Average and Median Age by Artificial Intelligence Category – Q1 2017

The following graph shows average and median age in the Artificial Intelligence sector. The graphic includes data through October 2016.

Average and Median Age by AI Category
Average and Median Age by AI Category
The above graph summarizes the average and median age of companies in each Artificial Intelligence category. The Speech to Speech Translation category has the highest average age at around 14 years, and the Speech Recognition category has the highest median age at around 10 years.

We are currently tracking 1539 Artificial Intelligence companies in 13 categories across 71 countries, with a total of $9.9 Billion in funding. Click here to learn more about the full AI landscape report and database.

Artificial Intelligence Companies Founded by Year – Q4 2016

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Artificial Intelligence Companies Founded by Year
The above graph summarizes the number of Artificial Intelligence companies founded in a certain year. 2013 ranks at the top with around 188 companies founded in that year alone. 2014 is the runner-up with 173 companies founded in that year.

We are currently tracking 1503 Artificial Intelligence companies in 13 categories across 73 countries, with a total of $9.3 Billion in funding. Click here to learn more about the full Artificial Intelligence landscape report and database.

Artificial Intelligence Startup Landscape Trends and Insights – Q4 2016

A report providing an overview of the Artificial Intelligence startup landscape, graphical trends and insights, and recent funding and exit events. Click here to see this entire deck on our new blog.

We are currently tracking 1500 Artificial Intelligence companies in 13 categories across 73 countries, with a total of $9.1 Billion in funding. Click here to learn more about the full Artificial Intelligence landscape report and database.

Artificial Intelligence Category Innovation Quadrant – Q4

Artificial Intelligence Category Innovation Quadrant
Artificial Intelligence Category Innovation Quadrant
Our Innovation Quadrant provides a snapshot of the average funding and average age for the different Artificial Intelligence categories and how they compare with one another.

  • Heavyweights: Categories with high average funding and high average age. These categories are comprised of companies that have reached maturity with significant financing.
  • Established: Categories with low average funding and high average age. These categories are comprised of companies that have reached maturity with less financing.
  • Disruptors: Categories with high average funding and low average age. These categories are comprised of companies that are less mature with significant financing.
  • Pioneers: Categories with low average funding and low average age. These categories are comprised of companies that are less mature with earlier stages of financing.
The definitions of the Artificial Intelligence categories represented in the above Innovation Quadrant are as follows:

Deep Learning/Machine Learning (Platforms): 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: Companies that build algorithms that process human language input and convert it into understandable representations. Examples include automated narrative generation and mining text into data.

Speech Recognition: Companies that process sound clips of human speech, identify the exact words, and derive meaning from them. Examples include software that detects voice commands and translates them into actionable data.

Computer Vision/Image Recognition (Platforms): Companies that build technology that process and analyze images to derive information and recognize objects from them. Examples include visual search platforms and image tagging APIs for developers.

Computer Vision/Image Recognition (Applications): Companies that utilize technology that process images in vertically specific use cases. Examples include software that recognizes faces or enables one to search for a retail item by taking a picture.

Gesture Control: Companies that enable one to interact and communicate with computers through their gestures. Examples include software that enables one to control video game avatars through body motion, or to operate computers and television through hand gestures alone.

Virtual Assistants: Software agents that perform everyday tasks and services for an individual based on feedback and commands. Examples include customer service agents on websites and personal assistant apps that help one with managing calendar events, etc.

Smart Robots: Robots that can learn from their experience and act autonomously based on the conditions of their environment. Examples include home robots that could react to people’s emotions in their interactions and retail robots that help customers find items in stores.

Personalized Recommendation Engines: Software that predicts the preferences and interests of users for items such as movies or restaurants, and delivers personalized recommendations to them. Examples include music recommendation apps and restaurant recommendation websites that deliver their recommendations based on one’s past selections.

Context Aware Computing: Software that automatically becomes aware of its environment and its context of use, such as location, orientation, lighting, and adapts its behavior accordingly. Examples include apps that light up when detecting darkness in the environment.

Speech to Speech Translation: Software which recognizes and translates human speech in one language into another language automatically and instantly. Examples include software that translates video chats and webinars into multiple languages automatically and in real-time.

Video Automatic Content Recognition: Software that compares a sampling of video content with a source content file to identify the content through its unique characteristics. Examples include software that detects copyrighted material in user-uploaded videos by comparing them against copyrighted material.

We are currently tracking 1498 Artificial Intelligence companies in 13 categories across 73 countries, with a total of $9 Billion in funding. Click here to learn more about the full Artificial Intelligence landscape report and database.

