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  #1  
Old 12-06-2017, 11:18 AM
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Question What opportunities are there in AI for non-PhDs?

AI seems to be even hotter than data science. 300k - 500k seems to be the going rate for an entry level AI specialist, but I think that's for people with PhDs in the field.

A PhD is just going to be impractical, but I've been considering a masters. What opportunities would there be then? Or now? I don't need 500k (yet). Heck, I'd even do it for the well below market rate of 200k or even 150k just to get my foot in the door. A buddy of mine who recently got his fellowship quit his job to go back to school to get in, and he was already making good money as an actuary.

Well, I've been reading through Russell and Norvig's text...one step at a time I guess, lol. To start, I figure it would be good to at least be familiar with the basics because it's only going to get more and more important as AI grows.
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Old 12-06-2017, 11:42 AM
Locrian Locrian is offline
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"AI" used to encompass a lot of things, many of which you already do. Lately it seems synonymous with deep learning - is that how you're using it here?

I've become interested in deep learning as well. This is a shift - for a long time I couldn't figure out what the use case for DL actually was. My reading now suggests my confusion was because of its flexibility. Now I'm hearing about people replacing entire stacks of analytics encompassing multiple types of forecasting, mathematical optimization and more, all with one DL model. That got my attention.

I probably won't be into it nearly as much as you are, so if you updated this or another thread with your learnings and thoughts I'd love to hear them.
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Old 12-06-2017, 11:54 AM
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I don't know why you think people on this board would know more about the question than you do.
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Old 12-06-2017, 12:04 PM
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Originally Posted by Locrian View Post
"AI" used to encompass a lot of things, many of which you already do. Lately it seems synonymous with deep learning - is that how you're using it here?
I agree it's kind of vague. Some use cases may clarify things - I went to an AI conference and there were startups that were trying to automate claims by getting computers to quantify claim amounts from photos of damaged cars. Microsoft is working on chat bots that simulate how a claims adjuster would be talking to you if you were filling a claim.
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Old 12-06-2017, 12:10 PM
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I don't know why you think people on this board would know more about the question than you do.
I come from commercial insurance, which is in the stone ages compared to where personal lines is when it comes to technology. I figure since a lot of pl actuaries are here, they'd have more exposure to no-touch claims and underwriting tech.
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Old 12-06-2017, 02:46 PM
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Originally Posted by Colonel Smoothie View Post
AI seems to be even hotter than data science. 300k - 500k seems to be the going rate for an entry level AI specialist, but I think that's for people with PhDs in the field.

A PhD is just going to be impractical, but I've been considering a masters. What opportunities would there be then? Or now? I don't need 500k (yet). Heck, I'd even do it for the well below market rate of 200k or even 150k just to get my foot in the door. A buddy of mine who recently got his fellowship quit his job to go back to school to get in, and he was already making good money as an actuary.

Well, I've been reading through Russell and Norvig's text...one step at a time I guess, lol. To start, I figure it would be good to at least be familiar with the basics because it's only going to get more and more important as AI grows.
Opportunities in AI for non-PhDs are great. Just ask Tanmay Bakshi.
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Old 12-06-2017, 09:59 PM
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From what I read those big salaries are going to PhDs and Postdocs from a small number of research groups. If you were to break into that world, you'd probably be working as a software engineer. There are plenty of experienced software engineers in silicon valley, so I doubt they would go out of their way to hire an actuary (let's be realistic, right?). I think it would be better to focus on applications of AI in the insurance world and establish yourself as a domain expert.
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Old 12-07-2017, 12:37 AM
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From what I read those big salaries are going to PhDs and Postdocs from a small number of research groups. If you were to break into that world, you'd probably be working as a software engineer. There are plenty of experienced software engineers in silicon valley, so I doubt they would go out of their way to hire an actuary (let's be realistic, right?). I think it would be better to focus on applications of AI in the insurance world and establish yourself as a domain expert.
I've thought about two possible ways. One was to serve as a domain expert for insuretech startups, which I already do for my current job - so with that route I can either keep doing what I am doing now, or get hired by a client. One downside to this however, is the pay really isn't all that much better than what a regular actuary would make, if at all. If I can get equity at a startup, and, assuming my gf doesn't quit her job, it would be a risk worth taking.

Another option is to just take a job as a regular software engineer to gain experience even it means going down a rung, temporarily, which I may be willing to do if I have reasonable chance of making big bucks.

What do you think about the masters? After I finish my exams, I was thinking about applying to GT's CS program which is cheap enough to be fully funded by my employer. There are some big name schools that charge like, $60k per year, which I believe are out of the question. But there are some schools at are in between, notably the state schools that have good programs. Is a masters worth pursuing at all or should I just go straight into software dev? Or both?
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  #9  
Old 12-08-2017, 07:16 PM
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Originally Posted by Colonel Smoothie View Post
I've thought about two possible ways. One was to serve as a domain expert for insuretech startups, which I already do for my current job - so with that route I can either keep doing what I am doing now, or get hired by a client. One downside to this however, is the pay really isn't all that much better than what a regular actuary would make, if at all. If I can get equity at a startup, and, assuming my gf doesn't quit her job, it would be a risk worth taking.

Another option is to just take a job as a regular software engineer to gain experience even it means going down a rung, temporarily, which I may be willing to do if I have reasonable chance of making big bucks.

What do you think about the masters? After I finish my exams, I was thinking about applying to GT's CS program which is cheap enough to be fully funded by my employer. There are some big name schools that charge like, $60k per year, which I believe are out of the question. But there are some schools at are in between, notably the state schools that have good programs. Is a masters worth pursuing at all or should I just go straight into software dev? Or both?
What is your definition of "big bucks"? Give us some numbers.
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Old 12-08-2017, 07:33 PM
MathStatFin MathStatFin is offline
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As for the masters programs, my initial thought was that you should consider the University of Chicago. The tuition is pretty insane imo. Their program doesn't really seem all that good and the faculty there seemed to be focusing on things you will not find particularly relevant.

I'm just wondering why you're considering computer science instead of statistics. Is it also 60K a year for the statistics program? They have an excellent stats department.

What exactly are you interested in? I think there's a difference between "AI", "Machine learning" and "statistical learning". You need to focus on the course offerings before committing to a specific program since you might end up taking a lot of courses that will not be interesting to you or even relevant to your career aspirations. For instance, the stats departments don't really deal with "AI". From an academic point of view, "AI" usually refers to specific topics within computer science like deep learning, robotics, computer vision, etc. It's not something that you will be doing in a stats department (as far as I can tell).
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