Dipyaman Sanyal

AI and Cloud Computing have transformed our lives drastically. Every day, people use digital assistants like Siri, Google Home, and Amazon's Alexa, which execute spoken commands such as playing music on a connected speaker, buying a product online, etc. However, AI algorithms have one major drawback- they rely on cloud servers to perform computations and can't power mobile phones, computers and other devices whose applications use AI. Hence AI has limited relevance in areas of sparse connectivity. Another issue caused by AI applications hosted in the cloud is privacy. There are scenarios in which sending personal data to the cloud for processing by ML applications might not be a possibility. Hence, doing ML inference on edge then becomes important. In this session Gunnvant will talk about the edge ML ecosystem that is in place today and is accessible to tinkerers and hobbyists. A discussion on how anyone can get started with writing AI applications on edge and embedded devices will be undertaken. The session will also focus on hardware options that are in place, the software ecosystem that exists, the capabilities and limitations of contemporary stack.