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I think the most effective strategy here is to provide accessible tools to solve problems with ML, and provide accessible education material to get people started. Not just grad students, not just engineers -- but everyone who's in a position to build things and solve problems
This is also a strong form of power decentralization. "Decentralization" seems to be a keyword of interest these days, although people often use it to pitch businesses and products that use decentralized computing to *centralize* power into the hands of a small number of insiders
ML/AI doesn't always need massive computing power and data. But to think it does is proof that only the big target items are being worked on and the smaller ML/AI components not needing such mass of comp and data are being ignored. Components "plug-in" designed for end users use.
Consider ML/AI to be an extension of automation, where it cab provide small scope dynamic component(s) that can be used within other non-ML/AI automation(s). Then imagine a non-ML/AI automation core that can be used to integrate various automations w/ & w/o ML/AI.
This non-ML/AL core can be used to do many different automation but an long-running ethics violation in software needs to be corrected enable users to do so in a common consistent manner. Seems to me ML/AI typical user-oriented tools have yet to be developed (like plug-ins).
On this note, can you envision a possible future in which big companies (a la Google, etc) have developed cloud based AutoML tools to a sufficient degree that they will be the go-to for most applications, with current "data scientist" jobs being relegated to boutique firms?
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