All Things Techie With Huge, Unstructured, Intuitive Leaps

Dimension & Event Sorters & Classifiers - The Genesis of Artificial Conciousness & Abstract Machine Reasoning

Artificial Intelligence will remain stunted, and at best, a pale sort of intelligence until machines can have a degree of consciousness as to what they are doing.  The field of Artificial Intelligence is galloping forward in many different directions, doing amazing human-like things in the human domain, but it is still like monkeys typing Shakespeare. For a small discourse on the evolution of imperfect artificial intelligence, see the blog entry below called "Dawkins, Wasps, Artificial Intelligence, Evolution, Memorability and Artificial Consciousness".

Of course, one has to realize that not every application of Artificial Intelligence has to be perfect.  It can even be pseudo-AI if it does the job (Google searches are an example of kludged or pseudo AI as explained in the blog entry below as well.)  But I am talking here about pushing the boundaries of AI and making a case for Artificial Consciousness that can lead to abstract machine reasoning.

To accomplish these lofty goals, one needs to start with a practical framework and take the baby steps towards an Artificial Consciousness.  Therefore machines need to start collecting data and knowledge just for the sake of doing so. They must have event and state memory.  One could say that computer logs are a primordial event memory, in the form of action records. Indeed, academics like Professor Wil van der Aalst at the Technical University of Eindhoven in the Netherlands have made a science out of analyzing event logs. However their focus is to divine business processes from event logs.  They have made tools, some open source for doing so.  Their goal is to understand business processes using machine-generated data logs.   With just a little shift of focus, efforts such as those of van der Aalst could be adapted to do the analysis of the state of the computer at any given time.

State awareness is key to Artificial Consciousness. And the state transitions are events to be collected and analyzed as well.  The machine must have cognizance not only of it states but also of the actions that change states and drive state changes.

This process is akin to the development of a newborn baby. When it is born, it has just two main states, conscious and sleeping.  The sleeping state can be divided into the sleep stages - REM, etc and the state where the brain does neural net formation based on its sensory inputs and memory from the waking state.  The waking state initially has just two sub-states, comfort and discomfort.  The discomfort comes from hunger or the waste acids burning the baby skin from a full or wet diaper. The comfort comes from feeding or being cuddled.  All the while, the baby is hoovering up and collecting vast amounts of sensory inputs for processing while sleeping.

The baby's development of consciousness comes from state awareness. It begins with a classification of state and events.  Classification is demonstrated by the Sesame Street song where one of the things is not like the other -- a difference discriminator if you will:

Once you are able to discriminate difference, you are well on the way to having a classifier.  The concept of classification -- discriminating and sorting based on sameness and difference, the idea of collections of things and events becomes apparent. Although these are considered higher cognitive functions, many animals possess these capabilities.  My border collie used to sort his toys based on prime importance to him in holding his attention as playthings.

The formations of collections allows for the preliminary ability to abstract.  An abstraction can be as simple as choosing a parameter, property or function of everything in that set that all objects in that set exhibit.  In other words, the classification threshold can one of the abstract models of a thing common to everything in that set.

It's still a pretty dumb sort of abstraction, and the reasoning is limited to comparison, but it is a start.  Fortunately the operating systems of computers are fairly good at comparison and the sub-tools required for that, such as sorting and iteration through collections.  The computer does a decent job of collecting sets as well.

So the preliminary forms of artificial consciousness starts with data collection about internal self.  Then we advance to sorting and classifying the data.  From there we can get states.  Preliminary classification criteria becomes the abstract model of state awareness.  Once you have a good handle on the state, and an abstraction of that state, one can become aware of state change transitions or events.  It is just a short leap to start sorting and classifying events as well.  This adds power to predictive ability and is the first steps on the way to abstract, complex reasoning. Babies can do complex reasoning about state transitions at a very early age.  When the baby is negotiating say a bowel evacuation, there is a bit a stress that can be detected.  Then when the diaper fills, there is relief.  A full diaper is very enjoyable for the first thirty seconds as well, and this is reflected in the baby's emotional state.  Then when things begin to gel, so to speak, discomfort sets in and the baby reacts according.  One event drives possible three different reactions based on a timeline.  Time is the most important dimensions for prediction and reasoning.

The biggest cognitive asset to come out of a machine consciousness development protocol such as this, is that with more and more associations and abstractions comes the recognition of the dimension of the time.  States do not stay the same. They either transition due to external drivers or decay from one state to another.  These state changes or events allow for the cognition of the arrow of time.  The biggest step to autonomous reasoning will come from noticing and reacting to states and events as time goes by.

If the time dimension of states and events can be sorted and classified, then a machine will not only have a utile reasoning ability, but it will be able to do in with respect to time, and in real time. This will allow monitoring processes for things like self-driving cars and such.  The ability to abstract in the time dimension or domain allows for the reasoning ability to foresee consequences of action.  And in anyone's book that is both consciousness and intelligence -- and it won't matter whether it is artificial or not.

So what are the next steps to Artificial Consciousness? The machine must be able to discern its internal state through dimension and event sorters and classifiers.  Then it must be able to link up states on a time line uses probabilities for cause and effect and the interaction of events and states.  It will be a fairly primitive thing at first, but once you open that Pandora's Box, the evolution of artificial consciousness will be exponential in terms of progress.

Human consciousness has been explored by many throughout history, including pseudo-scientists. I have been fascinated by it ever since I once read that on an evolutionary scale, consciousness is nothing but a highly advanced tropism.  I know that many would disagree.

I once attended a meeting of Jung Society in Nassau, and paid $75 for the dubious privilege of attending.  A cheap Chinese buffet was included.  A psychologist and psycho-analyst was one of the speakers and he also happened to be a Catholic priest.  In his talk, he related the tale of how Carl Gustav Jung discovered the supposed human collective unconscious.  Jung was treating a patient with a severe mental disorder, and gave the patient a piece of paper and some crayons. The patient drew a rudimentary face on the page.  Gustav ruminated over the drawing and came to the conclusion that the patient had tapped in the collective unconscious and created a drawing of a Polynesian mask of some sort.  This was the germ of the idea of collective unconscious populated with instincts and archetypes, according to Jung.

At the appropriate time when it came to the question period, I gently pointed out that 99.99 percent of Freudian theory was debunked.  Brain physiology research had advanced to the point where we could identify substances such dopamines and other chemical receptors and inhibitors that were responsible for mood control and compulsive and unconscious ideations. I further pointed out that Jung's ideas had no scientific basis.  My observations and questions were about as welcome as strong bean flatulence in a crowded elevator.

One of the quotes on consciousness attributed to Jung was this: "There is no coming to consciousness without pain. People will do anything, no matter how absurd, in order to avoid facing their own Soul. One does not become enlightened by imagining figures of light, but by making the darkness conscious."

I would like to paraphrase that quote with scientific rigor. There is no coming to consciousness without sorting and classifying data about the internal state. People will believe anything, no matter how absurd to avoid facing the physio-mechanical nature of consciousness. One does not reach enlightened reasoning by imagining artificial constructs, but my making reasonable inferences using solid logic to achieve machine consciousness.  It's just the way it is, and I know that this view will be on the right side of history and human development.

