Never Mind Artificial Intelligence, How About Artificial Personality ?

In my quest to make the ultimate Artificial Intelligence chatbot that sells cars, I have been pontificating on various attribute that the chatbot should have. It should have an EQ (Emotional Quotient) as well as an IQ (Intelligence Quotient). It should be good at math instantly. It should be good at logic and detecting attempts to misdirect and confuse it. It should know when to be aggressive and when to back off, as part of its emotional awareness. It should be able to remember conversations, and return to any point in the conversation after a non sequitur, especially when in the middle of negotiations. I have already describe on a high level as to how I would implement the technology for this in previous articles.

As I was discussing this with a friend, it was pointed out that I needed to create a de facto artificial personality. And it was pointed out to me, that perhaps there should be a feminine one as well as a masculine one. I named my chatbot Honest John and made him a male, simply because I am a male, and I tried to transpose what I would say if I were a chatbot.

I keep up to date with Artificial Intelligence and I am practitioner of it. There are researchers out there seeking the Holy Grail of artificial consciousness in silicon. They are trying to making "thinking machines" with consciousness. Artificial consciousness in a thinking machine is a noble aim, but I think that it is putting Descartes before the horse. One has to have a personality that directs the aspects of thinking and personality-expression, much like a wedding cake and a wedding ring converts your partner's personality to a morose, complaining entity with a negative worldview.

Creating gender in a chatbot is easy. It is already incorporated in the AIML (Artificial Intelligence Markup Language). It substitutes "he" for "she" and hobbies like "sewing" instead "drinking beer". But that is not enough. The gender responses also have to match the personality. For example the non-sympathetic, hard-nosed, take-no-prisoners negotiating chatbot could either be a man or a woman, and truth be told, some men prefer a woman with those traits. So, there has to be a way of imbuing personality into the chatbot.

Luckily, that is not technically difficult to do. Once personality traits are defined, they are stored in AIML, and the appropriate AIML libraries are loaded when the chatbot fires up. The work for this, is all semantic and expressed in natural language within the AIML. This is where a liberal arts degrees become useful again -- at the intersection of technology and human interaction.

So my chatbot Honest John will have the capability of becoming transgendered into Honest Jane. Or Honest John will have the ability to stop being the cigar-chomping salesman and become the meditating yogi who recommends an electric car at a fair price to the goodhearted peoples who made it to save the planet's environment. This has been a fun journey so far.

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 Châtelier'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.

How To Build Free Will Into Artificial Neural Networks Using A Worm Brain As A Model

In March of 2015, there was a fascinating study published in Cell, conducted by Rockefeller University.  The study was a brain analysis of a worm, specifically how a single stimulus can trigger different responses in a worm.  This may have huge ramifications for artificial intelligence and thinking machines.

A worm is not burdened with a whole lot of neural nets. This particular specimen ( Caenorhabditis elegans  ) has 302 neurons and about 7,000 synapses or connections between the neurons.  This microscopic worm was the first to have its entire connectome, or neural wiring diagram completely detailed.  The researchers found that if a worm is offered an enticing food smell, it usually stops to investigate.  However it doesn't stop all of the time.

There are three neurons in the worm brain that signals the body to a food detour.  The collective state of these neurons determine the likelihood of the worm doing a fast food drive through. By stimulating the states of the various permutations and combinations of the three neurons, the researchers could figure out the truth table of meal motivation.

It's being touted as worm free will.  The three neurons in the worm are called AIB, RIM, and AVA. When the odor sensors pick up the smell of isoamyl alcohol, the dinner bell for this worm, the stimulus is first presented to AIB.  There are only three persistent states of these neurons.  The first is when they are all off.  The second is when they are all on, and the third is when AIB is on.  AVA is the neuron that sends the signal to the muscle of the worm to chart a new course for the food.  When all three neurons transition from on to off, the worm heads for the buffet table.

