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Implementation of Artificial Intelligence on Arduino
- Artificial Intelligence (AI) is making strides in various industries, and its integration with affordable hardware like Arduino boards is opening up new possibilities for smart device development.
- Technologies like Tiny Machine Learning (TinyML) enable AI models to run on microcontrollers with limited resources, allowing for applications like voice and gesture recognition.
- Platforms such as Arduino, supported by TensorFlow Lite and Teachable Machine, simplify the implementation of AI for hobbyists and professionals by providing accessible tools.
- Practical AI applications on Arduino include speech and gesture recognition, as seen in boards like Arduino Nano 33 BLE Sense and Arduino Nicla Voice.
- Neural networks on Arduino, leveraging tools like TensorFlow Lite, enable tasks such as obstacle avoidance and speech recognition with optimized models for resource-constrained devices.
- The utilization of TensorFlow Lite for Microcontrollers facilitates real-time inference on Arduino, enhancing system responsiveness and efficiency.
- Training neural networks on Arduino requires collecting sensor data, model training on a more powerful computer, optimization for Arduino deployment, and performance testing for accuracy.
- Artificial Intelligence on Arduino enhances projects with analog inputs, allowing for flexible responses and adaptability, especially in robotics and automation.
- The AI integration on Arduino opens up new horizons for interactive and autonomous projects, enabling developers to explore innovative applications with limited resources.
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