I've read claims that they are desperately trying to hire. https://mobile.twitter.com/TaylorOgan/status/140705191831739...
We detached this subthread from https://news.ycombinator.com/item?id=27584719 .
[censored by dang]
Andrej Karpathy (Tesla): CVPR 2021 Workshop on Autonomous Vehicles [video]
111–120 of 214 posts
Re: Andrej Karpathy (Tesla): CVPR 2021 Workshop on Autonomous Vehicles [video]
#112Earlier quoted context omitted.
Waymo has published detailed safety performance data of their Arizona operations: https://waymo.com/safety/performance-data You can read their other safety whitepapers in https://waymo.com/safety
Arizona roads are also mapped to extreme precision, have very wide lanes, and are optimized for cars. Waymo has prioritized low intervention by being overly cautious and avoiding hard maneuvers (like many left turns). That doesn't work when they scale up to any other set of normal roads, especially as density and complexity increases.
They don't avoid left turns. There are plenty of videos from Chandler, AZ of Waymo performing unprotected left turns perfectly fine.
They will always map roads to precision, whether it's Arizona or San Francisco. Why is that a problem? You should either look at their CA disengagement reports over the years or wait until they roll out a service in SF (where they've been testing heavily). That will show how safe they are in dense environments.
Re: Andrej Karpathy (Tesla): CVPR 2021 Workshop on Autonomous Vehicles [video]
#113I see a lot of people here are stuck on the perception side of things. There's a lot more to self driving than just the sensor suite and perception. There's a lot of work that needs to be done in the planning and controls department prior to the time we get full vehicle autonomy. Andrej's work is impressive, but I wish we'd see more research into the latter. Then again this is CVPR so...
Re: Andrej Karpathy (Tesla): CVPR 2021 Workshop on Autonomous Vehicles [video]
#114Earlier quoted context omitted.
The fallacy here is that the scale of the neural network used by Tesla is sufficient to capture the problem of driving given enough training. There is no guarantee that a reasonably priced neural network can encompass the task of driving. Having training data beyond a certain point is overrated, and Tesla's advantage in gathering it is overstated. Other companies are capturing this data as well. Is there any indicati…
It seems as if the people gobbling up the "Tesla has the data! Autopilot will keep getting better!" line have never trained a neural network in their life. Models converge. Loss stops decreasing, regardless of more incoming data. Extreme manual data cleaning effort becomes required to prevent overfitting. Model architecture has to change and hyper parameters have to be tweaked. Then you're back at square one as far a…
I would agree if their increase in data was linear, but it is increasing by orders of magnitude, which should have qualitative consequences for what they're able to accomplish as they claw their way through 9s. I don't see how it's possible to get progressively more 9s without scaling in both data and compute.
The point of the higher scale isn't just more data, it also makes it easier to solve the unbalanced data problem, because rarer and rarer scenarios will appear in large enough numbers to work with.
Re: Andrej Karpathy (Tesla): CVPR 2021 Workshop on Autonomous Vehicles [video]
#115Earlier quoted context omitted.
The fallacy here is that the scale of the neural network used by Tesla is sufficient to capture the problem of driving given enough training. There is no guarantee that a reasonably priced neural network can encompass the task of driving. Having training data beyond a certain point is overrated, and Tesla's advantage in gathering it is overstated. Other companies are capturing this data as well. Is there any indicati…
It seems as if the people gobbling up the "Tesla has the data! Autopilot will keep getting better!" line have never trained a neural network in their life. Models converge. Loss stops decreasing, regardless of more incoming data. Extreme manual data cleaning effort becomes required to prevent overfitting. Model architecture has to change and hyper parameters have to be tweaked. Then you're back at square one as far a…
> And, in fact, quite insulting to the intelligence of even the most casual ML engineers.
Exactly, casual ML engineers. The issue of plateauing tends to occur because there is no more novelty to be had in the data. What mega-experiments like GPT and similar have shown us is that actually you can keep adding novel data and keep improving the model. Kinda inelegant, yet effective. The problem is, most institutions can't add more novelty beyond a certain scale, since that usually means shoveling more money at data storage and compute, on top of the novelty collection.
Tesla merely has to open the money tap to get more of both compute and storage, and let the real-time data flow in.
