It sounds like the author of this article in for a ... bitter lesson. [1]
[1] http://www.incompleteideas.net/IncIdeas/BitterLesson.html
[1] http://www.incompleteideas.net/IncIdeas/BitterLesson.html
Waymo is an example of a system which has machine learning, but the machine learning does not directly drive action generation. There's a lot of sensor processing and classifier work that generates a model of the environment, which can be seen on a screen and compared with the real world. Then there's a part which, given the environment model, generates movement commands. Unclear how much of that uses machine learning.
Tesla tries to use end to end machine learning, and the results are disappointing. There's a lot of "why did it do that?". Unclear if even Tesla knows why. Waymo tried end to end machine learning, to see if they were missing something, and it was worse than what they have now.
I dunno. My comment on this for the last year or two has been this: Systems which use LLMs end to end and actually do something seem to be used only in systems where the cost of errors is absorbed by the user or customer, not the service operator. LLM errors are mostly treated as an externality dumped on someone else, like pollution.
Of course, when that problem is solved, they're be ready for management positions.
I doubt an expert machine’s accuracy would change if you threw more energy at it, for example.
Is this at all ironic considering we power modern AI using custom and/not non-general compute, rather than using general, CPU-based compute?
The architectures before transformers were LSTM based RNNs. They suck because they don't scale. Mamba is essentially the successor to RNNs and its key benefit is that it can be trained in parallel (better compute scaling) and yet Mamba models are still losing out to transformers because the ideal architecture for Mamba based LLMs has not yet been discovered. Meanwhile the performance hit of transformers is basically just a question of how many dollars you're willing to part with.