Bringing Artificial Intelligence to the Edge

We encounter artificial intelligence in almost all our daily tasks: speech-to-text, photo tagging technology, fingerprint recognition, spam classification. We see it contributing to cutting-edge innovations: precision medicine, injury prediction, use-cases like predicting diabetic retinopathy and autonomous cars.

By allowing machines to learn, reason, act and adapt in the real world, artificial intelligence and machine learning are helping businesses unlock deeper levels of knowledge and insights from massive amounts of data. Most AI algorithms need huge computing power to accomplish tasks from huge amounts of data. For this reason, they rely on cloud servers to perform their computations, and aren’t capable of accomplishing much at the edge, the mobile phones, computers and other devices where the applications that use them run. There is another reason why AI developers are switching to cloud service providers these days: reliability. However, despite the enormous speed at processing reams of data and providing valuable output, artificial intelligence applications still have one key weakness: their brains are located thousands of miles away. This limitation makes current AI algorithms useless or inefficient in settings where connectivity is sparse or non-present, and where operations need to be performed in a time-critical fashion.

This session talks about how to develop machine learning models and bring artificial intelligence/machine learning to easily run complex deep learning models edge devices with low processing capabilities.