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 

Future Engineering : An Electronics Reader

Future Engines for Innovative Electronics Landscape

Big Data and the future of Epidemiology

People are always the source of any data systems. And hence mobile phones cannot be ignored while thinking about moving from information into insights. MIT in the Technology Review online magazine speaks about different use cases of deriving insights from the mobile phone usage. Researchers in MIT are dreaming that the data mining strategies in this direction can even be the future of epidemiology itself. On the research on genetics of malaria parasite based on Africa, they have studied the cell phone call data. It can be helpful to understand phenomena like ethnic divisions in Nairobi slums and the spread of cholera in Rwanda.

HetNet – Network Mix for tomorrow

Portable devices and programming languages are the backbone of the connected devices. Now comes portable telecommunication networks, thanks to HetNet. Mobile experts define the HetNet as a network with complex interoperation between macro cell, small cell, and in some cases WiFi network elements used together to provide a mosaic of coverage, with handoff capability between network elements. A Wide Area Network can use macro cells, Pico cells, and/or femtocells in order to offer wireless coverage in an environment with a wide variety of wireless coverage zones, ranging from an open outdoor environment to office buildings, homes, and underground areas. In short HetNets can offer better cell associations and load balancing. On a product end, HetNets can enhance the capabilities of FaceTime, VideoSkype and related streaming applications.

Codassium- Coding in Online Streams

Web RTC Technology has stirred up an excitement in the online collaboration space. It enables audio/video streaming and data sharing between browser clients (peers). Codassium is a live tool that works on WebRTC. As a set of standards, WebRTC provides any browser with the ability to share application data and perform teleconferencing peer to peer, without the need to install plug-ins or third-party software. WebRTC components are accessed with JavaScript APIs. Currently in development are the Network Stream API, which represent an audio or video data stream, and the PeerConnection API, which allows two or more users to communicate browser-to-browser. 

Mozilla Servo – Browsing in Parallel Computers

Mozilla dreams of future versions of Firefox to be able to “take advantage of tomorrow’s faster, multi-core, heterogeneous computing architectures,” writes Mozilla CTO Brendan Eich on the company’s blog. To make that happen Mozilla is developing a new browser engine dubbed Servo. Servo is not an extension of Gecko, Firefox’s current rendering engine, but an entirely new beast written specifically to take advantage of modern, massively parallel processing hardware. Mozilla Servo is written in Mozilla’s homegrown Rust programming language, a C++ style language that attempts to provide more security by avoiding memory corruption and buffer overflows, a common attack vector in today’s browsers. 

World’s Very first Website – Comes Back

Twenty years ago on April 30th, CERN Research Lab in Europe, published a statement that made the World Wide Web freely available to everyone. To celebrate that moment in history, CERN is bringing the very first website back to life at its original URL. We can see the very first webpage that Tim Berners-Lee and the WWW team ever put online, point your browser to http://info.cern.ch/hypertext/WWW/TheProject.html.

PS: Content is Social. Social is Me.

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

Data Diary #4

Information Machines and Metadata Strategy: Some early thoughts

How much of engineering is required for designing / evolving +information machines so that they can always differentiate between data and +metadata and further go beyond to create knowledge and insights out of it. While thinking of this question. I cam across the role played by Search Engines in this. No doubt, search engines are information machines. They learn the #semantic graphs of information relationships in a sea of indexes. Let me bring some metrics here. How will we measure the effectiveness of search engines as information machines. Do we need some axis to plot / visualize their effectiveness.

One of my favorite questions will be how much of data / information / relationships / indexes / semantic webs can be converted to working knowledge by Search engines? Rather can they do this task at all without the intervention of human interactions, at all ! 

Going by the same lines, my categorization / differential positioning of +metadata with respect to data will not be merely based on the relationship between the meaning and associative positioning of data nodes. It will be rather based on how one node of data connects the other node of data to a third node of data. So data and +metadata should always have more than one connecting dot between them. Thus when we use a search engine to find all the +metadata about a particular data node, it should find the meta data based on this semantic graph. 

+Gokul Alex 

@gokulgaze, PS: Views are my own and do not subscribe to any organization or institution

Data Diary #3:

Network Equipment Providers's (NEP) and Next Generation Content

We have already witnessed the wonders of streaming media and video streams. Now comes the real time video streams. At the recently concluded Mobile World Congress, Peter Linder,VP, Fixed Broadband and Convergence, Ericsson speaks about how much Ericsson is enthusiastic about the possibility of live and multimedia content in mega sporting events such as FIFA World Cup, Olympics and so on.

Ericsson Speaks on Next Generation Content

This speech is a pointer to the direction in which Network Equipment Providers must be moving. This is nothing but out of the realization that Voice communication networks can no longer be the only revenue generator or business value accelerator. Voice -Data convergence, Content explosion and the entrance of traditional NEP's in this ecosystem opens up interesting possibilities of competition in the content aggregation and content analytics space.

Already we have a lot of players in the content aggregation and content analytics space. How can NEP's leverage their experience and infrastructure to create new opportunities and user experience ? And that is something worth study.

+Gokul Alex 

Data Diary #2

Data Visuals : Dreams unlimited!!

Visuals are there everywhere, from primitive Ajanata, Ellora caves to the huge Bulletin board of the metropolis. As some cultural theorists say, we live in the world of images. And it is un-disputable that images speak fancier than words, if not louder. And as days progress, data is becoming more and more ubiquitous and visible in all the quadrants of life. It is not that all these data has been born into our networks all of a sudden. Instead, we are becoming more and more conscious about the possibility of sharing and transforming the data. 

Hence these two universes are colliding and co-creating a new paradigm called data visualization. By the time you analyse the thoughts, you can visit the below page to know the mushrooming tools in the data visualization space. This shows the demand in this space. Periodic Table of Data Visualization Tools

Data visualization is a field with exciting features and powers in Web Intelligence. Web Intelligence is slowly transforming the Business Intelligence space all together. Data Visualization as a strategy stands very much coherent to IBM's thought of simplifying life. Gone are the days when we need the expertise of complex queries to mine the data from huge data stores. Now the imperative is to leave the complexities to a selected few data scientists and to empower the rest of the world to focus on the beauty of the data. By the time I finish this introduction on data visualization, at least some of us may be started exploring or experiencing in our daily work or social media life. 

In Public internet we come to see its capabilities through tools such as Klout, Cloze, Vizify, Orange etc.Thus Social Business is no longer confined to the interfaces of text analytics and streaming hash tags. It has become more intuitive and info-graphic in nature. Image analytics may be a tough ask for the present computing circuits. But any day, we can see it happen. Visualizaton need not be confined to data alone as meta data can also be visualized. At then end of the day, only your imagination is your boundary!

Twitter: @gokulgaze

Note: All the views are purely personal ...