Re: Andrej Karpathy (Tesla): CVPR 2021 Workshop on Autonomous Vehicles [video]
#116Tesla's decision not to use the LIDAR as a safety feature (i.e. having reliable high-resolution data about things the car can collide with) is so incredibly indefensible, since solving the last 1% of this using only vision likely requires a general artificial intelligence Prediction: Tesla will be the last of all major auto manufacturers to get to L5 autonomy. Time interval between when Tesla L5 FSD is finally availa…
No one is going to get to a true L5 for a long time. That is totally irrelevant. It's a war of attrition. Whoever can monetize L3/L4 and can scale without any vehicle upgrade cost is going to win. It's pretty obvious lidar is very very silly since it doesn't scale.
It is also pretty easy to see that Tesla doesn't have to hit L5 to have won autonomy. It just has to successfully monetize L3/L4.
Re: Andrej Karpathy (Tesla): CVPR 2021 Workshop on Autonomous Vehicles [video]
#117[deleted]
Re: Andrej Karpathy (Tesla): CVPR 2021 Workshop on Autonomous Vehicles [video]
#118Tesla's decision not to use the LIDAR as a safety feature (i.e. having reliable high-resolution data about things the car can collide with) is so incredibly indefensible, since solving the last 1% of this using only vision likely requires a general artificial intelligence Prediction: Tesla will be the last of all major auto manufacturers to get to L5 autonomy. Time interval between when Tesla L5 FSD is finally availa…
I strongly disagree. By all measures I've seen (including a couple of slides in the OP's video), Tesla's self-driving is far safer than human driving: the number of accidents and deaths per mile driven are something like an order of magnitude lower (i.e., around 10x safer). I mean, the machine never gets distracted, tired, sleepy, emotional, drunk, etc., so it is a LOT LESS likely to crash on boring, monotonous road…
>Tesla's self-driving is far safer than human driving: the number of accidents and deaths per mile driven are something like an order of magnitude lower (i.e., around 10x safer).
Lies, damn lies, and statistics.
Tesla here, is, again, being funny with the numbers. They LOVE to cite autopilot ON death statistics as being "10x safer than normal driving". What they fail to note is that Autopilot can ONLY be on while driving on a limited access highway. Highways are much safer to drive on than a mix of ALL ROADS, which is where the baseline figure comes from.
Another confounding factor is the price of the vehicle. The average CONFIGURED Tesla with the FSD package today costs what? $65k? More? Those X's and S's are $100k+. Nobody is buying that base Model 3. The point is that Tesla drivers are 1) Older and 2) Wealthy. Wealthy, older people get in far fewer car crashes than the average driver. In fact, car crash fatalities are really driven by two groups: drunks (or pill addicts), and young (teenage) men. Not saying it's IMPOSSIBLE to have a substance abuse problem and own a Tesla, but the average Tesla owner is less likely to have these issues. It's also less likely to own a Tesla while young.
So, Tesla autopilot stats should be compared to other comparably priced vehicles while driving on the highway ONLY. That would actually be a fair, honest comparison. I believe a recent outgoing BMW 5 series chassis finished its entire life without a single fatality in the US. That's right -- 4-5 years of service in the US without a single death. Turns out, wealthy people who drive expensive family sedans don't get in a lot of fatal highway crashes.
Here's a Forbes article (sorry) doing some of the back-of-the-napkin math. They estimated that in Q3 2019, autopilot really wasn't any safer than manual driving.
https://www.forbes.com/sites/bradtempleton/2020/10/28/new-te...
Re: Andrej Karpathy (Tesla): CVPR 2021 Workshop on Autonomous Vehicles [video]
#119Re: Andrej Karpathy (Tesla): CVPR 2021 Workshop on Autonomous Vehicles [video]
#120Earlier quoted context omitted.
The fallacy here is that the scale of the neural network used by Tesla is sufficient to capture the problem of driving given enough training. There is no guarantee that a reasonably priced neural network can encompass the task of driving. Having training data beyond a certain point is overrated, and Tesla's advantage in gathering it is overstated. Other companies are capturing this data as well. Is there any indicati…
It seems as if the people gobbling up the "Tesla has the data! Autopilot will keep getting better!" line have never trained a neural network in their life. Models converge. Loss stops decreasing, regardless of more incoming data. Extreme manual data cleaning effort becomes required to prevent overfitting. Model architecture has to change and hyper parameters have to be tweaked. Then you're back at square one as far a…
>to pile on more (unlabeled!) data
given the nature of that data you can get a lot of unsupervised mileage, so to speak, out of it.