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Tencent Says There Are Only 300,000 AI Engineers Worldwide, But Millions Are Needed (theverge.com)
An anonymous reader quotes a report from The Verge: It's well-established that talent is in short supply in the AI industry, but a new report from Chinese tech giant Tencent underscores how great the need might be. According to the study, compiled by the Tencent Research Institute, there are just 300,000 "AI researchers and practitioners" worldwide, but the "market demand" is for millions of roles. These are unavoidably speculative figures, and the study does not offer much detail on how they were reached, but as a general trend they fit with other, more anecdotal reports. Around the world, tech giants regularly complain about the difficulty hiring AI engineers, and the demand has pushed salaries to absurd heights. Individuals with just a few year's experience can expect base pay of between $300,000 and $500,000 a year, says The New York Times, while the very best will collect millions. One independent AI lab told the publication that there were only 10,000 individuals worldwide with the right skills to spearhead serious new AI projects.
Tencent's new "2017 Global AI Talent White Paper" suggests the bottleneck here is education. It estimates that 200,000 of the 300,000 active researchers are already employed in various industries (not just tech), while the remaining 100,000 are still studying. Attendance in machine learning and AI courses has skyrocketed in recent years, as has enrollment in online courses, but there is obviously a lag as individuals complete their education.
Tencent's new "2017 Global AI Talent White Paper" suggests the bottleneck here is education. It estimates that 200,000 of the 300,000 active researchers are already employed in various industries (not just tech), while the remaining 100,000 are still studying. Attendance in machine learning and AI courses has skyrocketed in recent years, as has enrollment in online courses, but there is obviously a lag as individuals complete their education.
Change the way AI is done.
It doesn't have to be so esoteric: make it "visible" as layered voting machines where each factor "votes". Use data layouts similar to spreadsheets and relational database reports so that "regular" office workers can study, arrange, relate to, and adjust factor weightings, mask weightings, and routing paths (similar to "hidden layers") as needed.
Color coding, similar to Excel's conditional formatting can make high-match and low-match factors stand out for test cases or trouble-shooting.
Staff can be divided similar to the processing tree. For example, in vision recognition, one group can focus on people identification, another on furniture and building identification, another on outdoor patterns, etc. The idea of one giant do-it-all monolithic neural-network is not practical if we want rank-and-file AI and dissect-able AI. Bring in modularity and divide-and-conquer techniques.
You may need an experienced AI domain specialist to help divide up tasks and provide factor (test) guidelines or drafts, but once staff have their basic assignments they can focus and tune without being caught up in the big picture and way-out theory.
Table-ized A.I.