Artificial Intelligence (AI) is a field of study dealing with teaching computers to behave more like humans or other animals, and it’s been around since the 1950s. Machine Learning, on the other hand, is a subset of AI that deals with computer algorithms that learn from experience to get better at a task or function, such as recognizing faces.
Collecting user data to predict browsing and shopping habits using machine learning has been helping businesses make huge leaps in marketing growth in the last few years. However, collecting, storing, and managing all that “big data” has proven to be troublesome for some companies.
Additionally, the Internet of Things (IoT) is an industry term for connecting non-browser based and non-backbone computing devices to the Internet. While it might not be apparent in our lives as consumers, it has helped industry by being able to automate the monitoring of expensive equipment and track assets as they move about the world.
Edge AI is a somewhat new term that combines these concepts. Instead of moving tons of raw data to a remote server, locally networked computers and embedded devices can utilize machine learning techniques to help alleviate some of the stress of network traffic.
If you would like to see a blog post discussing the ideas presented in this video, see here: https://www.digikey.com/en/maker/projects/what-is-edge-ai-machine-learning-iot/4f655838138941138aaad62c170827af
Getting Started with Machine Learning Using TensorFlow and Keras
Intro to TensorFlow Lite Part 1: Wake Word Feature Extraction
Intro to TensorFlow Lite Part 3: Speech Recognition on Raspberry Pi
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