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“One AI works to learn if a landing zone is safe, while another AI learns how to land,” he says. Instead, as Holvey from Bell tells us, the AI practices in AirSim, a simulated environment, where it can practice thousands of landing identification scenarios in minutes. It is unrealistic for unmanned aircraft to practice identifying landing zones and landing in the real world for three key reasons: safety concerns, potential damage to the aircraft and the amount of time it would take to train in enough real-world scenarios. All this training is done safely in the cloud, where we can not only train for core scenarios but also for any possible edge case -no matter how unlikely- before operating in the real world. Once created, this simulated world can be instantiated thousands of times and allows us to “overclock” reality, thus accelerating AI training millions of times over. Imagine needing to drive tens of millions of miles in a car to learn how to drive safely on the highway!Īccurately simulating our environment, machines and sensors captures not just the visual aspects but also its physics. AI needs millions of cycles to learn a new skill. Machine teaching enables any engineer to safely add autonomy to new or existing machines.Īutonomous systems operate in the real world, but the real world is not a safe place to train a machine. You can get the AI to understand what decisions to make about altitude and pitch based on the identified landing zone it sees.” Matt Holvey, Senior Manager of Intelligent Systems at Bell, had this to say: “We’re using Project Bonsai because it allows us to quickly create and teach an AI just like if we were training a pilot on what to look for. Bell is using Microsoft autonomous systems and AirSim to work toward safer autonomous flight vehicles, starting by focusing on landings. Along with computer vision and cloud computing from Azure, Project Bonsai and AirSim helped Bell create an AI solution that can practice in a simulated environment. The Bell team identified Microsoft technologies that could help them work toward their goal of transforming their business model from helicopters to autonomous flight vehicles. For example, instead of building special cages for robots, the robots learn to climb stairs, open doors, navigate a dynamic warehouse, land a drone or work together with other systems and humans. The resulting AI solution can now control the machine and make it adapt intelligently to the real world, in the same way a human would. With that input, our platform uses reinforcement learning against a simulated environment to build the AI solution. Our tools allow an expert to express what needs to be learned and how to learn it. Engineers, experts in their own fields, can transfer their years of experience into an AI solution without going back to college to learn AI.īut how do you teach a machine? Most machines today are hardcoded in functionality and have basic or rigid control systems. We knew we needed a different approach, so we looked to provide open, reusable and powerful tools and a platform that would enable anyone to teach intelligence to machines without being an AI expert. Many early efforts to create autonomous systems were developed as siloed, bespoke applications by a team of experts. This malleability enables us to craft abstractions and layers of capability that allow creators to quickly build any type of autonomous systems that can be imagined.
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Software is the most malleable medium that there is. The potential to help our customers innovate with this technology and bridge the divide between the physical and digital worlds is limitless.
We were surprised to learn how little was currently being executed in the market compared with the vast potential of autonomous systems to change so many industries. We started this journey two and half years ago with the acquisition of a startup called Bonsai and combining the team with our AirSim research effort. Within this, we are focused on bringing human-taught intelligence to machines that operate in the real physical world around us, bridging the world of bits to the world of atoms. It laid the foundation for the fourth industrial revolution, that of artificial intelligence. The third industrial revolution of computing (after steam and electricity) saw the most rapid advancement in the history of mankind. Then as well as now, I had a fundamental belief in software as a means for innovation, with which amazing ideas could be brought to life quickly.
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When I started at Microsoft in the early ’90s, at the dawn of the PC and Internet era, I couldn’t have imagined all of the waves of innovation that were to come.