Dialectic logic has been studied by philosophers like Hegel, Marx, Althusser, Adorno, Habermas and so on. It was sufficient enough to explain the dynamic behavior in linear systems. In linear signals and systems, causality, probability, stochastics and randomness etc. can be explained by dialectic logic. It has found its applications across the field of formal linguistics, physics, economics, culture etc. However in the era of dense real and surreal experiences and imaginations, something more specific than dialectic logic has become necessary. Hence I am positioning an asymptotic logic as an epistemic category in the realm of universal pragmatics which is a wider and deeper fabric than ideological lattices. Asymptotic logic is running deep through dialectic systems.
Natural Language Processing (NLP) and Understanding (NLU) has become an essential toolset for the search engines of our times. There are a lot of sophisticated frameworks such as Seq2Seq, FastText, Glove, Word2Vec, BERT etc. for implementing NLP / NLU based models. Everything starts with a good language / linguistic / meta-linguistic model. We have to decide the parameters ( features ) and weights for them in this creative endeavour.
When it is about search engines and their information architecture, fundamental relationships between data, metadata, taxonomy, tokens, topology etc. remain quite significant. Hence I would suggest to give importance to find-ability, link-ability & usability when we encode and enumerate the parameter set ( feature space ) for a search engine, be it a document search engine, image search engine, face search engine, voice search engine or any other kind.
The notions of find-ability, link-ability, & usability will manifest in numerous dimensions and dynamics for various formats mentioned earlier. In this age of fake news and falsified information, trustability also become quite important. If we are factoring the data dimensions into the feature set, we can consider the big data features like volume, velocity, variety and veracity into consideration. I would say they are not essential for a simple and small scale search engine.
Find-ability elucidates the content discovery aspects such as navigation, sitemap, query structure, result set etc. Link-ability summarises the inter-objective and inter-subjective aspects of content, relationships between data and metadata, compactness of taxonomy etc. Usability is an angle towards the accessibility, visibility, experience, cognisance, consumption etc. related to the end-user engagements with the search engine.
This note is quite a brief collection of spontaneous thoughts on the aspects behind a good feature space for an NLP based search engine. Rather than starting with an arbitrary hypothesis or model, it is better to evolve a logical and linguistic framework for NLP based search engines.
Internet is such a simple word, so minimal, so technical, so unexciting, so basic, so primitive, so rudimentary such that we have plugged into it so swiftly, so effortlessly, so continuously, so seamlessly in many forms and formats. I never new networking was such as powerful thing before experiencing internet. It has been the biggest dream I have lived. It is the largest reality that cannot deny. It is the messiest maze I have been to.
What is internet for you ? a cave. ? a maze ..? a ladder ..? a gaze ..? a haze …? It could be anything and everything. I have started surfing internet in 1997. By the standards of today, it is quite late for a 16 year old boy from India. I hailed from a remote town in the state of Kerala for whom a personal computer has been a distant luxury even in the engineering studies. Hence internet came to my life as a career imagination during college days.
It has inspired my curiosity for knowledge by many folds. When I look back into my tryst with Internet, it has been an evolutionary experience, if evolution is a simple metaphor.
Now when I reflect upon the internet as a macrocosm, as a cosmos, as a firmament, it is full of leeches. It is full of fissures. It is full of fulcrums. It is full of fractals. It is full of serpents. It is full of ashes. It is full of tunnels.
It is not what is. Internet has been a refuge. It has been an ensemble. It has been a mirage. It has been a fume. It has been a dust. It has been a lot of different entities. I will narrate them in this log of blogs. Keep surfing !!
