineers, is doing something. Freshly minted from their Global Initiative on Ethics of Autonomous and Intelligent Systems, is the 290-page first edition of Ethically Aligned Design: A Vision for Prioritizing Human Well-Being with Autonomous and Intelligent Systems. If that title sounds like a mouthful, it ought to. The issues that need to be addressed, to prevent the summoning of the demon that Elon Musk warns of, are complex.


Principles That Lead To The Ethics Of Artificial Intelligence

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What good is technology if it doesn't take care of well being of humans. In the field of technology & research, bodies like IEEE and some NPOs have ensured, from time to time, that an "ethics" framework is in place before there is mass adoption of any technology. Neural networks, machine learning, computer vision & natural language processing based products existed even before the times of commoditizing of Artificial Intelligence (AI). However, the breathtaking landscape of AI is solving multiple problems, yet the corporate world has pushed the envelop too far. The idea of putting this article out is to make leaders and industry veterans enforce and ensure that their teams are abiding by the ethics framework for building Artificial Intelligence based products/solutions.


'Neural Lander' uses AI to land drones smoothly

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Landing multi-rotor drones smoothly is difficult. Complex turbulence is created by the airflow from each rotor bouncing off the ground as the ground grows ever closer during a descent. This turbulence is not well understood nor is it easy to compensate for, particularly for autonomous drones. That is why takeoff and landing are often the two trickiest parts of a drone flight. Drones typically wobble and inch slowly toward a landing until power is finally cut, and they drop the remaining distance to the ground.


The U.S. military wants your opinion on AI ethics

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The U.S. Department of Defense (DoD) visited Silicon Valley Thursday to ask for ethical guidance on how the military should develop or acquire autonomous systems. The public comment meeting was held as part of a Defense Innovation Board effort to create AI ethics guidelines and recommendations for the DoD. A draft copy of the report is due out this summer. Microsoft director of ethics and society Mira Lane posed a series of questions at the event, which was held at Stanford University. She argued that AI doesn't need to be implemented the way Hollywood has envisioned it and said it is imperative to consider the impact of AI on soldiers' lives, responsible use of the technology, and the consequences of an international AI arms race.


Machine teaching - How people's expertise makes AI more powerful

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Most people wouldn't think to teach five-year-olds how to hit a baseball by handing them a bat and ball, telling them to toss the objects into the air in a zillion different combinations and hoping they figure out how the two things connect. And yet, this is in some ways how we approach machine learning today -- by showing machines a lot of data and expecting them to learn associations or find patterns on their own. For many of the most common applications of AI technologies today, such as simple text or image recognition, this works extremely well. But as the desire to use AI for more scenarios has grown, Microsoft scientists and product developers have pioneered a complementary approach called machine teaching. This relies on people's expertise to break a problem into easier tasks and give machine learning models important clues about how to find a solution faster.


Machine teaching: How people's expertise makes AI even more powerful

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Most people wouldn't think to teach five-year-olds how to hit a baseball by handing them a bat and ball, telling them to toss the objects into the air in a zillion different combinations and hoping they figure out how the two things connect. And yet, this is in some ways how we approach machine learning today--by showing machines a lot of data and expecting them to learn associations or find patterns on their own. For many of the most common applications of AI technologies today, such as simple text or image recognition, this works extremely well. But as the desire to use AI for more scenarios has grown, Microsoft scientists and product developers have pioneered a complementary approach called machine teaching. This relies on people's expertise to break a problem into easier tasks and give machine learning models important clues about how to find a solution faster.