Another obvious example is actuaries and any number of other analysts in the insurance industry. According to Google[1], actuaries earn an average of just over $97k/year. While this may not strictly be an "investing area," it's a clear target for automation.

1. https://www.google.com/search?q=actuaries

Part of the reason actuaries make so much is that they are certified and legally required to sign off on analysis, similar to the way civil engineers put their signature (and reputation) on their work. But you would be surprised how many decisions come down to "actuarial judgement", having good business sense, intimate knowledge of insurance rules and regulations...and not doing anything special with the data. Hell, some actuarial methods are the same as they were in the 70s. Data science is slowly eating their lunch in predictive modeling areas, but I wouldn't be surprised if they're still around after data science itself has been automated.

I work on a team of actuaries in a more data science-y role. Believe me, I had the same impression initially: "Can't we automate some of this stuff?". But I've since come to see their value.

It's not that actuaries will be eliminated. It's that 2 actuaries and 2 data scientists will do the work of 20 actuaries.

How many actuaries does a major insurer employ? Would they like to halve that number?

I don't know about actuaries, but I do remember there was an article lately about an insurance company in Japan that replaced hundreds of workers with IBM's Watson.

https://www.theguardian.com/technology/2017/jan/05/japanese-...

That's really interesting. In my circle of friends, most of us can't point to a single success of Watson, despite it's very publicized capabilities. This may only be a small company, but almost certainly larger insurance agencies & medical foundations must be on to this.

Actuaries have quite a high regulatory wall protecting them from other human competitors. But probably not from computers.

(I don't know about the legalities, but the laws requiring companies to have a actuary sign off on stuff probably can be satisfied by having on part time actuary shared between multiple companies sign off on computer generated analysis.)

I know in banking, regulators are satisfied with ML models making decisions about bank capitalisation, so long as the models are annually audited by an external auditor (eg. KPMG, EY)

I assume they will need to sign off on the model... I expect to see a lot of this in the future.

It's not so much the actuaries but the claims adjusters whose jobs will be automated:

http://www.inc.com/kevin-j-ryan/artificial-intelligence-repl...

Computers are already making trades based on natural language articles.

EX of some research from 2008: http://www.seas.upenn.edu/~cse400/CSE400_2007_2008/DavdaMitt...

You see real world movement within seconds of the release of some public data. Which is far to soon for humans to read much of anything.

I'd have to wonder what type of safeguards vet against intentional false data released to poison the well of other HFT bots?

Maybe it's not too important yet because HFT bots only need to game the slower mass of "dumb (reactionary) money"

Not a whole lot. [1]

[1] http://www.theatlantic.com/sponsored/etrade-social-stocks/th...

Absolutely, but isn't this type of software generally used to augment existing jobs, rather than replace them? The program can make basic inferences like "10 negative GE articles -> Sell GE stock", but you still need a human to make more general decisions about an industry as a whole, related companies, how long-term an issue is, etc. Basically, nobody's job is as simple as reading the news and deciding whether articles are positive or negative. In the case of this Goldman Sachs story, some people's jobs really were as simple as looking at the performance of equities and making decisions based on that alone.

>Absolutely, but isn't this type of software generally used to augment existing jobs, rather than replace them?

Is there a difference? If a person does 10x as much you need 1/10th as many of them.

These algorithms are only good at front running the reaction of the market, which is fairly predictable in the short term when a relevant piece of news is published. They are nowhere for making long term investment decisions.

AI everywhere is as naive as software everywhere. It costs a lot of money to develop and maintain software professionally. It's uneconomical to staff an IT team to replace every routine job there is with software.

It will be the same with AI. If you apply enough bright people's mind for long enough, we can possibly automate certain niche jobs, but that's going to be completely uneconomical. You need to pay these AI specialists, and retain them so that you can adapt your algos as the problem moves, and there needs to be more than one in case one goes or is ill. You can easily spend more that the salaries you are going to spare.

Other problems are I believe too vast and open ended for AI to come up with anything useful. What company will be successful in 5 years? What is the right level of renumeration for the management of that company? Where is innovation going next? You really think that AI is anywhere near answering that kind of questions?