I know tears can be written
I know palindromes exist
I know alphabets are stubborn
I know words are turbulent
I know waves can be erased
I know epics can be foiled
I know ellipsis can be noiseless
I know silence can be spellbound
I know footsteps can be leaderless
I know parody can be rhythmic
I know tragedy can be episodic
I know echoes can be solenoids
I know saliva can be colloidal
I know larvae is a liquid self
I know lava is a molten self
I know skin is withering self
I know shades are stymied self
I know tears are written in neural lines
I know they viper whispers
I know tears can be unwritten
Errors, Errors, Episodic Errors
Ellipsis, Ecilipse of Ellipsis
I shall remain knowing
Until the last solenoid coils
Unto the last of the equinox
Intelligence is a hot topic in technical and business circles. Intelligence is no longer an intellectual or philosophical or psychological topic of discussion. Intelligence is now regarded as the fuel for the future engines of progress for humanity. Artificial intelligence is expected to fuel the most important machines of our time. Be it IBM Watson, or Apple Siri, or Amazon Alexa or Google DeepMind, Artificial intelligence is the driving force for all these advanced information machinery. When we are seeing a society and economy dominated by the constellation of advanced machinery and their information architecture, it is quite natural to believe that the combination of artificial intelligence and advanced information machinery is going to rule this world. Even if we accept the autonomous nature of information machinery powered by artificial intelligence, the question of energy and its dialectic relationship with intelligence comes into play.
How does energy influences intelligence? Does this question sound odd ? When I mention intelligence, it is not only artificial intelligence. The terrain of intelligence touches upon natural intelligence, human intelligence, societal intelligence, personal intelligence, organic intelligence, cellular intelligence, atomic intelligence, sub-atomic intelligence, particle intelligence, material intelligence, fluid intelligence, entropy intelligence, fractal intelligence, quantum intelligence and so on. Artificial intelligence we are talking about is just a sub-set of human intelligence in the personal and inter-personal level. It is not even the complete mapping of human intelligence at the societal, personal and organic level. If we take into account these multitude categories of intelligence, the next question is on the dialectic relationship between energy and intelligence.
The relationship between energy, matter and space time is of profound implications. At the quantum mechanical level, energy influences space time properties such as angular momentum, velocity, wave length, frequency etc. On the galactic dimensions, space time curvature together with matter influences the nature of electromagnetic waves. The precise nature of energy, matter and space time are meshed together in the extreme dimensions, be it microscopic or macroscopic. This dialectical relationship between matter and energy is manifested in different space time conjectures differently.
Social Networks are part of our life big time. We spend hours surfing through the profiles, walls and news feeds of our friends and foes alike. Social networks create micro experiences of vivid emotions, in fact a bewildering variety of emotions and flurry of thought. There is a lot of impulsive expressions in social networks. Fine, what about the formation and distribution of knowledge in social networks?
Information exist in the digital systems, but knowledge is always created through human act of cognition and re-cognition. It can only be represented and absorbed in social networks. Hence the full life-cycle of information exchange and knowledge management should materialize in social networks.
Not all information becomes knowledge, but some grains of knowledge get created and distributed through social networks. The challenge is that people seldom reflect and analyze the knowledge within the social networks. Information gets assimilated within seconds, perhaps like the attention span of a Gold fish. However, knowledge takes time to gets developed in human mind. It is not just the analytical insights that we are talking about. It is about the collection of hypothesis, inductions, deductions, inferences, critical analysis and reflections that we sum up as a knowledge collective.
The unique nature of knowledge is that it is not something a machine can produce. An act of human cognition and re-cognition is required to create a quantum of knowledge. Knowledge is generated in the human mind, not in the human – machine interfaces. It is a kind of space-time integration of information and impulses in the semantic ocean of collective unconscious.
Perhaps nature of knowledge is the single most beautiful entity that quantum physics should have studied. Unfortunately, quantum physics had always its own plastic affections to uncertainty and could not go beyond the ramifications about quantum consciousness. Knowledge is a dialectic melange of synchronic and diachronic coefficients of collective unconscious that gets superposed with the synaptic impulses from our experiences. Our synaptic impulses are nothing but the cerebral expressions of information exchanges. There is a semiotic continuum at work in human act of cognition and re-cognition that transforms every genetic expressions to powerful symbols that human mind can easily relate. It is all together a beautiful nature at work in the formation of knowledge in human mind.
