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  #91  
Old 12-20-2017, 07:21 PM
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Originally Posted by clarinetist View Post
CS,

If you were really serious about going down the AI route, you'd leave your actuarial job and go work for a startup full-time, or you'd go to grad school and bolster your technical background.

I'm not sure what you're expecting. Leaving actuarial science is not a seamless transition. I went through that, and I'd say it paid off quite well for me. But it took time, and I had to get some grad school in before it did.

But here's the thing - if you're going to go all the way to FCAS, chances are, there will be a new fad by the time you obtain that credential and you'll be posting a similar thread on the AO about that new fad. You'll probably be enrolled at GA Tech by then. Notice: not now, but after you finish your FCAS, because right now, your main priority is getting your FCAS. There is not enough time in the day to do both an FCAS and a Master's degree. By the time I was enrolled in the Master's program, I already threw out the idea of paying dues for an ASA and doing APC, etc.

Also, I had some exposure to GA tech's masters courses when I did Udacity for a very short time. They don't mess around. It is probably (can't say for certain because I didn't enroll in it) more difficult than the Master's program that I'm in right now.

As it is said, "talk is cheap." If you're going to pursue anything AI-related, now's the time to do it.
I don't plan on doing full blown AI research. As AI grows, I fathom there will be a lot of jobs in AI that don't require being an academic.

I know people are going to accuse me of having "not a real AI job" similar to how people like to engage in pissing contests on who is and isn't a "real data scientist" or who gets to be called an actuary. I find those discussions to be silly and I really don't care. I just want to things that are interesting and get paid for it.

I like chasing new ideas. For the most part, it's made my day job interesting and stimulating.
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Originally Posted by Wigmeister General View Post
Don't you even think about sending me your resume. I'll turn it into an origami boulder and return it to you.

Last edited by Colonel Smoothie; 12-20-2017 at 07:24 PM..
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  #92  
Old 12-20-2017, 07:30 PM
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Originally Posted by PeppermintPatty View Post
I think it depends on your career path. I don't see traditional actuaries needing a PhD. I'd guess an MBA would matter long before the PhD for most positions. And I think there will still be a lot of those positions in the future. But if you want to be competing with the data scientists at predictive modeling, you might need a PhD.

By the way, I've poked a finger into a lot of parts of the actuarial pie, including some GLMS models, reserving, large account modeling, reinsurance pricing and reserving, even a little non-traditional stuff. The most interesting and mathematically challenging stuff I've done was large account pricing. The most creative stuff I've done was reserving (and some non-traditional stuff that had me put on my reserving hat) -- although I've certainly seen mindless drone-like reserving, as well. The predictive modeling was okay, kinda on par with picking trends for pricing in complexity and interest, with more data annoyances and a little more need for domain knowledge, but it wasn't my favorite by a long shot.
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Originally Posted by jas66Kent View Post
You already need a graduate degree in Europe in order to advance in Actuarial. This is still not largely true in the US and UK, but it is trending in that direction.
Yeah I don't see PhD being a prerequisite. Very few people would be willing to put up with exams on top of that for an EL salary. People who are that smart choose other careers. I think the profession will destroy itself from credentialism before it gets that competitive.

Masters, though, I'm not so sure. More likely in the US, it'll be like Canada where students graduate nearly credentialed with 2 internships/co-ops under their belt.
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Don't you even think about sending me your resume. I'll turn it into an origami boulder and return it to you.
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  #93  
Old 12-20-2017, 08:13 PM
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Originally Posted by Colonel Smoothie View Post
I don't plan on doing full blown AI research. As AI grows, I fathom there will be a lot of jobs in AI that don't require being an academic.

I know people are going to accuse me of having "not a real AI job" similar to how people like to engage in pissing contests on who is and isn't a "real data scientist" or who gets to be called an actuary. I find those discussions to be silly and I really don't care. I just want to things that are interesting and get paid for it.

