24 ms·

If you believe that they are doing false advertising around FSD, could they also be doing false advertising about this?

sidcool 7y ago
It's highly unlikely. This is a public event and they will be held even more accountable than when Elon tweets something. Lying at such a stage would risk their reputation. So I say it's unlikely, not impossible though.
- navigatesol 7y ago
  >Lying at such a stage would risk their reputation. But the thing is, the fans never say it's a lie. Battery swaps, Solar Shingles, Alien Dreadnought factory producing so quickly you need strobe lights to see machines, Full-Self Driving, profits forever, robot taxis...these are not lies, they are simply things that haven't happened yet. Years ago the guy literally walked onto a Hollywood set and demonstrated the Solar shingles. Now they are writing down Solar City and getting in trouble for not producing enough or providing the promised jobs at the subsidized factory in New York. Lie?
  - jsight 7y ago
    The only bit there that I disagree with is the battery swap. They actually did that, and I think it pretty visibly demonstrated what a dead end that approach was likely to be. Otherwise, though, you are right, and they even prepped for that in the presentation. I always get it done, just not on time. So when this isn't done in 2020... well, 2022 then. They make great cars, IMO, but this stuff is more than a little annoying.
bronco21016 7y ago
I’m not sure they would risk lying about their EPA tested mileage rating after the VW scandal. It would surely mean the end of Tesla.
- MuffinFlavored 7y ago
  just how it was the end of VW?...
  - SmellyGeekBoy 7y ago
    VW have a lot more money in the bank and the support of the German government, two things that Tesla don't have.
gutnor 7y ago
Unlikely about battery capacity and range. Tesla have consistently delivered in that side of things. The core of their car business is solid. Their problem on the production side is not surprising. Missing deadlines and production targets is business as usual for "startups" (between quotes because they are massive, however still very much a new and small player in that field) "We will deliver million of widget in 2 years for $20" really means "we will deliver 100K widget in 5 years for $40" with the enthousiasm you expect of a company at that stage. Every claim around autonomous driving have been bullshit and they have doubled down on their bullshit instead of owning up. The grandiose announcement, Trumpish tweeting and technical presentation that carefully avoid talking about the current technical issue they must have solved ? That smells like they are trippling down. They avoid false advertising by kicking the can down the road further. eg: they aggressively talk down LIDAR because their current car do not have it but they are bound by their claim that every car is fully autonomous ready.
- mmsimanga 7y ago
  Missing deadlines and production targets is business as usual for "startups" I wouldn't just limit this to startups. Software development is a field where we sometimes struggle to deliver on time.
- Isinlor 7y ago
  Independent researchers have already proven Musk to be 100% right about LIDAR. Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving https://arxiv.org/abs/1812.07179 https://arxiv.org/abs/1812.07179 3D object detection is an essential task in autonomous driving. Recent techniques excel with highly accurate detection rates, provided the 3D input data is obtained from precise but expensive LiDAR technology. Approaches based on cheaper monocular or stereo imagery data have, until now, resulted in drastically lower accuracies --- a gap that is commonly attributed to poor image-based depth estimation. However, in this paper we argue that data representation (rather than its quality) accounts for the majority of the difference. Taking the inner workings of convolutional neural networks into consideration, we propose to convert image-based depth maps to pseudo-LiDAR representations --- essentially mimicking LiDAR signal. With this representation we can apply different existing LiDAR-based detection algorithms. On the popular KITTI benchmark, our approach achieves impressive improvements over the existing state-of-the-art in image-based performance --- raising the detection accuracy of objects within 30m range from the previous state-of-the-art of 22% to an unprecedented 74%. At the time of submission our algorithm holds the highest entry on the KITTI 3D object detection leaderboard for stereo image based approaches. Karpathy was also pointing to: Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras https://arxiv.org/abs/1904.04998 https://arxiv.org/abs/1904.04998 We present a novel method for simultaneous learning of depth, egomotion, object motion, and camera intrinsics from monocular videos, using only consistency across neighboring video frames as supervision signal. Similarly to prior work, our method learns by applying differentiable warping to frames and comparing the result to adjacent ones, but it provides several improvements: We address occlusions geometrically and differentiably, directly using the depth maps as predicted during training. We introduce randomized layer normalization, a novel powerful regularizer, and we account for object motion relative to the scene. To the best of our knowledge, our work is the first to learn the camera intrinsic parameters, including lens distortion, from video in an unsupervised manner, thereby allowing us to extract accurate depth and motion from arbitrary videos of unknown origin at scale. We evaluate our results on the Cityscapes, KITTI and EuRoC datasets, establishing new state of the art on depth prediction and odometry, and demonstrate qualitatively that depth prediction can be learned from a collection of YouTube videos. Tesla has also advantage of having radars and is able to do supervised learning of depth estimates for moving objects from video. I was also skeptical of Tesla self-driving capability, because they had some stupid ideas, like ignoring radar data based on GPS tagging from fleet behavior. But lack of LIDAR will not be the issue. Not even close. It seems like they are focusing on building proper pipeline for training neural networks. The question is whether neural networks as a technology can handle self driving. Reasoning based on "human brain can do it, so artificial neural networks can do it" is wrong. Natural neural networks and artificial neural networks, besides name share only very, very rough low level conceptual ideas. Moreover, our ANN architectures are probably missing most of what brains do on high level. So, I think this is still an open question - can ANN do it? If not, then nobody will have full self driving capability widely deployed any time soon. Because, even tough neural networks are not perfect, everything else is super brittle in comparison. LIDAR and hires maps are technologies that give working short-term solution, are sort of local minimum. LIDAR is super expensive and already proven to be not necessary. While, hires maps are super brittle and too capital intensive to be widely deployed. But if neural networks are sufficient Tesla will leave everyone in dust. There will be literally no competition. They do not need to learn electric cars manufacturing in order to deploy their technology widely, they are doing that already. They will not need to backtrack on LIDAR and hires maps solutions. Also, their decision to deploy self driving hardware to every car means that they have access to stupidly big amount of real world data from all varieties of environments all around the world. BTW - Extrapolating based what Tesla was doing before Karpathy joined is probably misguided. I was afraid that he will get lost in a big corporation, but he seems to be doing great job there. In my opinion technology that build with his supervision will be significantly better than what Tesla was doing before. But Karpathy is not a magician. So, Tesla has the biggest potential and the big question is: Are neural networks sufficient for self driving?
taneq 7y ago
It's pretty hard to fake a vehicle's range. It's a lot easier to oversell an experimental system which isn't available to the general public and won't be for 6 months - ∞.
- navigatesol 7y ago
  >It's pretty hard to fake a vehicle's range. No it isn't. There are so many variables that go into range, from road conditions, weather, driver habits, wind, that nobody actually gets the exact stated range anyway. I'm not saying they're faking it, but it certainly wouldn't be pretty hard.
  - umeshunni 7y ago
    But eventually people actually drive these cars and notice these things

