The bitter lesson is finally coming for the self-driving cars. The vision stack, 3D maps, lane selection grammar, occupancy networks, it’s maybe all about to give way to a single GPT looking at camera feeds and predicting the next steering wheel adjustment.
It’s mostly a latency problem at this point. The models are too big to run locally, but given that open-weight models like Qwen already exist, an open-weight, low latency equivalent to Astra can’t be too far out.
You might be interested to learn that the bitter lesson has already been grok'd by generations of autonomous car company engineers, and many or all have incorporated learned components (at minimum) in all their vehicle stacks.
There's also a very tangible limitation of the bitter lesson.
If, over time, compute climbs, and so compute-bound data-driven general architectures beat bespoke architectures (this is the bitter lesson), then it is not necessarily true that the most general architecture now beats all available bespoke architectures now (or even in the near/mid future - the crossover point is "eventually").
Bitter lesson is most tangible for long-running research directions. Sometimes you need something working as best as possible now.
But what might happen imo, is that these huge models might be much better at learning from data in an unsupervised manner, so a model based on Astra might become a much better driver in a short period of time (and given a smaller set of training data) - this is due to it understanding much more of the inputs, and being able to draw conclusions from it much more efficiently, thus the information available for training it is greater per sample.
Then the big model can teach a small model to become almost as good a driver.
This might be substantially more efficient way to train stuff, and might be fairly quick and straightforward.
In practical terms, I feel this means we can see huge jumps in capability overnight. And this is a general indicator of AI progress, not only in this narrow scope.
You're making a "might" argument for a more general, more CPU/Data intensive process, which I just call "bitter lesson". I wouldn't even say "might" i'd just say "yeah, eventually so!"
valine · · focus · HN ↗
It’s mostly a latency problem at this point. The models are too big to run locally, but given that open-weight models like Qwen already exist, an open-weight, low latency equivalent to Astra can’t be too far out.
jvanderbot · · focus · HN ↗
There's also a very tangible limitation of the bitter lesson.
If, over time, compute climbs, and so compute-bound data-driven general architectures beat bespoke architectures (this is the bitter lesson), then it is not necessarily true that the most general architecture now beats all available bespoke architectures now (or even in the near/mid future - the crossover point is "eventually").
Bitter lesson is most tangible for long-running research directions. Sometimes you need something working as best as possible now.
torginus · · focus · HN ↗
Then the big model can teach a small model to become almost as good a driver. This might be substantially more efficient way to train stuff, and might be fairly quick and straightforward.
In practical terms, I feel this means we can see huge jumps in capability overnight. And this is a general indicator of AI progress, not only in this narrow scope.
jvanderbot · · focus · HN ↗