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For me the biggest problem, as manager to build AI team, is everyone talks jargons of AI/DL through these kind of courses. How do I find out who knows in depth?

For me the biggest problem, as manager to build AI team, is everyone talks jargons of AI/DL through these kind of courses. How do I find out who knows in depth? Do you have any intriguing questions you have that can quickly separate the wheat from chaff? Is there any Questionnaire on AI/DL that help select (AI expert)/prepare (for Job interview)?

krapht 7y ago
Same as you'd select any other professional where you don't know how to do their job. 1) Work experience. 2) Educational pedigree. There's also the idea you should hire a program lead who does have demonstrated experience, and let them do the technical interviews of their underlings... At the companies I've worked for doing scientific programming work, during my interview I had to give a presentation to the engineering team over lunch concerning a technical problem and take questions. If a candidate was a fresh graduate we had a requirement they have 1) a Master's and 2) they would present on any research papers they had published, UNLESS 3) they had interned at our company and everyone liked them, then they could skip the Master's requirement.
- commandlinefan 7y ago
  > research papers they had published I wonder how academic publishers feel about this growing trend to require not just a degree, but now peer-reviewed, published, research papers to get a job. The sentiment behind it is, of course, if you’re really smart, you would be published, so if we look for people who are published, we know they’re smart. The reality, though, is that once this catches on, journals are going to be flooded (even more than they already are) with desperate attempts to get something, anything, with somebody’s name on it since that’s another checkbox they have to tick before they can eat. Just like what happened with higher education.
o10449366 7y ago
Ask them about statistics and when it's appropriate to use certain models over others. It's basic, but pretty revealing if a candidate doesn't understand basic tradeoffs between bias and variance when it comes to model selection.
- krapht 7y ago
  Knowing about the bias-variance tradeoff is like asking a candidate to pass Fizzbuzz. It doesn't tell you anything except that they aren't completely fraudulent. Going into more detail isn't something that a non-technical manager type would be able to do.
ps101 7y ago
As a non-technical manager it will be very difficult - just make sure that the selection process has a technical component. As a technical manager (and opinions here will vary), focusing on linear algebra is a great filter, because it's essential to anything ML but it's rarely taught in most online courses.
sharcerer 7y ago
this might be relevant for you. https://www.deeplearning.ai/ai-for-everyone/ https://www.deeplearning.ai/ai-for-everyone/

krapht 7y ago

Same as you'd select any other professional where you don't know how to do their job. 1) Work experience. 2) Educational pedigree. There's also the idea you should hire a program lead who does have demonstrated experience, and let them do the technical interviews of their underlings... At the companies I've worked for doing scientific programming work, during my interview I had to give a presentation to the engineering team over lunch concerning a technical problem and take questions. If a candidate was a fresh graduate we had a requirement they have 1) a Master's and 2) they would present on any research papers they had published, UNLESS 3) they had interned at our company and everyone liked them, then they could skip the Master's requirement.

commandlinefan 7y ago
> research papers they had published I wonder how academic publishers feel about this growing trend to require not just a degree, but now peer-reviewed, published, research papers to get a job. The sentiment behind it is, of course, if you’re really smart, you would be published, so if we look for people who are published, we know they’re smart. The reality, though, is that once this catches on, journals are going to be flooded (even more than they already are) with desperate attempts to get something, anything, with somebody’s name on it since that’s another checkbox they have to tick before they can eat. Just like what happened with higher education.

o10449366 7y ago

Ask them about statistics and when it's appropriate to use certain models over others. It's basic, but pretty revealing if a candidate doesn't understand basic tradeoffs between bias and variance when it comes to model selection.

krapht 7y ago
Knowing about the bias-variance tradeoff is like asking a candidate to pass Fizzbuzz. It doesn't tell you anything except that they aren't completely fraudulent. Going into more detail isn't something that a non-technical manager type would be able to do.

ps101 7y ago

As a non-technical manager it will be very difficult - just make sure that the selection process has a technical component. As a technical manager (and opinions here will vary), focusing on linear algebra is a great filter, because it's essential to anything ML but it's rarely taught in most online courses.

sharcerer 7y ago

this might be relevant for you. https://www.deeplearning.ai/ai-for-everyone/ https://www.deeplearning.ai/ai-for-everyone/