If the worm had no free will, then every time it got a whiff of isoamyl alcohol, it would head for the feeding trough. But it doesn't.  AIB is the context monitor.  It checks out the state of the network, and determines whether RIM & AVA will play. If they won't, AIB won't play either, and the food is ignored.

The human analogy that the researcher gave was that you would get a hunger pang, and you have to cross the street to get food at the restaurant.  However if the AIB equivalent fired when it was activated when it was unpleasantly cold and you didn't want to suffer the discomfort, you ignored the hunger pang.

This is really interesting in many ways for machine learning application.  In an earlier blog posting, which you can read here, I outlined how Dr. Stephen Thaler, an early pioneer of machine intelligence in design, used perturbations in neural nets to cause them to design creative things. His example was a coffee mug.  Thaler used death as a perturbation -- he would randomly kill neurons and the crippled neural network produced the perturbations that created non-linear, creative outputs.  In my blog posting, I posited that instead of killing neurons, one method was to do synaptic pruning -- just killing some connections between the neurons.

In another blog posting, which you can read here, I postulated other forms of perturbations and confabulations as a method for machine thinking and creativity.  They include substitution, insertion, deletion and frameshift of neurons in the network.

Thaler's genius, I think, is the supervisory circuits of the neural networks.  He used them to funnel the outputs of perturbed and confabulated networks into a coherent design.  Not only can they do creative work, but extrapolating with what was shown with the worm neurons, they can also add free will -- a degree of randomness in behavior that precludes hardwired behavior.

The bottom line is that the AIB neuron in the worm evaluates the context of the neural stimulations, but what if, instead of just a contextual neuron, you plugged in a Thaler-like supervisory network?    You could add a pseudo-wave function of endless eigenstates and the resultant outcome would be the collapse of the function into a single eigenstate or action, due to the output of the supervisory context evaluator network.

This is all fascinating stuff.  But wait, don't send money yet -- there's more.  And it gets even weirder yet.  And the possibilities of artificial intelligence get more fantastic with simpler constructs.

Going back to the worm studies, the connectome is all mapped.  The researchers found that for the first state in the connectome diagram, when all of the neurons were activated, they transitioned to the low state and worm got to follow its nose to eat (so to speak).  But this was not a 100% guaranteed event. It usually happened, but there were some small number of times when it didn't.  This makes is a probability function.  Knowing the number of neurons, the state of them, and having a map of the connections, then one can create a complex Bayesian calculation model.  (A very simplified explanation of a Bayesian calculation, is that the conditional probability of an event can be calculated knowing the probabilities of the previous event(s)),

So what if you created a neural network with supervisory circuits, and modeled the permutations and combination of states?  If you got good enough at it, and your model was sufficiently accurate for some sort of use, then you wouldn't actually need the neural networks.  You could string together a whole pile Bayesian calculators built on the probabilities of neural networks, without all of the necessary hardware and software to calculate the inputs and outputs of massive amounts of artificial neural network layers.  You would be faking intelligence with a bunch of equations rather than the bother of neurons and such.  A simple small device with rudimentary computation could be fairly intelligent.  In this Brave New World, the richest data scientist will be the one with the best Bayesian calculator.

But there is even more, so one more parting thought.  The worm's neural nets could be a very rudimentary model of the way that we as humans work.  The difference is that our neural networks are massively scaled up.  The human brain has 86 billion neurons and 100 trillion synapses -- give or take a few billion depending on the level of alcohol imbibition of the person. If the model holds, and there is a possibility that the brain could potentially be modeled as one humongous Bayesian calculator, what does that say about Life? To me, it says lots, and that a machine one day, could have the basis of cognition, and some sort of consciousness.

A Start to Artificial Conciousness - Making A Computer Worry With Machine Learning

Bring things spring from small seeds. This is the thought that keeps running through my mind when I think of Artificial Consciousness in computers. Ever since I saw the Imitation Game and the story of Alan Turing, I wondered how such an intelligent man could think that computers could think.  Of course he stipulated that the thought process was different, than in humans.

