Artificial Intelligence Report Highlights – Q2 2019

Here is our Q2 2019 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 dynamic report, visit us at www.venturescanner.com or contact us at info@venturescanner.com.

Explained – AI’s Explosive Funding Growth

Artificial Intelligence (AI) has become one of the hottest sectors in recent years, with its technology promising to revolutionize and automate every industry imaginable. We have been covering this trend, showing a massive increase in AI startup funding. As you can see in the graphic below, AI funding more than doubled from 2016 to 2017. Its funding more than tripled from 2016 to 2018.

AI Funding Over Time
AI Funding Over Time

This phenomenon then begs the question – what caused this explosive growth in AI funding? This explainer blog will help you understand the contributing forces behind the growth. It can be attributed to three factors:

  • The geopolitical battle between the US and China
  • The AI sector maturing over time
  • Specific AI functions gaining strong traction

US and China’s Geopolitical Battle for AI Dominance

The first factor contributing to the staggering AI funding jump is the geopolitical battle for AI dominance between the US and China. A plethora of news outlets, such as the Wall Street Journal and Forbes, are reporting on the technology battles between the US and China, especially around AI. Experts in those articles have echoed the sentiment that AI is expected to power the development of future business and national security strategies. Many also voiced the opinion that China may supplant the US as a technology leader as it makes significant headway in AI.

The data from our AI dynamic report reinforces the opinions expressed in the above publications. As you can see in the chart below, the lion’s share of AI startup funding is happening in the US and China, with China overtaking the US in 2018. The US jumped from $3B in 2016 to almost $8B in 2018, while China demonstrated even stronger growth, increasing 8-fold from $1B in 2016 to over $8B in 2018.

It’s also noteworthy that China’s AI funding in 2018 comprised 44% of the entire world’s AI funding, whereas the US’s AI funding comprised 41%. Clearly these two geographies are driving the AI revolution, with China now in the lead.

AI US-China Funding Comparison
AI US-China Funding Comparison

We should note a risk factor around the above conclusion. Some news outlets are reporting that 50% to 80% of Chinese companies exaggerate their funding by a factor of 2 to 10 to attract further investments and intimidate competition. While the accuracy of such claims was not further verified, they would certainly encourage us to treat the above conclusion regarding the US-China AI battle with some level of caution and scrutiny.

AI Sector Maturing As Funding Moves to Later Stages

The second contributing factor is the gradual maturation of the AI sector as funding events move to later stages. As demonstrated in the graph below, AI seed financings decreased from almost 70% of funding events in 2013 to below 30% in 2018. In contrast, Series B to Late Stage financings in AI have steadily increased from 15% of total funding events to 35%.

AI Funding Round Count Percentages
AI Funding Round Count Percentages

This continuous rise in mid to late-stage funding events indicates that the sector is maturing over time. More mature companies require larger funding amounts, which is consistent with the growth in overall funding that we are witnessing. Thus, the explosive funding increase from 2016 to 2018 can be partially explained by the AI sector’s gradual emergence as an established cornerstone in the modern technology landscape.

Specific AI Functions Seeing Massive Funding Increases

Venture Scanner organizes chaotic startup landscapes into understandable groupings. For AI, we have broken the sector down into 13 categories. These categories are defined by a specific technology function, such as Machine Learning or Natural Language Processing. Analyzing the AI categories has revealed the third contributing factor, that a small set of AI functional categories are behind the explosive growth seen in the above charts.

As demonstrated in the graph below, Machine Learning (ML) related categories have seen massive increases in funding, from around $4B in 2016 to around $15B in 2018. Computer Vision (CV) related categories also grew rapidly, from around $1B in 2016 to around $8B in 2018. Other AI categories, such as Smart Robots, NLP, and Recommendation Engines, also experienced large funding growth in 2017 and 2018.

AI Funding Over Time By Category
AI Funding Over Time By Category

Machine Learning Platform companies build algorithms that operate based on their learnings from existing data, while Machine Learning Application companies apply these self-learning algorithms to optimize specific business operations. By the same token, Computer Vision Platform companies build technology that analyzes images to derive information and recognize objects, while Computer Vision Application companies utilize this image processing technology in vertically specific use cases.

The fact that Machine Learning (ML) related categories and Computer Vision (CV) related categories are fueling the AI funding growth is consistent with our analysis that the AI sector is maturing as a whole. As specific AI technologies like ML and CV advance, more venture funding is needed to accelerate their development and adoption.