This is a long due post on design thinking since I kept delaying it thanks to numerous confusions I had about the concept. Enough is enough! Now it is time to let them out of my thinking hats !!
First confusion : Is there a (dis) harmony in the combination – ‘design + thinking’ ?
Considering the coherence of meanings, ‘design thinking = design + thinking’ does not augur well, at least for me. Why? You design, you think, both are end in itself. Then why do you need to do ‘design + thinking’ ? Thus I have ventured to think that design thinking is not equal to design + thinking. Now what? Is it design a function of ( thinking ) or is thinking function of ( design )? It is possibly the confluence of the two.
Let me elaborate. From a primary reading, I could define design thinking as something about thinking about various design aspects that contributes to some logical end. On the second read it appears to me as a way to design the process of thinking itself. If we go on spending more time over these two words, we can generate more logical meanings and associations. It is quite natural. Yet every time I think about design and thinking, numerous mathematical and logical combinations pops up in my mind. Why? Is it common for every combination of words. Is design thinking as ambiguous as innovation thinking? Is design thinking analogous to creative thinking?
Second confusion : What is the ideological existence of the category called ‘design thinking’?
Design thinking has become a highly successful ideology in the market place of ideas and innovations. There is so much hype around the concept of design thinking that people are prompted to use them as an ornamental and allegoric content in a loose manner. Similarly, Concepts like creativity, innovation, leadership, management, culture are some other terms which have been misused for so long. We cannot blame these terms for their sad plight. Neither we can say that these terms are open ended leaving enough room for interpretations and misinterpretations depending on the application context and intent. Why?
Third confusion : Is design thinking a static or a dynamic category?
Design and thinking: These terms are highly generic and societal. No contesting arguments on that. However the present overload of ambiguity and cacophony over the absolute meanings of these terms should throw light on the world in which these words are used. Some may differ that there is nothing such as an absolute meaning to any concept or categories. Meaning cannot be static or absolute. It varies with context and intent. Design and thinking varies with space and time. It varies with signifier and the signified. While agreeing that meaning cannot be absolute or static, we should critique the meaning of concepts and categories before we swallow them every time we see them in a context.
Fourth confusion : Is there a novel meaning for ‘architecture and design’ in the recent times?
Perhaps there is a counter argument that architecture and design has been mind blowing from time immemorial. On a second thought, we can see that in the previous civilizations and eras architecture and design were associated with the worlds and wonders of art and aesthetics. In this emerging 21st century, design thinking is no longer a subset of art and aesthetics, rather, it has become a conceptual category in itself. How?
Fifth confusion : Is design thinking societal or individualistic?
Design thinker and design thinking experiences cannot exist in isolation. It should be preceded and followed by other types and methods of thinking. Is it preceded by analytic thinking? Is it followed by organic thinking? Is communicative thinking a super set or sub set of design thinking? Is their a relation between relational thinking and design thinking? Is design thinking a meta narrative or a micro-narrative?
Thus my confusions around the realm of design thinking continues to grow and branch out into newer facets of arguments and sub-arguments. Despite the surge of confusions, I love the endless possibilities of thoughts and designs unraveled by the design thinkers and their creations, across the globe !
Unlike humans, computers scale like nothing before – Carl Bass on TEDx Berkley.
This quote is from a TEDx talk on the ‘New Rules of Innovation’ by Carl Bass, the president and CEO of AutoDesk Inc. It was quite an explorative session on innovation. He had spoken about a variety of topics around the world of innovation. One among his themes was the coinage ‘Infinite computing’. Infinite computing? the terms sounded interesting and weird to me at the same time ! I just googled by impulse and curiosity.