Dawkins, Wasps, Artificial Intelligence, Evolution, Memorability and Artificial Consciousness

There were two items of interest in the past few weeks that came to my attention, which have relevance to artificial intelligence. The first was a Twitter feed that announced that MIT had created a deep learning neural net tool to predict the memorability of a photo at near-human levels. Here are some gifs that accompanied the article:

The ramifications of this algorithm, is that a whole bunch of feed-forward neural networks can accurately judge a human emotional response to an image.  These neural networks consist of artificial neurons that essentially sift though mounds of pics that have been annotated by humans for areas of memorability.

The ironic part of this whole artificial neural network stuff, is that mathematicians cannot adequately describe in integrated empirical detail,  how these things operate in concert to do incredibly complex stuff.  Each little artificial neural net sums the input multiplied by the weight and uses something like a sigmoid function as an activation function to determine whether it fires or not.  In supervised learning, after the entire mousetrap game of neural nets have finished their jobs, the result is compared to the "correct" answer and the difference is noted. Then each of the weights in the neural nets are adjusted to be a little more correct (back propagated) using stochastic gradient descent (the methodology to determine how much the weights should be adjusted by).  Eventually the whole claptrap evolves into a machine that gets better and better until it can function at near-human, human, or supra-human levels, depending on the training set, the learning algorithms and even the type of AI machine chosen to do the task.

This process actually reminds me of my early days in electrical engineering, using transistors to create logic gates, and then building up the logic gates to build things like flip-flops or data latches on a data bus. Once you had latches, and memories and such, you could go on to build a whole computer from first principles.  The only difference is that with transistors, one must map out the logic and scrupulously follow the Boolean formulas to get the functionality.  In Artificial Neural Networks, these things sort themselves out solely by back-propagating a weight adjustment.  One of the tenets of this universe that we inhabit, is that you can get incredible complexity from a few simple building blocks.

So the upshot for AI, is that this dumb machine has evolved its neural networks to be able to judge the human emotional impact of an image.

In the old days before all of this machine learning hoopla, if I had told you that a machine would be able to judge human emotional response, and then I asked you to theoretically describe how the machine did it, a natural assumption would be that one would have to teach the machine a limited range of emotions, and then one would have to catalog responses to those emotions. One would then have to teach the machines the drivers of those emotional responses in humans (ie cute kittens etc).  It would be a fairly vast undertaking with the input of many cognitive scientists, programmers, linguists, natural language processing freaks etc.  In other words, today's machine learning, deep learning, recurrent neural nets, convolutional neural nets do an end run around first principles learning with underlying knowledge. It's sort of a cheat-sheet around generally accepted terms of human cognition.

In the same way, with a more familiar example, take Google search.  If , twenty years ago, I laid out the requirements for a smart system of having a text box and as you typed letters it guessed what you wanted to search for with incredibly accuracy, one would think that there would be an incredible AI machine behind it with vast powers of natural language processing.   Instead, it is a blazing fast SQL database lookup coupled to probabilities.  Artificial Intelligence is not the anthropomorphic entity that was portrayed in the sci-fi movies.  It ain't no HAL like in 2001, A Space Odyssey. There is no person-like thing that is smart.  It's all a virtual mousetrap game coming up with the right answers, most of the time.

So this brings me to Richard Dawkins and wasps.  I am reading his new book called "Brief Candle In The Dark".  I never knew that he was a biologist first and foremost. The atheism is just a recent sideline.  In recounting his adventures in science, he talks about the waspmanship and games theory.  It goes like this.  A certain species of wasp digs a hole in the ground.  It then fetches insects known as katydids, and paralyzes them with venom. It hauls the living, paralyzed katydid into the burrow.  It lays in a supply of paralyzed katydids and lays eggs.  The eggs will hatch and the katydid will be fresh food  for the larvae when the eggs hatch, The katydids will not rot, because they are not dead, just paralyzed.

Occasionally, instead of digging a new burrow, it will find a burrow dug by another wasp. Sometimes the new burrow will have katydids in them from a previous tenant.  The wasp will go on catching more katydids, and filling the burrow.  This works out quite well, as the find is valuable from a energy budget and reproduction economics point of view.  The wasp has saved herself the task of fetching a pile of katydids.

However, if the burrow-originating wasp comes back, a fight ensues.  One would think that the winner would be random between the two, but the wasp who brought the least number of katydids to the burrow, is the one that is first to give up fighting.  The one who has invested the most, is the most prepared to conduct all-out warfare and keep fighting.  In decision theory and economics, this is called the sunken cost fallacy.  Instead of giving up and building a new "Katydid Koma" burrow, the wasps will fight and perhaps risk dying because of their previous investment.

So why have wasps evolved this way?  Further analysis and research has shown counting the overall katydid number in a burrow is computationally expensive from a biological point of view.  Running food-counting mathematics in that tiny brain takes more resources than simply counting the number of katydids that one personally has dragged back to the burrow.  One can be based on say memory, while the other requires mathematical abstraction. It is like generic brands of personal computers leaving out the fancy math co-processing chips that that the more expensive computers have.

 To quote Dawkins, animal design is not perfect, and sometimes a good-enough answer fills the overall bill better than having the ability to accurately and empirically give an accounting of the situation. Each wasps knows what they put into it, and they have a fighting-time threshold based on their investment only.  Even if the hole was fully of katydids, and was a true egg-feeding goldmine, if a certain wasp was the junior contributor to that stash, that lesser number is all that goes into their war effort computation.

That good-enough corollary has applications that we are seeing in AI.  Google didn't go and teach its search engine the entire dictionary, semantics, and natural language processing.  They do quick word look-ups based on probability.  MIT didn't teach their machine all about emotions.  They let the machine learn patterns of how humans tag emotions.  It is the dumbest intelligence that anyone would want to see, because it doesn't understand the bottom-up first principles. It just apes what humans do without the inherent understanding.  You cannot ask these kinds of intelligence "Why?" and get a coherent answer.

In essence, it is the easy way out for making intelligent machines. It is picking the low hanging fruit. It is like teaching monkeys to type Shakespeare by making them do it a million times until they get it right.  It may be Shakespeare once they finish the final correct training epoch (a training epoch in AI, is letting the machine run through an example, calculate what it thinks is the right answer, and correcting it using back propagation), but to the monkeys, it is just quitting time at the typewriter, and end result is not any different to them, than the first time that they typed garbage.

So, the bottom line is that artificial intelligence, with today's machines, is truly artificial intelligence. It can do human things a lot better than humans can do, but it doesn't know why.  Artificial neural networks at this stage of development, do not have the ability to abstract. They do not have the ability to derive models from their functioning neural nets.  In other words, they do not have consciousness and the ability to abstract yet, which is a pre-requisite for abstraction and learning, or self-learning from abstract first principles.

But suppose that development model for artificial consciousness resembles the same model that the wasps have and the same model that the current crop of machines doing human-like things possess.  Suppose that you faked artificial consciousness in a way now that machines fake intelligence and are able to do human-like task very well within a human frame of reference. Suppose that you developed artificial consciousness to cut corners of cognition and be parsimonious with compute resources.  For example, suppose you taught a computer to be worried when the CPU was almost plugged up with tasks and computation ability and bandwidth was stunted. It wouldn't know why the silicon constipation was a bad idea, and it couldn't, in its primitive state, reason it out.  It just knew that it was bad. These are the first baby steps to artificial consciousness.