Conclusion: Geopolitical Battles, Sector Maturation, and Technology Advancement Contributed to AI Funding Increase

In summary, our analysis concludes that the top three contributing factors for the funding growth in AI are the geopolitical battle between the US and China, the sector maturing with its funding moving to later stages, and certain AI technology categories gaining significant traction.

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

The State of Artificial Intelligence in Six Visuals

We cover many emerging markets in the startup ecosystem. Previously, we published posts that summarized Financial Technology, Internet of ThingsBitcoin, and MarTech in six visuals. This week, we do the same with Artificial Intelligence (AI). At this time, we are tracking 855 AI companies across 13 categories, with a combined funding amount of $8.75 billion. To see the full list of 855 Artificial Intelligence startups, contact us using the form on www.venturescanner.com.

The six Artificial Intelligence visuals below help make sense of this dynamic market:

  1. Market Overview: Breakdown of Artificial Intelligence startup list into categories.
  2. Number of Companies Per Category: Bar graph summarizing the number of companies in each Artificial Intelligence category.
  3. Average Funding By Category: Bar graph summarizing average company funding per Artificial Intelligence category.
  4. Venture Funding in Artificial Intelligence: Graph comparing total venture funding in Artificial Intelligence to the number of companies in each category.
  5. Global Breakdown of Artificial Intelligence: Heat map indicating where Artificial Intelligence companies exist.
  6. Median Age of Artificial Intelligence Categories: Bar graph of each Artificial Intelligence category by median age.

1. Artificial Intelligence Market Overview

ai1

Deep Learning/Machine Learning Applications: Machine learning is the technology of computer algorithms that operate based on its learnings from existing data. Deep learning is a subset of machine learning that focuses on deeply layered neural networks. The following companies utilize deep learning/machine learning technology in a specific way or use-case in their products.

Computer Vision/Image Recognition: Computer vision is the method of processing and analyzing images to understand and produce information from them. Image recognition is the process of scanning images to identify objects and faces. The following companies either build computer vision/image recognition technology or utilize it as the core offering in their products.

Deep Learning/Machine Learning (General): Machine learning is the technology of computer algorithms that operate based on its learning from existing data. Deep learning is a subset of machine learning that focuses on deeply layered neural networks. The following companies either build deep learning/machine learning technology or utilize it as the core offering of their products.

Natural Language Processing: Natural language processing is the method through which computers process human language input and convert into understandable representations to derive meaning from them. The following companies either build natural language processing technology or utilize it as the core offering in their products (excluding all speech recognition companies).

Smart Robots: Smart robot companies build robots that can learn from their experience and act and react autonomously based on the conditions of their environment.

Virtual Personal Assistants: Virtual personal assistants are software agents that use artificial intelligence to perform tasks and services for an individual, such as customer service, etc.

Natural Language Processing (Speech Recognition): Speech recognition is a subset of natural language processing that focuses on processing a sound clip of human speech and deriving meaning from it.

Computer Vision/Image Recognition: Computer vision is the method of processing and analyzing images to understand and produce information from them. Image recognition is the process of scanning images to identify objects and faces. The following companies utilize computer vision/image recognition technology in a specific way or use-case in their products.

Recommendation Engines and Collaborative Filtering: Recommendation engines are systems that predict the preferences and interests of users for certain items (movies, restaurants) and deliver personalized recommendations to them. Collaborative filtering is a method of predicting a user’s preferences and interests by collecting the preference information from many other similar users.

Gesture Control: Gesture control is the process through which humans interact and communicate with computers with their gestures, which are recognized and interpreted by the computers.

Video Automatic Content Recognition: Video automatic content recognition is the process through which the computer compares a sampling of video content with a source content file to identify what the content is through its unique characteristics.

Context Aware Computing: Context aware computing is the process through which computers become aware of their environment and their context of use, such as location, orientation, lighting and adapt their behavior accordingly.

Speech to Speech Translation: Speech to speech translation is the process through which human speech in one language is processed by the computer and translated into another language instantly.

2. Number of Companies Per Category

ai2

The bar graph above summarizes the number of companies in each Artificial Intelligence category to show which are dominating the current market. Currently, the “Deep Learning/Machine Learning Applications” category is leading the way with a total of 200 companies, followed by “Natural Language Processing (Speech Recognition)” with 130 companies.

3. Average Funding By Category

ai3

The bar graph above summarizes the average company funding per Artificial Intelligence category. Again, the “Deep Learning/Machine Learning Applications” category leads the way with an average of $13.8M per funded company. The SEM category includes companies that help marketers with managing and scaling their paid-search programs.

