This post is the eleventh, and final, post in documenting the steps I went through on my journey to build an autonomous, voice-controlled, face recognizing drone. There are 10 other posts building up to this one which you can find at the end of this post.
Focus of this post
In this post I will share a video of the complete end-to-end demo and share details of the architecture which sits behind it. I will also share information on what I bought/used to bring this all together and relist all the different software, services and node packages in a single place.
Pulling It All Together
A lot of what we have been doing with this project is humanizing the way we communicate with machines/computers/things. That means talking and observing to drive intelligent interaction rather than using a mouse, keyboard or touch screen.
Our Autonomous Voice-Controlled, Face Recognizing, Drone is a smart drone which showcases, albeit crudely, how interaction with services filled with intelligence is going to evolve. It highlights the importance of cognitive services to the success of organizations in the future.
So with that said take a look at the entire end to end demo in the video below.
This post is the tenth post in documenting the steps I went through on my journey to build an autonomous, voice-controlled, face recognizing drone. There are 9 other posts building up to this one which you can find at the end of this post.
Focus of this post
Up until now we have been mostly working on controlling the drone, using the Microsoft Cognitive Services Face API to identify people and lastly making use of the Microsoft Cognitive Services API to convert text to speech and speech to text.
Ultimately we will have built an intelligent end-to-end IoT solution featuring analytics and visualization. The instructions here can be used to also understand how to get data in from other devices as well!
Let me just start out by saying that donut chart data visualizations are useless, in my humble opinion, and in this post I am going to try explain why I hold this view.
Before doing that let I want to state that the use of data visualizations can be very powerful when used correctly. Data visualization can help you “see” things in your data which conventional tables could not so easily expose. There is absolutely no doubt that a collection of good data visualizations deliver information more effectively than a list of tables. There is also no refuting the fact that a lot of data can be displayed on a great data visualization allowing an overview which is just not possible via tables. At the same time there are downsides especially if poor data visualizations require more time to understand than either a table or a different form of data visualization.