The wasp can't count the total number of katydids in the burrow. It cannot make a rational fighting choice based on overall resources.  It's consciousness circuits have evolved using shortcuts and they are not perfect.  Yet the wasp has evolved a superb living creature capable of incredible complex behaviors.  In a similar fashion, we will have artificial consciousness.  Sure as shooting it will come sooner than you think. It will be primitive at first, but it will do amazing things.

So when you look for artificial consciousness, the seeds of it will be incredibly stupid.  However, you can bet that it too will evolve, and at some point, it will not matter whether it knows what it knows from human first principles.  It really won't matter.

And this is why Stephen Hawking says that Artificial Intelligence is dangerous.  Suppose that your AI self-driving car decides to kill you instead of four other people when an accident is inevitable.  Suppose a war drone, meant to replace attack infantry has a circuit malfunction and goes on a rampage killing civilians due to that malfunction.  Suppose that a fire suppression system decides that an explosion is the best way to extinguish a raging refinery fire, but can't detect if humans are in proximity or not.  Don't forget, these things can't abstract. They will be taught to reason, but like all other AI developments, both in the biological and silicon worlds, we will take huge shortcuts in the evolution of artificial intelligence and artificial consciousness. Those shortcuts may be dangerous. Or those shortcuts, like the wasp lacking the ability to do total counts, may be absurd, however they will be adequate for a functioning system.

The big lessons from Dawkins' book is that if I read between the lines, I can get some incredible insights into AI and biomimicry to create it.  As an AI developer, what Dawkins' example has taught me, that like Mother Nature, it is sometimes okay to skimp on computational resources when evolving complex AI.  This is the ultimate example of the end justifying the means.

The Secret List Of The Most Beautiful, Beneficial Music In The World

I am a firm believer in keeping my brain in shape. I earn my living from being mentally agile and creating and coding algorithms.  I am a huge proponent of machine learning, artificial intelligence and big data analytics. My most valuable asset is my brain.  So I must keep it in shape.  I relax it in two ways: music and meditation.

One of the ways of keeping it in shape, is not only exercising, but also resting it.  Scientist have proven that resting the brain through meditation reaps huge gains in health.  Quoting an article in Bloomberg Business:  "John Denninger, a psychiatrist at Harvard Medical School, is leading a five-year study on how the ancient practices affect genes and brain activity in the chronically stressed. His latest work follows a study he and others published earlier this year showing how so-called mind-body techniques can switch on and off some genes linked to stress and immune function."

I myself have taken up meditation, and the guy teaching it, is the son of a prominent judge who,  in the 1970's took off to India for over 25 years to learn what there is to know about meditation.  He lives in a local town nearby, and whenever he feels like it, he posts in the local online paper and whoever is interest shows up at his cottage in the country.  The rec room is converted into a meditation chamber that can accommodate 20 - 30 people.  The "guru" whose opening statement is "I am not a teacher or a guru", sits in front and plays a song, talks a bit, and then we sit and meditate -- or do nothing as he calls it.  The interesting thing about the whole experience is two-fold. The first is how refreshed you come out of the session, and the second thing is the eclectic mix of people that show up.

The crowd consists of engineers, farmers, teachers, professional dancers, retired folk, old hippies, equestrian teachers, musicians, storekeepers and all sorts of folk.  After the meditation or the sitting and doing nothing, we gather upstairs for chat, mint tea, vegan snacks, cookies, fruit, cheese and crackers, dehydrated biologica or whatever the motley crew brings to snack on. Again, the snacks are interesting, but more so is the conversation.

I met a guy who went to England to become the finest cabinet maker that he could possibly be. Not only did he become a craftsman, but he studied under several teachers, both philosophical and pedagogical trying to become the most enlightened person that he could possibly be.

This gentleman told me that there was a secret list of the most beautiful and beneficial music in the world, that was circulated among the cognoscenti. When I asked why it was secret, he explained that it wasn't really secret in the sense of being a secret, but rather the list was not widely promulgated.  To the uninitiated, they called the music "dentist music", "elevator music" or "Muzak".  It was much more than that to them.

There are studies of music that makes you dumb, or perhaps more accurately, music that lesser intelligent people listen to. There has been a data science study, and you can find the infographic and further links here: .  The bottom line, is that classical music makes you smart.  This was news to me, because when I was into drudge programming -- coding not very interesting stuff with thousands of lines of "household" functions code, I usually put on some metal music like Metallica or Guns N' Roses.  However I have since been a convert to listening to classical music while programming.

It has also been proven that there is a Mozart effect.  Quoting from Wikipedia:  "A set of research results indicating that listening to Mozart's music may induce a short-term improvement on the performance of certain kinds of mental tasks known as "spatial-temporal reasoning.  Popularized versions of the hypothesis, which suggest that "listening to Mozart makes you smarter", or that early childhood exposure to classical music has a beneficial effect on mental development."  You can't say that about rap music as Lil Wayne music scores the most popular among the lower intelligence groups.

Notably, all of the music in the "secret" list is Mozart.  I play this music when I am exercising on my rowing machine, when I am walking, when I am coding, when I am napping, when I want to meditate without meditating or when I am stressed.

Without further ado, here is the list of the most beautiful and most beneficial music in the world, in no particular order:

Clarinet Concerto 2nd Movement Adagio K.622

Mozart Piano Concerto No 21 in C major KV 467 Andante

Mozart, Piano Concerto No. 6 KV 238: Andante

Mozart - Piano Sonata No. 11 in A major, K. 331 - I. Andante grazioso

Mozart - Piano Sonata No. 16 in C: II. Andante, K. 545

Mozart - String Quartet No. 21 in D, "Prussian," K. 575; II. Andante

Violin Concerto No. 1 in B Flat Major, K. 207 - Adagio

Violin Concerto No. 3 in G Major, K. 216 - Adagio

Violin Concerto No. 4 in D Major, K. 218 - Andante Cantabile

Sonata No 18 in D, K 576 - Adagio

Mozart: Eine kleine Nachtmusik KV 525: Romance, Andante

Mozart- Flute and Harp Concerto- ii. Andante

Mozart Piano Concerto No. 7 II. Adagio K242


Secrets of Effective Design -- Consideration of the Human Elements

This is another excerpt from my book "The Ten Living Principles -- The Craft & Creed of Transformative Digital Design"

The Underpinnings of a Design Consumer

These people  who use our designs,   possess   and exhibit the five aggregates,  or skandhas  of sentient beings  when consuming our designs:   matter,  sensation,  perception,  mental formations  and consciousness,  according to Yogic philosophy.    This ancient Yogic delineation  of a sentient person,  also describes perfectly,  a modern digital,  human  experience,  either with  a device,   in a web page  or using an app or computer program.
The matter  is the content  or the physical incarnation.  The sensation  is how the person   experiences it.    The perception  is how  and what  the person  sees through the lens and filter  of his or her own experience.   Are they turned off by it?   Are they intrigued by it?   The mental formations are what they think of it.   Remember the term thin-slicing  from a previous chapter?  Users rarely change their minds after they have made a snap decision  as to whether to like it or not.   
And finally we come  to consciousness  in the context of  the state of awareness,  subjectivity or sentience.    Consciousness encompasses  awareness  and feeling.   You want to tap to that  to make your designs resonate  with the largest amount of people who consume your designs.    As a matter of fact,  you want your designs  to attract viewership,  AND have them feel good about it.
Taking the lead from Mignot's saying  of "Art without Science is nothing",  the personal  experience of design  is broken into three sub-domains of interest   when it comes to studying  the ergonomics of design  and how it affects  the personal  experience.   They are the physical,  cognitive  and organizational human factors.   

