micro-ROS
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 ![Edge Impulse](ei_logo.png)
 
-This example demonstrates how to run an image classification neural network (built using Edge Impulse) on the Arduino Portenta H7 and publish the classification results using micro-ROS. The tutorial also shows how to add custom message types to micro-ROS and ROS 2. The motivation behind this demo is to offer a way to add AI functionality to ROS 2 robots without bogging down the main computational unit with additional neural nets. This is especially helpful when the offloading of computational tasks to a companion laptop (for instance) is not an option. 
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+This example demonstrates how to run an image classification neural network (built using [Edge Impulse](https://www.edgeimpulse.com/)) on the [Arduino Portenta H7](https://store.arduino.cc/products/portenta-h7) and publish the classification results using micro-ROS. The tutorial also shows how to add custom message types to micro-ROS and ROS 2. The motivation behind this demo is to offer a way to add AI functionality to ROS 2 robots without bogging down the main computational unit with additional neural nets. This is especially helpful when the offloading of computational tasks to a companion laptop (for instance) is not an option. 
 
 With this approach the MCU publishes inference results using a custom message type `EIResult` (Edge Impulse result), which is made up of multiple `EIClassification` items. The tutorial associated with this demo explains how to go about adding these custom message types. A single classification looks like this:
 
@@ -29,5 +31,11 @@ It contains all labels and their probabilities (together summing to 1). The size
 
 
 
+By running neural networks on MCUs and publishing their inferences using micro-ROS it becomes straightforward to experiment with a "distributed" approach to AI in robotics, where the central computer is concerned only with the results of the neural networks and not with sensor data ingest nor calculation.
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+![Traditional vs distributed approach](traditional_vs_distributed.png)
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+---
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 To run this demo yourself check out the video walk through and instructions given in [this repository](https://github.com/avielbr/micro_ros_ei).