Five Ways Your Safety Depends on Machine Learning

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Note: This article is based on a transcript of The Dr. Data Show episode, "Five Ways Your Safety Depends on Machine Learning" (click to view). Your safety depends on machine learning. This technology protects you from harm every day by guiding the maintenance of bridges, buildings, and vehicles, and by guiding healthcare providers and law enforcement officers. This puts you in good hands. Hospitals, companies, and the government use machine learning to combat risk, actively protecting you from all sorts of dangers and hazards, including fires, explosions, collapses, crashes, workplace accidents, restaurant E. coli, and crime.


New Training Model Helps Autonomous Cars See AI's Blind Spots

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A new training model developed by MIT and Microsoft can help identify and correct an autonomous car's AI when it makes potentially deadly mistakes. Since their introduction several years ago, autonomous vehicles have slowly been making their way onto the road in greater and greater numbers, but the public remains wary of them despite the undeniable safety advantages they offer the public. Autonomous vehicle companies are fully aware of the public's skepticism. Every crash makes it more difficult to gain public trust and the fear is that if companies do not manage the autonomous vehicle roll-out properly, the backlash might close the door on self-driving car technology the way the Three Mile Island accident shut down the growth of nuclear power plants in the United States in the 1970's. Making autonomous vehicles safer than they already are means identifying those cases that programmers might never have thought of and that the AI will fail to respond to appropriately, but that a human driver will understand intuitively as a potentially dangerous situation.


Let's Talk About Self-Driving Cars – The Startup

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This one is simple, it's when you completely drive yourself. Cars that we mostly drive today belong here, those are the ones that have anti-lock brakes and cruise-control, so they can take over some non-vital processes involved in driving. When the system can take over control in some specific use cases but driver still has to monitor system all the time is here, it's applicable to situations when the car is self-driving the highway and you just sit there and expect it to behave well. This level means that driver doesn't have to monitor the system all the time but has to be in a position where the control can quickly be resumed by a human operator. That means no need to have hands on a steering wheel but you have to jump in at the sounds of the emergency situation, which system can recognize efficiently.


Let's Talk About Self-Driving Cars – The Startup

#artificialintelligence

This one is simple, it's when you completely drive yourself. Cars that we mostly drive today belong here, those are the ones that have anti-lock brakes and cruise-control, so they can take over some non-vital processes involved in driving. When the system can take over control in some specific use cases but driver still has to monitor system all the time is here, it's applicable to situations when the car is self-driving the highway and you just sit there and expect it to behave well. This level means that driver doesn't have to monitor the system all the time but has to be in a position where the control can quickly be resumed by a human operator. That means no need to have hands on a steering wheel but you have to jump in at the sounds of the emergency situation, which system can recognize efficiently.


Crash Catcher: Detecting Car Crashes in Video – Insight Data

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Tasks that humans take for granted are often difficult for machines to complete. That's why when you're asked to prove yourself human through those CAPTCHA tests, you're always asked a ridiculously simple question, e.g., whether an image contains a road sign or not, or selecting a subset of images that contain food (see Moravec's Paradox). These tests are effective in determining whether a user is human precisely because image recognition in context is difficult for machines. Training computers to accurately answer these kinds of questions in an automated, efficient way for large amounts of data is complicated.