> This is also why using raw machine learning for automatic driving is a terrible idea.
Agreed. Train a billion-parameter model on million-feature sensor inputs from "normal" conditions, and it will drive really well until its massive over-fitting runs into slightly unusual circumstances, like a tumbleweed rolling across the road. Then it will do something completely unpredictable, and people will probably die. ML plus pervasive surveillance can automate a lot of routine work, but it has a serious problem with outliers.
"like a tumbleweed rolling across the road. Then it will do something completely unpredictable, and people will probably die."*
<sarcasm>
Seems a small price to pay for progress. We lost 37,000 people to auto accidents in 2016. I wouldn't be surprised if one or more of those deaths involved a mis-identified tumbleweed.</sarcasm>
Agreed. Train a billion-parameter model on million-feature sensor inputs from "normal" conditions, and it will drive really well until its massive over-fitting runs into slightly unusual circumstances, like a tumbleweed rolling across the road. Then it will do something completely unpredictable, and people will probably die. ML plus pervasive surveillance can automate a lot of routine work, but it has a serious problem with outliers.