Digital: Jobs of the Future

88% jobs lost in the US were lost to increased productivity due to automation. -Ball State University

That jobs have left the US in droves as a result of bad trade deals, is possibly based on faulty premise. The US manufacturing has become more productive and industrial output has been growing, despite a steep decline in factory jobs. US factories have been achieving this by gradually replacing human labor with robots.

Human welder earns around $25 per hour, equivalent operating cost per hour for a robot is around $8. -Boston Consulting Group

In 15 years, this gap will widen even more dramatically. This process is irreversible. PwC reports that 38% of the jobs in the US will be automated, 35% in Germany and 21% in Japan, by 2030. Automation threatens 69% of the jobs in India, 77% per cent in China, and in Ethiopia it is 85%, according to a World Bank research. PwC also estimates that more than 10 million UK workers are at high risk of being replaced by robots within 15 years as the automation of routine tasks gathers pace in a new machine age - nearly 30% of the workforce.

AI and robots will enable creation of new services that are difficult to imagine today.

As AI, machine learning, and intelligent robots become more pervasive, there will be new jobs in manufacturing, training, sales, maintenance, and fleet management of these robots. For young people, just sorting out their career goals, AI offers a wealth of opportunities. So how do we prepare for jobs that don’t yet exist?

1. If you’re a student -

• Math and physics are where you learn basic methods for AI, machine learning, data science, and many of the jobs of future. Take all math classes you can possibly take, including Calc I, Calc II, Calc III, Linear Algebra, Probability, and Statistics. Computer science, too, is essential - you’ll need to learn how to program. Engineering, economics, and neuroscience are helpful. You may also want to consider some areas of philosophy, such as epistemology, which is study of what is knowledge, what is scientific theory, and what does it mean to learn.
• These classes are not for simple rote memorization - you must learn how to turn data into knowledge. This includes basic statistics, but also how to collect and analyze data, be aware of possible biases, and to be alert to techniques to prevent self-delusion through biased data manipulation.
• Apply to PhD programs. Find a reputable professor who works on topics that you are interested in, or pick a person whose papers you like or admire. Apply to several PhD programs in schools of these professors and mention in your letter that you would like to work with that professor, but would be open to work with others.
• Engage with an AI-related problem you are passionate about. Start reading literature on the problem and try to think about it differently than what was done before. Before you graduate, try to write a paper about your research or release a piece of open source code.
• Apply for industry-focused internships to get hands-on experience on how AI works in practice.

2. If you’re already involved in a career and want to pivot to AI -

• You can get broad idea of what deep learning is about by going through tutorial lectures that are available online. There are plenty of online materials, tutorials, and courses on machine learning.
• You may also want to go back to school. If so, see the instructions above.

Stay physically fit and keep sharpening your saw. Age is just a number.