AI & Big Data
One can intuitively surmise artificial intelligence (AI) is today’s hot commodity, gaining traction in businesses, academia and government in recent years. Now, there is data — all in one place — that documents growth across many indicators, including startups, venture capital, job openings and academic programs. These bellwethers were captured in the AI Index, produced under the auspices of was conceived within Stanford University’s Human-Centered AI Institute and the One Hundred Year Study on AI (AI100).
One key measure of AI development is startups and venture capital funding. From January 2015 to January 2018, active AI startups increased 2.1x, while all active startups increased 1.3x, the report states. “For the most part, growth in all active startups has remained relatively steady, while the number of AI startups has seen exponential growth,” the report’s authors add. The trickle of venture capital into AI startups, another bellwether, also turned into a torrent. VC funding for AI startups in the US increased 4.5x from 2013 to 2017. Meanwhile, VC funding for all active startups increased 2.08x.
Another key measure, job openings, accelerated in AI. While machine learning is the largest skill cited as a requirement, deep learning is growing at the fastest rate — from 2015 to 2017 the number of job openings requiring deep learning increased 35x, the report’s authors state.
The AI Index also cited McKinsey data that demonstrated the types of AI solutions being deployed in organizations. In North American organizations, the main forms of AI include the following:
- Robotic process automation 23%
- Machine learning 23%
- Conversational interfaces 20%
- Computer vision 20%
- Natural language text understanding 17%
- Natural language speech understanding 16%
- Natural language generation 11%
Another interesting bellwether, downloads from AI-oriented open source solutions, is way up. The number of robot operating system (ROS) binary packages downloaded from ROS.org, an open source software stack for robotics. Since 2014, total downloads and unique downloads have increased by 352% and 567%, respectively. “This represents an increased interest in both robotics and the use of robot systems,” the report’s authors conclude. “Because the number of unique downloads is growing at a faster rate than the total number of downloads, we can infer that there are more ROS users, not just that ROS is more frequently used.”
Finally, another telling AI bellwether is AI course enrollment. The percentage of undergraduate students enrolled in introductory AI and machine learning courses has grown. While introductory AI courses tend to have a slightly larger proportion of undergraduate students than introductory machine learning courses (an average of 5.2% in AI versus 4.4% in ML), the number of undergraduate students in introductory machine learning courses are growing at a faster rate. Introductory AI enrollment was 3.4x larger in 2017 than it was in 2012, while introductory machine learning course enrollment was 5x larger than it was in 2012. “This depicts the growing importance of machine learning as a subfield of AI,” the report states.