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Digital Immortality

This is in continuation with the digital immortality post published here.

Summarizing the previous post, we stated that “it might be possible to achieve longevity, irrespective of the target machine we chose to live in, provided that we learn how to extract data holistically from the source machine (brain) and develop a package to restore data seamlessly to the new entity to maintain continuity.”

In this article, we try to put together some assumptions (based on connecting scientific facts) that are required to extract data from internalized memories within the brain.

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Self Learning Avatars

In early computing,  an avatar was a graphical representation of the user or the user’s alter ego or character. Examples were an icon or figure representing a particular person in a video game, Internet forum, etc. Now within the scope of AI, Avatars can be looked at as virtual embodiment of humans, which are driven more by artificial intelligence rather than real people.

Of the many ideas, the 2045 Avatar Initiative by Dmitry Itskov aims to create technologies enabling the transfer of an individual’s personality to a more advanced non-biological carrier(avatar), and extending life perhaps to the point of immortality.

Digital Immortality has long been discussed by Gordon Bell, and he describes it as storing (or transferring) a person’s personality to a more durable media, such as a computer, and allowing it to communicate with people of the future. The result might look like an avatar behaving, reacting, and thinking like a person based on a person’s digital archive. After the death of the individual, this avatar could have a static persona based on past data only (no new data gathered after the death of the person) or continue to learn and develop autonomously (by collecting new data and aggregating over past data).

According to Gordon Bell and Jim Gray from Microsoft Research, retaining every conversation that a person has ever heard is already realistic: it needs less than a terabyte of storage (for adequate quality). Martine Rothblatt envisions the creation of “Mindfiles,” which consists of a collection of data from all kinds of sources, including the photos we upload to Facebook, the discussions and opinions we share on forums or blogs, and other social media interactions that reflect our life experiences and our unique perspective.

To realize such an avatar, which can learn from files carrying external experiences of a particular individual, we will need to provide it with mindware (an AI platform) and enable it to learn using an unsupervised learning model. By bringing together popular theories such as Kohonen’s Self Organizing Map, Adaptive Resonance Theory (ART), Hopfields Network, and HHMM, we believe that  we should be able to achieve an autonomous learning avatar, that can learn from past data and exhibit the unique behavior of an individual’s persona.

We believe the RM2 Unsupervised Learning would be the ideal model  to bring avatars into functional existence. Our unified platform can learn from files (text, images, videos and audio) and be able to build a timeline of memories. These memories, and new ones, have accrued weights from extracted sentiments and moods, and these weights are updated with new avatar conversations.

The unique platform can be used even when we have figured out memory extraction directly from the human brain. The memory extraction process, which would mean extracting episodic memory by time sequences, would be the best way to achieve a very human-like avatar. Data extracted needs to be converted and organized as per the structure proposed earlier in order to allow avatars to behave, think and perform like humans.