It's highly unlikely. This is a public event and they will be held even more accountable than when Elon tweets something. Lying at such a stage would risk their reputation. So I say it's unlikely, not impossible though.

navigatesol 7y ago
>Lying at such a stage would risk their reputation. But the thing is, the fans never say it's a lie. Battery swaps, Solar Shingles, Alien Dreadnought factory producing so quickly you need strobe lights to see machines, Full-Self Driving, profits forever, robot taxis...these are not lies, they are simply things that haven't happened yet. Years ago the guy literally walked onto a Hollywood set and demonstrated the Solar shingles. Now they are writing down Solar City and getting in trouble for not producing enough or providing the promised jobs at the subsidized factory in New York. Lie?
- jsight 7y ago
  The only bit there that I disagree with is the battery swap. They actually did that, and I think it pretty visibly demonstrated what a dead end that approach was likely to be. Otherwise, though, you are right, and they even prepped for that in the presentation. I always get it done, just not on time. So when this isn't done in 2020... well, 2022 then. They make great cars, IMO, but this stuff is more than a little annoying.

bronco21016 7y ago

I’m not sure they would risk lying about their EPA tested mileage rating after the VW scandal. It would surely mean the end of Tesla.

MuffinFlavored 7y ago
just how it was the end of VW?...
- SmellyGeekBoy 7y ago
  VW have a lot more money in the bank and the support of the German government, two things that Tesla don't have.

gutnor 7y ago

Unlikely about battery capacity and range. Tesla have consistently delivered in that side of things. The core of their car business is solid. Their problem on the production side is not surprising. Missing deadlines and production targets is business as usual for "startups" (between quotes because they are massive, however still very much a new and small player in that field) "We will deliver million of widget in 2 years for $20" really means "we will deliver 100K widget in 5 years for $40" with the enthousiasm you expect of a company at that stage. Every claim around autonomous driving have been bullshit and they have doubled down on their bullshit instead of owning up. The grandiose announcement, Trumpish tweeting and technical presentation that carefully avoid talking about the current technical issue they must have solved ? That smells like they are trippling down. They avoid false advertising by kicking the can down the road further. eg: they aggressively talk down LIDAR because their current car do not have it but they are bound by their claim that every car is fully autonomous ready.