4. Venture Investing in Artificial Intelligence

ai4

The graph above compares total venture funding in Artificial Intelligence to the number of companies in each category. “Deep Learning/Machine Learning Applications” seems to be the category with the most traction.

5. Global Breakdown of Artificial Intelligence

ai5

The following infographic is an updated heat map indicating where Artificial Intelligence startups exist across 62 countries. Currently, the United States is leading the way with 415 companies. The United Kingdom is in second with 67 companies followed by Canada with 29.

6. Median Age of Artificial Intelligence Categories

ai6

The bar graph above summarizes Artificial Intelligence by median age of category. The “Speech Recognition” and “Video Content Recognition” categories have the highest median age at 8 years, followed by “Computer Vision (General)” at 6.5 years.

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

Venture Scanner enables corporations to research, identify, and connect with the most innovative technologies and companies. We do this through a unique combination of our data, technology, and expert analysts. If you have any questions, reach out to info@venturescanner.com.

Making Sense of the Artificial Intelligence Ecosystem

At this time, we are tracking 855 Artificial Intelligence companies across 13 categories, with a combined funding amount of $2.73B. These are companies and categories that involve anything and everything that is Artificial Intelligence. Below you’ll find our AI sector map as well as the categorical breakdown of the sector.

Artificial Intelligence Sector Map
Artificial Intelligence Sector Map

Deep Learning/Machine Learning Applications: Machine learning is the technology of computer algorithms that operate based on its learnings from existing data. Deep learning is a subset of machine learning that focuses on deeply layered neural networks. The following companies utilize deep learning/machine learning technology in a specific way or use-case in their products.

Computer Vision/Image Recognition: Computer vision is the method of processing and analyzing images to understand and produce information from them. Image recognition is the process of scanning images to identify objects and faces. The following companies either build computer vision/image recognition technology or utilize it as the core offering in their products.

Deep Learning/Machine Learning (General): Machine learning is the technology of computer algorithms that operate based on its learning from existing data. Deep learning is a subset of machine learning that focuses on deeply layered neural networks. The following companies either build deep learning/machine learning technology or utilize it as the core offering of their products.

Natural Language Processing: Natural language processing is the method through which computers process human language input and convert into understandable representations to derive meaning from them. The following companies either build natural language processing technology or utilize it as the core offering in their products (excluding all speech recognition companies).

Smart Robots: Smart robot companies build robots that can learn from their experience and act and react autonomously based on the conditions of their environment.

Virtual Personal Assistants: Virtual personal assistants are software agents that use artificial intelligence to perform tasks and services for an individual, such as customer service, etc.

Natural Language Processing (Speech Recognition): Speech recognition is a subset of natural language processing that focuses on processing a sound clip of human speech and deriving meaning from it.

Computer Vision/Image Recognition: Computer vision is the method of processing and analyzing images to understand and produce information from them. Image recognition is the process of scanning images to identify objects and faces. The following companies utilize computer vision/image recognition technology in a specific way or use-case in their products.

Recommendation Engines and Collaborative Filtering: Recommendation engines are systems that predict the preferences and interests of users for certain items (movies, restaurants) and deliver personalized recommendations to them. Collaborative filtering is a method of predicting a user’s preferences and interests by collecting the preference information from many other similar users.

Gesture Control: Gesture control is the process through which humans interact and communicate with computers with their gestures, which are recognized and interpreted by the computers.

Video Automatic Content Recognition: Video automatic content recognition is the process through which the computer compares a sampling of video content with a source content file to identify what the content is through its unique characteristics.

Context Aware Computing: Context aware computing is the process through which computers become aware of their environment and their context of use, such as location, orientation, lighting and adapt their behavior accordingly.

Speech to Speech Transition: Speech to speech translation is the process through which human speech in one language is processed by the computer and translated into another language instantly.

Venture Scanner enables corporations to research, identify, and connect with the most innovative technologies and companies. We do this through a unique combination of our data, technology, and expert analysts. If you have any questions, reach out to info@venturescanner.com.

Venture Investing in Artificial Intelligence

The following infographic compares total venture funding in our Artificial Intelligence sector to the number of companies in each category. Which AI categories do you think have the most traction and potential for growth? At Venture Scanner, we are currently tracking over 852 Future of TV companies in 13 categories across 62 countries, with a total of $2.71 Billion in funding. To see the full list of 852 Artificial Intelligence companies, contact us using the form onwww.venturescanner.com.

