Tag Archives: Artificial Intelligence

The Coming Age of Augmented Creativity

We are entering a new age, and everything gonna change.  In recent years we have seen the rise of machine learning thanks to advances in computer processing power and the accumulation of large amount of data from which the machines can learn.  These advancements have been significant, but they are nothing compared to what is to come.  Machine learning is growing exponentially; soon machines will know stuff that we don’t even know.  So does this mean that our digital servants, are about to become our digital masters?  The question on the minds of many is “are we on the verge of machines taking over?”…


Rule Based Systems

Computers took their first baby steps towards “Artificial Intelligence (AI )” when we started using them to implement simple diagnostic systems.

The earliest such systems were effectively a top-down design; usually built by simply encoding or storing “expert” knowledge in a large database.

These so-called “expert systems” however were really nothing more than table-look systems (if the patient has X, Y and Z symptoms then the patient has “Disease #23975648”).  Such systems were, in reality, merely rule-based systems; they were not really expert systems…

Complex Systems

True expert systems are systems that are capable of deep analysis and deep pattern recognition.  Expert systems are fast becoming an evermore sought after resource, because in our evermore interconnected world, we are increasingly going to have to deal with evermore complex systems, which means we are increasingly going to need access to expert systems that can “see beyond the rules”…

Most real problems always require some form of “expertise beyond rules”; because most real problems are so complex that there usually is no way of generating a clearly defined solution.  In fact in many cases the problems are so complex that there probably doesn’t exist a clearly defined solution — which seems to conflict with some of our basic assumptions about how things work…

To a certain extent it could be argued that for the last 400 years we have been living in a time dominated by the concept of “deterministic cause and effect”.  We believe in “rules of behavior”; we believe in the concept that clearly defined clauses will always have a clearly defined effect.  Mathematical Physics is the ultimate science of such deterministic cause and effect.  Physics prides itself on being the science that identifies “mathematical rules” of behavior that link cause to effect.  In reality however physics is really just the science of simple systems (systems without the friction of feedback), because, unlike simple systems, the behavior of complex systems (systems with feedback) do not obey clearly defined mathematical rules.

Beyond The Rules

So to tackle complex problems, and the behavior of complex systems, many  computer scientists are beginning to turn away from rule based systems (build from the top down) and instead are focusing their efforts on “Artificial Expertise” (generated from the bottom up).

Artificial expertise is basically an algorithmic way of producing “educated guesses” (intuitive guesses you might say) — a way of producing “Artificial Intuition”…

The basic idea that drives this artificial intuition is that machine knowledge is built, not by encoding linear rules of cause and effect but, by evolving a deep neural network to accumulation, and generate, nonlinear “Emergent Expertise”.

Practice Makes Perfect

We all know that to become a true expert at anything requires a lot of practice — learning the rules just doesn’t cut it.  True expertise requires practice because expertise always emerges from the bottom up.

For practice to be useful however, it needs to contain “feedback”; because practice without the evaluation of feedback is effectively useless – condemning us to do the same thing over and over again.  Practice with feedback is what we really mean when we talk about “training”

Over the last number of years we have witnessed the rise in the concept of so-called “Big Data”.  Big data is considered to be something of a game changer; but, in truth, most of the time the people who are using the term are really just writing reports.  Big whoop!  Very cutting edge!..

Nevertheless despite all of the over-hyped nonsense, there is actually some real value to be had from “big data” from an AI point of view…

In the past there were really just two things that were holding back true AI; and they were, the sheer computing power required for processing a bottom-up methodology, and, the diversity of data required to feed such a methodology.

However the recent explosion of big data means that we now have lots of “training data” to feed our deep neural networks.  This means that we now have lots of diverse actions and feedback that we can use to generate the digital equivalent of Malcolm Gladwell’s 10,000 hours of practice.

But the thing about digital practice hours is that our digital friends are not operating in the same fixed time environment as us.  With the right amount of data AI systems should be able build deep knowledge and expertise on just about anything (really really fast)….

Matrix of Cognitive Dynamics


So artificial expertise is fast becoming a very real possibility; but any machine/system that has the potential to learn from practice (to learn from the bottom up), also has the potential to become an expert at virtually anything…

And so as we move further and further away from rule-based systems to emergent-expert systems we are also moving closer and closer to a form of “Artificial Generalized Intelligence (AGI)”.

