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The analogy is a bit mixed up. Driving a car has been democratized. Actually going in and fixing/modifying a modern car is increasingly less accessible/democra

The analogy is a bit mixed up. Driving a car has been democratized. Actually going in and fixing/modifying a modern car is increasingly less accessible/democratized, though still possible with quite a bit of training. Car internals are getting more complex every generation. Likewise, using Google search / getting things recommended to you is obviously widely accessible. It is unclear how accessible ML research / going into the guts of a model actually is. While I don't doubt you've given talks about applying ML, I do have a little skepticism that the talks went any deeper than just application-related topics.

existencebox 10y ago
"While I don't doubt you've given talks about applying ML, I do have a little skepticism that the talks went any deeper than just application-related topics." Let me correct that misconception; every aspect of the algorithm was custom, from tokenization through classification through smoothing and postprocessing. It was a more simple model and process, but it was not exclusively application related. Far simpler than many of the deeper models I consume as a black box, for sure, but I'd defend my competencies in that I'm not exclusively a plug-and-play data engineer :) Let me also emphasize that I intentionally called out "driving" as opposed to "fixing". In fact I can't think of any car-fixing research going on just about anywhere, although I'm sure there is. My core point was that _using_ the tooling is key, and has been democratized in both driving and AI (the application layer aspect as you put it), the research point was merely an add-on that I've also seen a democratization of the knowledge needed to do even the basic level of novel algorithm development, even if I'm writing deep network papers or something of that ilk; I just find that to be the exception rather than the rule (per my "hardcore research is hard for the non-deeply-initiated once low hanging fruit has been pruned" comment)
- argonaut 10y ago
  To be clear, many of the things you just mentioned are feature engineering-related and don't have much to do with ML. Car construction/modification research is going on. It's what car companies are doing... Driving is not a valid analogy because by that logic AI was democratized 10 years ago because everyone "uses" Google search.

existencebox 10y ago

"While I don't doubt you've given talks about applying ML, I do have a little skepticism that the talks went any deeper than just application-related topics." Let me correct that misconception; every aspect of the algorithm was custom, from tokenization through classification through smoothing and postprocessing. It was a more simple model and process, but it was not exclusively application related. Far simpler than many of the deeper models I consume as a black box, for sure, but I'd defend my competencies in that I'm not exclusively a plug-and-play data engineer :) Let me also emphasize that I intentionally called out "driving" as opposed to "fixing". In fact I can't think of any car-fixing research going on just about anywhere, although I'm sure there is. My core point was that _using_ the tooling is key, and has been democratized in both driving and AI (the application layer aspect as you put it), the research point was merely an add-on that I've also seen a democratization of the knowledge needed to do even the basic level of novel algorithm development, even if I'm writing deep network papers or something of that ilk; I just find that to be the exception rather than the rule (per my "hardcore research is hard for the non-deeply-initiated once low hanging fruit has been pruned" comment)

argonaut 10y ago
To be clear, many of the things you just mentioned are feature engineering-related and don't have much to do with ML. Car construction/modification research is going on. It's what car companies are doing... Driving is not a valid analogy because by that logic AI was democratized 10 years ago because everyone "uses" Google search.