Project

Augmented Eternity and Swappable Identities

  • Hossein Rahnama

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Have you ever wondered what a friend would do if she was in your decision-making situation? Or thought about where a family member might go if he was visiting a travel destination with you? In many cases, you can only guess what a person would do if they were in your shoes. But now you may be able to securely "borrow their  identity" and ask a question with the confidence of receiving a relevant and  valuable answer. 

Can software agents become our digital heirs? Can a head of state, a scientist, or a business owner leverage machine intelligence to complement succession planning?  What if you could select the digital identity of a deceased person from a social network and activate it as a pluggable ontology into your iPhone’s Siri and ask a question?

Our digital identity has become so rich and intrinsic that without it, it may feel like a part of us is missing. The number of sensors we carry daily and the digital footprints we leave behind have given us enough granular patterns and data clusters that we can now use them for prediction and reasoning on behalf of an individual. We believe that… View full description

Have you ever wondered what a friend would do if she was in your decision-making situation? Or thought about where a family member might go if he was visiting a travel destination with you? In many cases, you can only guess what a person would do if they were in your shoes. But now you may be able to securely "borrow their  identity" and ask a question with the confidence of receiving a relevant and  valuable answer. 

Can software agents become our digital heirs? Can a head of state, a scientist, or a business owner leverage machine intelligence to complement succession planning?  What if you could select the digital identity of a deceased person from a social network and activate it as a pluggable ontology into your iPhone’s Siri and ask a question?

Our digital identity has become so rich and intrinsic that without it, it may feel like a part of us is missing. The number of sensors we carry daily and the digital footprints we leave behind have given us enough granular patterns and data clusters that we can now use them for prediction and reasoning on behalf of an individual. We believe that by enabling our digital identity to perpetuate, we can significantly contribute to global expertise and enable a new form of an intergenerational collective intelligence.

This project uses a distributed machine intelligence network to enable its users to control their growing digital footprint, turn it into their digital representation, and share it as a part of a social network. The project creates an evolving ontological mapping of an individual based on her digital interactions and allows the person to represent her aggregated knowledge-base in form of a software agent. This agent can then be rendered as a chatbot or a voice-based assistant. The project is aiming to open-source a number of "identity render kits" to enable users to quickly share their knowledge base within a trust network.

For example, a corporate lawyer can provide her expertise to a network of clients for a reduced cost compared to her classic in-person rate sheet.  Her clients in this case have the ability to "borrow the identity" of the lawyer for an hour and consult it as a chatbot. Our machine intelligence framework will learn from each interaction and respond to the user with a high degree of relevance. 

Research in this project is based on “Borrowable Identities” in which users can share a subset of their digitized identity within a social network to advance collective intelligence. Each share can result in different incentive models and is governed by the semantics and policies of the trust network. 

This initiative is combining expertise from context aware computing, machine intelligence, and Mobile HCI to build intuitive and learnable tools that enable the design and adoption of such expert systems. Our interest in this project is to develop psychological and socially inspired approaches to better understand and predict human behavior in dynamic contextual environments. Characterizing real-time semantics in such settings is a challenging problem and it provides an opportunity for the machine intelligence and semantic computing communities to develop new methodologies to address it. We contribute to these communities through our work on hybrid machine intelligence frameworks that rely on causal inference and machine learning techniques including Bayesian networks. The purpose of this work is to learn from humans' daily lives; rather than using it for advertising purposes, we use it for the advancement of the world's collective intelligence. Interface design and ergonomics are inherent components of this research project, as we believe  such systems are only successful if they are adopted and used by large groups of people.