AGI has been the stuff of science fiction novels and Hollywood movies, and is often equated with a dystopian future of machine rule.  However now that AGI is actually beginning to graduate from science fiction to science fact, it is worth remembering that there is more to human intelligence than Logical Reasoning and Intuitive Deep Pattern Recognition.



To be human is to be creative and we humans are capable not only of seeing and recognizing complex patterns of information, but also being able to “Design and Create” them.

The brain is a biological tool designed, by millions of years of evolution, to compress “data” into “information”, and subsequently integrate this information into an intuitive map of the external world.

Intuition therefore is in reality a totally natural but subconscious process of compression and integration; a subconscious mental process that tries to figure out what is real and how these real things fit together.  But there is a side-effect to this mental integration.  The side effect of trying to figure out how things fit together, is the potential awareness of “how things could  fit together”.

And so the subconscious process of information integration not only drives intuition, it also drives “imagination”; and consequently deep neural integration offers not only the potential to see hidden patterns of information, but also to “imagine the unseen”….

Emotional Intelligence

Neural integration drives imagination.  It is in this compression and integration of information that “thoughts” begin to emerge; and imagination is simply a conscious process of focusing on these subconsciously generated thoughts.

Our internal thoughts are like our own personal “internal data”, and when we focus on our thoughts we are effectively trying to extract information from our personal data.  But the question arises, “How do we go about evaluating what information can be extracted from internally generated thoughts without any form of external feedback?”….

Evolution once again comes to the rescue.  As a result of the emergence of thoughts and imagination, evolution obviously set to work on developing “Emotional Intelligence”

Over the last few million years, evolution has developed an internal feedback mechanism to evaluate internal thoughts.  Feelings are evolution’s feedback mechanism of choice.  Feelings and Emotional Intelligence are our internal mechanism for determining the “value” of thoughts and intuitions.

Diversity of Ideas

Traditionally our modern society has placed great store in the value of human reasoning, and has reserved merely a degree of curiosity in the value of human feelings and intuition.  However history has shown us that intuition has often been the seed of human insight, and many of our greatest leaps in understanding have been the result of such subconscious moments of inspiration.

And so it is with creativity; just as the integration of a diversity of information, can inspire “deep intuition”, so too the integration of a diversity of ideas can inspire “deep creativity”

Deep Creativity

To be human is to be creative; and deep creativity is nearly always a combination of both emotional intelligence and a diversity of ideas.

Most types of experts (be they people or systems) are in fact domain specific experts; but creativity finds it hard to breath in domain specific environments.  Creativity is almost always the result of the cross pollination of ideas; and creative innovation requires not only a diversity domain experience but also the ability to “sense” connectivity across seemingly unconnected domains.   Creativity in innovation requires being able to “feel” that maybe there is a certain value in the integration of a diversity of seemingly unrelated ideas…

And so it is that the natural and subconscious process of neural integration not only offers the potential for deep cognitive understanding of complexity, but also the potential for “Deep Creativity in Imagining the Complex Unseen”…

Matrix of Creative Dynamics


 Breaking the Rules

We have long known that anything repetitive or rule-based can essentially be carried out by a machine.   The arrival of “computing” machines prompted the concept of the computer database, which ultimately led to the concept of expert machine systems.

Early expert systems were really just glorified databases but recent developments in AI have shown us that if we really want to build true expert systems, we need to do so by evolving artificial intuition from the bottom up (because top down intelligence design just doesn’t work)…

As the field of artificial intuition systems develops further we will increasingly find that machines will able to exhibit emergent expertise and resultant creativity when it comes to problem solving.

But creativity is obviously not only limited to intuitive problem solving.  True creativity drives the imagination, and deep imagination is (nearly always) the interplay of a diversity of ideas and some degree of emotional intelligence.

On this score human evolution is way ahead of the game.  From birth every person on this planet carries with him, or her, a legacy of emotional software that time has hard-coded into his, or her, very DNA; and it is probably safe to say that we could equate this complex information repository to many trillions of hours of equivalent digital practice…

Evolutionary Thinking

As humans we have the potential for great creativity; but to be truly creative requires not only an imagination to see beyond the rules, but also a willingness to break some of these rules.  To be truly creative requires a willingness to entertain the idea that some to the so-called “rules” (and accepted wisdoms) which might actually be holding us back.  And this creative potentiality is the most likely future of work…

The future of work is likely to be less about reasoning and more about creativity.  And a future that involves artificial intelligence is less likely to be about machine rule and more likely to be about “machine assistance”; about machines capable of creative suggestion.

