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.
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
There is no space for silence
No silence is complete in time
No silence is salient enough
No silence is stellar in entirety
No silence is sober and seamless
It’s lost, it’s lost, a flower full of petals
There is space for sages, unlike silence
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.
An affine space, of livid systems
Is bound to echo, a locus limbs
Is burst to gaze, a primordial pivot
Is build to embed, elliptic notions
Is brittle to distill, vapors of vices
Is beam to perish, pristine poles
An affine space, a vivid lens
Rivets in a pulse, a prismatic visage
Beams, they darkened the edges of the surface
Emboldened, Emblematic, it is a fusion on the surface
Vestige of a spill, curled on to the surface
Surface is conjugal, coagulant and coalesced
It is hiding its fuming films beneath the bone
Sure, it is conjugal, ashes are fractals too
Tangent to the light, smoldered skins
Priming on the pelt, opaque cells of breath
Orthogonal to everything, prisms felt frenzy
Reflection, strange semantics of reflection
Meaningless glasses reflected for simple reasons
Etched out kernels are pivoted around the gaze
Third pole of the cross, fifth element of nature
Surface began peeling away, truths are born in manifolds
Venus, O Venus
Sun is a strange feeling full of fury
It fills your void with light and blindness
It is built up on hope, humidity and hills
This noon, I just wanted to tell you
It is your future, my past and a beam
I am home on your melting morning
I crawl and snarl like an earthenware
I may be a worm working on my larvae
I may be a drum beating on my own
I am home on your spellbound faces
Wishing you a heart full of sunflowers
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