As we previously noted, interest in AI has exploded. Its applications can be found in virtually every industry from marketing to healthcare to finance. On our AI research platform, we have classified the companies in the sector into functional categories. This blog post aims to examine these categories and how they compare with one another through a series of graphics.
Machine Learning Applications Is the Largest AI Category
Let’s start off by looking at the Logo Map for the AI sector. As of January 2018, we have classified 2,029 AI startups into 13 categories which collectively raised $27 billion in funding. The Logo Map highlights the number of companies in each category and a random sampling of these companies.
We have seen what the different categories in AI are and how many companies are within each category. What about their funding and maturity in relation to one another? Let’s look at our Innovation Quadrant to find out.
Most of the AI Categories Are Pioneers
Our Innovation Quadrant for the AI sector divides the categories within the sector into four different quadrants according to their average funding and average age. The Heavyweights are the categories with companies that have reached maturity with significant financing. The Established are those that have reached maturity with less financing. The Disruptors are less mature but with significant financing. The Pioneers are less mature and with earlier stages of financing.
We can see from our Innovation Quadrant above that most of the categories within AI belong in the Pioneers quadrant. The Smart Robots and Recommendation Engines categories have raised more funding and are in the Disruptor quadrant. Speech-to-Speech Translation and Speech Recognition are the most mature categories with significant funding.
We’ve now seen how the AI sector is categorized, and the relative stages of innovation for those categories. How do these categories stack up against one another in a holistic view? Let’s look at the Total Funding and Company Count Graph for AI.
Machine Learning Applications Category Dominates the Sector
The graph below shows the total amount of venture funding and company count in each AI category.
We can see from the graph above that the Machine Learning Applications category dominates all the other AI categories by far, with a total funding of $13 billion and 669 companies. This category is comprised of companies utilizing computer algorithms to automatically optimize some part of their operations. Some example companies in this category include CustomerMatrix, Ayasdi, Drive.ai, and Cylance.
Conclusion: The AI Sector is Thriving
The graphics above indicate that the Machine Learning Applications category stands out against other categories in terms of funding and maturity, while most other AI categories are pioneers and show huge potential for growth and development.
What are your thoughts on this? Let us know in the comments section below.
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