The kind of computation to which I refer isn't just basic computation; "deep learning" is an example of the type of computation that I would compare to life because the organizational structure of it's data points (a structure which emerges as the machine learns on it's own) is well beyond the complexity threshold of appearing to operate non-deterministically. — VagabondSpectre
So my argument is that essential to a semiotic definition of life is it is information which seeks out material instability. It needs chemical structure poised on a knife edge as that is what then allows the information to act as the determining influence. That is the trick. Information can be the immaterial part of an organism that gives the hardware just enough of a material nudge to tip it in the desired directions.
So yes, neural computer architectures try to simulate that. They apply some universal learning algorithm to a data set. With reinforcement, they can erase response variety and arrive at the shortest path to achieve some goal - like win a computer game. There is something life-like there.
But note that you then think that to become more life-like would involve a scaling up - add more information processing to head in the direction of becoming actually conscious or intelligent.
I instead would be looking to scale down.
Your DeepMind is still a simulation running on stable hardware and thus merely software insulated from the real world of entropic material processes. Sure, we can imagine the simulation being coupled to the world by some system of actuators or mechanical linkages. The program could output a signal - like "fire the missile". That could flick a switch that triggers the action. But it is essential that the hardware doing this job is utterly deterministic and not at all unstable. Who wants nukes hooked up to wobbly switches?
So while DeepMind might build a simulation of a learning system that feeds off the elimination of variety - and thus has to deal with its own artificial instability, the catastrophic forgetting problem - it still depends on deterministic devices outside its control to interface with the world. A different bunch of engineers is responsible for fabricated the reliable actuators that can take an output and turn it into the utterly reliable trip of the switch. I mean it makes no difference to the DeepMind computation whether anything actually happens after it has output its signal. A physical malfunction of the switch is not its responsibility as some bunch of humans built that part of the total system. DeepMind hasn't got the wits to fix hardware level faults.
But for life/mind, the organism is sensitive to its grounding materiality all the way down to the quasi-classical nanoscale. At the level of synapses and dendrites, it is organic. The equilibrium balance between structural breaking down vs structural re-constructing is a dynamic being influenced by the global state of the system. If I pay attention to a dancing dot on a screen, molecular-level stuff is getting tipped in one direction or another. The switch itself is alive and constantly having to be remade, and thus constantly also in a state of anticipatory learning. The shape some membrane or cytoskeletal organisation was in a moment ago is either going to continue to be pretty much still right or competitively selected for a change.
So my argument is that you are looking in the wrong direction for seeking a convergence of the artificial with the real. Yes, more computational resources would be necessary to start to match the informational complexity of brains. But that isn't what convergence looks like. Instead, the technology has to be pushed in the other direction - down to the level where any reliance on outside help for hardware stability has been squeezed out of the picture and replaced by an organismic self-reliance in directing the transient material flows on which life - as dissipative structure - depends.
Life and mind must be able to live in the world as information regulating material instability for some remembered purpose. It has to be able to stand on its own two feet entirely to qualify as life (as I said about a virus).
But that is not to say that DeepMind and neural network architectures aren't a significant advance as technology. Simulated minds could be very useful as devices we insert into tasks we want to automate. And perhaps you could argue that future AI will be a new form of life - one that starts at some higher level of semiosis where the entropic and material conditions are quite different in being engineered to be stable, rather than being foundationally unstable.
So yes, there may be "life" beyond life if humans create the right hardware conditions by their arbitrary choice. But here I am concerned to make clear exactly what is involved in such a step.
I do understand the non-linearity of development in complex and chaotic systems. Events may still be pre-determined but they may not predicted in advance because each sequential material state in the system contains irreducible complexity, so it must be played out or simulated to actually see what happens. (like solving an overly-large equation piece by piece because it cannot be simplified). — VagabondSpectre
It still needs to be remembered that mathematical chaos is a model. So we shouldn't base metaphysical conclusions on a model without taking account of how the model radically simplifies the world - by removing, for instance, its instabilities or indeterminancies.
So a reductionist takes a model that can construct "chaos" deterministically at face value. It does appear to capture much about how the world works ... so long as the view is grainy or placed at a sufficient distance in terms off dynamical scale. If you average, you can pretend that spontaneous fluctuations have been turned into some steady-state blur of action. So while analytic techniques fail (the real world is still a mess of chance or indeterminism), numeric techniques just take the assumed average and get on with the computation.
So chaos modelling is about eliminated actual complexity - of the semiotic kind - and replacing it with mere complexity. The system in question is granted known boundary conditions and some set of "typical" initial conditions are assumed. With the simulated world thus sealed at both ends, it becomes safe for calculation. All you need is enough hardware to run the simulation in the desired level of detail.
Machines which we build using mostly two-state parts with well defined effects are extraordinarily simple compared to those which seem to emerge on their own (using dynamic parts such as inter-connected memory cells with many states or strings of pairs of molecules which exhibit many different behaviors depending on their order). Even while I recognize the limits on comprehending such machines using a reductionist approach, I cannot help but assume these limitations are primarily owing to the strength of the human mind. — VagabondSpectre
This is in fact the big surprise from modern biophysics - at the ground level, life is far more a bunch of machinery than we ever expected. Fifty years ago, cells seemed like bags of chemical soup into which genes threw enzymes to make reactions go in desired directions. Now it is being discovered that there are troops of transport molecules that drag stuff about by walking them along cytoskeletal threads. Membranes are full of mechanical pumps. ATP - the universal energy source - is charged up by being cranked through a rotating mill.
So in that sense, life is mechanism all the way down. It is far less some soup of chemistry than we expected. Every chemical reaction is informationally regulated.
But the flip side of that is that this then means life is seeking out material instability at its foundational scale - as only the unstable could be thus regulated by informational mechanism.
If you are at all interested, Peter Hoffman's Life's Ratchet is a brilliant read on the subject. Nick Lane has done a number of good books too.
So there are two things here. You are talking about the modelling of informational-level complexity - the kind of intricate patterns that can be woven by some network of switches regulated by some set of rules. And there is a ton of fun mathematics that derives from that, from cellular automata and Ising models, to all the self-organising synchrony and neural network stuff. However that all depends on switches that are already behaving like switches - ie: they are deterministic and they don't add to the total complexity by "having a mind of their own".
But I am talking about life and mind as a semiotic process where the hardware isn't deterministic. In fact, it mustn't be deterministic if that determinism is what the information processing side of the equation is hoping to supply.
And where are our pretty software models to simulate that kind of world? Or rather, where are our actual "machines" that implement that semiotic notion as some actual device? In technological terms, we can do a fantastic amount of things at the software simulation level. But can we do anything life-like or mind-like at the self-assembling hardware actuality level?
Hell no. It's only been about 10 years that biology has even begun to grasp that this is such an issue.