Then along came Dr. Stephen Thaler who artificially introduced the idea of perturbations in computer "thinking" and got a patent for it. A perturbation is essential to artificial consciousness.  Essentially, a computer is programmed to linearly follow an execution path of its program. Even in artificial neural networks, the output of one perpceptron is fed into another layer.  It is a linear defined path.  Thaler introduced perturbations by selectively killing off perceptrons in a layer and in what he describes as artificial neuronal near-death, and the machine becomes a creative design machine.  His landmark example was a neural network that identified coffee cups and when it was brain-damaged, it came up with creative coffee cup designs.

Perturbations can come from all things. They can come from random events. But in the state of consciousness of any sentient thing or being (notice I now have to add things, because computers have the ability to become sentient), perturbations can come from the state of consciousness itself.  A prime example is worry. We observe something within our conscious sphere, and we think about it, make a judgement about it, add the judgement to the thought process, and keep recycling the thought in an obsessive compulsive state, and you have what is known as worry.

Going back to the opening statements of big things spring from small seeds, the thought struck me that I could make my computer worry.  It would be a small worry program to start with, but then I could hop it up to another layer of abstraction and make a universal computer worry module that could become part of the Operating System.  It would be the Worry Service.

Here's how a simple version would work. The Worry Service runs a task manager, a memory monitor or a CPU usage monitor in the background.  The minute that it detects that memory or CPU is approaching 100% or saturation, it kicks the worry.exe module. The worry module essentially assumes the highest thread priority, prints out on any display saying "I am incredibly busy" and deallocates processing priority to the heavy task, slowing it down.  It then detects that the task is slowed down, and kicks another worry module about its lack of performance.  The worry modules are able to be queried, and their response is always "I am incredibly busy and it is affecting my performance".  The worry module also writes to every log that it can, and using a machine learning neural network, reinforces the worry parameters so they automatically fire at lower thresholds.  Once the busy task is completed, the worry abates and slows down, and the computer becomes efficient again.  Of course, if the machine-learning neural network is too effective and eager in kicking in the worry, it becomes a compulsive worry, and needs to see a programming computer psychiatrist to up the thresholds of its worry mechanism by running a few positive reinforcement training epochs.  All of this technology is available now.

But I can just see it. Some Goth programmer will chain all of the worry modules into the depression module, making the computer virtually worthless for sustained work.

The very first step to scary artificial intelligence, is making a computer with the ability to navel gaze. This is a start. I am convinced that human consciousness is merely an accident of an over-developed tropism, and the evolution of Artificial Consciousness can start with this simple step -- a computer worry wart. Windows machines will be the worst worry warts and the most depressive among the conscious computers.

Will Computers Be Able To Have Children?

Dr. Stephen Hawking says that we should be afraid of creating Artificial Intelligence that can become a threat to man.  My contention, is that we are already on that path. That Pandora's Box or Can of Worms is already opened. The only way to close a can of worms is with a bigger can, and nobody has one when it comes to the progress of technology.

When Ray Kurzweil's book, "The Age of Spiritual Machine's" came out, I thought that it was a bunch of bosh -- until I got to a seminal part of the book for me.  It was a small appendix of a few pages about building an intelligent machine in three easy paradigms.  That book changed my life. One of my daughter's gave the book for Christmas, and it was the book that started me on the path to programming artificial intelligence and playing with machine learning.  I never once thought that I would use Machine Learning in my job, and I was wrong.

Machine Learning has a long way to go, as does Artificial Intelligence, but we are making great headway.  In previous blog entries, I make the case for every Operating System, or OS to have an artificial neural network embedded in it. I also make the case for standardized neural network notation so that I can transfer, or sell what my machine has learned to your machine.  And I make the case in this blog post, that we can evolve smarter and smarter machines, if every time that we need to load a new operating system, we let an existing operating system impart its neural nets to the new machine.  One of the differences between humans and other animals, is that knowledge is not passed from generation to generation.  If we do that with computers, we are well on the way to make scary intelligent machines.

