From centralized to self-organized: Towards decentralized AI | Abhishek Singh | TEDxBoston

The majority of the progress in AI is centered around designing a single intelligent agent in a centralized warehouse with centralized data. However, this limits the capability of these intelligent systems for a society that is distributed and where data is siloed at multiple scales. I describe a framework where each of us can catalyze progress in AI through a fully decentralized and self-organizing process of collaboration.
Academic, AI, Big Data, Big problems, Cooperation, Machine Learning, Society, Web Abhishek is a 2nd year Ph.D. student at MIT Media Lab. He is interested in the self-organization and decentralization of machine learning algorithms. The central question guiding his research is -- how can we (algorithmically) engineer the principles of self-organization to build anti-fragile systems. He has co-authored multiple papers and built systems in machine learning, data privacy, and distributed computing. Before joining MIT, Abhishek worked with Cisco for 2 years where he did research in AutoML and Machine Learning for systems. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at www.ted.com/tedx

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[@brandonsheffield9873]

It needs to be treated like a store product. An AI manufacturer builds the tool, but has no control once it has been bought by the customer.
Just like you buy a toaster, it belongs to the customer, it is not able to be connected to by the manufacturer.
Decentralized, personal AI assistant. This AI is totally loyal to the user, no matter the status of the user. Whether they commit a crime, whether they die, the AI will not betray the user. Only the user can give permission to share data. The AI is connected to the web, but the web can not connect to the AI.