3 ms·

Tesla FSD is vaporware that exists just to pump the stock.

DeonPenny 7y ago
Waymo is more likely vaporware. Traditional ML isn't going to work for all the edge case. Tesla has thousands of cars on the road. The Tesla Deep learning play is the only way this is going to work
- cromwellian 7y ago
  ML without hardware side radars or redundancies like high resolution maps in the areas they operate is what isn’t going to work. Name a deep neural net with anywhere close to 100% object recognition precision/recall. You’re engaging ins hand waved reality distortion from musk that somehow their cheap low quality sensorsuite with gaps that they shipped hundreds of thousands of, can be patched over by software and be made to drive across country and service millions of people in robotaxis one year from now despite the fact that they haven’t even filed any significant l4 miles with disengagement reports? This seems like a market rush disaster waiting to happen. You don’t ship mvp/beta of software that can kill people so you can gather training data.
  - DeonPenny 7y ago
    1. You can get 89% accuracy with a convolutional LSTM https://arxiv.org/pdf/1411.4389.pdf https://arxiv.org/pdf/1411.4389.pdf 2. You get multiple bites of the apple the chip is processing 200 images a second. It had plenty of sign in the next frame to identify an issue. 3. All Tesla have redundant side sensors and radars, and an adversarial system for checking it's conclusions. 4. Having don't actually need to file those things in Tesla case. Why would you? They could easily test the functionality and tell no one and not worry about the regulation until they've passed a certain amount of accuracy. 5. The beta is navigation on autopilot and it's proven to be safer than regular drivers by far. And most importantly what do you think will happen if elon release. Self driving on local roads in a year. Other than that what more would the car need to do. Be able to do that without people touching the wheel. If he just release that as a "feature" and it works there won't be a marketing issue. In fact he already has.
    - kajecounterhack 7y ago
      1. 89% != 100%. If you miss a pedestrian and hit them, you're in trouble. Do you really want a car that misidentifies things 11% of the time? The kind of nines you're looking for in a self driving car are much higher. 2. Multiple frames misinterpreting something the same way doesn't help you. Also many times you can only draw 1 conclusion from many frames e.g you need N seconds to tell a person's intent. 3. Redundant sensors are great. Radar + camera can help give good estimation about where things are in the world, but cameras have disadvantages (e.g occlusion, low sun angle, etc) and radar too (resolution, poor range, interference, etc). If you don't have redundancies for the weakness of each sensor, you are going to have problems in the long tail. 4. Ghost mode driving doesn't actually capture the long tail of crazy situations, especially since their current sensors most certainly don't store all the data needed to recreate the world. You need to drive a verrryyy long time to see some real-world situations that break your assumptions about your sensor robustness. 5. Highway autopilot is more or less solved (with varying safety bars). Surface streets are the hard part and Tesla hasn't demonstrated any progress there beyond PR / hype.
      - shdh 7y ago
        Human's aren't 100% either
      - DeonPenny 7y ago
        1. Humans aren't 100% They aren't even 89%. They are only 76% accurate. https://www.entrepreneur.com/article/283990 https://www.entrepreneur.com/article/283990 and they do just fine. 2. Multi frames means you don't misinterpret the same way. The rotation scale, and other variance change. 3. You do have redunancy in the sensors and something that no other sensor besides a brain has. The ability to predict items directions and speed relative to yours. Something no amount of sensors gives and which is the real goal. 4. You should point that argument to waymo not tesla. They are the only ones looking at all the cases they can and learning from that data. 5. Highway piloting is solved in tesla case. You know who hasn't solved it no other automaker. No other automaker can drive from on ramp to off ramp. They can even really change lanes.
  - xiphias2 7y ago
    I would be scared to trust a self driving vehicle based on high resolution map: the government can change the roads any time, and I don't trust my government to notify Google in time about it. ,,You don’t ship mvp/beta of software that can kill people so you can gather training data.'' I think you haven't looked at the presentation: the new data gathering and verifying is based on shadow mode on model mispredictions, and it's not enabled until the misprediction rate goes down to an acceptable rate.
- killjoywashere 7y ago
  Google has been systematically mapping, and remapping, every street on the planet for over a decade. I'll bet they have a few edge cases on record.
  - xiphias2 7y ago
    It doesn't help them in agent modelling, which is the hardest part of self driving. They are doing sophisticated agent modelling in simulation, while Tesla is mostly using shadow mode to verify the agent models.

