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.
I am totally convinced that the Ashley Madison hack was an inside job. The range of data came from the database servers, the email servers, and the internal document servers. Even good hackers could either get to the database, or get to the server, but not both without some help. And the access to the other servers, well its just incredulous.
The statement indicates that they know the protocols and where everything is hidden. I doubt that anyone will ever be caught, because only 1% of hackers are caught. The hacker(s) in this case have an alarming familiarity with the operation, that usually can only be gleaned from working there.
John MacAfee wrote that he thinks that the hacker is a disgruntled female employee or ex-employee. I wouldn't go so far as to say that the hacker is female, but the person is hugely technically inclined.
Here is the statement:
Avid Life Media runs Ashley Madison, the internet's #1 cheating site, for people who are married or in a relationship to have an affair. ALM also runs Established Men, a prostitution/human trafficking website for rich men to pay for sex, as well as cougar life, a dating website for cougars, man crunch, a site for gay dating, swappernet for swingers, and the big and the beautiful, for overweight dating.
Trevor, ALM's CTO once said "Protection of personal information" was his biggest "critical success factors" and "I would hate to see our systems hacked and/or the leak of personal information"
Well Trevor, welcome to your worst fucking nightmare.
We are the Impact Team. We have hacked them completely, taking over their entire office and production domains and thousands of systems, and over the past few years have taken all customer information databases, complete source code repositories, financial records, documentation, and emails, as we prove here. And it was easy. For a company whose main promise is secrecy, it's like you didn't even try, like you thought you had never pissed anyone off.
Avid Life Media has been instructed to take Ashley Madison and Established Men offline permanently in all forms, or we will release all customer records, including profiles with all the customers' secret sexual fantasies and matching credit card transactions, real names and addresses, and employee documents and emails. The other websites may stay online.
So far, ALM has not complied.
First, we expose that ALM management is bullshit and has made millions of dollars from complete 100% fraud. Example:
-Ashley Madison advertises "Full Delete" to "remove all traces of your usage for only $19.00"
-It specifically promises "Removal of site usage history and personally identifiable information from the site"
-Full Delete netted ALM $1.7mm in revenue in 2014. It's also a complete lie.
-Users almost always pay with credit card; their purchase details are not removed as promised, and include real name and address, which is of course the most important information the users want removed.
-Other very embarrassing personal information also remains, including sexual fantasies and more
-We have all such records and are releasing them as Ashley Madison remains online.
Avid Life Media will be liable for fraud and extreme personal and professional harm from millions of their users unless Ashley Madison and Established Men are permanently placed offline immediately.
Our one apology is to Mark Steele (Director of Security). You did everything you could, but nothing you could have done could have stopped this.
This is your last warning,
We are not opportunistic skids with DDoS or SQLi scanners or defacements. We are dedicated, focused, skilled, and we're never going away. If you profit off the pain of others, whatever it takes, we will completely own you.
For our first release, and to prove we have done all we claim, we are listing *one* Ashley Madison credit card transaction for each day for the past 7 years, complete with customer name and address (oneperday.txt) and associated profile information (oneperday_am_am_member.txt and oneperday_aminno_member.txt, selected rows from our complete dump of the AM databases). We are also releasing a hash dump and zone file for both domains, select documents from your file servers, executives' google drives, and emails, and the Ashley Madison source code repository. Also, since Ashley Madison stopped using plaintext passwords, we're also releasing the swappernet user table, which still has plaintext passwords:
1 example from this dump: "PERNELL GRAZETTE", with profile ID 23288650, who spitefully paid for Ashley Madison the day after valentine's day in 2014, lives at 10 charlotte st. Brockton, MA in the US, with email UPFRONT73@AOL.COM. He is not only married/attached, but is open to a list of fantasies from Ashley Madison's list: |29|44|39|37|7|, a.k.a. "Cuddling & Hugging", "Likes to Go Slow", "Kissing", and "Conventional Sex". He's looking for 'A woman who seeks the same things I seek: passion and affection. If you have such desires then we will get alone just fine','|54|11|9|' which means "Good Communicator", "Discretion/Secrecy", and "Average Sex Drive". He also says "I have only two personal interests on this site. Making sure that You are comfortable with me should I be so fortunate to hold your attention and making sure I take the role of discretion to an artform. I mean isn't this why we are here, to be as discreet as possible?" From the login table, we know his user ID is 'Heavy73' and password hash is '$2a$12$ndvz/F.EXyJKRYkrErX/w.EDgzF7cNkJcQvNeDGQylEMHRw2COLZO'.
As another, profile ID 48040 is listed as a "paid delete", which means a few of his profile text boxes are gone, but from purchase records we know it is "RICKIE RAMRATTAN" from "5499 Cosmic Crescent" "Mississauga","ON" "L4Z3P8" whose fantasies are |7|40|17|34|33|37|38|48|36|42|43|50|44|32|39|29|49|18|, which includes "Likes to Give Oral Sex", "Likes to Receive Oral Sex", "Light Kinky Fun", "Role Playing", "Erotic Tickling", "Erotic Movies", "Good With Your Hands", "Sensual Massage", and "Dressing Up/Lingerie" among others. You must be glad you paid for your profile to be deleted, huh?
