{"id":42825,"date":"2021-05-13T09:55:13","date_gmt":"2021-05-13T01:55:13","guid":{"rendered":"\/blog\/?p=42825"},"modified":"2022-07-29T15:29:57","modified_gmt":"2022-07-29T07:29:57","slug":"9-community-projects-tinyml-on-wio-terminal","status":"publish","type":"post","link":"https:\/\/www.seeedstudio.com\/blog\/2021\/05\/13\/9-community-projects-tinyml-on-wio-terminal\/","title":{"rendered":"TinyML Projects Roundup with Wio Terminal: What Is Your Next Embedded Machine Learning Idea"},"content":{"rendered":"\n

In recent years, machine learning<\/a> is a broad field that has seen tremendous progress. It is based on the principle that a computer can autonomously improve its performance on a given task by learning from data \u2013 sometimes even beyond the capabilities of humans. Machine learning\u2019s applications include but not limited to image classification, object detection, audio scene, speech recognition, weather or stock market forecasting, and anomaly detection.<\/p>\n\n\n\n

Edge AI refers to the use of artificial intelligence in the form of machine learning algorithms running directly on edge devices instead of the cloud. Bringing AI from the cloud to the edge, can not only reduce the requirement for latency, bandwidth, and power consumption (which means cost down) but also increase data security and reliability of the whole IoT system.<\/p>\n\n\n\n

As machine learning develops, many exciting possibilities will now extend to edge devices as well. Thanks to the TinyML<\/a>, a subset of machine learning that employs optimization techniques to reduce the computational space and power required by machine learning models, Edge AI is fitting artificial intelligence applications into smaller and less powerful computers, even ultra-low-power microcontrollers, such as our Wio Terminal.<\/a><\/p>\n\n\n\n

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