Artificial Intelligence Market Overview – Q4 2016

Artificial Intelligence Sector Map
Artificial Intelligence Sector Map
The above sector map organizes the Artificial Intelligence sector into 13 categories and shows a sampling of companies in each category.

Deep Learning/Machine Learning (Platforms): 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: Companies that build algorithms that process human language input and convert it into understandable representations. Examples include automated narrative generation and mining text into data.

Speech Recognition: Companies that process sound clips of human speech, identify the exact words, and derive meaning from them. Examples include software that detects voice commands and translates them into actionable data.

Computer Vision/Image Recognition (Platforms): Companies that build technology that process and analyze images to derive information and recognize objects from them. Examples include visual search platforms and image tagging APIs for developers.

Computer Vision/Image Recognition (Applications): Companies that utilize technology that process images in vertically specific use cases. Examples include software that recognizes faces or enables one to search for a retail item by taking a picture.

Gesture Control: Companies that enable one to interact and communicate with computers through their gestures. Examples include software that enables one to control video game avatars through body motion, or to operate computers and television through hand gestures alone.

Virtual Assistants: Software agents that perform everyday tasks and services for an individual based on feedback and commands. Examples include customer service agents on websites and personal assistant apps that help one with managing calendar events, etc.

Smart Robots: Robots that can learn from their experience and act autonomously based on the conditions of their environment. Examples include home robots that could react to people’s emotions in their interactions and retail robots that help customers find items in stores.

Personalized Recommendation Engines: Software that predicts the preferences and interests of users for items such as movies or restaurants, and delivers personalized recommendations to them. Examples include music recommendation apps and restaurant recommendation websites that deliver their recommendations based on one’s past selections.

Context Aware Computing: Software that automatically becomes aware of its environment and its context of use, such as location, orientation, lighting, and adapts its behavior accordingly. Examples include apps that light up when detecting darkness in the environment.

Speech to Speech Translation: Software which recognizes and translates human speech in one language into another language automatically and instantly. Examples include software that translates video chats and webinars into multiple languages automatically and in real-time.

Video Automatic Content Recognition: Software that compares a sampling of video content with a source content file to identify the content through its unique characteristics. Examples include software that detects copyrighted material in user-uploaded videos by comparing them against copyrighted material.

We are currently tracking 1464 Artificial Intelligence companies in 13 categories across 73 countries, with a total of $8.5 Billion in funding. Click here to learn more about the full Artificial Intelligence landscape report and database.

Artificial Intelligence Q1 Update in 15 Visuals

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We at Venture Scanner are tracking 957 Artificial Intelligence companies across 13 categories, with a combined funding amount of $4.8 Billion. The 15 visuals below summarize the current state of Artificial Intelligence.

1. Artificial Intelligence Market Overview

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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 representations. Examples include automated narrative generation and mining text into data.

Natural Language Processing (Speech Recognition): Companies that process sound clips of human speech, identify the exact words, and derive meaning from them. Examples include software that detects voice commands and translates them into actionable data.

Computer Vision/Image Recognition (General): Companies that build technology that process and analyze images to derive information and recognize objects from them. Examples include visual search platforms and image tagging APIs for developers.

Computer Vision/Image Recognition (Applications): Companies that utilize technology that process images in vertically specific use cases. Examples include software that recognizes faces or enables one to search for a retail item by taking a picture.

Gesture Control: Companies that enable one to interact and communicate with computers through their gestures. Examples include software that enables one to control video game avatars through body motion, or to operate computers and television through hand gestures alone.

Virtual Personal Assistants: Software agents that perform everyday tasks and services for an individual based on feedback and commands. Examples include customer service agents on websites and personal assistant apps that help one with managing calendar events, etc.

Smart Robots: Robots that can learn from their experience and act autonomously based on the conditions of their environment. Examples include home robots that could react to people’s emotions in their interactions and retail robots that help customers find items in stores.

Recommendation Engines and Collaborative Filtering: Software that predicts the preferences and interests of users for items such as movies or restaurants, and delivers personalized recommendations to them. Examples include music recommendation apps and restaurant recommendation websites that deliver their recommendations based on one’s past selections.

Context Aware Computing: Software that automatically becomes aware of its environment and its context of use, such as location, orientation, lighting and adapts its behavior accordingly. Examples include apps that light up when detecting darkness in the environment.

Speech to Speech Translation: Software which recognizes and translates human speech in one language into another language automatically and instantly. Examples include software that translates video chats and webinars into multiple languages automatically and in real-time.

Video Automatic Content Recognition: Software that compares a sampling of video content with a source content file to identify the content through its unique characteristics. Examples include software that detects copyrighted material in user-uploaded videos by comparing them against copyrighted material.

