Name: Anonymous 2012-10-07 8:13
http://arxiv.org/abs/1207.4803
On a computer, self-replicating automata (i.e. self-replicating software programs) execute replication by creating a new copy of their software program and storing the newly generated data in an already established and immutable hardware. In biology, information is copied and algorithmic instructions executed as mutable hardware is manipulated, created, and destroyed through biochemical interactions. In essence, bio-logical systems are unique because the information manipulates the matter it is instantiated in. In biology, there is no real distinction between hardware and software or between program and data; the program is the data and the data is the program.
Lisp, with its homoiconicity, once again confirmed for being ahead of its time.
This leads to a very different causal narrative then that observed in nonliving systems: life is characterized by a situation where almost all causal factors are context-dependent. The efficacy of information therefore permits multidirectional causality with causal influences running both up and down the hierarchy of structure of biological systems (e.g. both from state to dynamics and dynamics to state).
I wonder if this idea might provide future direction in coming up with a language feature that is better able to manage complexity in software systems.
On a computer, self-replicating automata (i.e. self-replicating software programs) execute replication by creating a new copy of their software program and storing the newly generated data in an already established and immutable hardware. In biology, information is copied and algorithmic instructions executed as mutable hardware is manipulated, created, and destroyed through biochemical interactions. In essence, bio-logical systems are unique because the information manipulates the matter it is instantiated in. In biology, there is no real distinction between hardware and software or between program and data; the program is the data and the data is the program.
Lisp, with its homoiconicity, once again confirmed for being ahead of its time.
This leads to a very different causal narrative then that observed in nonliving systems: life is characterized by a situation where almost all causal factors are context-dependent. The efficacy of information therefore permits multidirectional causality with causal influences running both up and down the hierarchy of structure of biological systems (e.g. both from state to dynamics and dynamics to state).
I wonder if this idea might provide future direction in coming up with a language feature that is better able to manage complexity in software systems.