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! ...

Transparency and Measurability :: The twin towers of Digital Economy

People and Technology: How they enrich a social network?

#Facebook, as the name goes, became this ubiquitous a social media only with a human face and a treasure chest of information. These two constitute the twin towers to build a powerful social media. These two ends should enrich each other in a social media platform. More people means more information, more information means more attractive to people. 

Without people, read & write web ( web 2.0 paradigm ) will just be a pipe dream. Social Media as a source of data opens up anew opportunities for collaboration, new insights, further more modes of communication and so on.

Yet making relevant decisions from social media remains a daunting task for many information management systems. We evolve complex algorithms and decision systems for this. We traverse the path of natural language and cognitive networks for this aim. Why?

The data formats and the metadata about people varies significantly across social media. This diversity often inspires the evolution of various ecosystems around each social networks. When #Pinterest gains momentum, the network society around it various substantially with #Twitter communes or #Google Plus hangouts.

Social Media Observatory: A step towards Digital Front Office

Beyond this multiplicity of modes of communication, a unified strategy for web intelligence from social media has a lot of challenges even today. The data exchange across social media need to be more transparent and measurable for this. Social Media Observatory concept is a landmark concept in this direction. An observatory is a platform which can access and plot the patterns and constellation of ideas and aspirations in social media.

This transformation is an imperative in making social media a viable channel for Smarter commerce, further leading to a comprehensive digital front office. This envisions the need for a front office digitization  (#FOD) strategy incorporating social media evolution as well. That will take us near the goal for a fully digitized globally integrated enterprise. A Digital enterprise cannot exist without a digitally segmented market and a digitized channel for network society. And that means the synergy of social media, big data, cloud and mobile platforms as a prelude and a stage setter. 

Big Data is not born in a Day !

Adieu to an Algorithmic Age::

Intelligence every where! Sensors every where! Data seems to be liberated from all the corners of universe. One may wonder, where was all these exabytes and petabytes of data hidden. Or is this internet universe that spawn monsters of data from nowhere? 

Absolutely not. Big Data is not born in a day ! This rich collection of data that we see accumulating in minutest measures of seconds was nothing but encapsulated in the abstractions of an algorithmic age. 

A Critique of Classical Computational Models:

In the scientific computing community, there is an emerging realization that we are moving ahead of an algorithmic age to an age of intelligence and adaptation. We emulate more and more the natural cognition. Data structures have grown beyond the linkages and contextual affinities of algorithms. We may look back for a while. What were we doing with information spaces all these while? Data structures in various information spaces where conditioned by a logic that could approximate a mathematical behavior.

In a way , Boolean logic is largely a mathematical behavior or an operational approximation for the convenience of calculation. Mathematical behavior can be modeled by approximations. Thus we created concept machines based on Boolean logic.Mathematical behavior or for that matter any behavioral logic is just the reflection of a larger set of conditions. If we look from a higher abstraction, we were trying to fix the multidimensional data structures into a partial projection of Boolean logic. Thus my argument is that Boolean logic was insufficient to capture the computational complexities of data streams.

Data Structures and Dialectical Logic

Then what is inherent in data structures: in reality, do they exist at all? I am not an exponent enough to lay down my arguments in mathematical formats. However, I may try in terms of dialectical logic here. Data as such is a representation of a physical entity or a cognitive process. It may have its own primary structure or it may be derived or dependent on a much more relatively invariant structure.  This integral between representational mathematical format ( symbol ) and the corresponding primary or secondary structure constitute a data structure. This visualization is in terms of dialectical logic. 

In the measures of physics, we may use the metrics of space time in many models. But when the data streams are dependent or realizations of energy structures the scenario becomes more interesting. The high energy physics and the future smart sensors may become the sources of such #energy structures. As the data explosions gains momentum, we may need inner eyes to capture the innate structures behind the 'Big Data' ecosystem.

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 

PS: Content is Social. Social is Me

Data Diary #5: Hyper Cubes of Information spaces

When, What and Where is Content ! 

3Ws of Data Science ...

When I was working on a meta data strategy for a data governance initiative, I came across the below interesting point :  

When to locate content , 

What is content and where to locate the content.

I believe these three thoughts works behind many of the search engine driven meta data strategies. They I came across a data dilemma  why do we see metadata becoming stale and stealth? And it made me to think about information spaces, their temporal properties and how to visualize them. Are they like the conventional space time conjectures and curvatures ? Then I realized that space-time is never an absolute metric of anything. 

Let's take a few information spaces. One imminent example that comes up in our mind is a library of wealth of information. Another one can be a stock market where numbers and stocks flock with finance capital. Yet another one can be a group of people assembled in a parliament or a conference.  And a very familiar example of a convenient information space is a data warehouse or a relational database. This is largely a information space sans soul of information. 

Connected Spaces

All these are information spaces and they define their metrics of content and metadata. Often information space is just associated with the needs of data visualization. This approach will provide only limited perspective of information spaces. Each information space is having a temporal or contextual aspect embedded or evolving around it. And it cannot be simplified in some relations of data structures. If data structures should meet this criteria, they should have a time variant structure associated with them.

Am I again going back to the traditional space-time? No, rather, just highlighting the necessity to accommodate  time in this situation. Every measurement system should know what it is going to measure. If this factor is not understood well, we will always witness an uncertainty or probability or chaos in measurement and results.

So if we design an information space to visualize content and metadata that locate them (When, What, Where), we need to know that all these co-ordinates themselves are manifestations of some other information spaces. Hence Information space cannot exist in silos. It exist through #Macro Connectors. The concept of macro connectors is not mine. This is proposed by MIT Media Lab. And it looks interesting. Information space thus becomes a connected space. 

So far so good. What do we achieve by extending these connections? How will information spaces work in cognitive computing / social computing environment.  Like light getting bend by gravity, I would love to say that information spaces get truncated and twisted, curled, diverged, converged by the real-time decisions of cognitive data nodes. Information overloading is just a behavior out of million possibilities in this information - cognition conjecture. 

Google: +Gokul Alex 

Twitter: @gokulgaze

PS: Content is social, Social is Me