One snippet from Wikipedia says
A quantum mechanical system which somehow uses an infinite superposition of states to compute a non-computable function.
Thus my preliminary analysis suggested to me that infinite computing can be achieved through the confluence of quantum mechanics and computational designs.
In this session, Carl Bass, had invoked this term to denote the scale at which the computational powers of machines are growing. He compared the rapid scale at which computers are growing in intelligence in comparison to us. It seems he had made a statement that the computational power of some of the most advanced machines of today is as powerful as the aggregate capacity for computation that existed a decade ago.
This comparison may panic at least someone among us after famous physicist Stephen Hawking has commended on AI. Apart from the hype around the race towards artificial intelligence and contradicting predictions, an interesting take away from such a discussion is our hope that even if machines become more intelligent than us, they should be kinder than us. More importantly we aspire that machines should be kinder to us. May be we can pause for a moment and think how kind we are towards the supposedly less intelligent species around us. Are we really showing loving kindness or compassion to the inferior species around us? At least let’s reflect about the kindness that we humans show towards people hailing from different races, cultures and religions. So connecting intelligence and compassion in the scope of AI may sound hollow and shallow.
Leaving the hypes and tropes of intelligence aside, let us come back to the theme called ‘infinite computing’. Yes, compared to our own past, today’s computers are way too smarter. Should we roll it back or destroy them or unplug them? No. It is sheer stupidity to stop the technological progress and our quest for imparting intelligence to machines. On the contrary, this is high time, we start pondering about the ethics we have left in the scientific and technocratic community.
What is driving our quest for artificial intelligence and deep learning machine? Is it the quest for enlightening ourselves and improving the lives as we proclaim. Or is sheer passion for more power and information to win a rat race in the market of momentary fluctuations, perpetual randomness and collective variances.
If it is a quest for knowledge and bettering humanity and nature, then learning machines will not be harmful to our pathways. We will find novel methods to harness the computational power and intelligence of these learning machines of our times. Like I mentioned in one of my earlier post in LinkedIn, we should learn from our learning machines with the right perspective.
Keeping these observations in perspective, I am thinking about the direction in which our machines are progressing. Infinite computing, hyper computation, Zeno’s machines etc are some models for future. We are not yet there. We are not yet sure if they are making sense at all. There are hypotheses floating around us. There are supporters and critics of hyper-computation. To those who are new to the construct called hyper computation, Wikipedia says “Hypercomputation or super-Turing computation refers to models of computation that go beyond, or are incomparable to, Turing computability. I suggest this construct should be revisited with the same vigor in reading and watching the ingenious life and works of Alan Turing, one of the foremost visionary in the field of computation and information theory.
We should move beyond the immediate benefits and perils of artificial intelligence or machine learning and try to understand the possibilities of constructing machines that can take computation to infinite scales and bounds. The simple reason is that, we are no way near the reality of intelligent machines. The hue and cry about the world ruled by machines is not grounded on rational investigations, rather based on irrational fear and ignorance about the history of knowledge, humanity and nature.
Photo Copyright: Flicker/PascalShodan
Constructing an artificial intelligence paradigm beyond the limits of cognitive computing
Nature is a beauty and the beast at the same time. Bewildering varieties of designs, and fathomless sources of information. This nature is full of vivid beings and diverse entities that presents social and engineering challenges of any scale you imagine. Let us observe some natural processes. Honey bees do construct their honeycomb in unimaginable precision and dexterity. Ants march in patterns so curious and laborious. Birds build nests using any material you can think of. The list of natural engineering marvels is endless as it goes. Behind these wonderful creations and engineering skills in nature, there lies a computational intelligence embedded in the nature. Animate and inanimate nature around us are harnessing the power of this computational intelligence for conditioning their internal and external reality. What is this natural computational intelligence essentially?
