The die is very much cast when it comes to the growth of machine learning. With the expansion of tech companies like Google, Amazon and Uber, artificial intelligence-based research and products is a growth industry that’s only just getting started.
Research from the International Data Commission forecasts that spending on AI and ML will rise from $12 billion in 2017 to $57.6 billion by 2021. The skyrocketing research funding into these fields is mirrored by the resulting patents being filed, with machine learning patents increasing by 34% between 2013 and 2017.
As the number of commercial products that are built on these architectures increases, so will the demand for engineers and researchers to work on them. This translates into handsome salaries at some of the world’s leading tech companies.
At the time of writing, the average salary for a machine learning engineer in the United States was listed on some employment sites at around $138,000. There is concern from some in the field of academia that high starting salaries and assorted perks could result in a brain drain from universities, with candidates who would previously have continued their research at established institutions being poached into careers.
In a Guardian article from November 2017, Maja Pantic, professor of affective and behavioural computing at Imperial in London, noted that one of her students dropped his PhD in its final year to go and work at Apple for a six-figure salary. “It’s five times the salary I can offer,” Paja said. “It’s unbelievable. We cannot compete. The creme de la creme of academia has been bought and that is worrying.”
Just how prevalent this is, though, is a matter of some debate. To get a better idea of the state of the machine learning field in relation to its industrial demand, Binary District Journal spoke to Chelsea Finn, PhD candidate at Berkeley. A respected machine learning academic, she will join the Stanford faculty in 2019, and has also worked at Google Brain.
Upsides and Downsides
Chelsea believes demand is outstripping supply at the moment. A particular skill set in demand is the intersection of machine learning with real robot hardware – with the ability to run experiments on physical robots.
“A lot of people are a bit afraid of working with real systems because they can take a fair amount of time to get set up,” Chelsea says, “and these might be people who prefer much faster prototyping in simulated domains.”
However, Chelsea believes that the matter of researchers forgoing the traditional extended academic pathways in favour of joining tech companies is an area that is not necessarily black and white.
“I think there…
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