Waymo is more likely vaporware. Traditional ML isn't going to work for all the edge case. Tesla has thousands of cars on the road. The Tesla Deep learning play is the only way this is going to work

cromwellian 7y ago
ML without hardware side radars or redundancies like high resolution maps in the areas they operate is what isn’t going to work. Name a deep neural net with anywhere close to 100% object recognition precision/recall. You’re engaging ins hand waved reality distortion from musk that somehow their cheap low quality sensorsuite with gaps that they shipped hundreds of thousands of, can be patched over by software and be made to drive across country and service millions of people in robotaxis one year from now despite the fact that they haven’t even filed any significant l4 miles with disengagement reports? This seems like a market rush disaster waiting to happen. You don’t ship mvp/beta of software that can kill people so you can gather training data.
- DeonPenny 7y ago
  1. You can get 89% accuracy with a convolutional LSTM https://arxiv.org/pdf/1411.4389.pdf https://arxiv.org/pdf/1411.4389.pdf 2. You get multiple bites of the apple the chip is processing 200 images a second. It had plenty of sign in the next frame to identify an issue. 3. All Tesla have redundant side sensors and radars, and an adversarial system for checking it's conclusions. 4. Having don't actually need to file those things in Tesla case. Why would you? They could easily test the functionality and tell no one and not worry about the regulation until they've passed a certain amount of accuracy. 5. The beta is navigation on autopilot and it's proven to be safer than regular drivers by far. And most importantly what do you think will happen if elon release. Self driving on local roads in a year. Other than that what more would the car need to do. Be able to do that without people touching the wheel. If he just release that as a "feature" and it works there won't be a marketing issue. In fact he already has.
  - kajecounterhack 7y ago
    1. 89% != 100%. If you miss a pedestrian and hit them, you're in trouble. Do you really want a car that misidentifies things 11% of the time? The kind of nines you're looking for in a self driving car are much higher. 2. Multiple frames misinterpreting something the same way doesn't help you. Also many times you can only draw 1 conclusion from many frames e.g you need N seconds to tell a person's intent. 3. Redundant sensors are great. Radar + camera can help give good estimation about where things are in the world, but cameras have disadvantages (e.g occlusion, low sun angle, etc) and radar too (resolution, poor range, interference, etc). If you don't have redundancies for the weakness of each sensor, you are going to have problems in the long tail. 4. Ghost mode driving doesn't actually capture the long tail of crazy situations, especially since their current sensors most certainly don't store all the data needed to recreate the world. You need to drive a verrryyy long time to see some real-world situations that break your assumptions about your sensor robustness. 5. Highway autopilot is more or less solved (with varying safety bars). Surface streets are the hard part and Tesla hasn't demonstrated any progress there beyond PR / hype.
    - shdh 7y ago
      Human's aren't 100% either
    - DeonPenny 7y ago
      1. Humans aren't 100% They aren't even 89%. They are only 76% accurate. https://www.entrepreneur.com/article/283990 https://www.entrepreneur.com/article/283990 and they do just fine. 2. Multi frames means you don't misinterpret the same way. The rotation scale, and other variance change. 3. You do have redunancy in the sensors and something that no other sensor besides a brain has. The ability to predict items directions and speed relative to yours. Something no amount of sensors gives and which is the real goal. 4. You should point that argument to waymo not tesla. They are the only ones looking at all the cases they can and learning from that data. 5. Highway piloting is solved in tesla case. You know who hasn't solved it no other automaker. No other automaker can drive from on ramp to off ramp. They can even really change lanes.
- xiphias2 7y ago
  I would be scared to trust a self driving vehicle based on high resolution map: the government can change the roads any time, and I don't trust my government to notify Google in time about it. ,,You don’t ship mvp/beta of software that can kill people so you can gather training data.'' I think you haven't looked at the presentation: the new data gathering and verifying is based on shadow mode on model mispredictions, and it's not enabled until the misprediction rate goes down to an acceptable rate.
killjoywashere 7y ago
Google has been systematically mapping, and remapping, every street on the planet for over a decade. I'll bet they have a few edge cases on record.
- xiphias2 7y ago
  It doesn't help them in agent modelling, which is the hardest part of self driving. They are doing sophisticated agent modelling in simulation, while Tesla is mostly using shadow mode to verify the agent models.