Very interesting article and trend. Many basketball and baseball teams are using similar methods. Currently, teams like the Boston Red Sox use predictive analytics to forecast a player’s future performance. This has had a major impact within baseball since teams are now not only paying players based on their past performance, but also leveraging forecasting data to determine a player’s future value.
Great article Saggitariutt! I am a car enthusiast and have owned several BMWs in my life. After reading your article and reviewing your cover picture (the BMW I8), I do have one concern about BMW’s approach. Many car enthusiast want their car to be 100% handmade (this pertains to very high-end vehicles, hence the Lamborghini Reventon, Austin Martin One, etc.). The reason being that when a car is handmade their are slight differences in engine performance from each car produced. This is valued because it adds a level of uniqueness to a mass produced car. BMW is trying to compete in this extreme luxury space by offering the I8, which looks similar to a car that costs 250k or more. BMW should use technology to help them produce most of their cars, but may want to consider leveraging manual labor when producing high-end performance vehicles.
Alexa, this is a good topic. Health care costs in the U.S are extremely high. In leveraging machine learning Johns Hopkins was able to become more efficient. My hope is that as more machine learning and artificial intelligence is embraced by health care facilities, efficiency will continue to increase and health care will cheaper for patients.
This is truly an insightful article. Using machine learning to grade written exams will remove inconsistencies. Throughout my schooling, I have been a strong believer that many teachers are inconsistent with their own grading, yet alone, one teacher vs. another. This makes being a student more difficult since you have to adjust your writing style from teacher to teacher and sometimes are unsure why a teacher is inconsistent with their grading.
As eluded to by the other comments, Spotify is a very popular application that uses machine learning to create music playlists. These playlists coincides with the demands of their user. This function has made my life a lot easier.
One thing that I am intrigue about is where Spotify will go next in regards to product offerings (i.e, Will Spotify be able to create vacation suggestions based on someone’s background, profile and traveling history?)
I fear that it will take at least 20 years before driverless cars (no assistance from humans) will be considered a normal means of transportation. As discussed in the article, there are major concerns surrounding the reliability of driverless cars and who will be at fault when accidents occurs (driverless car vs. human, driverless car vs. driverless car). The first time that a driverless car is involved in a fatal car accident, I predict that regulations and laws will tighten. How will driverless car manufacturer present there case in court? Would data logs be considered testimony, or even allowed, and how will the court interpret it? Subsequently, driverless car manufacturers will incur expenses deriving from law suits and the need to advertise to combat negative publicity. This will slow down the industry’s moment. I think the current state of the industry, where driverless cars have a driver as safety measure, is where the industry will stall.