Embedded AI (also known as Edge AI or TinyML) is the integration of artificial intelligence (AI) directly into devices that often have limited processing power or resources. Think smartwatches, home appliances, self-driving cars, and even tiny sensors.
How Embedded AI is Different:
Traditional AI in the Cloud: Most AI we’re familiar with involves sending data to powerful servers in the cloud (think data centers) for processing. These servers analyze the data, make predictions, and send the results back to your device.
Embedded AI on the Device: Embedded AI puts AI capabilities directly onto the device itself. This means the device can analyze data and make decisions without needing to rely on a constant internet connection.
Why Embedded AI Matters:
Examples of Embedded AI:
Embedded AI is opening up new possibilities. It’s making our devices smarter, more responsive, and capable of functioning in situations where cloud-based AI might not be practical.
OpenAI releases an AI strategy blueprint detailing what the US needs to do to stay…
For years, quantum computing has been touted as the Holy Grail of computational technology, a…
Back when I was just graduating college, if you’d asked me what my dream job…
Homegrown Indic language AI models promise to be a key driver of the Indian AI…
The Global AI Conclave, presented by Moneycontrol and CNBC-TV18, is set to bring together visionaries,…
Imagine waking up peacefully on a Thursday morning without the worry of rushing to the…