Venture Investing in Artificial Intelligence
Venture Investing in Artificial Intelligence

Venture Scanner enables corporations to research, identify, and connect with the most innovative technologies and companies. We do this through a unique combination of our data, technology, and expert analysts. If you have any questions, reach out to info@venturescanner.com.

An Image is Worth a Thousand Words

This series of articles examines innovation in the Artificial Intelligence (AI) space. Our first post provided an introduction to the Artificial Intelligence startup landscape, our second post reviewed the top trends and companies within the Smart Home Robots category, and our third post reviewed those within the Deep Learning/Machine Learning category.

We researched the Artificial Intelligence sector and identified 881 companies with $3.07 B in funding across 13 categories. To see the full list of 881 Artificial Intelligence companies, contact us using the form on www.venturescanner.com. This article demonstrates how our technology and analysts can help corporations exhaustively scan for all the relevant companies in a given sector, and provide deep dives into categories of interest to identify the best startups for them to work with.

This week we are going to examine the Image Recognition category within the Artificial Intelligence sector, which is comprised of 103 companies with $455M in funding. Image recognition is the method of processing and analyzing images to understand and produce information from them, as well as identifying objects and faces in those images.

Artificial Intelligence Map-Image Recognition
Artificial Intelligence Map-Image Recognition

Image Recognition Trends

Here are some of the major trends that we are seeing in the image recognition space:

  • Multiple Object Recognition: Google researchers have developed a program twice as effective at locating and distinguishing multiple objects of various sizes in a single image, such as identifying different fruits in a fruit plate.
  • Image Description: Teams at Stanford and Google have independently created technologies that are able to recognize and describe entire scenes of photographs and videos in prose with great accuracy.
  • Gesture Recognition: NYU and Facebook AI Lab Director Yann LeCun has built a deep learning model capable of predicting the position of human limbs in images. This could lead to better gesture control and motion capture systems.
  • Facial Recognition: Facebook developed a program called “DeepFace,” which could determine if two faces in photos are the same person with 97.25% accuracy.

Interesting Image Recognition Startups

We have selected a few companies to highlight, based upon our analysts’ evaluations of their traction, technology differentiation, funding amount, and founding team pedigree.

  • Vicarious focuses on image recognition, segmentation, and scene parsing technologies. The company announced it can crack any type of CAPTCHA with at least 90% accuracy. It has raised $72M from notable investors including Vinod Khosla, Elon Musk, Mark Zuckerberg, and Aaron Levie.
  • Clarifai specializes in deep learning algorithms for visual search. It is developing an API for developers to access its image search technology and plans to license its software to corporate users. Its notable investors include Google Ventures, Nvidia, and Qualcomm Ventures.
  • Cortica specializes in deep learning image recognition technology that automatically extracts the core concept in images and videos and maps them to keywords. The company has over 100 patent applications and has raised $37.9M.
  • AlchemyAPI focuses on deep-learning-based text analysis. It also offers deep-learning-based image recognition APIs for face detection/recognition, image tagging, and image link extraction. The company has raised $2M in February 2013.
  • Superfish is building visual search technology focused on consumer applications. It has created 3 visual search mobile apps for animal, furniture, and product recognition. The company has raised $19.3M from notable investors including DFJ.

Venture Scanner enables corporations to research, identify, and connect with the most innovative technologies and companies. We do this through a unique combination of our data, technology, and expert analysts. If you would like to learn more about how Venture Scanner could help you research and analyze any sector, contact us at info@venturescanner.com

Artificial Intelligence Sector Analysis (Landscape Overview)

Artificial intelligence has become an increasingly important sector in today’s technology industry, growing by 20% annually. The field includes the design and production of technology that could simulate human intelligence and act autonomously based on its own processing of environmental stimuli, rather than following programmed directions alone.

Through our exhaustive research of the sector, we have identified 630+ companies across 13 categories, 46 countries, and with $1.87B of raised funding. The 13 categories of the Artificial Intelligence scan are as follows:

Artificial Intelligence Map

In the following weeks we will examine the trends, issues, and top companies within three selected categories in the Artificial Intelligence scan (Smart Home Robots, Machine Learning/Deep Learning, and Image Recognition). If you would like to see all the 630+ companies across 13 categories or learn more about each category, contact us at info@venturescanner.com.

Venture Scanner enables corporations to research, identify, and connect with the most innovative technologies and companies. We do this through a unique combination of our data, technology, and expert analysts.