Artificial Intelligence Is Likely to Make a Career in Finance, Medicine or Law a Lot Less Lucrative – Entrepreneur

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Growing up, there’s a good chance you heard the mantra “go to a good school, get a good job, and make lots of money.” On the surface, that seems like sound advice. After all, college graduates, on average, earn almost $1 million more in their lifetimes than those with only a high school education.

Perhaps you were encouraged to get a professional degree to land a high paying job like a doctor, dentist, lawyer or something similar. This also seems like great advice, considering a professional degree holder typically earns more than $2 million more in their lifetimes than the average college graduate.

But that was then, and this now.

Related: The Future of Productivity: AI and Machine Learning

Thanks to rapid advances in robotics, automation and artificial intelligence, jobs are falling to machines left and right. And it’s not just blue-collar jobs that are being taken over by automation. It’s white-collar professions as well. According to an Oxford study, 47 percent of U.S. jobs could fall to automation in the next 20 years.

The safe, high paying jobs of the past are starting to look much less secure going forward. If you’re currently in one of the following professions, or going to school to get into these fields, you should think twice before continuing.

1. Finance.

If Wall Street is known for anything, it’s known for crazy high salaries and bonuses. For those who have wanted to get rich quickly post-college, there have been few better industries than finance. Alas, finance is one of the industries with the highest risk of automation.

Bridgewater Associates, the world’s largest hedge fund, announced late last year that it was going to be cutting staff in favor of more automation. It’s getting harder to compete with AI driven hedge funds like Sentient and high-frequency traders in general, and an impressive swath of financial management services are now being handled by robo-advisors like Betterment and Wealthfront, both of which are growing rapidly.

In fact, according to Angel List, there are more than 15,000 finance startups right now working to actively disrupt finance, many of them utilizing artificial intelligence and other forms of automation. If you’re in this field or planning to enter it, you might want to reconsider.

So, if not finance, then what? If you’re good with numbers and detail-oriented, you should consider getting into data science. Data scientist salaries are rising rapidly, and they’re considered the new rock stars of the tech world. Of course, you could always try getting into venture capital to ride the massive transitional wave that’s coming, but being a VC isn’t all it’s cracked up to be either. Either way, traditional finance jobs are on the way out.

Related: How AI Machines Coudl Save Wall Street Brokers’ Jobs

2. Medicine.

The work that doctors do is tremendously important, and on average, they’re very well paid for it. That said, there are numerous areas of medicine that are ripe for automation and improved efficiencies. One key example would be medical imaging and the fields of radiology, pathology and dermatology.

Using AI, IBM’s Watson is now considered at least on-par with a professional radiologist in terms of ability to analyze an image and diagnose a patient, and it can do the analysis much faster while considering vastly larger amounts of information than any human could ever hope to. This is fantastic news for the people who need a diagnoses, but not so great for medical imaging jobs. If you’re already in the field, or working to get into it, you could consider transitioning into some aspect of computer vision, be it research or training. If you can’t beat the machines, you can always help to make them better.

There are numerous other technologies, such as telemedicine and mobile medical devices, that will also heavily disrupt this field going forward. And while there will still be a strong need for certain medical skills going forward, you’ll need to be highly selective in what you choose.

3. Law.

Ahh, lawyers. The world could probably use far fewer lawyers, and the machines are well on their way to making that a reality. While you can still make a pretty penny as a lawyer, depending on your specialty, it’s worth noting that lawyers spend a lot of time gathering and parsing data, creating or reviewing legal documents and numerous other mundane tasks. For most lawyers, it’s far from a glamorous profession.

Much of this grunt work has already been automated, and there are more than 1,500 startups out there trying to streamline the legal world even further. While this won’t immediately eliminate all legal jobs, it means that it will take far fewer lawyers — and especially paralegals — to handle the same level of work.

Because lawyers tend to pay excellent attention to detail, and are highly versed in logic, a good alternative field would be programming. Programming languages are built around logic and require every bit as much attention to detail as any contract. Best of all, there are a ton of courses online that can help you learn, including some from top-tier universities like Stanford, MIT and even Harvard.

And of course, programmers are incredibly well paid and in high demand virtually everywhere. Here are a few of the most in-demand programming languages to help you along if you decide to make the switch.

Related: Advancing Automation Means Humans Need to Embrace Lifelong Learning

Jobs of the future.

There have been numerous times throughout history when a large number of old jobs have gone away, only to be replaced by new jobs as new technologies came along. Sometimes the transition from old to new is protracted enough to make a semi-smooth transition possible. That may or may not be the case this time around.

This list barely scratches the surface of the jobs at risk of automation, and I’d highly recommend that anyone with a job carefully consider the risk associated with their field. If your job is at risk of automation, now is the time to start retraining.

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