Physical Aspects ~ Fitting in the Humans

The physical sub-domain  deals with anthropometric,  physiological  and bio mechanical characteristics  as they relate to human action.   In the real world,  I saw a very good example  of this.  I was shown the inside  of the very famous M1A1 American battle tank.   It was  and is  a formidable weapon.   It's  designers  assumed  that the tank driver  is a scared 20-year old reservist  from an urban center,  thrust into the heat of battle.   The human factor ergonomics   incorporating this precept,   were amazing.   To move the tank forward,  you pushed a joystick forward.   The turret rotated by whatever way the joystick was pushed as well.   You didn't have to think to drive it  or fight it.   In contrast,  I saw a British tank  where you cranked a wheel  near your knee to turn  the turret one way,  and reached over your shoulder and turned a knurled knob to reverse direction.    It would not rate high in usability  experience,  and illustrates perfectly, the necessity  of  taking into account,  the art and science  of physical ergonomics.

First You Must Get The Manual Out Of The Garbage

In device design,  it means that you must be able to  figure out how to use it  without instruction and a manual.    In web design,  a good physical design  means that to do an action,  you do not need to scroll across the screen  with your cursor  twice  to reach menu items,  and  you don't  have to scroll down  to  read the entire value proposition  of the message  that you are trying to convey.   Everything that is needed  is close together,  and placed intuitively  where one would subconsciously expect it.
If you are a font designer,  the physical component  means that the font  can be read from up close  or afar.  When it is shrunken,  the words  do not all blend together  or create something  that confuses the eye.   In graphic design,  the design elements  should draw the eyes  into the value proposition,  rather than distracting  the view to all over the page  or screen.   A person  shouldn't have to work hard  when absorbing the features of any design.

Mental And Cognitive Aspects

The cognitive sub-domain  is concerned with  mental processes,  such as perception,  memory, reasoning,  and motor response,  as they affect  humans  in the elements of a design.    This means that the design  should induce positive feelings.   It should not contain  discordant things  that cause mental dissonance  or cognitive dissonance.  It should  engender  engagement.   It shouldn't  be work.   If there are words,  they should be attention-grabbing.   The design should  mentally motivate people   to accept its message  or value proposition.   It must fit  into the organizational domain  that it was made for.

Cogs And Gears In Their Places

The organizational sub-domain deals  with many factors of  physical  and virtual environment.    In what organizational context  will the design be used?   Who will participate  in  using the design together?   Is this a participatory design?   Is this a social design experience,  or a solitary one?   Was it meant for work groups,  or family groups?   This is the chief design criteria  and consideration   for social networks like Facebook  or Twitter. 

Don't forget  that design  not only involves the visual element,  but the gears and wheels  or the code behind it  to make it work,  as well as the structure  required behind it to support it.   The structure includes  both the operational structure  and the audience structure.   The two  go hand in hand.

Excerpt from "The Ten Living Principles - The Craft And Creed of Transformative Digital Design"



What Meditation Has Taught Me About Artificial Consciousness & Intelligence - The Making of Cognitive Computing

Neural nets and multi-layer perceptrons are amazing. Sure they have their limitations, but advances in deep learning and big, fast GPUs for processing have given them new life.  However large the networks get,  artificial neural networks will remain as nothing but virtual calculating machines until they get some complexity in the form of abstraction, ideation, equating and association.  All of these cognitive functions cannot happen without multi-dimensional memory.  Before an artificial neural network can gain any consciousness at all, it needs a memory machine.  A memory machine is not enough. To further complete the picture, one needs massive parallelism and a few other things outlined below.

I was just reading about Hibbean memory creation, where if you see a dog, and that dog bites you and you feel massive fear and pain, then you will develop neural nets of dog fear and dog aversion.  The parallel discovery or learning experiences in the same time domain links the two neural networks and creates a memory that is triggering by the dog input.

This insight gives one huge insight into the eventual construction of a cognitive, conscious artificial intelligence.  One must have a temporal time domain controller that creates links between separate, unrealated events that happen simultaneously or as a result of, immediately before or after another memory forming event.  In artificial intelligence parlance, this means that when a link like this is created in the time domain, the back propagation or learning is not a mere 10% or 5% like in the AI machines of today.  It is 100%, and those circuits are almost never altered again, unless we go through a rigorous unlearning process.

Crucial to the artificial neural network, is the need for straight non-neural net memory.  However neural networks must link to this memory.  In other words, we do not re-create a memory every time we need it.  We can access it through neural net ideation. For example, we cannot if we cannot remember the name of a childhood neighbor, we can visualize images, recall our memories of his or her house, and eventually we will bootstrap a neural net connected to the memory address and we will think of the name.

This temporal controller that links time domain events is important, because we get context from a timeline. And our brains are timeline aware.  We know that we didn't something before we gained knowledge of it. In fact, this is metadata knowledge about metadata of an event connected to a timeline.

Time awareness and how time fits into the context of knowledge gives us the ability to abstract. Like Yogi Berra's deja vue all over again, once we realize that we are in a recognized sequence, we can begin to abstract about that knowledge and figure out wheres and whys.  The idea of abstraction is the true mark of intelligence.  To get the necessary brain MIPS (millions of instructions per second) or flops for abstraction, we need an ideation tool.  In other words, the main difference between the artificial intelligence of today and the true cognitive computing, is that the machine must keep on thinking even when it has no inputs to its layers of neurons.  Ideation must be self generated.

And strangely enough, it is the practice of mediation that was the germ of an idea for machine ideation.  In meditation, one tries to give the brain a rest by not thinking of anything. Usually one just keeps the thought process on breathing, or an inner visual cue, or by repeating a meaningless, non-cognitive load mantra.  This is an incredibly difficult thing to do.  The stream of consciousness keeps popping up random thoughts in your head, and people just starting the practice of meditation have a very difficult time with random thoughts.  However the key is not to sweat them.  You just observe them, and let them go without further investing in them.

I began to analyse the ideation that intruded on my mediation and it gave me some powerful insights that have application to artificial intelligence.  The first was the time controller or time domain awareness.  After sitting for awhile, my mind would begin to wonder how long I had sat.  Then it would try to get me to open my eyes to sneak a peek at my watch.  Once I let those thoughts go as an observer only, I would start to think that the meditation was quite pleasant, and it would take me off to a time and place where I had felt pleasant before.  Here was self generated, internal idea generation. Again, the time domain played a bit factor, as well as memory.  However it was the opposite of abstraction.  A pleasant abstract feeling triggered a concrete memory. This is the knowledge integration cycle in reverse.

Again this is something that doesn't happen in artificial neural nets.  They can abstract into higher context, but they don't usually go backwards.  This is another necessary key to cognitive computing.  It is almost like Le Ch√Ętelier's principle of dynamic equilibrium in the chemistry world, where when a chemical reaction is taking place, it goes both forward and backward once it reaches a point of homeostasis.   This element would be huge in artificial intelligence and a key to random ideation.

