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What a great story! And congratulations on getting a great tool into use. Way back, I was getting to know about stock market analysis. I concluded that the re

What a great story! And congratulations on getting a great tool into use. Way back, I was getting to know about stock market analysis. I concluded that the recommended (technical) analysis was, by any individual recommender, just outside their area of mathematical competence. Someone with very little math would be impressed with (say) moving averages, but someone who understood some statistics would denigrate moving averages, but be impressed by Bollinger bands, etc. Since no one understands neural networks :-) everyone was impressed by them. Seems like a similar thing with AI/ML -- "Hey, this is beyond my level of understanding, it must be magic! Buy buy buy!"

ansgri 6y ago
This is a great insight. My company recently started working with agricultural analysts, and your example about moving averages is spot on. Initially, they also wanted the “magic” of NNs, and fortunately, after several prototypes, they understood that what they need is much simpler. As a result, after a couple of years, we’re starting to actually apply those NNs productively, having solved the simpler problems.
- chris_st 6y ago
  Well done! How did you get them to work with simpler models?
  - ansgri 6y ago
    Basically by convincing them that to make "the real AI" work, they would need a lot of high-quality data that they wouldn't be able to produce. After several iterations with less data-hungry statistical methods they finally realized that they need a real product real soon (they were some kind of innovation department within a large corporation) and realized that the thing they need most is just a centralized GIS with basic computational capabilities. Having successfully built that, those capabilities could be applied at scale and then we started experiments with more advanced analyses, this time more successful since both we had much more data and the customer became familiar with the data-intensive development.

ansgri 6y ago

This is a great insight. My company recently started working with agricultural analysts, and your example about moving averages is spot on. Initially, they also wanted the “magic” of NNs, and fortunately, after several prototypes, they understood that what they need is much simpler. As a result, after a couple of years, we’re starting to actually apply those NNs productively, having solved the simpler problems.

chris_st 6y ago
Well done! How did you get them to work with simpler models?
- ansgri 6y ago
  Basically by convincing them that to make "the real AI" work, they would need a lot of high-quality data that they wouldn't be able to produce. After several iterations with less data-hungry statistical methods they finally realized that they need a real product real soon (they were some kind of innovation department within a large corporation) and realized that the thing they need most is just a centralized GIS with basic computational capabilities. Having successfully built that, those capabilities could be applied at scale and then we started experiments with more advanced analyses, this time more successful since both we had much more data and the customer became familiar with the data-intensive development.