mmsimanga 7y ago
Missing deadlines and production targets is business as usual for "startups" I wouldn't just limit this to startups. Software development is a field where we sometimes struggle to deliver on time.
Isinlor 7y ago
Independent researchers have already proven Musk to be 100% right about LIDAR. Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving https://arxiv.org/abs/1812.07179 https://arxiv.org/abs/1812.07179 3D object detection is an essential task in autonomous driving. Recent techniques excel with highly accurate detection rates, provided the 3D input data is obtained from precise but expensive LiDAR technology. Approaches based on cheaper monocular or stereo imagery data have, until now, resulted in drastically lower accuracies --- a gap that is commonly attributed to poor image-based depth estimation. However, in this paper we argue that data representation (rather than its quality) accounts for the majority of the difference. Taking the inner workings of convolutional neural networks into consideration, we propose to convert image-based depth maps to pseudo-LiDAR representations --- essentially mimicking LiDAR signal. With this representation we can apply different existing LiDAR-based detection algorithms. On the popular KITTI benchmark, our approach achieves impressive improvements over the existing state-of-the-art in image-based performance --- raising the detection accuracy of objects within 30m range from the previous state-of-the-art of 22% to an unprecedented 74%. At the time of submission our algorithm holds the highest entry on the KITTI 3D object detection leaderboard for stereo image based approaches. Karpathy was also pointing to: Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras https://arxiv.org/abs/1904.04998 https://arxiv.org/abs/1904.04998 We present a novel method for simultaneous learning of depth, egomotion, object motion, and camera intrinsics from monocular videos, using only consistency across neighboring video frames as supervision signal. Similarly to prior work, our method learns by applying differentiable warping to frames and comparing the result to adjacent ones, but it provides several improvements: We address occlusions geometrically and differentiably, directly using the depth maps as predicted during training. We introduce randomized layer normalization, a novel powerful regularizer, and we account for object motion relative to the scene. To the best of our knowledge, our work is the first to learn the camera intrinsic parameters, including lens distortion, from video in an unsupervised manner, thereby allowing us to extract accurate depth and motion from arbitrary videos of unknown origin at scale. We evaluate our results on the Cityscapes, KITTI and EuRoC datasets, establishing new state of the art on depth prediction and odometry, and demonstrate qualitatively that depth prediction can be learned from a collection of YouTube videos. Tesla has also advantage of having radars and is able to do supervised learning of depth estimates for moving objects from video. I was also skeptical of Tesla self-driving capability, because they had some stupid ideas, like ignoring radar data based on GPS tagging from fleet behavior. But lack of LIDAR will not be the issue. Not even close. It seems like they are focusing on building proper pipeline for training neural networks. The question is whether neural networks as a technology can handle self driving. Reasoning based on "human brain can do it, so artificial neural networks can do it" is wrong. Natural neural networks and artificial neural networks, besides name share only very, very rough low level conceptual ideas. Moreover, our ANN architectures are probably missing most of what brains do on high level. So, I think this is still an open question - can ANN do it? If not, then nobody will have full self driving capability widely deployed any time soon. Because, even tough neural networks are not perfect, everything else is super brittle in comparison. LIDAR and hires maps are technologies that give working short-term solution, are sort of local minimum. LIDAR is super expensive and already proven to be not necessary. While, hires maps are super brittle and too capital intensive to be widely deployed. But if neural networks are sufficient Tesla will leave everyone in dust. There will be literally no competition. They do not need to learn electric cars manufacturing in order to deploy their technology widely, they are doing that already. They will not need to backtrack on LIDAR and hires maps solutions. Also, their decision to deploy self driving hardware to every car means that they have access to stupidly big amount of real world data from all varieties of environments all around the world. BTW - Extrapolating based what Tesla was doing before Karpathy joined is probably misguided. I was afraid that he will get lost in a big corporation, but he seems to be doing great job there. In my opinion technology that build with his supervision will be significantly better than what Tesla was doing before. But Karpathy is not a magician. So, Tesla has the biggest potential and the big question is: Are neural networks sufficient for self driving?

taneq 7y ago

It's pretty hard to fake a vehicle's range. It's a lot easier to oversell an experimental system which isn't available to the general public and won't be for 6 months - ∞.

navigatesol 7y ago
>It's pretty hard to fake a vehicle's range. No it isn't. There are so many variables that go into range, from road conditions, weather, driver habits, wind, that nobody actually gets the exact stated range anyway. I'm not saying they're faking it, but it certainly wouldn't be pretty hard.
- umeshunni 7y ago
  But eventually people actually drive these cars and notice these things