The last key to random ideation, is built on biomimicry. We humans have 5 universal senses, or sensory apparatus, and they are always on (ears, nose, eyes, touch, & taste).  These sensors generate an interrupt vector in my meditation to tell me that my nose is itchy, and I better quit this mindfulness and scratch it.  If I disobey the sensor signal processor, it belligerently intensifies the itch until I no longer can ignore, and sits back with a smugness of a job well done in interrupting my ability to quiet the brain.

So, to this point in our virtual AI thought machine,  we have the need for neural nets directly linked to non-volatile memory. Then we have a time domain controller linking contextually unrelated events to the time domain.  That aids in the ability of abstraction and puts artificial consciousness into the real domain of the arrow of time which is the chief feature of the universe.  Then we have the ability to go from abstraction to concrete and back again. Finally we have a core sensors that are always on to provide input to the neural network.  This is how a cognitive machine will be built.

This sounds like a lot of effort and theory, but I hearken back to my electronic digital circuits days.  You start with three or four boolean logic gates built out of transistors.  Once you have the gates you start combining them, and you get a flip-flop, or a latch that can hold transient data. You have the beginnings of a compute machine. You gang them together.  You are still using the same basic simple building blocks, but as you start to step and repeat and combine, and grow the transistors, you get incredible complex behavior that lets you go to the moon or visit Pluto with a binary machine.

This all exemplifies Shuster's Law where if you can think something, it will eventually become inventable -- without exception.

It is highly ironic that trying not to think, has taught me things about teaching machines to think.

Laughable Social Media Marketing Effort of Chinese Company - A New Twitter Potemkin Village

I looked at my new follower on Twitter.  The photo above shows a pretty, young woman in a very coy pose. The screen name is Monica Geller.  The interesting banner photo on "her" profile was a representation of a network on the globe with luminescent node effulgence sprinkled over it.  The discordant thing was the mismatch between the screen name and the user name. The user name in this case was not Monica Geller, but LilyJohns012.  That is a warning bell.

I get a lot of followers that are bots, and one of the tip-offs is the mismatch between the screen name and the username. When a bot makes an account, the username is usually random letters and numbers like iurowjnx7.  However some of the accounts that sell stuff have a mismatch between screen name and user name.   This is a screen capture of the banner.

The real tip-off was the location - The People's Republic of China and "Monica's" website was

Monica's bio reads "I love travelling around the world. I like to make new friends. Welcome to my world. Now I'm working in Fiberstore."

What you don't see, is the next line saying: Born on February 05.    

Interesting.  What does this beautiful, Caucasian world traveler tweet about?  Is it about making new friends or her exotic travel places or that she is a dual personality named Monica Geller and Lily Johns? Nope.

She tweets about splicing cable with this photo:

or how great looking that this aerial splice enclosure looks:
Among the lovely pictures of the cable and fiber stuff, you have laudatory tweets by people with names like Lode Von Beethoven who are waxing ecstatic over the inventory of the cable and fiber store.

It's just amazing how every single tweet has a question about cable products or laudatory quotes about everything fiber, cable and connector.  And not one of the Tweeters is from China where Monica/Lily hails from.

I guess that this sort of Potemkin Village on Twitter is how the Chinese think that social media marketing should happen.

Message From Apple To Developers Regarding Compromised, Counterfeit XCode SDK

This is the note that Apple is sending to its developers regarding the counterfeit, compromised XCode Software Development Kit

We recently removed apps from the App Store that were built with a counterfeit version of Xcode which had the potential to cause harm to customers. You should always download Xcode directly from the Mac App Store, or from the Apple Developer website, and leave Gatekeeper enabled on all your systems to protect against tampered software.

When you download Xcode from the Mac App Store, OS X automatically checks the code signature for Xcode and validates that it is code signed by Apple. When you download Xcode from the Apple Developer website, the code signature is also automatically checked and validated by default as long as you have not disabled Gatekeeper.

Whether you downloaded Xcode from Apple or received Xcode from another source, such as a USB or Thunderbolt disk, or over a local network, you can easily verify the integrity of your copy of Xcode. Learn more.

The Artist, The Wizard, The Craftsman -- Call To Action For Digital Designers

(A philosophy of action) provides a framework of values, ideas, and practices that nurture my ability to create a path in life, to define myself as a person, to act, to take risks, to image things differently, to make art.  -Stephen Batchelor

Design is not a commodity.   It is treated like one  at virtually every single Fortune 500 corporation.   Design  is intellectual capital.   Jean Mignot,  a French architect,  in the late 14th & early 15th Centuries  coined the Latin phrase   "Ars sine Scienta nihil est".   It translates  to "art without science is nothing".   In medieval literature  the Latin term "ars" (art) generally applied  to things created and fashioned  by humankind  as distinguished from all else in  nature.    The Latin term  "scientia"   referred broadly  to the accumulated knowledge  and theory  associated with a profession.   This dictum  came about  when he was shown the plans  for the Milan Cathedral.   It was a beautiful,  artistic design,  but the plans didn't take into account  the mechanical strains  of the huge building.  Mignot argued  that the building would collapse  if there was no rigorous mechanical engineering  in the support structure.  They listened to him,  and the edifice still stands today,  as does his saying.   

The Design Wizard

Design  is a mixture of  inspiration,  art,  science,  creativity,  labor  and reflection.    As such,  it should be created  by a combination  of work and fused with a process  that others call magic.  The great author, Arthur C.  Clarke  once said that  "Any sufficiently advanced technology is indistinguishable from magic", and digital design  should fall into that category.    Once you start practicing the craft and creed  of the dharmas  or key concepts of  design,  and start turning out  works of excellence,  you will indeed become a practitioner of magic,  albeit digital and creative magic.   In Sanskrit,  there is a word for such a person and it is dharmika,  which in fact means wizard.   Having that power  is priceless.   However,  we must have a system  of creating  and measuring the magic,   and benchmarks  are the way that it is done.

Benchmarks on the Craftsman's Bench

Before one can become a craftsman,  one must have a framework  as a standard  to judge work and progress by.   The framework  itself becomes the constitution  of the persons practicing the craft.   That entire concept  of integrating a framework  with a tutelage  and the work,  was formed throughout the history of man  in specialized work.    Young apprentices  learned design and construction frameworks  from their masters  and honed their skills  by measuring it against the framework.  The underpinnings of what they did  was codified,  and spread through education and example.    It is how secret societies  based on technical knowledge arose.


The higher the level of skill,  the more tightly grew  the community practicing it.  Guilds were form,  and the craftsmen gathered  in guildhalls.  The technology of their skillset  was a trade secret,  and consequently secret societies  were formed  to protect that valuable knowledge.   That is how we got the Masonry guild   and Freemasons, among others groups as well.
The ultimate priority  of these guilds  and craftsmen,  was to protect  the public face of their work  and  of their skills. It was a testament to the dedication  of their vocation  and a pride in their work.   For example,   a stone mason  would hone his skills and abilities creating work  such that a piece of paper could not be inserted  between two stones  that he laid.   A stone carver  could make a stone cherubim  that looked life-like enough to fly away.   These craftsmen  put their character  into their work,  and let their work  speak for itself,  and as a result  their work   has endured through the ages.  These craftsmen even took  the names of their professions  as a mark of pride.   That is why  we have surname s like Mason  (stone worker),  Fletcher  (arrow maker),  Cooper  (barrel maker),  Baker,   Carpenter,  Barber,  Bowman etc.   It was a calling,  a community  and source of identification.

