On Thursday afternoon, the United States became the country with the greatest number of known COVID-19 cases in the world. With millions out of work and the spread of the virus taking its toll, it's easy to feel overwhelmed and in anguish without ever getting off the couch. Inadequately supplied frontline healthcare workers are the heroes in the trenches of this war, but the world's scientific community is also considering how it can respond and provide solutions. People with expertise in AI, data science, and tech tools are in demand right now as the world scrambles for ways to avert disaster. In last week's newsletter, VentureBeat AI editor Seth Colaner characterized it as a kind of digital flotilla.

We have been experiencing an "AI Spring" (e.g. This was due to technological breakthroughs, commercialization of Deep Learning, and cheap computation. Such uptick in interest in AI was largely driven by the work from Alex Krizhevsky (a student of Geoff Hinton and co-worker of mine) and investment from firms like Google and Nvidia. We had similar AI Springs every decade since the 60s. However, AI Winters, defined by 1) skepticism and 2) cut in funding, followed every time.

Bangalore: An in-depth study on talent in the Machine Learning (ML) space by Zinnov, a global management consulting firm, revealed that while a few startups have a had success stories in their AI (artificial intelligence)/ML journeys, there still exists a deep chasm, and most startups and global enterprises haven't been able to succeed and/or scale, their ML initiatives. The AI/ML spend is predicted to touch $400 billion by 2020, according to industry estimates. Given this, it is more important for organizations to invest in the talent that will capitalize on this niche technology. However, acquiring and retaining, the right kind of ML talent continues to remain a significant challenge for organizations. Zinnov's study explained that a large contributor to this challenge is the skewed concentration of the niche ML talent.

Another way to expand your pool of "qualified" candidates is to look for individuals with nontraditional backgrounds. When companies are looking to hire talent like operations or salespeople, industry-specific experience is a definite benefit. While competition for the best candidate in these fields is still intense, the arenas are well-established enough to offer a more robust pool of suitable candidates, so the challenge is more manageable. But when it comes to a field as new as ML and AI, limiting your search to individuals with years of experience will quickly whittle the applicant pool down to next to nothing. Unless you're ready to shell out $500k as a starting salary, good luck trying to outcompete the Facebooks and Googles of the world. Instead, recruiters should seek out people who have crossed over industries, changed roles, or have, through a diversity of experiences, demonstrated that they are connectors and integrators. These candidates offer the adaptive thinking necessary to build the future of artificial intelligence as it unfurls. And, most people will completely overlook them.