2. Company Count by Artificial Intelligence Category

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The above graph summarizes the number of companies in each Artificial Intelligence category to show which categories are dominating the current market. The Machine Learning (Applications) category is leading the way with 263 companies, followed by the Natural Language Processing category with 154 companies.

3. Funding by Artificial Intelligence Category

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The above graph summarizes the total amount of funding in each Artificial Intelligence category. The Machine Learning (Applications) category is leading the market with over $2B in total funding, which is 3X the total funding of the second highest category, Natural Language Processing with $662M.

4. Venture Investing in Artificial Intelligence

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The above graph compares the total venture funding in each Artificial Intelligence category to the number of companies in the category. The Machine Learning (Applications) category is leading in both stats with over $2B in funding and 263 companies. Natural Language Processing is the runner-up in both stats with $662M in funding and 154 companies.

5. Artificial Intelligence Total Funding by Year

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The above graph summarizes the total funding raised by Artificial Intelligence companies each year. 2015 was the best year in Artificial Intelligence funding with almost $1.2B raised, with 2014 in the second place with a total of $1B raised.

6. Average Funding by Artificial Intelligence Category

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The above graph summarizes the average company funding in each Artificial Intelligence category. The Machine Learning (Applications) category leads the market with $17M in funding per company, followed by the Smart Robots and Gesture Control categories each with about $14M in funding per company.

7. Average Age by Artificial Intelligence Category

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The above graph summarizes the average age of companies in each Artificial Intelligence category. Speech to Speech Translation ranks as the most mature Artificial Intelligence category with an average age of 13 years per company, which is more than 1.5X the average age of the three runner-up categories (Gesture Control, Video Content Recognition, and Speech Recognition, each with an average age of about 8 years per company).

8. Median Age by Artificial Intelligence Category

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The above graph summarizes the median age of companies in each Artificial Intelligence category. Video Content Recognition ranks as the most mature Artificial Intelligence category with a median age of 7.8 years per company, followed by Speech to Speech Translation with a median age of 7.2 years per company.

9. Artificial Intelligence Company Count by Country

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The above map shows the number of Artificial Intelligence companies located in different countries. The United States ranks as the top country with 499 Artificial Intelligence companies, with the United Kingdom at a distant second with 60.

10. Artificial Intelligence VC Funding by Country

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The above map shows the amount of Artificial Intelligence venture capital funding in different countries. The United States has the most Artificial Intelligence VC funding at $4.2B, followed by Switzerland at $234M.

11. Artificial Intelligence Companies Founded by Year

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The above graph summarizes the number of Artificial Intelligence companies founded in a certain year. 2013 ranks as the top year with 118 Artificial Intelligence companies founded, followed by 2012 with 103 companies founded.

12. Artificial Intelligence Funding by Vintage Year

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The above graph summarizes the total amount of funding raised by the Artificial Intelligence companies founded in a certain year. Artificial Intelligence companies founded in 2010 have raised the most funding at $566M, with those founded in 2012 at a close second with $556M.

13. Artificial Intelligence Headcount Distribution

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The above graph summarizes the percentage of Artificial Intelligence companies with a certain employee headcount range. Companies with 1–50 employees make up almost 90% of the market.

14. Number of Artificial Intelligence Investments by Selected Investors

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The above graph summarizes the total number of investment rounds Artificial Intelligence investors participated in. Accel outperform all of its peers, having made 23 investments into Artificial Intelligence companies. New Enterprise Associates is the runner-up with 18 investments.

15. Number of Artificial Intelligence Companies Backed by Selected Investors

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The above graph summarizes the number of unique Artificial Intelligence companies funded by selected investors. Accel takes the top spot by having invested in a total of 20 unique Artificial Intelligence companies, which is almost 1.5X the number of companies invested by the runner-up, Intel Capital (14 companies).

As Artificial Intelligence continues to grow, so too will its moving parts. We hope this post provides some big picture clarity on this booming industry.

Note: If you missed it, you can also read our FinTech Q1 Update in 15 Visuals.

Venture Scanner is your platform for startup landscapes, data, and research. If you would like access to the full Artificial Intelligence landscape and dataset, visit www.venturescanner.com/artificial-intelligence or reach out to info@venturescanner.com.

Number of Investments by Top Artificial Intelligence Investors

The following infographic summarizes the number of companies invested by the top Artificial Intelligence investors. You could see that Intel Capital is in the lead by having invested in 16 companies, followed by Techstars which invested in 14 companies. At Venture Scanner, we are currently tracking over 897 Artificial Intelligence companies in 13 categories across 66 countries, with a total of $3.98 Billion in funding. To see the full list of 897 Artificial Intelligence companies, contact us using the form on www.venturescanner.com.

Artificial Intelligence investor count

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