I like chasing new ideas. For the most part, it's made my day job interesting and stimulating.
Or maybe AI is just hype and the whole thing will crash and burn.
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  #94  
Old 12-20-2017, 09:30 PM
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Or maybe AI is just hype and the whole thing will crash and burn.
Or maybe AI is not just hype but bungled AI implementations will still cause more than a few mismanaged companies to crash and burn.
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  #95  
Old 12-21-2017, 01:14 PM
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nonlnear is onto something.
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  #96  
Old 12-21-2017, 07:06 PM
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Originally Posted by nonlnear View Post
Or maybe AI is not just hype but bungled AI implementations will still cause more than a few mismanaged companies to crash and burn.
Yes like financial engineering pre 2009.
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  #97  
Old 12-21-2017, 07:07 PM
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Some of you may enjoy this

https://www.youtube.com/watch?v=Qi1Yry33TQE
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  #98  
Old 12-30-2017, 12:11 PM
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Originally Posted by Colonel Smoothie View Post
Yeah which is why GT appeals to me. Seems like a big win if I can get in.
What is the GT program that you are referring too? I'm interested as well.
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  #99  
Old 01-02-2018, 11:39 AM
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Originally Posted by Strider View Post
What is the GT program that you are referring too? I'm interested as well.
Georgia Tech Online Comp Sci Masters

Supposedly very rigorous.
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  #100  
Old 01-23-2018, 05:18 AM
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https://news.efinancialcareers.com/u...l-quarterbacks
Quote:
If you really know about artificial intelligence, you could earn as much as an NFL quarterback

by Dan Butcher 13 July 2017

Forget all the hype around artificial intelligence (AI), if you’ve really got the skills that Silicon Valley and Wall Street need, you could haul in millions of dollars a year.

“Right now, AI is an elitist sport – there are very few people who know how to practice it,” said Tom Eck, the CTO of industry platforms at IBM and a software developer who has been involved in AI as far back as the early ’90s. “The top-tier AI researchers are getting paid the salaries of NFL quarterbacks, which tells you the demand and the perceived value.”

While Eck, speaking at the Markets Media’s Summer Trading event in New York, didn’t specify whether he meant starting quarterbacks or their backups, he did say that the best AI researchers are earning as much as the highest paid position in the National Football League. For reference, there are 31 quarterbacks who will make more than $5m this year, and the highest-paid QB in the league – the Oakland Raiders’ Derek Carr, fresh off signing a new contract last month – will get a cool $25m this year. He’ll earn a minimum of $70m over three years and could get up to $125m over five.

Financial services, healthcare and advertising are the three biggest adopters of AI at this stage, according to Eck.

AI is the act of imbuing a machine or a piece of software with the capabilities that we consider human cognition, basically making a machine act like a human brain. If we take neurons and model them mathematically as a simple formula with nonlinear components, we can create an interesting rules-based system using “if this, then that” algorithms capable of pattern recognition, Eck said.

But it’s still at the early stage.

“AI is statistics-based – here’s a set of data; take the data, extrapolate and give me a prediction of the most likely thing to happen,” he says. “That’s about model-generating, given a set of data.

“The machine learning system has to come up with a generator that would spit out that data set, working backwards from the data to a mathematical model, and then it is no longer limited to the data you’ve observed – AI can predict what tomorrow’s equity prices will be.”

Deep learning uses the same neural-network type of approach as AI but at gargantuan sizes. All models are fueled by data,

“Big data really means more data than you have the capability to deal with: high volume, high velocity that you want to process quickly and variability,” Eck said. “The data science role not going away anytime soon – with data, it’s garbage in, garbage out.

“Deep learning is more challenging because of volume, velocity and variability,” he says. “The interesting thing about deep learning is its ability to operate on unstructured data.”

The next step in the process of evolution is cognitive computing, a system that’s imbued with the ability to understand, reason, recognize the context and learn – and it has to have a human interface such as a natural language base.
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