We already know that the future of work will likely see the rise of artificial intelligence and the demise of many boring factory and office jobs.  So what is going to be left for us to do?

Creativity!..  The future of human work is to be creative, in jobs that will require “Evolutionary Thinking and Design”.  The future of human work is likely to be all about “Creative Innovation augmented by Artificial Expertise”.  And in this new age of augmented creativity, it seems likely that it will not just be our current creative jobs that will thrive, but that all future jobs will be much less boring and far more creative…

Why this will be the Century of Complexity…


ALTHOUGH science may have spent the last 400 years honing its understanding of The Linear Dynamics of Cause and Effect”, the reality of everyday life in the 21st century is that the really interesting stuff is increasingly the result of “The Nonlinear Evolutionary Dynamics of Adaptive Integration and Emergent Complexity”…

Linear and Nonlinear Dynamics

“Physics” is the ultimate science of cause and effect.  Physicists like to describe their science as the hardest of “hard science” because physics can claim to be governed by hard and fast scientific “Laws”.  This of course would seem to imply that many of the so-called “soft sciences” are in some way not quite as elevated, not quite as good.

In truth however we could say that physics is an “easy science”, and the soft sciences are “difficult” because the “laws” of physics only really work in the absence of “noise”, and yet the everyday world of the soft sciences is full of noise because most everything is continually battered and buffeted by “constantly changing feedback” which can generate wild “nonlinear dynamics”.

In reality all dynamics have feedback (and resultant nonlinearity), it is just that some dynamics have much less feedback than others.  Physics is, in a sense, the science of “dynamics with negligible feedback”, the science of “linear dynamics” — or in other words it is the science of the nonlinear stuff that can be safely “compressed” into neat linear differential equations which express neat linear “cause and effect”.

New Paradigm

In the simplest possible terms, linear dynamics are dynamics where the effect is proportional to the cause, and nonlinear dynamics are where the effect can be disproportional to the cause.

Physics, it would be fair to say, has throughout its 400 year history, actively tried to steer clear of messy nonlinear dynamics, and in so doing has actively established a paradigm of linear dynamics; a linear paradigm of cause and effect.

But then, out of the blue, in the latter part of the 20th century, along came both “Chaos Theory” and “Complexity Theory” which between them seemed to hint strongly at a completely different paradigm; a nonlinear paradigm of “Integration and Emergence”…

Chaos Theory and Complexity Theory

Unfortunately however nobody seems to have been paying the proper attention, and so chaos theory and complexity theory, as they stand today, are still a bit of a mishmash of concepts and don’t really have agreed upon definitions.

Chaos Theory, for example, is generally associated with the relatively vague notion of the so-called “Butterfly Effect” (or as the academic community like to say “sensitivity to initial conditions”), but this association has, in my opinion, done more harm than good — for it is misguided, and its misdirection has merely served to mask the true nature of chaos.

Complexity Theory is similarly afflicted, but rather than analyse all the pros and cons of all the various definitions of both Chaos and Complexity, I will instead simply offer my own definitions…

Defining Chaos and Complexity

In my opinion chaos is not primarily characterized by sensitivity to initial conditions; but by emergence of decisions points and the resultant sensitivity to choice.  Chaos is simply “adaptive instability”; it is “unresolved internal adaptation to feedback, surfacing as turbulent diversity on the system level”.  So, in the simplest possible terms, we could say that

“Natural Chaos is Incompressible Adaptive Diversity”…

Complexity is simply the resolution of adaptive instability.  Complexity is the result of the “adaptive  integration of co-emergent diversity”, which ultimately results in “emergent complex systems” that have effectively “organised themselves into existence”.  So, in the simplest possible terms, we could say that

 “Natural Complexity is Self-Integrated Diversity”

Matrix of Universal Dynamics - Copyright - Kieran D. Kelly

Integration for Free

So is any of this important?  Absolutely it is!   We live in a world of nonlinear dynamics, some of it compressible, most of it not.  Physics and Chemistry generally deals with the compressible stuff, but Natural Evolution and Emergent Complexity deals with the rest…

To understand Natural Evolution and Emergent Complexity is to understand how in any system of adaptive entities many diverse things can randomly occur and be reinforced; but furthermore, and much more importantly, it also tells us that

With the co-emergence of diversity,

Complex-Integration comes for Free!…

This “Integration for Free” is evolution’s secret sauce.  Evolution drives the emergence of great diversity, which leads to the natural integration of co-emergent diversity, which drives the next level of emergence.  This constant interplay of integration and emergence means that evolution naturally ratchets-up complexity over time, and consequently “the complex whole is forever becoming greater than the sum of its less complex parts”

Natural Creativity

Some years after its publication, the English philosopher Herbert Spencer summarized Darwin’s theory of evolution as being the “Survival of the Fittest”, but unfortunately this description, although popular, is somewhat misleading.

