Digital immortality (or “virtual immortality“) is the hypothetical concept of storing (or transferring) a person’s personality in more durable media, i.e., a computer, and allowing it to communicate with people in the future. The result might look like an avatar behaving, reacting, and thinking like a person on the basis of that person’s digital archive. After the death of the individual, this avatar could remain static or continue to learn and develop autonomously.
From another perspective, digital immortality could also mean the hypothetical concept of transferring your personality into a machine so that even after the death of the physical body, you can continue to live out of the machine (continue to learn and develop autonomously.)
Digital Longevity is a humbler version of Digital Immortality, as immortality brings in endlessness to the whole equation.
In order to turn this hypothetical concept to reality, it is important to understand the extraction and migration of personality (data) from a human brain to a machine or to an organic body. This is akin to the ETL (Extract, Transform, Load) functions applied to transfer raw data from a source server to a data warehouse on a target server and then prepares the information for downstream uses.
However since we are still looking to figure or working on a theoretical hypothesis on how data is stored in the human brain, the data migration process is not going to be easy. Solutions in brain computer interfaces have sure shown the way on how we can tap into output data or responses triggered in the brain and pass it on to a computer/machine to perform a particular action. But the real challenge would be to tap into the brain’s raw data (data warehouse) in order to construct memories, or to understand object associated weights, or the computation behind the responses triggered by the brain.
As the data touch-points are limited, we can be sure that the data attributes are limited and the entire knowledge structure is created using this attributes. The human brain structures knowledge based on data collected through touch-points (eyes, ears, nose, tongue and skin) by building relationships and applying weights for simpler computations. So the important step in migration would be to extract this data holistically without disturbing the relationships and weights, in order to reuse this data. Loss of data could corrupt memories and patterns, which would mean relearning again after the data is transferred to the target machine. So it would be very critical to backup and restore the entire unified structure
It can also be true that the brain employs a very easy structure in order to store and synthesize quickly, which is demonstrated in our thinking and actions. A simple storage and retrieval mechanism backed by simpler computation techniques helping us compare, reason and deduce answers, would also imply that the brain’s output (intelligence) is designed to achieve maximum energy optimization.
Imagine a human brain having to compute between 50 parameters to deduce an answer; that would consume a lot of energy, draining the entire system. Hence, you can consider that the brain uses simpler smart techniques to compute for a smaller size to arrive at answers in a short time span.
Even learning employs simpler methods to establish information and intelligence using the basic concepts of mimicking. You can see mimicking as a general rule of learning among all cellular models. Mimicking, which can be further broken down into sequencing, relationship and strength, is by itself a linear process which gives the ability to replicate an action with available data parameters collected during observation. Imagine a brain having to employ machine learning techniques like clustering, dimensionality reduction and others for every learning exercise. It would drain all its energy computing these techniques and hang up the machine.
Hence, it is imperative to arrive at smart ways to store and synthesize data exactly like the brain, in order to build a similar structure in the machine to migrate data with its native structure intact. This will ensure that we dont face any data loss and can facilitate continued learning for downstream usage.
However, it leaves me with a few questions answered.
Will there be a rewarding system for my actions in the new machine? Will I experience various states of mind that i enjoyed or disliked? Will I feel the sudden rush of hormones when i achieve something?
Well, i cannot seem to get an answer here. However, replicating cellular features and automation might bring in these possibilities too, facilitating a complete transition.
We could even choose to migrate data to another organic body, and continue migrating to a younger version as and when the existing body starts to age. This might create an option for complete transition of data along with emotional weights, which would mean you can continue to achieve emotional states even in the new body.
To summarize, it might be possible to achieve longevity, irrespective of the target machine we chose to live in, provided we learn how to extract data holistically from the source machine (brain) and develop a package to restore data seamlessly to the new entity and thus maintain continuity.