A look at where AI is heating up,
from healthcare to entertainment

Download the State of AI Report with all the top trends and startups

$44.6B across 5037 deals from Q3'13 to Q3'18

Hover over each square to see the number of deals in each quarter. CB Insights customers can click on each square to view the corresponding deals on the CB Insights platform. (Last updated on 10/9/2018)


*The coloring corresponds to equity rounds only. The map includes startups that have raised at least one equity round since 2012, and excludes AR/VR, on-demand taxis, and hardware-focused robotics startups.

5 min

WTF is artificial intelligence? (Don't worry, we got you...)

The definitions of what artificial intelligence is and what it’s capable of accomplishing have been constantly changing over the years with corresponding changes in technological capabilities. Access to massive amounts of data and advanced hardware processing capabilities have ushered in a new era of AI applications.

What it's NOT: We're not looking for the next terminator startup. Most of it is still a lot of number crunching, taking into account more variables than a human possibly can to categorize new data and predict trends, among other things.

Chatbots: Not all bots are AI bots. If it only follows a command (eg, !add or !fetch) to do a specific task, it's not a self-learning algorithm. If it is constantly learning and improving its answers as you interact with it, you can call it an AI-powered bot. So, is Microsoft's Clippy an AI bot? Maybe 20 years ago, it seemed like an intelligent interface. Today, think of Tay and the reasons the product was rolled back so quickly.

Machine learning: Machine learning is a set of algorithms used to make a system “artificially intelligent,” enabling it to recognize patterns from large datasets and apply the findings to new data. Deep learning is a subfield of machine learning, which uses several layers of neural networks (algorithms that mimic the human brain). 

Machine learning can be used to train computers to understand and analyze human language, including text and voice (Natural Language Processing or NLP), to identify and analyze images (image processing and computer vision), or for time series analysis, among other things. 

Vision: You're trying to get a system to "see" like people do. Say, for example, an e-commerce search engine that allows you to upload a picture of your favorite blue top. The algorithms then recognize the object (a top not a pant), its attributes, look for other images with similar attributes and then give you suggestions on where to buy it from. This may seem like a very simple, "is this really AI" kind of task, but here are some slides on Google's deep learning evolution that explain how its image recognition abilities improved over time. Other advanced vision systems are used in robotics and autonomous cars for object detection and collision avoidance. 

Natural language processing/generation: Understanding and/or interacting in human language. Apart from chatbots mentioned above, NLP is used in voice-enabled smartwatches and smart home applications, context-specific searches, finding semantic similarities of words and phrases, among other things.