Element AI, a software provider based in Montreal, Canada had published the Global AI Talent Report for 2019, at the start of April. The report said that its survey had captured a 66% increase in the number of people who declared themselves as artificial intelligence (AI) specialists on their LinkedIn profiles. There had also been a growing trend of researchers publishing at top machine learning conferences. High-impact researchers are mostly concentrated in the US, follows by China, the UK, Australia, and Canada. 

Interestingly, according to the report, AI talent pool is extremely versatile. About one-third of the researchers are working with employers who are away from the area where they received their PhDs. The AI community is also ready to accept individuals who have moved across disciplines. However, the supply is still not meeting the demand. In a separate survey by Microsoft and Times Higher Education published this March, 89% of the 111 AI researchers and university administrators expressed difficulties in hiring or retaining AI experts. 

A Tug of War between academia and industry? 

As more companies are tapping onto the hype of AI, it means those who are trained in the area will have wider career options. In recent years, we witness a gradual loss of researchers to private sectors. Apart from the opportunities and great compensation packages, being in the industry has also offered AI talents a chance to be exposed to different kinds of challenges and data that are harder to come by in a lab setting. 

The tug of war for AI experts is also present across various industries. As Tushar Mehrotra, Senior Vice-President of Analytics, Optum told AIMed at the recent Healthcare Information and Management Systems Society (HIMSS) conference. AI talents, in particular, data scientists, have very transferrable skills. Thus, when healthcare is looking at capturing these individuals, they are not just competing with other healthcare organizations but the non-healthcare related ones too. This has created a huge hurdle for recruitment. 

Nevertheless, some regard the trend as a door for more partnerships and job flexibility. Anima Anandkumar, Co-Director of Decision, Optimization and Learning at the California Institute of Technology and Director of Machine-Learning research at NVIDIA told Nature, “there’s unprecedented openness between academia and industry: more collaboration, researchers moving back and forth, and people like me with dual roles”. 

It’s all about a healthy AI pipeline

Oren Etzioni, Chief Executive of Allen Institute for AI in Seattle, Washington said creating a healthy AI pipeline would save many of such unnecessary competition. It will also enable more people to benefit from the AI ecosystem. One of which is immigration, he told Nature, at University of Washington where he works, more than 40% of enrolled Ph.D. students are foreigners. Yet, the US administration is restraining some of these overseas nationals from coming into the country or allowing them to stay after they have completed their programs. 

Similarly, more women and minority should be encouraged to do a Ph.D. or to increase their presence in AI industry. Diversity is what AI needs most at the end of the day. Both universities and industries should give experts more freedom to work more closely to prevent them from choosing a side as a result of prospect and funding. 

Author Bio
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Hazel Tang

A science writer with data background and an interest in current affair, culture and arts; a no-med from an (almost) all-med family. Follow on Twitter.