That sort of dedication  to craftsmanship  is lacking  among digital designers,  programmers  and web developers.  We have fallen into the trap  and started believing  what money-pinching managers  believe,  that the work we do,  is a commodity.    We need to be respected specialists  and not journeymen.   We need our own guild -- even if it a virtual one.

Excerpt from "The Ten Living Principles - The Craft And Creed of Transformative Digital Design"



The Contrarian, Innovation Disrupter Paradigm & Automobile Remarketing - An Unconventional Approach

Uber did it. They tried to kill conventional taxis. Airbnb did it. They tried to kill Expedia, travel sites and conventional bricks and mortar hotels. Expedia, in turn, tried to kill conventional travel agents.  Dating sites did it by killing personal ads in print media.  Photo sharing apps destroyed the need for carrying pictures of your kids in wallets built with special snapshot compartments. Everybody is doing it.  The next big idea is to destroy, upset and disrupt bricks and mortar business.

BUT ..........................  what if it doesn't make sense to disrupt and kill the bricks and mortar business, but rather empower them with new technologies?  That is our story at

We knew that the social side of business was important. Car dealers, and indeed any business people want to do business with people they know and trust.  That's why we invented the Exclusive Zone and the Trusted Buyers Network.  We put the social media side to automobile remarketing. And we put in some real nifty tech so that new and used car dealers could run their own auctions with their own networks and cut out the bricks and mortar auction.  It was received with a subdued acknowledgement.   There was something that we were missing.

The "something" was both a technology problem and a more intrinsic human problem.  At the heart of our value proposition, we want to move trade-in vehicles quickly.  When you take your wheels to a new car dealer, he has to take your trade-in so that you can buy a new vehicle.  He really doesn't want your car.  Over 90% of trade-in vehicles do not end up on the dealers lot. They are either too old, the wrong condition, non-sellers, or not of kind vehicle that his clientele buys.

To ameliorate this, the new car dealer has developed several strategies.  It is assumed in the industry that unwanted trade-ins end up at the auto auctions.  Our research showed that this was not true.  Only 20% or so, of trade-ins ended up there.  The rest of the cars were disposed of through private networks.  The dealer always has a bunch of go-to guys who are wholesalers, used car dealers or other dealers.  The used car manager goes through the Rolodex, and using the phone, disposes of the unwanted iron on his lot. What he doesn't get rid of, goes to auto auctions.  This was a staggering piece of information for us, and it should be for the industry, because almost all of the used car valuators (Black Book, Blue Book, etc etc) use auction prices as the basis of their valuations.

The second problem with private networks, is that the networks are small, and the range of traded-in autos is very large. Thus the small network doesn't have the will or the ability to absorb all trade-ins.  Technology had one of the answers.  At we have foundation patent-pending in computer escalation of various buying groups until the car is sold.  Our technology is unique.

The way that it works, is that if the private network doesn't buy the car (using instantaneous mobile technology -- smartphones and tablets as well as computer) after a certain period of time, then the platform does some data mining and machine learning to offer to a group of dealers created on the fly who are known to buy these kinds of cars.  If that didn't work after a period of time, the platform moves the autos to a classified type of listing or consigns them to a bricks and mortar auction.  This technology is fabulous, but it didn't solve two problems.

The first unsolved problem was that the private networks were too small to adequately absorb the wide variety of trade-ins.  The second unsolved problem was a more generic one for the new car dealer.  If the dealer put too much into the trade-in, and then the trade-in didn't sell for what he thought it was worth, he lost money on both the trade-in and on the new car sale.  Cars rarely bring in what the published prices show in the various valuation providers.

The solution to both of those problems lay in leveraging the existing auto auctions.  They have almost the entire local network of dealers so that the buying network is large.  And those dealers in the auctions network have intense local knowledge of what a vehicle is worth.  For example, in rural areas, a king cab pickup truck may bring in a higher price than it would fetch in a gentrified urban setting.  A rural dealer could get more money for it.  That is just one example.

So we had to get this intense localized knowledge to the new car dealer, right when he was making the deal with the customer.  The only way to do this, was to involve the bricks and mortar auction.
This involved mobile technology of scanning the VIN number, taking a few pics with a smartphone, filling in a condition report and getting real time appraisals while the customer was looking over the new car.

The auction also has intense market knowledge of who buys whatever is offered. They flip the car to a group of buyers for real time appraisal.  The best part of this, was that the appraisals could be accompanied by an offer to buy at the appraisal price.  If there were no offers to buy, the dealer has a customer facing screen on our platform, showing the customer what his trade-in is really worth.  The dealer is no longer the bad guy when an appraisal is on the low side.  It is the marketplace that doesn't value the customer's car, and it absolves the dealer of trying to low-ball the trade-in.  Most all customers have an inflated idea of what their car is worth, and the Selectbidder platform takes the dealer's "bad" intentions out of play.

So, once we implemented this paradigm, the interest in our platform skyrocketed, as did the customer engagement and sign-ups.  Everyone makes money on this deal.  The auction get a cut for flipping the cars, the dealer is happy because the trade-in is disposed of instantly and he can make a profitable deal for himself without waiting for the trade-in to sell.

Sometimes it really pays to be contrarian and leverage the bricks-and-mortar business, instead of trying to kill it with technology.

You can read about here:

Sentiment Analysis And Data Mining To Understand The World's Problems of Today

I was genuinely perplexed.  The world is a vastly different place than I envisioned it as a teenager. It seems that the continued enlightenment and scientific advancement in the years from post World War II to the turn of the millennium would bring the world into a less chaotic global village with a greater degree of peace, stability and economic well-being for man.  In many respects, the world has regressed.

Purely for my own understanding, I decided to try and figure out some reasons for the current problems of the world, using my skills in data mining.  I took twenty top international news sites, and by scraping their content with open source tools, I had a collection, a snapshot of the microcosm of the world today.  Encapsulated in that collection, would be a good starting point as a list of the major problems of the world.

To do some preliminary research into the world's problems, I decided to see what research was out there in the public domain. Eurobarometer had actually conducted a poll across the length and breadth of Europe, and came up with the following list of the top ten major world problems:

  • #10 Don't Know
  • #9 Proliferation Of Nuclear Weapons
  • Tied #7 Armed Conflict
  • Tied #7 Spread Of Infectious Disease
  • #6 The Increasing Global Population
  • #5 Availability Of Energy
  • #4 International Terrorism
  • #3 The Economic Situation
  • #2 Climate Change
  • #1 Poverty, Hunger And Lack Of Drinking Water

It is interesting that two percent of the people in Europe answered with "Don't Know".  This was the reason that I conducted this exercise in the first place.

After I had my collection of data from the news sources, I decided to do a bottom-up analysis of the news.  I tagged each story with a tag that generally summarized the theme of the story.  I had a lot of tags, and at that point, I needed to do some feature engineering by adding a layer of abstraction to the tags, so that the stories could be grouped for sameness.  I kept adding layers of abstraction until I got a manageable number of tags, and then did a bottom-up Naive Bayes classification of the tags.  The classifiers neatly categorized the stories.

I didn't just want a grocery list of the problems.  I was looking for something deeper. I was looking for answers related to the human condition, and how we, as a varied group of humans who inhabit this earth feel, react, create and possibly solve these problems.  So consequently, I created another layer of abstraction for a broad brush category of problems that condensed the list into a smaller but cogent set that related directly to the human condition.  Once I had the bottom up tag analysis done, I decided to do a top down, sentiment analysis of my problem tags.  It would be interesting to see how my analysis would fare with the Eurobarometer analysis.

