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"AI" (or whatever rebranding it gets) always works in cycles, with a phase of excitement and overpromising followed by a phase of apparent underdelivering and s

"AI" (or whatever rebranding it gets) always works in cycles, with a phase of excitement and overpromising followed by a phase of apparent underdelivering and skepticism. But what actually happens is that the innovations just become part of the normal tooling, and stop being called "AI". At some point there is no need to hire a "data scientist", as any python programmer is already expected to know how to use numpy, pandas, sklearn and keras, just like before it was already expected for them to do any kind of data manipulation with SQL without requiring a dedicated database expert.

PLenz 7y ago
The value is a "Data Scientist" isn't that they know how to use a tool - it's that tell know why to use _that_ tool (technique) and not this other one.
- ddragon 7y ago
  I'm definitely not denying the worth of the particular skillset, just like of good DBAs. But as with any skillset, there are diminishing returns. You can get fairly easily someone at the point where they can merge data from multiple sources, create automatic reports with graphs, make simple similarity clustering, regressions and expert systems even if in suboptimal ways and most companies don't really need more than that. They can even learn to integrate cloud/black box solutions for image/speech recognition without having any idea of how to write one from scratch. Of course, if a company wants to truly innovate in the area it will need PhDs or people with great dedicated knowledge in ML/Statistics/Particular Domain, if it needs to scale it will need good data engineers to create the data pipeline together with DBAs and experts in each tools (like Spark/Flink), but for most companies the basic above is already a great improvement to what they had before.
- lumost 7y ago
  Always be wary of people who's skill is telling others what to do. You may find the people they're telling already know.

PLenz 7y ago

The value is a "Data Scientist" isn't that they know how to use a tool - it's that tell know why to use _that_ tool (technique) and not this other one.

ddragon 7y ago
I'm definitely not denying the worth of the particular skillset, just like of good DBAs. But as with any skillset, there are diminishing returns. You can get fairly easily someone at the point where they can merge data from multiple sources, create automatic reports with graphs, make simple similarity clustering, regressions and expert systems even if in suboptimal ways and most companies don't really need more than that. They can even learn to integrate cloud/black box solutions for image/speech recognition without having any idea of how to write one from scratch. Of course, if a company wants to truly innovate in the area it will need PhDs or people with great dedicated knowledge in ML/Statistics/Particular Domain, if it needs to scale it will need good data engineers to create the data pipeline together with DBAs and experts in each tools (like Spark/Flink), but for most companies the basic above is already a great improvement to what they had before.
lumost 7y ago
Always be wary of people who's skill is telling others what to do. You may find the people they're telling already know.