Our natural beings measure, compile, decipher, encrypt, optimize, forecast, predict themselves and engage with the world around them in numerous computations. Thus every minutest entity in this material world has an abundance of computational capacity within.It is interesting to note that 99% percent of these natural computations are unconscious, subconscious, spontaneous and very instinctive. This realization is very important as cognitive computation forms only a mere 1% of the entire natural computation happening on this universe.
Our recent progresses in the area of artificial intelligence have enabled us to develop a lot of neural algorithms and a few cognitive computers like IBM Watson etc.But nature is way beyond the powers of cognitive computing frameworks. Natural computation is largely instinctive and unconscious in itself. One key challenge is that natural computing is so much hardwired and intrinsic to the beings that they don’t visibly expose the internal elements of computation in their life processes.
Hence on a primary observation we can say that nature has intentionally encapsulated computation in its powerful patterns and designs. Some may attribute evolution as the reason for this gradual emergence of abstraction in natural computation. Thus data and logic associated with natural computation is well abstracted by nature. When we interact with these layers of abstraction in the nature, we get a lot of knowledge about the internals of these patterns and designs.
Artists among us get inspired and venture to create forms of fine art that inspire humanity further to create forms of art and engineering. When engineers among us understand nature, we assimilate those abstractions in our mental models and venture to create forms of figures. When philosophers among us explore this natural abstraction, we try to create finer forms of abstraction from the highest level of perspective possible. When scientists among us interact with nature, we rationalize and speculate with nature such that we dissect our knowledge of reality itself. Hence nature remains as the most elementary and abundant source material for all sort of human and natural computations. Thus both cognitive and instinctive computers will need to rely on nature to further its scope and boundaries of knowledge.
In our recent times, we have made a lot of progress with cognitive approach to natural computation. It is imitating the complex cognitive activity of mind that deals with semantic structures. We are also successful in constructing engineering models that imitate the computational behavior of neurons and the processes that happens in neural-synaptic end points. When we consider these behaviors in natural entirety, these exhibits are not an end in itself. Natural computation progresses beyond cognition. Cognition is an advanced natural process. We have a lot of natural computational processes which are wide spread, rudimentary, adaptive and essentials to the genetic and stem cell structures. We need to explore them as well. Thus understanding natural computation calls for a dialectic approach to the forms of real life processes. Both advanced and primitive processes should be comprehended and modeled and unified with a dialectical unity in perspective.
Picture Courtesy: Flicker/Pavlina Jane
Social enterprise is an enterprise which has its reach and influence beyond the boundaries of its own policies and physical networks. In other words, social enterprise permeates into the society. In this 21st century, our society is increasingly networked through devices, channels and deeper engagements between stakeholders. Hence major telecom service providers envisage our future society as ‘network society’. Terms like social enterprise and social business gain more prominence and wider meanings in the terrains of networked societies.
As per the definitions of social capital scenario, labor hires capital in a social enterprise. Hence capital will be circulating more frequently and intensely than labor in a social enterprise. Due to this pace or acceleration, naturally accumulation of capital will differ from conventional enterprises. More than through static accumulation, capital will grow through circulation in a social enterprise scenario. Hence it’s growth cannot be measured at a specific geographic location in a social enterprise. Rather, capital will be distributed across the key locus points of circulation of capital. NGOs, civil society organizations etc are some good examples for social enterprise. This capital that circulates non-linearly in a social enterprise can be simply defined as social capital. To govern social capital, thus we need a decentralized power structure within the organization.
Thus NGOs and similar organizations that aspire to build a networked society of social enterprises will be following a decentralized power structure. This seems to be one of the primary reasons why NGOs actively support a decentralized power structure and political economy world wide. As the world around us is increasingly becoming a networked global society, we will see the conflict between the accumulating and circulatory tendencies in the finance capital formations. In such a situation, it is sure that social capital and social enterprises will face challenges in confronting the socioeconomic and political pressures from the global finance capital controlled by trans-national trade networks and multi-national corporate companies. It will be a money ball game worth watching !!