What ‘light loving moths’ forgot to tell you about ‘experience design’

‘Light loving moths’ and the world of design?, why this weird comparison! Yes, you may be thinking for a while. As we know there is a common belief that moths, flies, beetles are attracted towards light and that leads them to their unfortunate miseries at the candle light. When we understand that it is by their sheer inability to understand the circular light emitting behavior of the artificial light, these little insects fall prey to the candle fire and other hazards caused by artificial sources of light. Those insects are actually in search of the moonlight that has a constant angle of radiation. 

This occurrence in nature often reminds me about the hypes of nurturing and manufacturing customer experience design. Primarily design has two layers; one physical and second ideological. When physical layer of design act as the necessary act to optimize the functional and material aspects of the subject matter or the object manifestation, ideological layer act as the force that can create a demand or customer appeal beyond its actual capacities. Hence when we make all sort of fuzz about design, design thinking, truth in design we should understand the layer in which we are applying our design efforts and logic.

How IBM Watson helped me select the right mobile apps: A science fiction

How IBM Watson helped me select the right mobile apps

This is a fictional narrative on cognitive mobiles. With Supercomputers like +IBM Watson, cognitive computing has become an immediate reality. Can these cognitive computers solve our real life problems and confused mind? This article presents a dream where a man's misery with mobile app is resolved with the help of a cognitive computer! Please read more and share your thoughts at the IBM Mobile Business Insights blog! ...

Some Early thoughts on the Boundaries of Circuit Computing

Computational machines, their digital senses are hovering are all around us. We make decisions, we experience and negotiate, we travel and triangulate through their prowess. In other words, we as a collective of social knowledge embed intelligence and knowledge into their circuitry and digital logic. Their arises my question; how is this digital logic grounded in the larger set of mathematical logic. Is this digital logic a linear crystallization of natural computing algorithms? 

As we reach the boundaries of traditional algorithms of natural data set, the emerging frontiers of decision sciences are appearing as the phantom ghosts from nowhere. And at times we think they are coming from nowhere in the past and we call them data explosion and sensor revolution. Where has internet hidden all these Phantoms from the past.

Recently I read that mathematical logic has a lot of formalism derived from the brilliant approaches of the 20th century mathematician Hilbert, who tried to consolidate many of the mathematical problems under the ambit of a unified theory of mathematical theory. Thus we need to understand the formalism and whether their were some limitations on the approaches in the problem solving approaches of Hilbert methodology. 

Why all this question now? It is because of the very reason that the theoretical computational models need to take a new turn as we are seeing cognitive computing as the future approach to the decision making algorithms of future needs. When we shift the gears from circuit computing to cognitive computing, there must be a realization of the underlying mathematical logic and its complexity inherent. Our investigations must begin at the very root of the mathematical logic which derived its powers from the formalist approach. 

+Gokul Alex 

Unwind the Mind : Data Dynamics of Natural Computers

Data Diary (5): Natural Computers

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 ... 

+Gokul Alex 

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