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It's not just a matter of hardware. People didn't really know how to train neural networks more than 1-2 layers deep at the time. This is also before convolutio
by tachyonbeam 10y ago
It's not just a matter of hardware. People didn't really know how to train neural networks more than 1-2 layers deep at the time. This is also before convolutional neural networks were invented by LeCun.
This network is literally 30x32 pixels => two NN layers => steering decisions. No convolution, no semantic segmentation. It's a very cool technical demonstration, but we've come a long way since, on every front.
- omarforgotpwd 10y agoYes, agreed. Software had also not advanced sufficiently.
- deleted 10y ago[deleted]
- cr0sh 10y ago...and apparently it still took a good amount of time to train. If you dig enough thru the various papers and such written about ALVIIN, you'll find links to older projects, and a couple of "follow on" ones as well. One of the earlier projects that (I believe) led to ALVINN was the CMU Terragator - this was a custom 6-wheel chassis about the size of a large quad-cycle that was designed to navigate around the CMU campus. It was basically a testing platform for autonomous vehicle technologies. IIRC, this was around 1984 or so. After the Terragator, but prior to ALVINN (and ultimately led to the funding for ALVIIN) there was a project to develop a radio-controlled vehicle that could navigate through an unstructured environment (a cluttered alleyway, IIRC) called "DAVE". These projects and more are all referenced in the NVidia End-to-End CNN paper.