4 ms·

I really like the expression "metadata analysis". It's very succinct, describes very much what AI/ML often boils down to in 2 (rather) simple words. I will try

by Tarsul 5y ago

I really like the expression "metadata analysis". It's very succinct, describes very much what AI/ML often boils down to in 2 (rather) simple words. I will try to remember this. The marketing guys in cooperation with journalists won the battle for now but words will be replaced by others again and again and even change their meaning, so maybe next time the shiny new technology will get more adequate wording? (well, maybe not in these times where clickbait wins it all)

topspin 5y ago
The minute 'AI' solves a problem the problem is removed from that class of problems that require 'AI'. If the problem is difficult enough we might award the new solution its own name.
- cgearhart 5y ago
  “Moving the goalposts” has long been identified as a problem by AI enthusiasts, but the opposite is also often true—an AI is built to solve a limited or highly restricted form of a problem, and then proponents claim that the goalpost has been moved when it’s observed that the general problem remains unsolved. It feels to me that we started calling ML “AI” when deep learning became powerful enough to work on less clearly structured problems like vision and NLP—but “find patterns in complex data” does not seem powerful enough for what I would consider “intelligence” (artificial or otherwise). I don’t think that stateless/idempotent ML is capable of what most people would recognize as intelligence; in part because I suspect a history is required for a system to be self-correcting over time.
  - dynamite-ready 5y ago
    I'm starting to appreciate this sentiment. The way most ML success stories are presented, you'd think it really is all a case of finding enough data, and let the computer learn to extract something useful from it. That is not the hard part. The hard part, is finding a dataset that is amenable to the training process. Or at the very least, determining if a collection has any 'educational' value at all. Then, by the time you get to that point, you're probably already looking at metadata, or a simple pattern that could possibly be encoded in a database query. I have some faith in some of these new discoveries (Alphafold is a remarkable case study), but in many cases, the effectiveness of ML seems to be overstated.

by Tarsul 5y ago

topspin 5y ago

The minute 'AI' solves a problem the problem is removed from that class of problems that require 'AI'. If the problem is difficult enough we might award the new solution its own name.

cgearhart 5y ago
“Moving the goalposts” has long been identified as a problem by AI enthusiasts, but the opposite is also often true—an AI is built to solve a limited or highly restricted form of a problem, and then proponents claim that the goalpost has been moved when it’s observed that the general problem remains unsolved. It feels to me that we started calling ML “AI” when deep learning became powerful enough to work on less clearly structured problems like vision and NLP—but “find patterns in complex data” does not seem powerful enough for what I would consider “intelligence” (artificial or otherwise). I don’t think that stateless/idempotent ML is capable of what most people would recognize as intelligence; in part because I suspect a history is required for a system to be self-correcting over time.
- dynamite-ready 5y ago
  I'm starting to appreciate this sentiment. The way most ML success stories are presented, you'd think it really is all a case of finding enough data, and let the computer learn to extract something useful from it. That is not the hard part. The hard part, is finding a dataset that is amenable to the training process. Or at the very least, determining if a collection has any 'educational' value at all. Then, by the time you get to that point, you're probably already looking at metadata, or a simple pattern that could possibly be encoded in a database query. I have some faith in some of these new discoveries (Alphafold is a remarkable case study), but in many cases, the effectiveness of ML seems to be overstated.