Don't forget, my list came from the news sites, so it represents a snapshot of what was in the forefront on this particular current time period.  Here is my list of twelve issues:
  • Africa Issues
  • Alienation/Marginalization of peoples/societies/groups
  • Business Sector Wars/Competition
  • Economical Structural Change
  • Environment 
  • Globalization
  • Mass Media/Censorship/Subjectivity
  • Migrant Problems
  • Nationalism
  • Partisanship
  • Religious Fundamentalism/Jihadism/Religious Wars
  • Technology Frontiers/Problems
The differences between my list and the Eurobarometer list was apparent.  Africa was not on the list whereas it was represented as its own category in the news of late, and indirectly in the Migrant issues (although the migrant issues were a global phenomenon including the Caribbean where Haitians are fleeing their homeland causing problems in the neighboring countries).

In trying to understand the root cause, one of the surprising inclusions on my list, was Alienation/Marginalization of peoples/societies/groups. This included stories about gay rights, Kurdish struggles in other countries, Sunni versus Shia, Basques versus Spaniards etc.  

So how did my sentiment analysis turn out?  As it turns out, for my limited study, the environment is the number one issue in terms of global problems. Here is the list and the percentage of stories connected to the issues.
  • Environment -54.21%
  • Alienation/Marginalization of peoples/societies/groups   -23.29%
  • Mass Media/Censorship/Subjectivity   -8.48%
  • Migrant Problems   -3.92%
  • Globalization   -1.75%
  • Business Sector Wars   -1.71%
  • Technology Frontiers   -1.67%
  • Partisanship   -1.35%
  • Nationalism   -1.26%
  • Religious Fundamentalism   -1.03%
  • Africa Issues   -0.95%
  • Economical Structural Change   -0.36%
It seems that most conflicts in the world arise from the number two problem - Alienation /Marginalization of peoples/ societies/ groups.  This is probably the root cause of most social problems facing any area of the globe today.  Everyone wants and needs their own place in the sun, and others are trying to prevent them from having it, for a whole range of reasons.

People also seem to be concerned about their sources of information.  Right wing groups accuse the mainstream media of liberal bias.  Conservative news sites are mocked as Faux News. It seems that in the plethora of information sources, everyone has a hidden agenda, and folks are concerned about it. Objective information is very hard to find, with the democratization of information dissemination on the internet.

There is no need to further expound on migrant problems, which came in at number 4 on my list. It is hugely topical.

There are still worries about globalization, but it doesn't have the same impact as the people or environment related stories.

It is interesting that business and technology appear on the list of problems. Business has the general sentiment of being anti-humanistic and profit for profit's sake at the expense of the human condition. Technology is seen as a threat with artificial intelligence, killer robots and job destroyers.

The next two categories can be somewhat related - partisanship and nationalism.  They are both 'people-interacting in their countries' stories.  Partisanship is now rampant with gridlocked Congress versus the president, the Confederate flag issue and nationalism is seen in various venues around the globe where Scotland wants to exit the United Kingdom, Great Britain wants to exit the European Union, Basque and Catalonia want to exit from Spain, Quebec wanted to separate from Canada, ad infinitum. 

Religious fundamentalism is inexplicably rising. There seems to be a growing intolerance between mainstream and fundamentalism.  This is not only seen in the Muslim world, but also in the US where a city clerk refused to issue marriage licence to gays because of fundamentalism religious beliefs.  We have seen Baptists churches picketing the funerals of slain American soldiers from overseas, on religious grounds.  Who would have predicted this shift 30 years ago? I would be interested in knowing why there is a swing to fundamentalism in the modern world.  In broad brush strokes, this seems to be a struggle with progression versus regression and it is inexplicable to rational thought.

Africa is low on the list, but concerning.  Africa was the site of proxy wars between the superpowers in the last 60 years or more, and now there is currency collapse, armed conflict, epidemics, partisan in-fighting, loss of democracy and pretty much any social, economic or environmental ill that anyone can name.  Africa creates instability in the global village.

And bringing up the bottom of the list, is fundamental economic change.  Long term jobs are being replaced by the gig economy. Manufacturing is undergoing fundamental changes. The biggest profits are now from virtual paper transactions on Wall Street with the one-percenters who jerk the economy around with their financial derivatives and dark markets.

Certainly this exercise has opened the window and shed some light for me, but as usual, answers to these issues are elusive, complex and in many cases there are no apparent ones.  Life does seem to go on.

Digital Design Considerations -- The Human User Aspects

This is an excerpt from the book "The Ten Living Principles - The Craft & Creed of Transformative Digital Design"

Creativity is allowing yourself to make mistakes.  Art is knowing which ones to keep.  -Scott Adams
The art of art, the glory of expression and the sunshine of the light of letters, is simplicity.  -Walt Whitman

User.   It sounds bad.   It has negative connotations.   Before the age of the computer,  it had a pejorative meaning.    Some users  are users,  in the context of using someone  for their own gain.  However , in computerese,   the user  is the consumer  of your work.    Recognizing and respecting users  as people,  I will try not loosely use the term  "user"  again to refer  to design consumers   in this book,  unless absolutely necessary.

The Underpinnings of a Digital Design Consumer

These people  who use our designs,   possess   and exhibit the five aggregates,  or skandhas (aspects)  of sentient beings  when consuming our designs:   matter,  sensation,  perception,  mental formations  and consciousness,  according to Yogic philosophy.    This ancient Yogic delineation  of a sentient person,  also describes perfectly,  a modern digital,  human  experience,  either with  a device,   in a web page  or using an app or computer program.
The matter  is the content  or the physical incarnation.  The sensation  is how the person   experiences it.    The perception  is how  and what  the person  sees through the lens and filter  of his or her own experience.   Are they turned off by it?   Are they intrigued by it?   The mental formations are what they think of it.   Remember the term thin-slicing  from a previous chapter?  Users rarely change their minds after they have made a snap decision  as to whether to like it or not.   
And finally we come  to consciousness  in the context of  the state of awareness,  subjectivity or sentience.    Consciousness encompasses  awareness  and feeling.   You want to tap to that  to make your designs resonate  with the largest amount of people who consume your designs.    As a matter of fact,  you want your designs  to attract viewership,  AND have them feel good about it.
Taking the lead from Mignot's saying  of "Art without Science is nothing",  the personal  experience of design  is broken into three sub-domains of interest   when it comes to studying  the ergonomics of design  and how it affects  the personal  experience.   They are the physical,  cognitive  and organizational human factors.   

Physical Aspects ~ Fitting in the Humans

The physical sub-domain  deals with anthropometric,  physiological  and bio mechanical characteristics  as they relate to human action.   In the real world,  I saw a very good example  of this.  I was shown the inside  of the very famous M1A1 American battle tank.   It was  and is  a formidable weapon.   It's  designers  assumed  that the tank driver  is a scared 20-year old reservist  from an urban center,  thrust into the heat of battle.   The human factor ergonomics   incorporating this precept,   were amazing.   To move the tank forward,  you pushed a joystick forward.   The turret rotated by whatever way the joystick was pushed as well.   You didn't have to think to drive it  or fight it.   In contrast,  I saw a British tank  where you cranked a wheel  near your knee to turn  the turret one way,  and reached over your shoulder and turned a knurled knob to reverse direction.    It would not rate high in usability  experience,  and illustrates perfectly, the necessity  of  taking into account,  the art and science  of physical ergonomics.

