3 ms·

I think the more interesting question is how much human context is necessary in decreasing accident rates. The signal question and tunnel answer hinted at that.

by babesh 5y ago

I think the more interesting question is how much human context is necessary in decreasing accident rates. The signal question and tunnel answer hinted at that. Some context is very local and some context is general at the level of humans. Examples: human eyes will have trouble adjusting to the sudden darkness of tunnels so some people will tend to brake suddenly; that person looks old and will probably have slower reaction times so watch out for the upcoming sharp turn; that person looks like they are on their phone and may cross the lane suddenly; watch out for this intersection because young humans cross it after school without looking so slow down below the speed limit. This human understanding doesn’t seem to be directly represented by the system without explicit architecting on their part. A more general intelligence would begin to automatically learn these. A human intelligence would automatically model these or learn from experience or read about it. As mentioned, the current system has some advantages over humans: more eyes, doesn’t get tired or distracted, faster reaction time. I guess we shall see when these advantages cover up the disadvantages.

fpgaminer 5y ago
I agree, though I think there's an obvious path towards that higher order understanding of the road that humans have. Suppose they eventually have this current system dialed in and they get really good, accurate bounding boxes around all interesting objects on the road. So now, in addition to their 10 second samples of video data they're collecting, they start collecting 10 second samples of scene representations. These samples of scene representations are time series of how various objects in the scene are moving and behaving over time. Many examples of just what you describe: cars with older drivers having slower reaction times; cars with distracted drivers driving recklessly; etc. Now you train a model on all that data, asking it to make predictions through time. It's going to quickly pick up on the same or similar cues that humans do. It sees an older person in a car and says that slower, cautious paths through the scene are more likely for that vehicle. They see a large, lifted truck and assume a 90% probability of a "cut off every car possible" path through the scene. Etc. So I see what Karpathy is building now as a foundation upon which they can build the higher order stuff.
- babesh 5y ago
  Yes, building the next level of abstractions and inferences.

by babesh 5y ago

fpgaminer 5y ago

I agree, though I think there's an obvious path towards that higher order understanding of the road that humans have. Suppose they eventually have this current system dialed in and they get really good, accurate bounding boxes around all interesting objects on the road. So now, in addition to their 10 second samples of video data they're collecting, they start collecting 10 second samples of scene representations. These samples of scene representations are time series of how various objects in the scene are moving and behaving over time. Many examples of just what you describe: cars with older drivers having slower reaction times; cars with distracted drivers driving recklessly; etc. Now you train a model on all that data, asking it to make predictions through time. It's going to quickly pick up on the same or similar cues that humans do. It sees an older person in a car and says that slower, cautious paths through the scene are more likely for that vehicle. They see a large, lifted truck and assume a 90% probability of a "cut off every car possible" path through the scene. Etc. So I see what Karpathy is building now as a foundation upon which they can build the higher order stuff.

babesh 5y ago
Yes, building the next level of abstractions and inferences.