Too bad for those men, they're cheating dirtbags and deserve no such discretion. Too bad for ALM, you promised secrecy but didn't deliver. We've got the complete set of profiles in our DB dumps, and we'll release them soon if Ashley Madison stays online.
And with over 37 million members, mostly from the US and Canada, a significant percentage of the population is about to have a very bad day, including many rich and powerful people.
Well, Noel? Trevor? Rizwan? What's it going to be?
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.
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.
How to Design Books To Be Read On A Mobile Device
This is an excerpt from a chapter in my book called "The Ten Living Principles --The Craft And Creed of Transformative Digital Design". In this book, I have applied the principles of the latest research about how people read on a digital screen. This aids in up to 40% more absorption of content. You might see how this is done, with this example.
Something Completely Different Backed By the Latest Research
notice something different about the layout design of this book.
Something about the way this book is formatted.
This book has a unique value
proposition. It is the first book that I know of, that has been specifically
designed to transmit information when read from an electronic reader rather
than a paper book. I won't spell out the exact details, but you probably have noticed some subtle changes or something "different" in the
layout. You may have noticed phrasing, parsing, hinting, billboarding, white spaces, synchronous asymmetry and a soupçon of randomness and chaos. The design and science of digital reading has taken a great leap forward with the effort put into this book. It has
to do with the way we read things on electronic screens like smart phones or computer displays, and the result should make your task of absorbing this information a lot easier.
It may be slightly slower to
read, but the absorption of
information is significantly greater.
How You Read on a Digital Screen
In this Brave New electronic world, we are inundated with data, which we integrate into information. We have to do a lot more reading than our ancestors did. Everything from emails, to articles, to whitepapers and even SMS text messages vie for attention in our brains. There is a report stating that the amount we need to read has tripled in the past twenty years. That trend is not only continuing, but accelerating. So our adaptation, is a non-linear solution devised by our brains.
Nothing Is Linear
The way that the brain adapts to this flow of information, when reading from the screen of a device, is to do what is called non-linear reading. It is a type of skimming that uses both pattern recognition and meme processing to get the gist of what is written. For example Oxford Uinevritsy povred taht a prseon can raed a snetnece as lnog as the frist and lsat lteters are itncat. That iss because we don't sound out the word, but recognize it as a pattern. So we can skim quite accurately without reading all of the words. However, newcomers to the English language will find the mixed up sentence above, almost impossible to decipher. Our native ability to discern the contents of a sentence is amazing. This is sufficient for most cases, except in cases where every sentence carries a cogent, cognitive load -- like a book of information.
This process of skimming while reading, works great for a novel but nonfiction books do not fare very well on screens. Professor Ziming Liu of San Jose University found that we have adapted our reading behavior using screens to spot keywords, browse, scan and selectively fragment-read. This has negative effects on nonfiction, because we lose the capacity to read in-depth. Professor Andrew Dillon a professor at the School of Information at the University of Texas, Austin found that there is a cost to reading on screen in terms of attention and understanding.
Boosting The Words Into Your Brain From The Screen
Researchers found that absorption of information and hence the understanding of what was written suffered. Much of this arose from the dynamics of a screen with multiple sources of incoming things to read and a very distractive milieu. As a result, we are very poor time managers when it comes to reading on a screen.
Hooked On Notifications
We don't take the time to read slowly because our devices demand our attention, and we have not learned how to deflect and apportion time and management thereof for electronic information. Like Pavlov's dogs, we have become conditioned to automatically react when our smart phone announces that a new text has arrived. It is to the point that we rudely interrupt the people in front of us or in our company to check our devices, in spite of the fact that they have presence priority. It is our brain changing because of our uses of these devices.
The Brain Gain
In a further chapter, I deal with neuroplasticity and how we can change our brains for the better. Humans did not evolve to read, but after the printing press was invented, and paper information became widely promulgated, we became pretty good at it. The plasticity of our brains is such that we are always adapting. So if we continuously train the brain to read as if it was always reading from a screen, ultimately comprehension will suffer -- unless a design comes along to counter that. You have one such design in your hands right now. And, it perfectly illustrates a part of the craft and creed of transformative digital design.
Note: I will give you a hint on how to format a digital book to be read on the screen. In my book, the sentences are chunked in discrete meme parts with asymmetrical white spaces, and the paragraphs are chunked, USA Today-style, into small bites with an explanatory heading. I predict that this is the future of content delivery.
See the blog entry below on how and where to get the book.
