How many researchers are expanding the capabilities of artificial intelligence (A.I.) systems?
According to new data from Canadian startup Element AI, there are roughly 22,000 PhD-level researchers in the artificial intelligence field. “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,” read the Bloomberg article breaking down the data. “In addition, to make the cut, people needed coding skills in programming languages such as Python, TensorFlow or Theano.”
That pool expanded to 90,000 researchers when Element AI incorporated PhDs earned before 2015, as well. As Bloomberg points out, that number is well below the 200,000-300,000 researchers estimated by Chinese internet firm Tencent in December 2017.
Which number is correct? It’s almost immaterial: If A.I. is truly the next big technological revolution, the world is going to need far more than a few hundred thousand researchers to carve the path forward. The salaries for those with the rarest A.I.-related skills are already stratospheric, thanks to demand; at the Markets Media’s Summer Trading event in 2017, for example, Tom Eck, CTO of industry platforms at IBM, said that top-tier A.I. researchers make as much as NFL quarterbacks.
“Right now, A.I. is an elitist sport—there are very few people who know how to practice it,” Eck added.
The most recent Dice Salary survey also suggests that some of the skills undergirding A.I. are quite valuable. For example, those tech professionals who specialize in Python can expect to pull down $103,191 per year, on average. Highly esoteric skillsets can likewise command six-figure salaries (if you’re curious about how much your own skills are worth, check out Dice’s Salary Predictor).
For those who don’t have the time or funds to earn a PhD, but who nonetheless want to participate in this evolution, check out some of the latest toolkits that allow you to experiment with computer vision and other A.I. technologies. For example, Google recently unveiled Cloud AutoML, a platform for creating customized machine-learning models. Facebook’s research division has also open-sourced tools such as Detectron, its computer-vision system.
Even if you’re not a researcher, in other words, you can still plunge into A.I., provided you have the time and inclination. The future awaits.