Thanks for the insights. I can definitely see what you're saying from the perspective of a software engineer.
I wonder if those same concepts translate over to onboarding people who might have a weaker programming background, such as mathematicians / theorists who may have a more difficult time making the switch. For example if they've been using the same toolset their entire careers.
This last point isn't in response to your comment, but more a response to the ideas presented in the article. I don't necessarily buy the idea that the data is incentive enough to researchers at the top of their fields to leave what they're doing to go work at Google. Surely they have access to plenty of public datasets large enough to accomplish what they want to accomplish. So the requirement for switching tools may be a much bigger hurdle when trying to recruit for ML. Maybe I'm wrong in assuming they are targets for employment at Google.
I wonder if those same concepts translate over to onboarding people who might have a weaker programming background, such as mathematicians / theorists who may have a more difficult time making the switch. For example if they've been using the same toolset their entire careers.
This last point isn't in response to your comment, but more a response to the ideas presented in the article. I don't necessarily buy the idea that the data is incentive enough to researchers at the top of their fields to leave what they're doing to go work at Google. Surely they have access to plenty of public datasets large enough to accomplish what they want to accomplish. So the requirement for switching tools may be a much bigger hurdle when trying to recruit for ML. Maybe I'm wrong in assuming they are targets for employment at Google.