Last year, the Montreal-based startup Element AI estimated that there were fewer than 10,000 people worldwide with the necessary skills to design artificial intelligence/machine learning systems, but the Chinese internet conglomerate Tencent Holdings later estimated the total number of AI researchers and practitioners at between 200,000 and 300,000 people.
Element AI came out with a new estimate on Wednesday, Jeremy Kahn reports at Bloomberg, putting the number of AI specialists with recently-earned PhDs at 22,000, of whom 3,000 are looking for work. With less restrictive parameters, however, the total number of AI experts could be four times greater:
Element AI said it scoured LinkedIn for people who earned PhDs since 2015 and whose profiles also mentioned technical terms such as deep learning, artificial neural networks, computer vision, natural language processing or robotics. In addition, to make the cut, people needed coding skills in programming languages such as Python, TensorFlow or Theano.
Among the critical skills in today’s job market, data science expertise is perhaps the most coveted in terms of high demand and short supply. As businesses in a wide variety of industries find new applications for data analytics, the limited pool of specialized data scientists can work pretty much anywhere they want and command a highly competitive salary. This September, New York University is launching a new PhD program in data science both to address this skills shortage, particularly in New York’s financial sector, and shape the field of data science as an independent academic discipline, Ivan Levingston and Taylor Hall report at SF Gate:
It’s one of the first such programs in the nation and builds on master’s degrees at NYU and other schools. MIT is gearing up a doctoral degree that includes data science, and Harvard plans to jump into the field with a master’s program in 2018. In the near absence of degree programs, investment firms must sort through the wannabes and find skilled data scientists from fields like physics and math.
“The term is a fairly loose term, and it can mean anything from somebody who’s an extreme expert in machine learning all the way down to someone who’s really more of a data analyst, preparing and cleaning data and producing charts, and it can mean everything in between,” said Matthew Granade, who oversees Point72 Asset Management’s data science unit, Aperio.
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Artificial intelligence expertise is one of the hottest commodities in the talent market today, and large, wealthy tech companies are taking the lead in hiring the best minds in AI, to the point of raising concerns about smaller firms being unable to compete. Just last month, Microsoft Ventures launched a fund dedicated to investing in AI startups and Uber bought a small AI startup to turn into its in-house research lab.
While the startup scene is one important source for these AI hires, universities are another. In November, the Wall Street Journal reported that tech giants were poaching AI experts from academia at such a rate as to raise concerns that this hiring frenzy might jeopardize the growth of the AI talent pool by leaving behind a shortage of teachers for the next generation:
The share of newly minted U.S. computer-science Ph.D.s taking industry jobs has risen to 57% from 38% over the last decade, according to data from the National Science Foundation. Though the number of Ph.D.s in the field has grown, the proportion staying in academia has hit “a historic low,” according to the Computing Research Association, an industry group.
On the other hand, Lauren Dixon adds at Talent Economy, the high technology industry has been moving toward a less secretive approach to AI development, with projects like OpenAI aiming to democratize the field. That increased openness was necessary to attract star academics into the private sector, and because of it, their employment at private companies may not hinder the free exchange of knowledge as much as some fear:
At the Atlantic last week, Ed Yong looked into a troubling trend in academic science, where more and more people from underrepresented groups are earning PhDs, but the representation of minorities on university science faculties is not improving at anything near the same rate:
Kenneth Gibbs Jr., an immunologist and science-policy expert at the National Institute of General Medical Sciences, … gathered figures on the numbers of Ph.D. graduates and assistant professors in the science departments of medical schools throughout the country, from 1980 to 2014. The data were stark. During that time, the number of newly minted Ph.D. holders from underrepresented groups grew by nine times, but the number of assistant professors from those groups grew by just 2.6 times. No such gulf existed for well-represented groups like whites and Asians; there, the Ph.D. graduate pool grew by 2.2 times while the assistant professor pool rose proportionally, by 1.7 times. …
But why does the gap exist? Donna Ginther from the University of Kansas wonders if it’s partly because Gibbs focused on medical schools, most of which do not guarantee salary with tenure, and so might be unattractive when compared to other alternatives. Perhaps scientists from minority groups are just seeking employment elsewhere. Gibbs counters that this is unlikely, since almost every sector of academia struggles with faculty diversity. Hiring practices are a likelier culprit.
University science departments not only fail to hire underrepresented minorities; they also do a poor job of retaining them; Gibbs’ research shows that even if these departments were to become substantially better at recruiting minority professors, their diversity won’t improve unless those professors get the support they need to stay.
The problem here may be one of accountability. Are these universities holding themselves and their peers accountable for having a diverse workforce that represents the population they serve? If not, how can they do so?