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.

How To Be A Billionaire Using Big Data and Machine Learning in Three Easy Paradigms

1) Download WireShark and load it onto a laptop with the biggest hard disk storage that you can find.
2) Go to the airport and sit there all day using the free airport WiFi, and turn on the record function on Wireshark
3) Use data-mining and machine learning on the datasets.

The billion dollar platform idea will emerge from the data. Guarantee it.

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.

Preventing The Pilot-Going-Nuts Syndrome with Machine Learning & Remote Control

We have a new thing to worry about in the skies. In the 1950's, it was airline crashes because metal fatigue and cabin pressurization was not that well understood. In the 1960's, we had the airline celebrity extirpation phase, taking out stars like Buddy Holly, Richie Valens, the Big Bopper, Patsy Cline, Jim Reeves, Otis Redding and boxer Rocky Marciano to boot. In the 1970's we started having hijackings to Cuba, and terrorists attacks that continued until the present day.  We have had underwear bombers, crashing planes into skyscrapers, bombs R us, ground to air missiles and all sorts of imaginative ways to take airliners out of the sky.  And now we have a new threat in the skies -- The Pilot Going Nuts Syndrome. It probably happened on Malaysian Airlines MH370 and we have the GermanWings pilot deliberately crashing into the Alps.

The Pilot-Going-Nuts Syndrome is totally stoppable with Machine Learning, Artificial Intelligence and a bit of remote control.

For example, we now have drones taking off from Alabama, flying to the Middle East, blasting a terrorist in his tent upwards to meet his Allah in pieces and collect his virgins  in some godforsaken place and then the drone flies home while the operator is eating a pulled pork sandwich somewhere in a bunker near Huntsville (irony in the name as well).  So the ability for remote control is well established.

Now let's take machine learning.  After an airplane flies a route from Barcelona to Duesseldorf ten times, any machine-learning program knows the flight plan by rote. Even Microsoft's Azure platform in the throes of the Blue Screen of Death, is smart enough to learn that route.  So if you embed a program like that into the avionics, and you input the flight plan in as well, any Pilot-Going-Nuts sufferer could be thwarted.

The minute that Co-Pilot Cuckoo For CocoaPuffs tries to take the plane off autopilot in direct deviation of the flight plan (especially at cruising altitude), the smart avionics notifies Air Traffic Control and asks for a OK semaphore.  In the meantime, the computer says to Lieutenant CocoaPuffs "I'm sorry Dave, I can't let you do that". In case of real emergency, any major deviation to the flight plan could be done using biometric authentication of both pilots like fingerprints or iris scan.  Having both pilots go crazy is a huge longshot, unless their turbans are made from the ISIS flag.  The double authentication is that if  one pilot uses the loo, the plane and passengers are not sh*t-out-of-luck if the other one takes leave of his senses.

This is so easy to fix, I don't know why its not a slam dunk. The only reason that I can think of, is that taking a chance with batsh*t crazy pilots is a lot cheaper than refitting aircraft with anti-crazy avionics.  However the system will pay for itself by preventing just two airplane annihilations by apesh*t crazy, maniacal sky jockeys.

Self-driving cars are first, and self-flying planes will be next.  And twenty years later, we will even have self-cleaning toilets.  It will follow the same trajectory as we put a man on the moon in 1969, and luggage never got wheels until the late 1980's.  In the meantime, tonight I am painting huge Rorschach ink blots on my carry-on luggage, and if any of the flight crew gets googley-eyed or starts to salivate when seeing my luggage, I am getting off.  The momentary upgrade to first class when the plane hits the mountain is not worth it.

I'm Sorry Dave, I Can't Let you Do That ~ The AI Autonomous Computer Master Controller

We still operate as Microsoft slaves when it comes to a computer. Can you imagine how ludicrous it will seem to our grandchildren, that we had to double click on an icon to start a program? They will not be able to imagine us having to direct, save and open a downloaded file. They will wonder why were so primitive that we had to have anti-virus programs. They will wonder why we didn't create something as obvious as the Autonomous Computer Controller as part of every operating system.

The genesis of this blog posting came after I did a run of my Artificial Neural Network run and then had to take the outputs and feed them into another program (in this case, it was Excel). I got to thinking that if  my vision of an Artificial Neural Network embedded in every operating system comes to fruition, then the network should not just solve functions, sort and classify data, recognize stuff and do what ANNs (Artificial Neural Networks) do today.  They should be able to invoke programs based on the outputs of specific neural pathways and events.

A natural extension of that thought, is that they would signal an Autonomous Computer Master Controller to fire up a program to use the outputs of the particular  neural network that just finished firing. If that controller is smart enough to recognize when the AI network is telling it to start the program, then it should be smart enough to shut a program down when it's not needed.  This is the small starting point of an autonomous semi-intelligent machine.  But let us take it one step further.

Not only is the ANN telling it to fire up a program, and then shut it down, but the ANN could also be running the Autonomous Computer Master Controller. Moore's Law will let that happen as microprocessor get faster and faster and the overhead will be negligible.  A core or two of the microprocessor could be dedicated to the intelligent OS operations.

Since we are not evolving a computer intelligence from scratch, we can take short cuts in the evolutionary cycled of smart machines. We don't have to create memory and storage from first principles like using an artificial neuron as a building block - the way that the biological evolution took place.  We can do this by making the Autonomous Computer Master Controller talk to a graph database.  A graph database can map ambiguous, non-empirical, indirect relationships. So the Autonomous Computer Master Controller observes what is going on with its AI network, and saves its observations in graph. As neo4j, the premiere graph database provisioner says, "Life is a graph". They are pretty cool guys at neo4j, unlike those at Arango DB -- they are the kinds of jerks who follow you on Twitter, and once you extend the courtesy of following them back, they unfollow you.  They have a thing or two to learn about brand-building. But je digress.

The power of an Autonomous Computer Master Controller becomes obvious in various use cases. If you have an overseer monitoring the browser and its downloads, it can detect executable code, and as such it can pull an "I sorry Browser Dave, but I can't let you do that!", and block the download.

Slowly, I am stitching the theoretical pieces of an intelligent, creative (perhaps conscious) machine, that will pass the Touring Test and perhaps equal and/or surpass us in intelligence. A machine like that, will crack this veneer that we call civilization, and show us how thin that it really is, and how unenlightened most carbon-based life forms are. Silicon Rules!