AI will shine in similar areas where software shines. Problems that are simple enough to be understood by computer scientists (and that narrows down this vast world quite a bit), and that are broad enough to justify the economics of making high investments to build data, or where there is already ample of existing and relevant data. Driving a car. Cleaning or building a house. Replicating certain hand movements. Things like that. I doubt an expert on a narrow and specialised domain has much to fear from AI.

> It will be the same with AI. If you apply enough bright people's mind for long enough, we can possibly automate certain niche jobs, but that's going to be completely uneconomical.

The thing is, it doesn't have to be an all-or-nothing proposition. If you can write software to make a team of ten traders 10% more productive, it may be possible to do the same job with only 9 traders.

Over time, those productivity gains compound and you have literally cut in half the number of people on your trading floor.

I've seen that happen to FX traders, and some fairly straight-forward automation. No AI was required.

AI is a very flexible term, all software that makes choices technically qualifies as AI even if the benchmark is moved over time. But, remove the buzzards and rejecting a loan using a single if statement based on a Fico score really is machine decision making at work.

It should be noted that demand is not a static quantity. If a technology allowed a person to create more per-unit cost will be cheaper, raising demand (people on the bottom will still lose jobs, but it's not linear).

But this is about responding faster, not responding more often. It helps you react to a story before everyone else, but there's only so many stories in a day.

Or you can have 10× productivity.

You have that either way. The question is do you need 10x the output?

Ah but in 2008 these news articles were still written by humans :-)

Yes. Not to mention that spreads are so tight that the only real challenge for a cash trading desk is how to read the flow, and manage inventory, into events. Computers don't do a great job of interpreting doublespeak and insinuation, which is a substantial part of earnings calls and other releases of information.

Derivatives trading has grown as cash trading has shrunk. And the banks haven't figured out how to automate that business yet.

Computers don't need to understand natural language, they just need a little guidance towards what the data means. You can train a neural network, or you can just write some solution-specific code. A lot of people are not that hard to replace.

i agree 100%. i think the only reason traders can be replaced by machines is because these traders didnt perform better than monkeys shooting darts.

yeh you can't send a robot of an on site visit to look the management in the eye

Not really. The world is data. Anything you need to know about the world is in a table somewhere, and the web has done a great job of making the planets data accessible to machines.

Other areas may be more complex, but complexity isn't as big a barrier as it may seem.

Also, those areas are probably not as complex as people think they are. I'm reminded of the AI that started making breakthroughs in oncology by looking at the cells around a tumour, despite the scientific consensus that there was nothing to be learned from the cells around a tumor.

> Anything you need to know about the world is in a table somewhere

No it isn't. If I read an article about the Volkswagen emissions scandal, what table do I look in to figure out how much consumers will care, and how aggressive various governments will be with their punishment? When Disney buys the rights to Star Wars, where should I look to see if it's worth what they paid for it? Should I rely on data from 30 year old movies, or look at merchandise sales or something? If I start seeing a lot of articles about climate change and the dangers of fossil fuels, should I sell my stock in Exxon?

Not that humans are particularly good at this stuff, but I don't think computers will be as good or better for a long time.

I think a lot of people get hung up on how humans make decisions, with the expectation that you need to be able to understand the same ideas to perform the task.

Even if a computer can't figure out the stats you mention, maybe it doesn't matter? Maybe just reacting fast enough to what people in the market are doing is good enough? Maybe simple sentiment analysis on articles on Disney around the web is a good enough proxy for share price increase.

Sounds like that's giving the humans who are left in the market 10x the power.

You're making the mistake of being specific. Think heuristic.

> Anything you need to know about the world is in a table somewhere

Not yet.

The problem is understanding data. A human can learn a new word's meaning by reading its definition in a dictionary; a computer can't.

It's a dictionary. I'm pretty sure a computer can look it up faster than a human can.

Access to raw text is not the hard part.

Even so, that doesn't stop one human helping the computer to understand, and thus replace x more humans.

Spoken like a true data scientist. Hope there's room for culture in there somewhere.

Sorry to burst your bubble, but culture is data too.