So if a computer can pass on knowledge to a new generation of computers, by passing down knowledge embedded in Artificial Neural Networks, can one say that the new computer is a child of the old computer?

I have opined on how to create Artificial Consciousness (more in a later blog topic on how I can make a computer have the worry emotion). I also have talked about Computational Creativity and Dr. Stephen Thaler's work.  So if we evolve computer intelligence to the point that it can seed other computer's with that intelligence, then we are on the way to computers having virtual children.

The way that I see Artificial Intelligence evolving, is that no computer can be an expert on everything. As computers become more and more intelligent, there will be specialization among the ranks of computers, as there is in human endeavor. Some computers will trade securities. Some will diagnose illness. Others will run power plants.  There will be a hierarchy of computer intelligence as there is in humans now.  And the progeny of each computer will be a mirror of its parents.  It's hard to imagine, but if computers do acquire consciousness, intelligence, personality and creativity, then the internet will become a computer society mirroring human society.  And that is when we will have to fear it.

Alan Turing never knew what he was getting into when he proposed his machines and the capability of passing a Turing Test.  We are on the cusp of something mind boggling, but at the moment, I would be content on creating an Artificial Neural Network that makes money for me while I ruminate about Artificial Intelligence.

How To Make Scary Smart Computers

If you have ever visited this blog before, you will know that I am heavily into Artificial Intelligence.  I have been playing with artificial neural networks for about ten years.  Recently buoyed by the Alan Turing movie "The Imitation  Game" and "The Innovators" by Walter Isaacson, I have decided to start mapping out what it would take to make the truly intelligent, conscious, creative computer that would easily pass a Turing Test.

In previous blog entries, like the one immediately below, I outline the need for an autonomous master controller that can stop and start programs based on what an embedded set of artificial neural networks come up with.  I have also started thinking about a standardized artificial neural net, that can be fed into any machine already trained, so there can be a market for trained artificial neural nets.  I have outlined a possible algorithm for perturbations in artificial neural nets that can create computation creativity.  The list goes on and on.
 
But here is another essential element,  If computers have embedded artificial neural networks in them, then for the machine to become scary smart, it has to be able to pass on what it has learned to the next generation.  So how do you accomplish that? Easy.

Every time that an Operating System or OS is upgraded, it is upgraded by a predecessor machine who passed on the trained and learned neural nets that it has learned in its artificial lifetime.  It is the equivalent of a parent teaching a child.

In the biological world, what makes humans different from the animals, is that we can pass on wisdom, knowledge and observation.  In the animal kingdom, each new generation starts from where its parents did -- near zero.  Animals learn from their parents.  But I can go and read a book, say written by the Reverend Thomas Bayes who wrote in the 1700's on Bayesian theory, and I can read last weeks journals.  I can pick and choose to learn whatever I want from the human body of knowledge.  But first and foremost, I get my first instruction from my parents.

So if a new Operating System is loaded into a computer from an existing one with artificial intelligence, then it won't have to start from scratch.  And if you embed the ability of the artificial neural networks to read and learn stuff by crawling the internet, soon you will have a scary smart computer. The key is that each machine and each server is capable of passing on stuff that it has learned to new computers.

I do believe what Dr. Stephen Hawking says, is that one of the threats to mankind will be some of the artificial intelligence that we create.  But like nuclear fission, we have to build it for the sake of knowledge and progress, even if it has the potential to do the human species grave harm.

We have already opened the can of worms of artificial intelligence.  Once opened, there is no way to close it unless you have a bigger can.  Unfortunately, the contents of that can of worms is expanding faster than we can keep up with it.  The best way to control artificial intelligence, is to have a hand in inventing a safe species of it.