Evolution is not about the “Survival of the Fittest”; evolution is about the “Integration of the Optimally Adapted” (or more precisely, the optimal integration of optimally adapted diversity).

There is a subtle difference between these two descriptions; the former implies anti-synergistic competition, while the latter implies synergistic collaboration.  When we look at the natural world it is obvious that Nature favors integrated diversity over uniformity.  Mother Nature does not employ an asymmetric “winner takes all” strategy, but prefers a more chaotic, but ultimately more creative, strategy of “mutual reinforcement”…

Accelerated Evolution

More and more in the early part of this 21st century we are being made to realize the creative power of complexity dynamics and its potential for system self-integration and emergence.  In some arenas such as a multicultural society, the economy, technology, the arts, and even our daily lives, self-integration and emergence is a source of great diversity and creativity; but in other areas such as financial markets, terror networks, and the global climate it can be a source of great instability and destruction.

Over the last 400 years cause and effect has told us a lot about the dynamics of simple systems void of feedback, but the dynamics of complex systems alive with feedback is a subject that is becoming increasingly relevant and important to understand.

Essentially natural complexity is, in fact, “self-integrated information”.  All natural complex systems are characterized by the fact that they have “low thermodynamic entropy, but high information entropy”; consequently these systems can be considered to be highly organized but unpredictable nonetheless.

So while most people might think about evolution in terms of “an ecosystem of plants and animals”, the reality is that plants and animal (and the ecosystem itself) are really just “information structures”, and evolution simply keep creating evermore complex information structures (structures that are ever more difficult to mathematically compress).

Evolution is fundamentally a “universal process of change”.  Evolution is not just about biology, but about all “information creation”.  Evolution created us, and we in turn create complex information structures.  In fact it could be reasonably argued that in our ever-more rapidly interconnecting world, we are likely fast approaching a phase transition in human development, a transition to a whole new age ; “An Age of Accelerated Evolution and Information Creation”.  And this coming age will be dominated not by the old linear paradigm of predictable cause and effect, but by a whole new Nonlinear Paradigm of unpredictable “Integration and Emergence”…

Algorithmic Search

The new physics of the 21st century and beyond, will be the physics of self-integrating systems and accelerated evolution.  Understanding this “new physics of evolution” will be essential if we want some control over our ever-increasing inter-connected, co-dependent world.  Artificial Intelligence (AI) is seen by many as a means of dealing with complexity and already AI is being used to get computers to learn, but ultimately this will turn out to be really rather small potatoes; the really big pay-off will come from getting computers to explore.

After studying chaos and complexity for so many years, it strikes me that the universe is not fundamentally (as it so often suggested) purely mathematical, but is more generally “algorithmic”.  The process of evolution is, as Darwin himself more or less suggested, a continual process of the emergence of ever greater complexity; a process which would seem to suggests that Mother Nature is, in fact, ceaselessly executing a form of “Algorithmic Search”, constantly seeking out the most successful combinations of integrated diversity.

If this is indeed the case then it begs the question, “Is what Nature finds simply random, or are some things more likely to be found than others?”  Well, as it turns out, chaos suggests the latter…

The discovery of chaos has alerted us to an algorithmic universe that was previously hidden from our awareness; a nonlinear universe of “complex strange attractors”.  The existence of such algorithmic attractors begs yet another question, “are there some, or even many, hidden gems (or dangers for that matter) in this nonlinear universe that we are as yet unaware of?”

In these early days of the 21st century, we are only just beginning to reach the computational power needed to address this question; and although computational exploration of complex system behavior is still an activity very much in its infancy, it is destined to grow to great importance because, for good or bad, it is safe to say that

The 21st century will be a century that embraces the Creative Power of Natural Evolution and Complexity Dynamics…

Matrix of Emergent Dynamics - Copyright - Kieran D. Kelly