AI: Where the Brains Are


AI will catalyze sea change in TMT, effecting nearly every existing consumer and business application and enabling a host of new ones that could remake entire industries ( Talent – scientists with the intellect, skills, and experience needed to design and “train” the deeply layered complexes of interrelated algorithms comprising deep learning systems – are one of the three key ingredients to leading edge AI (data and powerful computing platforms are the others). All else equal, companies with more talent can build more complex programs to address more complicated problems with better results in less time across more applications than the competition. Skepticism muted interest and funding in the area until the mid-2000’s, an “AI Winter” that has limited the population of true experts in the field, many of whom have been poached by commercial enterprises, constraining universities’ capacity to turn out new Ph.Ds. We have built a data base of over 850 of the most cited AI scientists, 44% of whom work outside of academia. #1 GOOGL has 83 of them, followed by MSFT (54) IBM (19), FB (19), AMZN (14) and BIDU (8). GOOGL’s roster of heavy hitters also attracts top young talent – it runs an internal “post-doc” education program in deep learning. At the recent ICML academic conference, GOOGL authors contributed 22 of 322 papers selected for presentation and won 2 of the 4 best paper awards. MSFT (17), IBM (9), and FB (4) also presented more than 2 papers. We believe that this excellence positions these companies to lead in finding broad, efficient and optimized solutions for the most promising arenas for AI, including natural language interfaces, image/video recognition, predictive modeling, and autonomous vehicles. Some companies with less talent can effectively address smaller, constrained applications, but most will have to buy AI from the leaders.

Experienced scientific talent is a prerequisite for leading edge AI. Deep learning systems have three key ingredients – big data bases, powerful processing platforms, and programmers with expertise and experience in the idiosyncratic technology. Unlike traditional software, built bottom up from functional modules that can be written independently, deep learning demands an understanding of the whole system. Leading edge systems have thousands of individual algorithms, each working on an aspect of the problem but interacting with each other in layers. The program iterates over large data sets, with scientists periodically changing the individual algorithms and their relationships with each other in order to “train” the system to ever more optimal results. All things equal, companies with more and better talent can address more complex problems more completely and find better solutions more quickly.

Deep learning scientists are in short supply. AI had a history of promising much, but delivering little. By the late ‘80’s, when the shortcomings of the expert systems approach had become apparent, backlash yielded an “AI winter” that lasted into the new millennium. Deep learning funding dried up, and promising CS students were steered away from the discipline. According to leading figures in the field, there are fewer than 100 true thought leaders – most already poached by a handful of companies. These experts are attracting the next tier of scientists from the close-knit academic community, concentrating talent and constraining the ability of universities to turn out new Ph.Ds.

GOOGL leads for experienced talent. We have built a data base of thousands of scientists who have been cited by academic papers for their work in AI. Of these, 855 have been cited at least 5,000 times, a significant indicator of their experience and influence. More than 35% of these experts have left academia, with GOOGL the largest employer by far with 83. Following behind are MSFT (54), IBM (19), FB (19), AMZN (14) and BIDU (8). GOOGL’s brain trust leads thousands of AI proficient engineers spread over dozens of teams applying learning technology to existing businesses and new opportunities, while supporting highly touted internal post-grad AI training programs that supplement its aggressive hiring in the tight market for recent grads with relevant experience.

Talent leadership evident in intellectual contributions. 322 papers were selected for presentation at the recent ICML conference, the top annual gathering for AI academics. 34% of the papers were written or co-written by scientists working in the private-sector, 22 by GOOGL employees, including 2 of the 4 papers judged to be the best presented at the conference. MSFT was involved in 17 papers, IBM with 9, ADBE with 5, and both YHOO and FB with 4. AMZN and BIDU each had 3, while TWTR, Hulu, and recent CRM acquisition MetaMind contributed 2 apiece, with 15 other companies presenting one paper each.

Top players addressing generalized solutions. GOOGL, MSFT, FB, IBM, AMZN, BIDU, and BABA have all built extensive AI capabilities with hundreds of experienced engineers. We believe that these companies are in position to push leading edge deep learning solutions against broad, complex use cases – uncompromised natural language interfaces, translation, autonomous machines, detailed image classification, complex predictive modeling, etc. – simultaneously addressing multiple projects. GOOGL is the clear leader, but MSFT is the clear #2, with the others well behind and fairly clustered in the capabilities of their talent rosters.

The next tier will specialize and compromise. Companies like AAPL, TWTR, ADBE, CRM, NEC, NVDA, QCOM, and MBLY have a handful of experts and fairly small teams of trained deep learning engineers. These companies look to leverage open source technologies with focused research of their own to address very specific applications, typically constraining the application to simplify and accelerate development – e.g. voice interfaces that recognize limited commands or self-driving systems restricted to highway driving. All else equal (i.e. available data, processing platforms, ecosystem partnerships), these companies are at significant disadvantage for the broad, complex applications being pursued by the top players.

Everyone else will buy AI from the leaders. Companies in traditional sectors, like automakers, media companies, banks, telecom carriers, retailers, etc., barely show up on the lists of published AI experts. With scarce supply of trained deep learning engineers, these organizations are not desirable employers for candidates that are as often looking for opportunities to enhance their own skill sets by working with known experts as they are in grabbing the highest salary. We do not believe that any of these companies is likely to succeed in implementing significant AI capabilities on their own, and expect many of them to partner with top players to counter obvious threats to their core businesses. For companies like IBM, MSFT, GOOGL and AMZN, this could represent a substantial future opportunity.

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