I’ve found myself to be in quite a lot of agreement with Erwin Schrödinger’s view on life essentially being an open system which tries to keep itself ordered (causing negative entropy changes within its boundary) via homeostasis while exchanging energy and matter with its environment.
The key word here is homeostasis: resistance to change due to external factors and keeping key processes well-regulated. But, in my view, this definition fails to capture a characteristic of life which is as fundamental as any of the other textbook-listed characteristics: the ability of an organism to repair itself.
This is an ability shown by the simplest of cells to the most complex of animals. Now, why is this a key impediment for life that is truly artificial (i.e., one that does not rely on existing biological material / frameworks that nature has already figured out: proteins, phospholipids, RNA, DNA etc)?
First of all, the best way we know of to encode objective functions (say, one of “survival” – whether that’s defined through maximizing time-lived or maximizing offspring-count among other things) is via mathematical constructs. And, perhaps, the best way so far we have found of encoding the algorithms to respond to stimuli is in the coefficients of a system of nonlinear equations (“neural networks”). Those neural networks are best deployed onto deterministic computing hardware built out of silicon (I believe these can be deployed into biological systems as well – but I’m not that well-versed with say, DNA computing and such). So, now you can have a machine with a silicon chip as its “brain” and motors / transducers as its actuators and various sensors as its “eyes and ears”.
But here we run into a problem: does such a machine know how to repair itself? Even if it does, does it have the materials required to make more copies of itself or of its constituent devices readily available (by “readily available”, I mean not at prohibitive energy costs)? One can argue that life as we know it on earth was perhaps one of the most efficient ways to create an ordered system, resilient to exogenous forces, with the materials readily available on Earth. Put another way, the “activation energy” hill that life found was probably one of the lowest around on our planet, if one were to use the materials that were abundant.
Therefore, at least on Earth, I believe any artificial life will look pretty similar (or let’s say “organic”) to the naturally-evolved life that we are and see around us. Not the titanium-clad robots that adorn many sci-fi movies. One could, arguably, have robots which have been initially provided with human-built giant robot factories, silicon foundries and all the chemicals/materials supply-chain that could go into building all the components to build more of such robots and the robots themselves know how to use them completely independently of any outside intervention. But that sounds supremely inefficient.
Moreover, when you look at that giant setup, you can’t help but be awestruck by the fact that almost all living organisms just require an individual or two to fully create highly capable life!
Now a couple of other stream-of-consciousness points that I have about life:
- I think reproduction evolved as a way to not be localized spatially as resources are spread all over geographically and one organism cannot access them all (rooted plants being the best example). It also makes sense from a survival perspective to have versions of your genetic information be resilient to localized natural disasters.
- The one puzzle I have is that where is the survival function encoded in organisms. I mean yes, it is in the DNA/RNA, but what reading of that code gives an organism a “purpose” to survive? And is that function encoded in entropic terms?
One aspect that also bears mentioning is possibly the non-deterministic computational aspects of life: as Roger Penrose would have it, perhaps consciousness (and one can extend that to all of life) is inherently quantum in nature. In which case, this becomes an even harder problem to solve!
Sum over paths 