The soul and the extended phenotype

I had an interesting thought today when I was thinking about the 'soul' and machine learning. Today's epiphany revolves around two key ideas:
a.) why so many people in AI/Robotics are obsesed with Machine Learning (experience at CMU)
b.) how a particular genotype has broad impact on the rest of the world (Dawkins' Extended Phenotype)

Let's start with the evolutionary theory first. According to Richard Dawkins, the term 'extended phenotype' refers to the influence of a gene beyond the organism which is a container for the gene. Dawkins says that we have to look beyond the effects of a gene on the organism which serves as a container for the gene; we have to look at the cases where the gene influences the survival of an organism by its broad-reaching influence. In The Selfish Gene, Dawkins discusses interesting cases where a virus (with its own genes) will infect an organism and through its own extended phenotype it will help the the organism survive. This is a case where the virus helps the organism and the organism helps the virus.

Imagine this process of mutual symbiosis has been happening over millions of years as species evolved. In the same sense that Dawkins uses the term 'extended phenotype' to denote that the gene has a long reach outwards into the world, we can also look at the long reach of the world inwards. If the outside environment is made up of organisms which possess their own genes, then just as much as we are influencing them they are also influencing us. Over the insanely long amount of time that we have been evolving, the outside world has touched us deeply. We have acquired new genes simply because we are creatures which interact with the world (and the world interacts with us). There is a part of the outside world 'inside' us. This intimate relationship we have with the environment is a result of us evolving with the world. This pantheistic view that a part of the world is inside of us is what gives us our 'soul.'

Now I have something to say about Machine Learning. ML refers to algorithms that change their internal state once they observe some data. This is analogous to the process of the world becoming a part of us throughout the process of evolution. Machine Learning is concerned with algorithms that are trying really hard at building up a soul.

These esoteric ideas can be rendered pellucid via the artificial neural network analogy. An ANN contains hidden nodes whose weights are updated when new input/output pairs are presented. These weights are actually dependent on the input/output pairs. Sometimes these weights correspond to latent variables (hidden states) of the world, but it is only important to realize that these weights are highly correlated with the types of input/output pairs that have been used to update them. Consider person 'A' who spent their entire life in NYC (they were looking at buildings and crowded city scenes their entire life), and person 'B' who spent their entire life in the Sahara Desert (they were looking at sand dunes all of their life). Clearly, person 'B' will have a hard time getting their way around a metropolitan area while person 'A' will struggle at finding his/her way in any type of desert. This is because the spatio-temporal patterns that they have been accustomed to seeing have been engraved in their hidden weights. NYC is in some sense 'inside' of person 'A' while the Sahara is inside of person 'B'.