Average and Median Age by Artificial Intelligence Category – Q2 2017

The following graph shows average and median age in the Artificial Intelligence sector. The graphic includes data through April 2017.

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 13 years, and the Speech Recognition category has the highest median age at around 9 years.

We are currently tracking 1743 Artificial Intelligence companies in 13 categories across 70 countries, with a total of $14.5 Billion in funding. Click here to learn more about the full Artificial Intelligence market report.

Artificial Intelligence Market Overview and Innovation Quadrant – Q1 2017

The following post highlights how Venture Scanner categorizes the Artificial Intelligence (AI) startup landscape, and presents our Innovation Quadrant showing how those categories compare to one another. The data for this post is through April 2017.

Artificial Intelligence Q1 2017 Logo Map

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

Artificial Intelligence Innovation Quadrant Q1 2017

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

  • Heavyweights: These categories are comprised of companies that have reached maturity with significant financing.
  • Established: These categories are comprised of companies that have reached maturity with less financing.
  • Disruptors: These categories are comprised of companies that are less mature with significant financing.
  • Pioneers: These categories are comprised of companies that are less mature with earlier stages of financing.

The definitions of the AI categories are as follows

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

We are currently tracking 1,731 Artificial Intelligence (AI) companies in 13 categories across 69 countries, with a total of $13 Billion in funding. Click here to learn more about the full Artificial Intelligence market report.

Artificial Intelligence Startup Market Trends and Insights  – Q1 2017

Here is our Q1 2017 summary report on the Artificial Intelligence startup sector. The following report includes a startup landscape overview, graphical trends and insights, and recent funding and exit events.

We are currently tracking 1,731 Artificial Intelligence (AI) companies in 13 categories across 69 countries, with a total of $13 Billion in funding. Click here to learn more about the full Artificial Intelligence market report.

Where in the world are Artificial Intelligence startups? Q1 2017

The analyses below summarize where Artificial Intelligence (AI) innovations are occurring. The graphic includes data through October 2016.

Artificial Intelligence Q1 2017 Company Count by Country

The above map shows the number of AI companies located in different countries. The United States ranks as the top country with around 740 companies.

Artificial Intelligence Q1 2017 VC Funding by Country

The above map shows the amount of total AI startup venture capital funding in different countries. The United States has the most VC funding at around $6B.

We are currently tracking 1,727 Artificial Intelligence companies in 13 categories across 69 countries, with a total of $13B in funding. Click here to learn more about the full AI landscape report.

Artificial Intelligence Companies Founded by Year – Q1 2017

The following graph shows the founding year distribution in the Artificial Intelligence sector. The graphic includes data through October 2016.

Artificial Intelligence Companies Founded by Year
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 1700 Artificial Intelligence companies in 13 categories across 70 countries, with a total of $13 Billion in funding. Click here to learn more about the full Artificial Intelligence landscape report and database.

Artificial Intelligence Funding Trends – Q1 2017

The following graphs highlight recent trends in Artificial Intelligence (AI) startup funding activity. The graphics include data through October 2016.

AI Funding by Year Q1 2017

The above graph summarizes the total funding raised by AI startups for each year. 2016 is the best year with around $2.5B in funding. 2015 comes in at second place at just under $2B in funding.

AI Vintage Year Funding Q1 2017

The above graph summarizes the total amount of funding raised by AI companies founded in a certain year. Companies founded in 2012 have raised the most funding at around $1.4B.

We are currently tracking 1,675 Artificial Intelligence companies in 13 categories across 70 countries, with a total of $13 Billion in funding. Click here to learn more about the full Artificial Intelligence report and database.

Artificial Intelligence Funding by Round – Q1 2017

The following two graphs summarize the rounds of funding going into the Artificial Intelligence (AI) space. Please note these graphics are made using data through October 2016.

artificial-intelligence-funding-amount-by-round
AI Funding by Round by Year – Funding Amount

The graph above shows the total amount of VC funding broken out by round. In recent years, we’ve seen a general increase in the amount of funding across the board, with growth in the amount of practically all funding stages.

AI Funding by Round by Year - Event Count
AI Funding by Round by Year – Event Count

The graph above shows the total count of funding events broken out by round. Similar to the earlier graph, we’ve seen a general upward trend over these past few years. Earlier stage deals (Seed, Series A) make up a larger share of the total count.

We are currently tracking 1630 Artificial Intelligence companies in 13 categories across 73 countries, with a total of $12.2 Billion in funding. Click here to learn more about the full AI landscape report and database.