Information space continues to enrich my imagination to new manifolds. The recent news that a new start up named Aysadi has come up with a Topological approach to machine learning and Big Data analytics, is really really an interesting conjecture. It is said that DARPA, NSF and Stanford were involved in this research for a long time. I hope this will be a good trend where we will have an open approach to data science. This should be beyond the dependency on specific tools.
This inspired me to go through some nuances of topological learning and created lots and lots of questions in my 'unstructured' mathematical understanding. The stress on various 'in variance' conditions in topological analysis makes me believe that we are far from the best approach. A comprehensive approach to a data problem should not be defining a boundary to its explorations and insights. Yet my comment remains largely naive as I am not an authority or trained in topology.
Continuing from our previous post on information - cognition conjecture, I have landed on a cyclical condition. With the advent and advance of cognitive computing and neuroscience, we are creating anew computing machines driven by human cognition. So we can state that cognition can control computation and therefore information too. On the other side of the coin, can information control cognition. In simple terms the answer is yes, a plain yes. If so, can we create a cyclical information - cognition cyclical machine ? This should be a machine where cognition initiates information processing and then information processing generates new re-cognition.
When I try to rationalize this order, I believe this is happening in all our day to day lively transactions. Going on the same lines, how many machines can claim to do this natural computing cycle to maximum approximation to the real world. And what is the most effective model to observe the data flow in this cognition - information - re-cognition cycle. Knowledge ( Neural Signals, Thought Processes ) in ( Cognition ) - (Language, Semantics, Syntax) in ( Information ) - ( Semiotics, Visuals, Shapes, Numbers, Senses, Emotions ) in Re-cognition seems to be data dynamics. Natural computing demands more rigorous modelling for data dynamics. In pursuit of more natural thoughts ...