I agree with you here Data and measurement is the single most important part of the process.
From my experience working in an industrial plant which has been involved in several machine learning trials a lot of the time there are attempts made to use complex modeling techniques to make up for a lack of measurements.
Something I question is whether the outcome would have been better if the money which was invested into hiring AI consultants was spent on better plant instrumentation.
Industrial Instruments are not cheap something like a PGNAA analyser (https://en.wikipedia.org/wiki/Prompt_gamma_neutron_activatio...) is an expensive capital purchase and I suspect some people have unrealistic expectations that AI and machine learning can replace things like this.
I think there is some middle ground where AI complements better sensors (maybe instrument companies should be pushing this). I've yet to see any of the data experts push back and say something like "actually you need to measure this better first before we can model it."
From my experience working in an industrial plant which has been involved in several machine learning trials a lot of the time there are attempts made to use complex modeling techniques to make up for a lack of measurements.
Something I question is whether the outcome would have been better if the money which was invested into hiring AI consultants was spent on better plant instrumentation.
Industrial Instruments are not cheap something like a PGNAA analyser (https://en.wikipedia.org/wiki/Prompt_gamma_neutron_activatio...) is an expensive capital purchase and I suspect some people have unrealistic expectations that AI and machine learning can replace things like this.
I think there is some middle ground where AI complements better sensors (maybe instrument companies should be pushing this). I've yet to see any of the data experts push back and say something like "actually you need to measure this better first before we can model it."