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Andrej Karpathy

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Terminator857

Active Member
Aug 5, 2019
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Quote: I forgot how cool European cities are. More compact, denser, more unique / interesting, cleaner, safer, pedestrian/bike friendly, a lot more pedestrian only plazas with people relaxing / hanging out. A lot more of outside is an outdoor living space, not just transportation space.
/quote
Funny how is knocking cars indirectly and directly knocking streets for cars. I agree with Karpathy that U.S. should be more pedestrian, bicycle friendly, and less car friendly.
 
@karpathy: Exactly. But NLP has run far ahead of vision on showing impressive transfer learning to tasks outside of the self-supervised objective. Vision is bit behind I think partly due to required scale (many many more pixels than words). Papers like MAE are close

@ranig: Text is already “compressed by design” so much easier to process in scale. Vision requires lots more processing power so easy to see why text is leading the way. On the positive side, it allows vision to skip “mistakes”
made in text

@karpathy: Yes in NLP humans did the hard work of compression into discrete tokens. In vision the pixels are extra plentiful, raw (uncompressed), and also have a lot more distracting entropy - e.g. structure in clouds, trees, etc. Could simulate it in NLP by sprinkling in 10X random tokens.
 
I have been saying the same on the FSD forums, especially the Phantom Braking issue. There is too much information to process, which leads to it making decisions on factors which we humans are ignoring very conveniently as we have been trained. The Tesla is still in training, and being trained by the FSD Beta group who have a high safety score.
 
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Quote: I forgot how cool European cities are. More compact, denser, more unique / interesting, cleaner, safer, pedestrian/bike friendly, a lot more pedestrian only plazas with people relaxing / hanging out. A lot more of outside is an outdoor living space, not just transportation space.
/quote
Funny how is knocking cars indirectly and directly knocking streets for cars. I agree with Karpathy that U.S. should be more pedestrian, bicycle friendly, and less car friendly.
Really helps to put in context that so many European cities were built many centuries before cars existed. Maybe barely big enough for people walking or on horseback (without a cart). A lot of the streets suck on a bicycle too.

I will be the first to say I hate densely populated cities. I hate hearing all my neighbors fighting, yelling, playing their music, etc. It is like listening to hundreds of conversations in your head all the time. It takes a real toll on you. Most cities don't have enough green space. Add in that just because you have less cars, doesn't mean you don't have congestion in other ways. I have been in a lot of non-US cities where there was even more congestion with bikes than with cars.

As for safety, can't say I agree with all that either. I worked in Denmark and Sweden (and other place in Europe). I was in Malmo for a while. Don't just have to worry about shootings, but hand grenade attacks there along with bombings. The locals kept telling me Malmo was safe. Just don't go to the outskirts of town. Don't go out at night, if female walk in pairs, don't dress provocatively, etc. etc.


God help you if you are in the police department (or anyone else) and talk about the increased number of rapes and what groups are committing them in Sweden as well. Europe is its own worst enemy in many ways. The US is getting there too.
 
@karpathy: Exactly. But NLP has run far ahead of vision on showing impressive transfer learning to tasks outside of the self-supervised objective. Vision is bit behind I think partly due to required scale (many many more pixels than words). Papers like MAE are close

@ranig: Text is already “compressed by design” so much easier to process in scale. Vision requires lots more processing power so easy to see why text is leading the way. On the positive side, it allows vision to skip “mistakes”
made in text

@karpathy: Yes in NLP humans did the hard work of compression into discrete tokens. In vision the pixels are extra plentiful, raw (uncompressed), and also have a lot more distracting entropy - e.g. structure in clouds, trees, etc. Could simulate it in NLP by sprinkling in 10X random tokens.
That is what I have been saying- there is too much data to process into usable information and that is why things like Phantom Braking are occurring.
 
Andrej says FSD saved him from collision with motorcycle.
Quote:
I’ve seen similar events play out in clip telemetry many times, but a few minutes ago is the first time Autopilot prevented an almost certain collision for me personally. Experiencing it in real life is something else.

I was lane changing while a motorcyclist very aggressively lane changed and accelerated from behind the car behind me. Autopilot aborted the lane change and was right.
 
Andrej has been working out or was it Wegovy?
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