3 ms·

You aren't alone - I don't take well to unsolicited advice, unless it's about avoiding traffic :)

by shamino 10y ago

lucb1e 10y ago
More often than not, Google Maps (I assume you're referring to) does about as good a guesstimate as any human in the area would. I'm starting to feel like some good old brains would serve us well sometimes. Take Google Maps' traffic again, it can tell me the traffic now. It can tell me expected traffic tomorrow. But it doesn't understand that if I depart at 8am and I arrive at 10am, then at 9am that traffic jam is worse than when I departed. I'm better at planning this route myself this way. Kudos to the people who built the whole model (almost certainly using some sophisticated algorithms) that automatically detects traffic when there's enough data, probably correlating it with their map data and stuff, but this is an elementary feature apparently nobody there thought to implement. It's not all just training sets and enough CPU power, some human intelligence to provide it with the concept of time would be smart, too. Now this might seem like one silly example, but in many cases it's like this. People promote machine learning and neural nets and all sorts of automated training in situations where some research and an if statement or five would have worked as well. Another simple example: we had security monitoring class and one of the topic was machine learning. Let your intrusion detection system learn normal traffic patterns and alert on, say, traffic spikes on Sunday nights when there should be few if any people working. I feel like looking at traffic data and some if statements would be more useful, especially since you can then integrate it with holidays and events and whatnot. Indeed, one of the warnings in the course was that "during events this will go off as well". Yeah, that's why we shouldn't use it here in the first place. This just a hammer and screw issue.
- nibs 10y ago
  The other thing that Google Maps could add, while we are talking about unsolicited advice and the human element of making traffic decisions, would be sports games. My commute at around 7PM is usually clear, unless there is a sports game, in which case it is a nightmare for a very specific period of time before the game. Further, a big team generates a sold old game which generates more traffic than a mid-week game against an out of division team. Would be fun to program although rather complex.
- ma2rten 10y ago
  People promote machine learning and neural nets and all sorts of automated training in situations where some research and an if statement or five would have worked as well. As a Machine Learning Engineer / Data Scientist I would always evaluate my model against a baseline (in this case the if statements) and only put the model in production if it outperforms the baseline.

You aren't alone - I don't take well to unsolicited advice, unless it's about avoiding traffic :)

by shamino 10y ago

You aren't alone - I don't take well to unsolicited advice, unless it's about avoiding traffic :)

More often than not, Google Maps (I assume you're referring to) does about as good a guesstimate as any human in the area would. I'm starting to feel like some good old brains would serve us well sometimes. Take Google Maps' traffic again, it can tell me the traffic now. It can tell me expected traffic tomorrow. But it doesn't understand that if I depart at 8am and I arrive at 10am, then at 9am that traffic jam is worse than when I departed. I'm better at planning this route myself this way. Kudos to the people who built the whole model (almost certainly using some sophisticated algorithms) that automatically detects traffic when there's enough data, probably correlating it with their map data and stuff, but this is an elementary feature apparently nobody there thought to implement. It's not all just training sets and enough CPU power, some human intelligence to provide it with the concept of time would be smart, too. Now this might seem like one silly example, but in many cases it's like this. People promote machine learning and neural nets and all sorts of automated training in situations where some research and an if statement or five would have worked as well. Another simple example: we had security monitoring class and one of the topic was machine learning. Let your intrusion detection system learn normal traffic patterns and alert on, say, traffic spikes on Sunday nights when there should be few if any people working. I feel like looking at traffic data and some if statements would be more useful, especially since you can then integrate it with holidays and events and whatnot. Indeed, one of the warnings in the course was that "during events this will go off as well". Yeah, that's why we shouldn't use it here in the first place. This just a hammer and screw issue.

nibs 10y ago
The other thing that Google Maps could add, while we are talking about unsolicited advice and the human element of making traffic decisions, would be sports games. My commute at around 7PM is usually clear, unless there is a sports game, in which case it is a nightmare for a very specific period of time before the game. Further, a big team generates a sold old game which generates more traffic than a mid-week game against an out of division team. Would be fun to program although rather complex.
ma2rten 10y ago
People promote machine learning and neural nets and all sorts of automated training in situations where some research and an if statement or five would have worked as well. As a Machine Learning Engineer / Data Scientist I would always evaluate my model against a baseline (in this case the if statements) and only put the model in production if it outperforms the baseline.