The number of jobs that machine learning could render redundant over the coming decades is a growing cause for concern. According to research by PwC, 38% of US jobs will be automated by 2030, while other parts of the world fare little better. In Germany, it is 35%, and in the UK, 30%. However, it may be inevitable that jobs will be lost, but as with all periods of great technological advance, new jobs will also be created. Many of these will in fact be focused on developing and supervising machine learning algorithms, helping businesses to integrate and implement the technology and bring in efficiencies hitherto unimaginable.
This is already happening to an extent. According to the job search website Indeed.com, June 2015 to June 2017 saw a 500% rise in the number of job postings in the field of AI. Of these job postings, 61% in the AI industry were for machine learning engineers, 10% were for data scientists and just 3% were for software developers.
However, machine learning is suffering from the same problem STEM has suffered from since time immemorial: A lack of qualified people to exploit it to its full potential. There is a dearth of people who understand first where is appropriate to apply it, and second how to apply it. According to a survey from Tech Pro Research, just 28% of companies have some experience with AI or machine learning, and more than 40% said their enterprise IT personnel don’t have the skills required to implement and support AI and machine learning.
Machine learning is, as you would likely imagine, extremely complicated, and not something your run-of-the mill computer engineer is going to be capable of without proper training. It requires someone with a background in computer science, likely with a doctorate in the sciences, as well as a significant amount of practical experience working with data at scale. Given that there is already a dearth of qualified data scientists, there is little to suggest that the situation is going to be any different when it comes to machine learning. And this is already hampering the technology.
Just 15% of organizations manage to bring their big data projects to production, according to Gartner analyst Nick Heudecker, and he believes this number is likely to be far lower when it comes to machine learning. His fellow Gartner analyst Merv Adrian blames this on the lack of available talent, noting recently that, “For me it’s mostly about skills. Missing skills.” Sumit Gupta, IBM VP for Cognitive Systems, agrees, arguing that, “We really need to nurture and harness partners and startups with the skill sets to implement these AI offerings.”
Even The US Government has expressed concerns about the lack of AI talent. At a recent Senate Commerce, Science and Transportation subcommittee hearing, both Washington policymakers and AI experts agreed that the lack of tech talent could see the US overtaken by the rest of the world when it comes to AI technology.
Read the source article at Innovation Enterprise.