This article was originally published on ETFTrends.com. Watch a complimentary webinar below to learn more about Apache Spark's MLlib, which makes machine learning scalable and easier with ML pipelines built on top of DataFrames. In MapR Technologies video, you'll get to learn about the following: Review Machine Learning Classification and Random Forests Use Spark SQL and DataFrames to explore real historic flight data Use [...]
One of the most stressful parts of traveling happens between heading to the airport and waiting to board your flight, as you start checking to see if your flight is on time. Flights already shows delays, and now we're sharing reasons for those delays and delay predictions too. Using historic flight status data, our machine learning algorithms can predict some delays even when this information isn't available from airlines yet--and delays are only flagged when we're at least 80% confident in the prediction.
To illustrate this point by example, Smith turns to another vehicle: a time machine. "Imagine that someone invents a time machine," he writes. "Does she break the law by using that machine to travel to the past?" Given the legal principle nullum crimen sine lege, or "no crime without law," she doesn't directly break the law by the act of time-traveling itself, since no law today governs time-travel. This is where ethics come in.