These are some key graphs and data elements that I’ve selected from the AI Index 2018.
“The AI Index is an effort to track, collate, distill and visualize data relating to artificial intelligence.
It aspires to be a comprehensive resource of data and analysis for policymakers, researchers, executives, journalists and the general public to develop intuitions about the complex field of AI.
The graph below shows the number of active venture-backed U.S. private startups in a given year. The blue line (left-axis) shows AI startups only, while the grey line (right-axis) shows all venture-backed startups, including AI startups. The graph plots the total number of startups in January of each year. Excluding instances where startups are removed from the data set (see appendix for details), the number of startups is cumulative year-over-year.
From January 2015 to January 2018, active AI startups increased 2.1x, while all active startups increased 1.3x. For the most part, growth in all active startups has remained relatively steady, while the number of AI startups has seen exponential growth.
The graphs below and on the following page show the results of a McKinsey & Company survey of 2,135 respondents, each answering on behalf of their organization. The graph displays the percent of respondents whose organizations have embedded AI capabilities in at least one function or business unit. Respondents can select multiple AI capabilities. See data for Asia Pacific, India, Middle East and North Africa, and Latin America on the next page.
While some regions adopt certain capabilities more heavily than others, AI capabilities are adopted relatively equally across regions. We look forward to tracking how company adoption changes over time.
The graphs below and on the following page show the results of a McKinsey & Company survey of 2,135 respondents, each answering on behalf of their organization. The graph shows the percent of respondents whose organizations have piloted or embedded AI capabilities within a particular business function. Respondents can select multiple functions. See data for Manufacturing, Supply-chain management, and Risk on the next page.
Organizations tend to incorporate AI capabilities in functions that provide the most value within their industry. For example, Financial services has heavily incorporated AI in Risk, while Automotive has done so in Manufacturing, and Retail has done so in Marketing / sales. This implies that the rate of AI progress for specific applications (e.g., Manufacturing) will likely correlate to uptake in industries where that specialization is particularly important.”