Artificial Intelligence (AI) has seen a lot of buzz in the news recently. As previously noted, funding into the AI sector grew exponentially in the past few years. We’ve also observed that funding amounts and funding counts have shifted to later stages, indicating that the sector is maturing.
We will now examine the different components of AI and how they make up this startup ecosystem. On our AI research platform, we have classified the companies into 13 categories. This blog post illustrates what these categories are and which categories have the most companies. We will also look at how these categories compare with one another in terms of their funding and maturity.
Let’s start off by looking at the Sector Map for the AI sector. As of March 2018, we have classified 2161 AI startups into 13 categories that have raised $32 billion. The Sector Map highlights the number of companies in each category. It also shows a random sampling of companies in each category.
We see that Machine Learning Applications is the largest category with 762 companies. This category contains companies utilizing algorithms that learn and optimize automatically from data. These companies tackle issues in specific use cases such as detecting banking fraud or identifying top retail leads. Some example companies include Sift Science, SparkCognition, Sumo Logic, and BenevolentAI.
We have seen what the different categories making up this sector are and the number of companies in each. What about their funding and maturity in relation to one another? Let’s look at our Innovation Quadrant to find out.
Our Innovation Quadrant divides the AI categories into four different quadrants.
We see that the Pioneers quadrant has the most AI categories with 8. The Pioneer categories are in the earlier stages of funding and maturity. Speech Translation and Speech Recognition landed in the Established quadrant. Both of these speech technology-related categories have reached maturity yet with less financing.
Smart Robots, Recommendation Engines, and Machine Learning Platforms are in the Disruptors quadrant for acquiring significant financings at a young age.
We’ve now seen the AI categories and their relative stages of innovation. How do these categories stack up against one another? Let’s look at the Total Funding and Company Count Graph.
The graph below shows the total amount of venture funding and company count in each category.
As we’ve seen in the Sector Map and the above graphic as well, the Machine Learning Applications category leads the AI sector with 762 companies. The above graphic also shows that Machine Learning Applications leads in funding with almost $16 billion. Some of the best-funded companies in this category include Toutiao ($3B), Argo AI ($1B), Indigo ($359M), and Wecash ($328M).
The funding in Machine Learning Applications is more than 267% of that in the next category, Machine Learning Platforms. These two categories are related yet have different functions. Machine Learning Applications companies apply self-learning algorithms to optimize specific business operations. Machine Learning Platforms companies build these self-learning algorithms or their underlying infrastructure.
From the above analysis, we can see that Machine Learning Applications dominates the AI sector in total funding and company count. It’ll be interesting to see how the AI landscape will change and develop throughout the rest of 2018.
What are your thoughts on this? Let us know in the comments section below.
To learn more about our complete Artificial Intelligence report and research platform, visit us at www.venturescanner.com or contact us at [email protected].
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