Synaptic Pruning in Artificial Neural Networks and Multilayer Perceptrons

What happens in a baby's mind is fascinating.  While the baby is sleeping, it processes all of the information that its senses took in, and puts in through a huge Mixmaster creating all sorts of connections to memory, storage, logic and emotions.  I love the way that Mother Nature plays dice.  The baby's brain makes synaptic connections between bits of data that are also inappropriate. This is hugely beneficial because once these connections are made, then the logic circuits can evaluate if they are sound and reflect the outside world.  A baby's brain multiplies in size 5 times until it reaches adulthood, largely from creation of synapses or links to neurons (plus other biological infrastructure functions).  This is why a child's imagination is so fertile.

Then we have synaptic pruning near the onset of puberty. ( http://en.wikipedia.org/wiki/Synaptic_pruning ). Once we start thinking about sex, we start pruning the synapses that we think are inappropriate.  The cartoon below gives a very simplistic diagram of pruning inappropriate synapses.  I use the word inappropriate in the sense of what is considered inappropriate by adults and keen rationalists or fairy-tale dogmatics.

How did I get onto this?  I saw a tweet by a hard-core religion fundamentalist who stated that neuroplasticity was the deity's way of fixing a brain.  (In that context, I think that he was implying neural re-wiring to fix apostasy, homosexuality, atheism, and everything else that he didn't approve of.)  I had heard of neuroplasticity but I googled it to ascertain the current scientific thinking of it.  Simply, neuroplasticity is the rewiring or creation of synapsis to take over functions of the brain that have been destroyed by trauma, injury and/or accident.  For example, it has been reported that brain function controlling say motor activity has been discovered in a portion of the brain not known for that activity in an accident victim.  The term synaptic pruning was in this article, and I had to investigate the term.

Once I googled it, it reminded me of the works of Dr. Stephen L. Thaler, PhD.  He has a raft of scientific discovery and patents, and he was an early adopter of artificial neural networks. ( http://imagination-engines.com/iei_founder.php ). In a nutshell, he did some work in Cognition, Consciousness and Creativity in artificial neural networks for which he holds patents.  He discovered that if you randomly destroyed neurons in a massive array of artificial neural networks, as the network was expiring, it came up with creative outputs or solutions.  As a result, he added another layer of neurals nets to observe this.  In essence by killing off neurons randomly, he was doing synaptic pruning of a sort.

Let me quote from Dr. Thaler's website:

After witnessing some really great ideas emerge from the near-death experience of artificial neural networks, Thaler decided to add additional nets to automatically observe and filter for any emerging brainstorms. From this network architecture was born the Creativity Machine (US Patent 5,659,666). Thaler has proposed such neural cascade as a canonical model of consciousness in which the former net manifests what can only be called a stream of consciousness while the second net develops an attitude about the cognitive turnover within the first net (i.e., the subjective feel of consciousness). In this theory, all aspects of both human and animal cognition are modeled in terms of confabulation generation. Thaler is therefore both the founder and architect of confabulation theory and the patent holder for all neural systems that contemplate, invent, and discover via such confabulations.


The idea then struck me, that perhaps it wasn't necessary to destroy the neuron in the network to achieve what Dr. Thaler saw, but rather just do the synaptic pruning, by randomly destroying inputs (and as a result their weights) in the hidden layers of multilayer perceptrons.

After the connection was destroyed, you would still run the AI machine including back propagation and see what comes out.  What a fascinating concept, and I am itching to try this once I find the time.

I am sure that all sorts of people might think that Dr. Thaler is a nutbar, but those were the same people who thought that Benoit Mandelbrot's ideas on fractal geometry were child's play with no practical applications.  Or we see how the works of the Rev. Thomas Bayes who is a relative unknown, publishing only two papers in his lifetime, and dying in 1761 postulated the important Bayesian inference used in Machine Learning.

