The following two graphs summarize the rounds of funding going into the Artificial Intelligence (AI) space. Please note these graphics are made using data through July 2017.
The graph above shows the total amount of VC funding broken out by round. From 2006 to 2016, we saw a general increase in the overall sector funding, with the total amount peaking in 2016. Earlier stage funding rounds (Series A, B, and C) made up most of the funding amount.
The graph above shows the total count of funding events broken out by round. From 2006 to 2016 we’ve seen a general upward trend that peaked in 2014 and 2015, and then declined slightly in 2016. Earlier stage funding such as Seed, Series A, and Series B events make up the majority of funding event counts.
We are currently tracking 1917 Artificial Intelligence companies in 13 categories across 70 countries, with a total of $21.5 Billion in funding. Click here to learn more about the full Artificial Intelligence market report.
The above analysis summarizes the total number of investment rounds Artificial Intelligence investors participated in, and the number of unique AI companies funded by selected investors. New Enterprise Associates takes the lead with 20 investments, followed by Khosla Ventures and 500 Startups. 500 Startups takes the lead in the number of AI companies backed with Intel Capital and NEA following.
We are currently tracking 1,275 Artificial Intelligence companies in 13 categories across 70 countries, with a total of $7.4B in funding. Click here to learn more about the full AI landscape report and dataset.
We at Venture Scanner are tracking 1104 Artificial Intelligence companies across 13 categories, with a combined funding amount of $6.27 Billion. The 15 visuals below summarize the current state of Artificial Intelligence.
1. Artificial Intelligence Market Overview
We organize Artificial Intelligence into the 13 categories listed below:
Deep Learning/Machine Learning (General): Companies that build computer algorithms that operate based on their learnings from existing data. Examples include predictive data models and software platforms that analyze behavioral data.
Deep Learning/Machine Learning (Applications): Companies that utilize computer algorithms that operate based on existing data in vertically specific use cases. Examples include using machine learning technology to detect banking fraud or to identify the top retail leads.
Natural Language Processing (General): Companies that build algorithms that process human language input and convert it into understandable...