I think you could shorten this quite a bit, be a little gentler about the value of logging, and retitle it “the danger of descriptions”. All reports of observations (and all observations) are suspect. This is an unsolved problem.
I agree that we may question any descriptions, even our own.
However, I emphasized error descriptions not because we find them more suspicious than other kinds of descriptions, but because error descriptions have the unique quality of implicitly asking for corrective action.
We tend to believe that, in an ideal state, a system emits no error descriptions—so actions based on them tend to get prioritized over actions based on other kinds of information.
Because of this, an error description provides a more virulent vector for manipulation of agency to gain traction. This makes them especially dangerous, particularly when learning and adaptation play an active role in continuous error-correction schemes.
As to whether or not it’s quite rude and too harsh of me to describe as a cargo-cult those whose beliefs include, “more logging is always better” and “more detailed logs with better metadata are more useful” etc,, please consider the following.
When I google “log noise” then the top hits I find are:
Logreduce, a Python machine-learning library that claims it “saves debugging time by picking out anomalies from mountains of log data”.
An OpenStack conference talk on using machine learning to reduce log noise and reduce the tedium of the process of “figuring out what went wrong” video link.
I hope you can see what my concern is. First, the cargo-cult created infinite haystacks of useless, irrelevant logs. Second, they now propose to put AI in charge of finding the all-important needle. Can you see where this is going?
I think you could shorten this quite a bit, be a little gentler about the value of logging, and retitle it “the danger of descriptions”. All reports of observations (and all observations) are suspect. This is an unsolved problem.
Thank you for the feedback.
I agree that we may question any descriptions, even our own.
However, I emphasized error descriptions not because we find them more suspicious than other kinds of descriptions, but because error descriptions have the unique quality of implicitly asking for corrective action.
We tend to believe that, in an ideal state, a system emits no error descriptions—so actions based on them tend to get prioritized over actions based on other kinds of information.
Because of this, an error description provides a more virulent vector for manipulation of agency to gain traction. This makes them especially dangerous, particularly when learning and adaptation play an active role in continuous error-correction schemes.
As to whether or not it’s quite rude and too harsh of me to describe as a cargo-cult those whose beliefs include, “more logging is always better” and “more detailed logs with better metadata are more useful” etc,, please consider the following.
When I google “log noise” then the top hits I find are:
Logreduce, a Python machine-learning library that claims it “saves debugging time by picking out anomalies from mountains of log data”.
An OpenStack conference talk on using machine learning to reduce log noise and reduce the tedium of the process of “figuring out what went wrong” video link.
Mute uninteresting log noise with machine learning
etc.
I hope you can see what my concern is. First, the cargo-cult created infinite haystacks of useless, irrelevant logs. Second, they now propose to put AI in charge of finding the all-important needle. Can you see where this is going?