3 ms·

I find a lot of applied AI use-cases to be "same as this other method, but more expensive".

by Morizero 2y ago

Terr_ 2y ago
Better to spend $100 in op-ex money than spend $1 in cap-ex money reading a journal paper, especially if it lets you tell investors "AI." :p
- mattnewton 2y ago
  Your engineers cost <$1/hr and understand journal papers?
  - Terr_ 2y ago
    The 100-vs-1 is a ratio.
relativ575 2y ago
Use cases such as?
- Morizero 2y ago
  I'm in an AI focused education research group, and most "smart/personalized tutors" on the market have similar processes and outcomes as paper flashcards.
kjkjadksj 2y ago
That was happening even when they were still calling it machine learning in the papers. Longer before that still. It’s the way some people reliably get papers out for better or worse. Find a known phenomenon with existing published methods, use the same dataset potentially using new method of the day, show there’s a little agreement between the old “gold standard” and your method, and boom, new paper for your cv on $hotnewmethod you can now land jobs with. Never mind no one will cite it. That’s not the point here.
miki123211 2y ago
It's often vastly more expensive to inference, but vastly cheaper and faster to train / set up. Many LLM use cases could be solved by a much smaller, specialized model and/or a bunch of if statements or regexes, but training the specialized model and coming up with the if statements requires programmer time, an ML engineer, human labelers, an eval pipeline, ml ops expertise to set up the GPUs etc. With an LLM, you spend 10 minutes to integrate with the OpenAI API, and that's something any programmer can do, and get results that are "good enough". If you're extremely cash-poor, time-rich and have the right expertise, making your own model makes sense. Otherwise, human time is more valuable than computer time.

3 ms·

I find a lot of applied AI use-cases to be "same as this other method, but more expensive".

by Morizero 2y ago

I find a lot of applied AI use-cases to be "same as this other method, but more expensive".

Terr_ 2y ago
Better to spend $100 in op-ex money than spend $1 in cap-ex money reading a journal paper, especially if it lets you tell investors "AI." :p
- mattnewton 2y ago
  Your engineers cost <$1/hr and understand journal papers?
  - Terr_ 2y ago
    The 100-vs-1 is a ratio.
relativ575 2y ago
Use cases such as?
- Morizero 2y ago
  I'm in an AI focused education research group, and most "smart/personalized tutors" on the market have similar processes and outcomes as paper flashcards.
kjkjadksj 2y ago
That was happening even when they were still calling it machine learning in the papers. Longer before that still. It’s the way some people reliably get papers out for better or worse. Find a known phenomenon with existing published methods, use the same dataset potentially using new method of the day, show there’s a little agreement between the old “gold standard” and your method, and boom, new paper for your cv on $hotnewmethod you can now land jobs with. Never mind no one will cite it. That’s not the point here.
miki123211 2y ago
It's often vastly more expensive to inference, but vastly cheaper and faster to train / set up. Many LLM use cases could be solved by a much smaller, specialized model and/or a bunch of if statements or regexes, but training the specialized model and coming up with the if statements requires programmer time, an ML engineer, human labelers, an eval pipeline, ml ops expertise to set up the GPUs etc. With an LLM, you spend 10 minutes to integrate with the OpenAI API, and that's something any programmer can do, and get results that are "good enough". If you're extremely cash-poor, time-rich and have the right expertise, making your own model makes sense. Otherwise, human time is more valuable than computer time.