I've used a quote from Deming before, and I'll use it again.
Deming, from Out of the Crisis (1986):
People with master's degrees in statistical theory accept
jobs in industry and government to work with computers. It is
a vicious cycle. Statisticians do not know what statistical
work is, and are satisfied to work with computers. People
that hire statisticians likewise have no knowledge about
statistical work, and somehow suppose that computers are the
answer. Statisticians and management thus misguide each other
and keep the vicious cycle rolling. (p. 133)
The last time I used this in the context of data scientists. Now it's AI. I'm seeing this at my employer now as well. They think AI will save them, and want to use it for so many things. But the problem is that most people don't really know what to do with it, and most work won't really benefit from it (it could, but not the way they're going about it which is mostly throwing buzzwords at the wall). It was the same problem with statisticians and with data scientists.
Statisticians could help us do our work better, but not the way they did it. Data scientists could help us do our work better, but not the way they did it. AI will have the same problem for most businesses who choose to follow trends and fads rather than evaluate the actual value and utility of the technology and subject matter. And will be of greatest benefit to those who hire experienced people. This was the problem with both statisticians and data scientists for many companies. They'd hire to fill a slot, not for expertise.
In many ways it's mirrored in companies trying to transition to DevOps as well. Filling a slot. Rather than comprehending whether the approach is valid for them, what the approach actually entails, and properly evaluating who should fill the relevant positions.
Deming, from Out of the Crisis (1986):
The last time I used this in the context of data scientists. Now it's AI. I'm seeing this at my employer now as well. They think AI will save them, and want to use it for so many things. But the problem is that most people don't really know what to do with it, and most work won't really benefit from it (it could, but not the way they're going about it which is mostly throwing buzzwords at the wall). It was the same problem with statisticians and with data scientists.Statisticians could help us do our work better, but not the way they did it. Data scientists could help us do our work better, but not the way they did it. AI will have the same problem for most businesses who choose to follow trends and fads rather than evaluate the actual value and utility of the technology and subject matter. And will be of greatest benefit to those who hire experienced people. This was the problem with both statisticians and data scientists for many companies. They'd hire to fill a slot, not for expertise.
In many ways it's mirrored in companies trying to transition to DevOps as well. Filling a slot. Rather than comprehending whether the approach is valid for them, what the approach actually entails, and properly evaluating who should fill the relevant positions.