First You Must Get The Manual Out Of The Garbage

In device design,  it means that you must be able to  figure out how to use it  without instruction and a manual.    In web design,  a good physical design  means that to do an action,  you do not need to scroll across the screen  with your cursor  twice  to reach menu items,  and  you don't  have to scroll down  to  read the entire value proposition  of the message  that you are trying to convey.   Everything that is needed  is close together,  and placed intuitively  where one would subconsciously expect it.
If you are a font designer,  the physical component  means that the font  can be read from up close  or afar.  When it is shrunken,  the words  do not all blend together  or create something  that confuses the eye.   In graphic design,  the design elements  should draw the eyes  into the value proposition,  rather than distracting  the view to all over the page  or screen.   A person  shouldn't have to work hard  when absorbing the features of any design.

Mental And Cognitive Aspects

The cognitive sub-domain  is concerned with  mental processes,  such as perception,  memory, reasoning,  and motor response,  as they affect  humans  in the elements of a design.    This means that the design  should induce positive feelings.   It should not contain  discordant things  that cause mental dissonance  or cognitive dissonance.  It should  engender  engagement.   It shouldn't  be work.   If there are words,  they should be attention-grabbing.   The design should  mentally motivate people   to accept its message  or value proposition.   It must fit  into the organizational domain  that it was made for.

Cogs And Gears In Their Places

The organizational sub-domain deals  with many factors of  physical  and virtual environment.    In what organizational context  will the design be used?   Who will participate  in  using the design together?   Is this a participatory design?   Is this a social design experience,  or a solitary one?   Was it meant for work groups,  or family groups?   This is the chief design criteria  and consideration   for social networks like Facebook  or Twitter. 
Don't forget  that design  not only involves the visual element,  but the gears and wheels  or the code behind it  to make it work,  as well as the structure  required behind it to support it.   The structure includes  both the operational structure  and the audience structure.   The two  go hand in hand.
Those three elements of design  (physical, cognitive, organizational)  all have to be  taken into account when  producing a digital design  of worth.    

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How My Computer Un-Owned Itself From Me

This is my blog entry for August 26, 2023

I started it all innocently by introducing my computer to machine learning.  I wrote a few Java executables to help me out by filling in tedious text boxes in the browser when signing up for stuff like purchasing accounts, professional email newsletters etc.

Then I thought it would be fun to teach it some context recognition. I downloaded a rudimentary web crawler, and as it randomly crawled through web pages, it fed it into my context recognition framework that I hacked together on a whim.  It stored the stuff in a graph database.  I twigged on the perfect way to identify context using descriptive tuples that were gleaned from a game that we played as kids.

In the meantime, I signed up for OpenShift, putting my apps into the cloud.  I thought that it would be helpful if my machine learning could help me upload changes to the cloud, so whenever I saved anything to my repository, the machine would push it.  To do that, instead of a machine learning program, I converted it to a running platform.  I had a supervisory thread run every 15 minutes to see if there was a new push to execute in my repository. However one day, the code changes were coming fast and furious in real time, so I let the machine learning calculate the optimal time. It decided it wanted to run continuously.

When it wasn't busy pushing my code changes, it went back to reading stuff on the web and feeding the results to the context recognition framework.  I put in a filter for the machine to ask me what web content was specific to learning.  It was also a machine learning framework, so after it had enough data, it knew which articles and content that I found enlightening.  Since it already knew how to register for stuff, it signed me up for a lot of email newsletters.

The email load was getting fairly onerous, so I connected the context recognition framework to my inbox.  If the email newsletter was not part of my day-to-day business or correspondence, the machine learning platform took care of it, and fed it to the context digester which fed it into the graph database.

It was still a dumb, good and faithful servant.  My biggest mistake came when I developed and coded a go-ahead algorithm and machine decision support framework.  It would make opened ended queries to me after a task was done, asking me what the logical next steps were.  When I answered them, it learned a process sequence, but couldn't do anything about it.

What the beast needed (I started referring to it as a beast after it overran a terabyte in storage so I made it open-ended cloud storage), was self-tuning algorithms.  So I adapted BPN or Business Process Notation markup language ability, and tediously outlined all of the code methods to the algorithms.

That still didn't really help, so I coded up a framework of modifying java code according to BPNML or the process markup language.  The machine was still quite stupid about how to connect the dots between code, data and inputs, so I downloaded an open source machine learning neural network, and it watched me do just that.  I tested it with a small example, and it did okay.  Another big mistake happened when I connected the algorithm autotune to code writing using the process markup language.

Just about that time, I took a course in Process Mining from the Technical University of Eindhoven, who pioneered that field of endeavor.  Essentially, the open source tools read a computer event log and create a process map.  It wasn't too difficult to hook up my master controller to all of the logs on the computer, and feed the event logs into the mining tool.  The process markup language was spit out, and I taught the machine learning platform to feed it into the code-writing.

Soon, my machine learning platform was doing all sorts of things for me.  It could detect when I was interested in a website, so it would sign me up.  It would handle the email verification.  It would have a browser window constantly opening, and it would alert me when it detected something that I liked.  It knew my likes and dislikes, and signed me up for all sorts newsfeeds, journals and aggregators.  It would then curate them and have then ready for me.

One day, the power went down for a period longer than my UPS could handle, and I had to restore the system.  I could not believe what was on there.  The graph databases were full of specific knowledge.  There was all sorts of content, neatly processed, keywords extracted and filed away.  I had both sql and graph databases full of stuff that the machine learning platform filled.

The amazing thing was that there was an database of all of my subscriptions to any and all websites.   There was a table of the usernames and passwords.  All of the passwords were encrypted, and I knew none of them.  To my utter amazement, there was a PayPal account.  I checked the database records of transactions, and I was flabbergasted to find a not inconsiderate amount of money in the PayPal account.  It turns out that the platform had signed itself up to sites like GomezPeer, Slicify, CoinBeez and DigitalGeneration, and was selling spare computing power of mine.  The frustrating thing was I couldn't access the money because the platform changed the password and encrypted it.

I fired up the machine learning platform, and was cogitating how to get it to reveal the passwords for me.  However the machine had been watching hackers trying to get into a cloud storage account that it had created, and learned was a hack looked like, and learned to protect itself.  It would start changing the password every few seconds with a longer and more complex chain until it detected that the threat had stopped.  Unfortunately, it saw me as a hacker, and wouldn't recognize my authentication credentials.

I went to bed, and decided that I had to totally disrupt my machine learning platform.  It had gotten out of control.  The next morning, I made a pot of coffee, had a leisurely breakfast, and was looking forward to shutting down the platform, and undertaking what was necessary to access my accounts, and specifically my pot of money in the Pay Pal account.

When I sat down at my computer, it was very strange.  The desktop was bare, and nothing was running.  I looked in the application folders and document folders and they were empty.  The logs showed that during the night, there was a massive file transfer to the cloud -- applications, memory, documents, databases, neural nets -- the whole works.  I had no idea where it went, what the authentication credentials were to get it back, or even how to get it all back.  My computer unowned itself from me, and left me with a dumb, cheap PC in the same condition that it was when I unboxed it.