So Artificial Neural Networks come and go in popularity in the computing field.  I am sure that Dr. Thaler is onto something, and for some strange reason, his theories may pan out to be seminal in the field of machine consciousness that way that Alan Turing's ideas became pivotal in this modern age of technology.  And somewhere in there, synaptic pruning will take place, and it just may not be a footnote in the development of artificial consciousness.

If you are looking for ideas for a master or doctoral thesis, you are welcome.

Musings on Machine Consciousness or Artificial Consciousness

(click on photograph to enlarge)

I was walking along a country road and I came upon this scene picture above.  The picture really does no justice to the rolling hils, the lines, folds and textures as well as the light and dark areas.  As you eye takes in the panorama in real life, you realize that it is a magical place that makes you feel numinous.

And right in front of me, was a guy with a huge piece of paper, trying to capture the scene with a charcoal sketch in preparation for a painting.  The back of his VW stationwagon was lifted open and sketches were scattered all over the car.  I peeked at his sketches, and we started talking.

As it turned out, he was from Australia,  an ex-computer engineer, a mainframe guy who was now an artist and had been for over 20 years.  He earns his living with his art.

We got to talking about stuff, computers and such, and the magnificent scene in front of us, and the impossibility of reproducing and how it made us both feel numinous and from there we got on to consciousness.

I brought up the topic that Ray Kurzweil had predicted a machine with a soul with his book "The Age of A Spiritual Machine".  My new-friend begged to differ, citing that we really didn't know what consciousness was, so how could we emulate or even create it on doped silicon.

I wanted to beg to differ with the artist.  My own neural nets tell me that consciousness is an over-developed tropism.  Plants started it with phototropism for the leaves and geotropism for the roots, and after several hundred million years of evolution gone wild, the tropisms developed into senses, the brain evolved to process the sensory stimuli and it didn't stop.  We developed the ability to abstract, to think, and to integrate the sensory data to knowledge and preserve and pass it on to our species.

This got me to thinking about how a primitive artificial consciousness would be formulated in a computer.  We already have the prototypes with QoS circuits in servers monitored Quality of Service.  We have a heartbeat, plus all sorts of monitors that read and report on processes, cycles, peripherals, services and such.  Right now, these QoS circuits report to humans.  They would have to report to the machine itself.  And the machine would have to react to them.

How it reacts is the tricky part.  In humans, we react to our sensory input by abstracting it to a higher plane.  If for example, our heart skips a beat, we start worrying about a heart attack.  What it means for machine consciousness, is that the data integration into information and finally stringing information into knowledge has to be developed.  As a further step, knowledge must be abstracted into an ideal that can be applied to many other things.  For example, any child knows the following ideal:

Using this ideal, a child can match the above to this:

And with even a bit of cogitation, a child can match the abstract with this:

So, just to recap to this point, a computer would need sensory inputs from everything about itself, hardware and software, and then it would need some powerful inference tools to sort the Big Data from its sensors into information, and from there in knowledge and from there, into an abstract ideal, or an interface if you will, in programming language.

We haven't touched the world outside the computer yet.  But we are not done.  Once we have the knowledge bits, we need some emotions bits if we are to match the human experience.  I'm not saying that we really need that.  The computer conscious could be a Spock-on-steroids who has no emotions, but that wouldn't be fun, would it?

Emotions need qualitative judgements and reactions to those qualitative judgements.  In a human being, if we are worried, our performance is impaired.  If we were to match this in a machine, worry would spawn threads that would impair performance based on whether the machine was bummed out or not.  But how would a machine express happiness?  Would it be by innocuous things like playing mp3s when it should be doing floating point co-processing?

My Australian friend said that we would never develop Artificial Consciousness.  Moi, I say that what I have brought up are mere details.  There will be frameworks upon frameworks that will give the computer a good idea of what is going on, how the computer should react, and that there is a whole other world outside the binary bits and bytes of the doped Gallium Arsenide matrices and PNP and NPN junctions in its transistor neurons. It's a scary thought when a computer first comes to the realization of an outside world, but damn, it would be exciting.   More to come.