We’ve previously highlighted that artificial intelligence (AI) funding has seen explosive growth in recent years. When we take a closer look at the funding trends for each category within AI, we notice two key takeaways:
We’ll highlight these takeaways with some graphics and discussions below.
To start off, let’s review the amount of funding raised this quarter by each category within artificial intelligence.
The above graphic highlights that the Machine Learning Platforms category leads the sector in Q3 funding with $1.9B. The Computer Vision Platforms category follows in second place with $1.6B in Q3 funding.
Machine Learning Platform companies build self-learning algorithms that operate based on existing data. They include predictive data models and software platforms that analyze behavioral data. Some example companies include C3 IoT, DataRobot, Sentient, and AYASDI.
Let’s now investigate how the AI categories’ funding compare with each other historically.
The graph below shows the all-time funding for the various artificial intelligence categories. The Q3 funding and growth rates of these categories are also highlighted.
As the bar graph indicates, the Machine Learning Applications category leads AI in total funding at $19B. This is more than twice the funding of the next category, Machine Learning Platforms at almost $9B.
Machine Learning Application companies utilize self-learning algorithms to optimize vertically-specific business operations. Examples include using machine learning to detect banking fraud or to identify relevant sales leads. Some example companies are Sift Science, SparkCognition, Sumo Logic, and BenevolentAI.
In summary, the two machine learning-related categories are leading the AI sector in funding. Let’s see how the the rest of 2018 shapes up for artificial intelligence!
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