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Apr 23, 2019, 11:30 AM
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Mechanism envy


The Tesla autonomy show had a lot of nuggets about what the typical self driving car startup does. There's a chip which is a scaled up version of a GPU, using stock IP that doesn't use very current feature sizes, but marketed like crazy as a revolution. Something never mentioned before, but which slowly oozed out over the last 5 years is how training the networks still entails manually annotating thousands of images. They have some interpolation, some LIDAR to generate annotations, but most of it is mass Bangalore labor.

A more telling feature was the use of neural networks in place of all conditional statements. Determining if a car was changing lanes was an exercise in thousands of sets of car coordinates manually classified as lane changes. They couldn't just classify it based on x > threshold. Enormous effort was spent doing it with a neural network. How much more effort would have been required to make a pose classifier based on neural networks instead of if statements?


There's still no magic in using a neural network to detect a path. It has to be trained with thousands of images of every section of path in every lighting condition. They all have to be manually annotated or interpolated with path coordinates. All the network does is compress the training set & replay it. The network has to be more like millions of neurons instead of hundreds.
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