Predicting the future: election results and more

Ken Jennings graciously losing to IBM's "Watson" on Jeopardy

Not only can computers steal our jobs and win at Jeopardy, they can also predict the future with frighteningly accurate results. Yesterday, Professor Daniel Katz gave a fascinating presentation on quantitative prediction in my legal informatics class. His focus was primarily in the legal field but the explanation of how we've arrived at a point in time where legal prediction is possible is applicable to many areas.

His slides are available below. I would highly recommend a quick skim of even just the first 60 or so slides if you're not interested in the legal field for a general overview of the rise of technology. (To my fellow law students, the information contained past slide 60 may disturb you but I urge you to power through!)

Legal Challenges of Autonomous Driving

On September 25th earlier this year, California became the third state in the US, following Nevada and Florida, to pass legislation in allowing the operation of driverless cars on the road. Two short years earlier, Stanford Artificial Intelligence Lab director Sebastian Thrun unveiled that Google had created the world’s first autonomous car. In testing, the cars, which were manned by trained operators, drove around California, from Mountain View to Santa Clara and on to Hollywood Boulevard. They successfully navigated the Pacific Coast Highway, crossed the Golden Gate Bridge, and even made it around Lake Tahoe. The test cars logged over 140,000 miles in total. The following year, Thrun gave a TED talk explaining how autonomous cars could save lives, time, and fuel:

The National Highway Traffic Safety Administration reported 32,885 motor vehicle deaths in 2010, the leading cause of death among teenagers. Furthermore, the majority of car accidents can be attributed to driver error and other human factors (from 57% to as high as 90%) that would not be present in driverless cars.