Machine Learning-Related Categories Lead Artificial Intelligence Funding

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:

  • The Machine Learning Platforms category leads the sector in Q3 funding
  • The Machine Learning Applications category leads the sector in all-time funding

We’ll highlight these takeaways with some graphics and discussions below.

The Machine Learning Platforms Category Leads AI In Q3 Funding

To start off, let’s review the amount of funding raised this quarter by each category within artificial intelligence.

Artificial Intelligence Latest Quarter Category Funding
Artificial Intelligence Latest Quarter Category Funding

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 Machine Learning Applications Category Leads the Sector in All-Time Funding

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.

Artificial Intelligence Total Category Funding
Artificial Intelligence Total Category Funding

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!

To learn more about our complete artificial intelligence report and research platform, visit us at www.venturescanner.com or contact us at info@venturescanner.com.

Artificial Intelligence Sector Overview – Q3 2018

The artificial intelligence sector has experienced explosive funding growth in recent years. This blog post examines the different components of the AI sector and how they make up this startup ecosystem. We will illustrate what the categories of innovation are and which categories have the most companies. We will also compare the categories in terms of their funding and maturity.

Machine Learning Applications Is the Largest Artificial Intelligence Category

Let’s start off by looking at the Sector Map. We have classified 2,316 artificial intelligence startups into 13 categories that have raised $45 billion. The Sector Map highlights the number of companies in each category. It also shows a random sampling of companies in each category.

Artificial Intelligence Sector Map
Artificial Intelligence Sector Map

We see that Machine Learning Applications is the largest category with 866 companies. These companies utilize self-learning algorithms to optimize business operations in vertically specific use cases. 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.

Let’s now look at our Innovation Quadrant to find out the funding and maturity of these categories in relation to one another.

The Pioneers Quadrant Has the Most Artificial Intelligence Categories

Our Innovation Quadrant divides the artificial intelligence categories into four different quadrants.

Artificial Intelligence Innovation Quadrant
Artificial Intelligence Innovation Quadrant

We see that the Pioneers quadrant has the most artificial intelligence categories with 8. These categories are in the earlier stages of funding and maturity. The Disruptors quadrant has 4 categories that have acquired significant financing at a young age. The Established quadrant has Speech to Speech Translation as its one category. This category has reached maturity with less-than-average financing.

We’ve analyzed the artificial intelligence categories and their relative stages of innovation. Let’s now look at how they stack up against one another in terms of their total funding versus company counts.

Machine Learning Application Startups Have the Most Funding

The graph below shows the total amount of venture funding and company count in each category.

Artificial Intelligence Total Funding by Category
Artificial Intelligence Total Funding by Category

As the above graphic implies, Machine Learning Applications also leads the sector in total funding with $19 billion. Its funding is more than twice the funding of the next category, Machine Learning Platforms with $9 billion. These two categories are related yet have different functions. Machine Learning Application companies apply self-learning algorithms to optimize specific business operations. Machine Learning Platform companies build these self-learning algorithms or their underlying infrastructure.

Conclusion: The Machine Learning Applications Category Leads Artificial Intelligence

As the analysis above demonstrates, the Machine Learning Applications category leads in total companies and funding. We’ll see if any of the other categories catch up during the rest of 2018.

To learn more about our complete artificial intelligence report and research platform, visit us at www.venturescanner.com or contact us at info@venturescanner.com.

Artificial Intelligence Startup Highlights  – Q3 2018

Here is our Q3 2018 summary report on the artificial intelligence startup sector. The following report includes a sector overview and recent activity.

To learn more about our complete artificial intelligence report and research platform, visit us at www.venturescanner.com or contact info@venturescanner.com.

Mid-Year Artificial Intelligence Exits Analysis

In Q2, AI had the second highest exit activity on record. Now armed with the data through June 2018, we’re performing a mid-year status check on how this year is shaping up.

Based on analysis on our AI research platform, we see that exit activity in the first half of 2018 is slightly down from 2017.

2018 Mid-Year AI Exit Activity Lower Than 2017 But Higher Than 2016

Let’s take a closer look at the number of AI exit events by year.

artificial-intelligence-exits-by-quarter
Artificial Intelligence Exits By Quarter

The above graphic shows 32 exits in the first half of 2018. For the past three years, Q3 and Q4 accounted for 46% of total exit events on average. If that trend holds, 2018 exits finish the year slightly lower than 2017, but higher than 2016. We’ll see if the second half of the year changes this trend!

To learn more about our complete artificial intelligence report and research platform, visit us at www.venturescanner.com or contact us at info@venturescanner.com.

Artificial Intelligence Startup Highlights  – Q2 2018

Here is our Q2 2018 summary report on the artificial intelligence startup sector. The following report includes a sector overview and recent activity.

To learn more about our complete artificial intelligence report and research platform, visit us at www.venturescanner.com or contact info@venturescanner.com.

Machine Learning Categories Lead AI Funding

Last quarter we observed that the artificial intelligence sector is maturing. This quarter we are conducting a deeper analysis on our AI research platform to examine funding by category. Our analysis shows two important observations:

  • Machine Learning Platforms and Computer Vision Platforms lead the sector in Q2 funding
  • Machine Learning Applications dominates the sector in all-time funding

We’ll explain these observations with some graphics and discussions below.

