IEEE Future Directions
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From the previous post it should be clear that a full mind uploading is very far in the future, assuming that it will ever be possible (and at the present state of technology and knowledge it is not). Subset of mind uploading (like the intention of moving a hand or the identification of the image of a cat) have already been proven using current technologies and we can expect in the coming decade an extension of capabilities that will greatly improve Brain Computer Interfaces (but this is not a Mind Uploading).
There is, however, a different approach to mind uploading that uses a completely different set of technologies: mind virtualisation. With mind virtualisation we mean the possibility of extracting a number of characteristics from a mind, as observed from the outside, to develop a model of that mind that can be used to simulate future behaviour.
This approach is based on a broad set of artificial intelligence technologies, like machine learning, deep neural networks, sentiment analyses, image recognition, applied both to extracting the characteristics to create the model and to the simulation of the mind based on the model.
It should be noted that these technologies, associated with statistical analyses, are already used in market forecast as well as in election forecast. Here the key point is the use of statistics applied to a multitude of individuals averaging out the noise in the signal to get accurate forecast.
In the coming years we are going to see these technologies applied to forecast the behaviour of a single individual, basically creating a mind virtualisation. Notice that in the case of single individuals we cannot use statistical analyses to average out and eliminate the noise but we can apply machine learning to obtain the same result by creating multiple images of the mind separated in time. As the possibility of monitoring increases, through wearable, ambient sensors, activity tracking (including semantic tracking) the number of points available to the machine learning will increase leading to more accurate results.
Additionally, the virtual mind can be used to continually predict behaviour and the system can learn from divergence from the expected behaviour. This approach is now commonly used in speeding up the learning of machines with amazing results.
A short term application of this “mind virtualisation” is in the area of digital twin based education.
The ethical and privacy issues are obvious and need to be tackled.
Notice that the availability of mind virtualisation can become an asset to a person, leading to augmentation of that person cognitive capabilities and decision making capability since one could “simulate” at a high speed his own mental processes stimulated by a broad variety of stimuli.
Through mind virtualisation we can simulate a mind and the interaction among minds. This latter can include human minds as well as “machine minds” (i.e. machine behaviour that in intelligent machine can be very complex) and understand/predict the overall symbiotic behaviour.
This is becoming a crucial point in areas where a loose cooperation between a human and a machine is needed, like self driving car at level 4: the car is capable of autonomous driving but a human driver is needed to take over in some cases. The problem is that the human will grow ever more confident of the machine (we have already seen this happen at level 3) and will not be ready to take over when need arise. The possibility of simulating, using mind virtualisation, the behaviour of that particular person under a variety of situations can greatly improve safety by stimulating the symbiotic relation in a specific way fitting that particular person.