The framework is a distillation of the Steve Jobs recipe for changing the world. These are the same type of principles that the ultimate master designer of all time, Steve Jobs followed. It is a fusion of self-improvement, Eastern Thought, cutting-edge science, and human factors engineering coupled with attributes of beauty, simplicity and truth. It changes you, the way that people see you as a designer and the way that people interact with technology. It puts a whole new spin on making ideas happen, where the onus is on the designer to transfer the Eureka moment to reality in an elegant, graceful, intelligent and artistic way.
Whether you are a programmer, graphic artist, writer, industrial designer, robotics engineer, sales and marketing guru, inventor, web developer, student or artist, or whatever discipline you follow that requires you to design anything, you can't jump to the big leagues without a plan and some practice. This book gives you both. It is time that we took back the work of digital from the meat factories of the cubicle farm, and become renowned craftsmen, who hallmark our revolutionary work with excellence.
The Ten Living Principles that will make you the best person AND design artisan that you can possibly be. It shows you how these Ten Living Principles translate to a physical or digital design. It reveals the two most necessary elements that every transformative design today must have. It spells out in the three attributes that all designs must have after the Eureka moment to be successful. It gives you the freedom to open up and let the best of you shine.
In keeping with these principles, this book has the value proposition of an intelligent design. It is probably the first book that is specifically designed and laid out to be read on a digital screen. If you have ever wondered what the future of reading for human beings will look like, this gives you a small glimpse of what to expect, based on the latest Human Factors research . It could be the template for all new eBooks.
But even more than all of the above, this book is a call to action. It is time to hallmark the digital world with personal excellence in execution of design. If there is one thing that the world needs more of, is transformative digital designers. It could be you!
Nook (Barnes & Noble):
As I am sure you have seen, we are reaching a point where there will be a major disruption within the automotive industry: the self driving car. From an optimistic point of view, the automotive industry will continue as usual for a period of time (10 years? 20 years? maybe even 30 years or more). However, from a pessimistic point of view, the transition will have many unforeseen consequences.
First the good news for people involved in the automotive industry: the change can not possibly happen overnight. It will be a very slow process as the initial self-driving cars will be very expensive (as is the case for most tech when first introduced). Unless corporations fund (or governments subsidize) a major initiative to adopt self driving cars, I would imagine that for the next 10 to 20 years at least that business will continue as usual. Some rich people will have fancy self driving cars, and everyone else will continue to buy regular cars. Some may ask "what if the car companies stop making regular cars?". My viewpoint is that where there is a demand, companies will produce product to meet the demand. Many people like to drive cars, so the pattern will continue. This will also continue to drive the after market of cars (used cars, wholesale cars, etc).
The self driving car, while a nice concept, also has the potential to create a dystopian future similar to what people read about in science fiction novels. Imagine for a moment a world where only the upper class can afford the self driving cars, and these same upper class use their wealth and political influence to obtain roads/highways/freeways which are to be used only by self driving cars. The American government in particular would enjoy this idea, as they could charge a toll for these roads.
This also creates the potential for yet more class warfare. Rich people on toll roads with self driving cars, safe from these so called "dangerous people driven cars" , while everyone else (lower class in particular) continue using regular cars....abandoned technology on poorly maintained roads (as possibly the government would put more resources into the self driving routes). In a world already highly divided by class/race/religion...this is not good.
Where does this leave people who can not afford the self driving cars, especially if we get to the point where General Motors or BMW stops making cars that have a self driving feature? The used car market. What happens when big auto abandons support for older cars? I believe people will get creative. I can see a future where aftermarket car parts are made by 3d printers. Some people however will view this as a side effect of the self driving car market. One group will want the future, the "safe" self driving car with all it's convenience. Another group will rebel against the concept, and do whatever they can to keep their regular car going so they can continue driving.
Based on the article from Vox.com that has recently being going around relating to DMCA and what car companies will do about the software in current modern cars, copyright law also comes into play when considering the future of the car. My take is that copyright will not stop people. The younger generation having very little regard for copyright is my main basis for this thought. People (in particular the young) will find a way to circumvent car software and do as they wish. Perhaps a new kind of auto mechanic? Time will tell. This idealism could also be applied towards self driving cars: people will find a way to bypass future software if it gets to the point where there is no self driving mode. Illegal steering wheel modifications for the car of the future? Possible startup idea right there.
A final thought (and this may be the one you find most interesting): the Third World. At this time the pros and cons of a self driving car is a very first world problem. As you know, people in various developing nations can not even afford a regular car, much less a self driving car! There are many countries that do not even have a good infrastructure for regular self driving cars (or public transit, but that is another matter entirely). I suppose where I am going with this is if America/EU/Japan/China start adopting self driving cars, there should still be a HUGE market for selling used cars in places like the Philippines, Africa, and various parts of South America. Suddenly an out of reach "regular" car could become affordable in the developing world. Perhaps all of the used cars of America will end up being sold in places like Cambodia and Honduras? Again, only time will really tell. Affordable used cars also can be used for job creation/economic growth. Somebody would of course need to create more infrastructure to handle additional cars on the road.
Labels: self driving cars