Machine Learning Platforms and Computer Vision Platforms Lead AI in Q2 Funding

To start off, let’s scrutinize the AI funding by category in Q2.

Artificial Intelligence Current Quarter Category Funding
Artificial Intelligence Current Quarter Category Funding

The above graphic shows that both Machine Learning Platforms and Computer Vision Platforms lead the sector in Q2 funding with $1.5B each. Machine Learning Applications and Smart Robots follow in the second and third places with $1.4B and $1B, respectively. It’s also noteworthy that there is a steep drop-off after Smart Robots, as its funding is 3.4 times higher than the next category, Speech Recognition.

So we’ve witnessed how different AI categories stack up in their Q2 funding. But how do these categories’ funding compare with each other historically? Let’s investigate that in the next section.

Machine Learning Applications Dominates AI in All-Time Funding

The graph below shows the all-time funding for different AI categories. The quarterly funding and growth rates of these categories are also highlighted.

Artificial Intelligence Total Category Funding
Artificial Intelligence Total Category Funding

The bar graph indicates Machine Learning Applications completely dominates the sector with $17B in total funding. This is more than twice the funding in the next category, Machine Learning Platforms.

In addition, the line graph demonstrates that Computer Vision Platforms saw the highest growth rate in Q2 at 48%.

Conclusion: Machine Learning Categories Are At the Forefront of AI Funding

In summary, we have analyzed the AI funding amounts in different categories. We’ve discovered that Machine Learning Platforms and Computer Vision Platforms lead the sector in Q2 funding. In addition, Machine Learning Applications dominates AI in all-time funding. It’ll be interesting to see if any other AI categories will catch up in the rest of 2018.

To learn more about our complete artificial intelligence report and research platform, visit us at www.venturescanner.com or contact us at info@venturescanner.com.

Deep Learning Applications and Computer Vision Platforms Lead AI Exit Activity

Last quarter we reviewed artificial intelligence exit trends and saw strong growth. We now dig in one level deeper on our AI report and research platform to examine exits by category. We conclude that Deep Learning Applications and Computer Vision Platforms are at the forefront of AI exit activity.

This conclusion was derived from two takeaways:

  • The Deep Learning Applications category leads in the number of exits
  • The Computer Vision Platforms category leads in acquisition amount

We’ll illustrate these takeaways with some graphics that show AI exit activity by category.

To help set the stage, the graphic below shows AI exit activity over time. As you can see, the sector’s exit activity experienced strong growth over the past few years.

Artificial Intelligence Exits by Quarter
Artificial Intelligence Exits by Quarter

Deep Learning Applications Leads AI in the Number of Exits

Let’s examine the exit events for each AI category. Exit events include both acquisitions and IPOs. The below graph highlights the number of AI exit events by category.

Artificial Intelligence Exits by Category
Artificial Intelligence Exits by Category

This graph shows that the Deep Learning Applications category leads the sector with 71 exit events. Natural Language Processing comes next with 46 exit events.

Deep Learning Applications includes companies that utilize computer algorithms to optimize operations in vertically specific use cases. Examples include using deep learning technology to detect banking fraud or to identify relevant sales leads. Some example companies are Sift Science, SparkCognition, Sumo Logic, and BenevolentAI.

Let’s now see how AI categories compare with one another by acquisition amount.

Computer Vision Platforms Leads AI in Acquisition Amount

The graph below shows the acquisition amounts in different AI categories.

Artificial Intelligence Acquisition Amounts by Category
Artificial Intelligence Acquisition Amounts by Category

We can see from this graph that the Computer Vision Platforms category leads all the other AI categories by far. The total acquisition amount in this category is around $16 billion. Computer Vision Platform companies process images to algorithmically derive information from them and recognize objects. Some example companies in this category include Cortica, Blippar, Kairos, and Clarifai.

Computer Vision Platforms has seen some large acquisitions in recent years. Mobileye was acquired by Intel in March 2017 for around $15 billion. Movidius was acquired by Intel in September 2016 for $400 million. Magic Pony Technology was acquired by Twitter in June 2016 for $150 million.

The acquisition amount in Computer Vision Platforms represents 72% of all AI acquisition activity. It’s noteworthy that its acquisition amount is more than ten times the next category, Deep Learning Platforms. Additionally, Computer Vision Platforms’ acquisition amount is highly concentrated, with 15/16 of the amount coming from the $15 billion Mobileye acquisition.

Conclusion: Deep Learning Applications and Computer Vision Platforms Lead AI Exit Activity

In summary, we have examined AI exit activity by the number of exit events and acquisition amount. The Deep Learning Applications category leads the sector in the number of exit events. The Computer Vision Platforms category leads in acquisition amount. It will be interesting to see which other categories take the lead in AI exit activity in the rest of 2018.

To learn more about our complete artificial intelligence report and research platform, visit us at www.venturescanner.com or contact us at info@venturescanner.com.