Russian Dolls ...

Perhaps the easiest way to think about artificial intelligence, machine learning, neural networks, and deep learning is to think of them like Russian nesting dolls. Each is essentially a component of the prior term.

Russian Nesting Dolls Analogy for AI, ML, DL and Neural Networks

That is, machine learning is a subfield of artificial intelligence. Deep learning is a subfield of machine learning, and neural networks make up the backbone of deep learning algorithms. In fact, it is the number of node layers, or depth, of neural networks that distinguishes a single neural network from a deep learning algorithm, which must have more than three.

As seen by IBM's graphic delineating the relationships of various components needed to create AI, it becomes obvious the enormous role analog plays in enabling AI to bring in data in real time as analog measures and is perfectly suited to deal with the chaos of reality while digital counts, a process requiring absolute precision in order to function. When the two are connected, the entity becomes a neural network.

Most deep neural networks are feed-forward, meaning they flow in one direction only from input to output. However, you can also train your model through backpropagation; that is, move in opposite direction from output to input. Backpropagation allows us to calculate and attribute the error associated with each neuron, allowing us to adjust and fit the algorithm appropriately.

As stated before, the NN is the analog component of AI, the prime driver as to how realtime data comes into the system ready to be analyzed in realtime by the ever evolving code residing in the hardware.

As per MIT ...

Deep learning is in fact a new name for an approach to artificial intelligence called neural networks, which have been going in and out of fashion for more than 70 years. Neural networks were first proposed in 1944 by Warren McCullough and Walter Pitts, two University of Chicago researchers who moved to MIT in 1952 as founding members of what’s sometimes called the first cognitive science department.

Modeled loosely on the human brain, a neural net consists of thousands or even millions of simple processing nodes that are densely interconnected. Most of today’s neural nets are organized into layers of nodes, and they’re “feed-forward,” meaning that data moves through them in only one direction. An individual node might be connected to several nodes in the layer beneath it, from which it receives data, and several nodes in the layer above it, to which it sends data.

To each of its incoming connections, a node will assign a number known as a “weight.” When the network is active, the node receives a different data item — a different number — over each of its connections and multiplies it by the associated weight. It then adds the resulting products together, yielding a single number. If that number is below a threshold value, the node passes no data to the next layer. If the number exceeds the threshold value, the node “fires,” which in today’s neural nets generally means sending the number — the sum of the weighted inputs — along all its outgoing connections.

This is why AI will never be fully understood as it takes software to write software able to cope with the realtime vagaries of the real world in realtime, something human programmers cannot do in any way, shape or fashion. 

The Dark Secret at the Heart of AI

Keith Rankin

Last year, a strange self-driving car was released onto the quiet roads of Monmouth County, New Jersey. The experimental vehicle, developed by researchers at the chip maker Nvidia, didn’t look different from other autonomous cars, but it was unlike anything demonstrated by Google, Tesla, or General Motors, and it showed the rising power of artificial intelligence. The car didn’t follow a single instruction provided by an engineer or programmer. Instead, it relied entirely on an algorithm that had taught itself to drive by watching a human do it.

Getting a car to drive this way was an impressive feat. But it’s also a bit unsettling, since it isn’t completely clear how the car makes its decisions. Information from the vehicle’s sensors goes straight into a huge network of artificial neurons that process the data and then deliver the commands required to operate the steering wheel, the brakes, and other systems. The result seems to match the responses you’d expect from a human driver. But what if one day it did something unexpected—crashed into a tree, or sat at a green light? As things stand now, it might be difficult to find out why. The system is so complicated that even the engineers who designed it may struggle to isolate the reason for any single action. And you can’t ask it: there is no obvious way to design such a system so that it could always explain why it did what it did.

If one doesn't understand how something works, how does one control the entity in question? You can't.

Toons/rev XXX

Tech never sleeps.

Channeling Goebbels.

It's a tithe, right?

The Memory Hole circa 2023.

Misinterpreting The Art of War.

The law of initial conditions ...

At first glance, it seems the world is united against Russia as seen by this NYTimes graphic along with the one below but initial conditions regarding Russia's inept war with Ukraine no longer applies in 2023 as The West Tried to Isolate Russia. It Didn’t Work.

In 2023, the unanimity of countries supporting the US viewpoint is moot at best.

In closing, Russia has enough economic support to continue waging war no matter the cost.

Read the entirety of the NYTimes piece as the information the US spews out is skewed to the max, something akin to how Nam was sold to us rubes.

In closing, assume nothing, question everything as it's the smart thing to do.

Frankenfoods ...

Photo by Jonathan Borba on Unsplash

Ah, frankenfoods, you know, the kind of crap we all love, devoured with extreme relish, particularly when the Super Bowl's on, AKA the worst food day of the year in America, is the root cause of obesity, a prime driver of related health problems killing millions all over the world 24/7.  Channeling this fact relating to the alteration of our food, better known as the western diet, is more than just appropriate as frankenfood not only applies to meat and fish due to how said entities are processed (think antibiotics & steroids) but also to other commodities packaged to last eons thanks to modern technology as it's cheaper to produce these items than to depend on a diet of largely unprocessed foods like nuts, breads, veggie/herbs and fruit of all varieties. 

For yours truly, MacDonalds, Burger King and Wendy's were the star-point of processed foods and why 69% of Americans are overweight, 36% of which are catagoriezed as being obese. 

The percentage of overweight or obese citizens in the United States 

compared to the rest of the world, according to a new Lancet study.

Credit: Lesley Marker

Hall’s study drew a clear link between junk food and excess calorie consumption, but it can’t tell us why people on the ultra-processed diet ate more. After he published the results, Hall was flooded with suggestions from other scientists. Some thought it was because junk food is more calorie-dense. Since processed foods are often deep-fried and high in fat, they pack in more calories per gram than whole foods. Or maybe it was because junk food was eaten more quickly; in the study, people on the ultra-processed diet ate significantly faster than those eating whole foods. Other scientists thought that additives might be playing a role, or that junk food changed the gut microbiome in a way that influenced calorie intake.

A big factor might be the effect that ultra-processed foods have on our brain. Alexandra DiFeliceantonio is an assistant professor at the Virginia Tech Carilion School of Medicine who studies how junk food interacts with the brain’s reward systems. “We know a lot more about fat, sugars, and carbohydrates, and how those are signaled in the gut and to the brain. We know a lot less about the role of ultra-processing in altering any of those signals,” says DiFeliceantonio.

Her hypothesis is that since ultra-processed foods are rich in easily available calories, they induce a potent reward response in our brains that keeps us coming back for more.

Question, can anyone truly resist the allure of MacDonald fries? 

Lest not forget GMOs. 

GMO Salmon Photo of a genetically engineered Salmon. Created so that it continuously produces growth hormones and can be sold as a full size fish after 18 months instead of 3 years.

Photograph by Paulo Oliveira/Alamy Stock Photo

A genetically modified organism (GMO) is an animal, plant, or microbe whose DNA has been altered using genetic engineering techniques.

For thousands of years, humans have used breeding methods to modify organisms. Corn, cattle, and even dogs have been selectively bred over generations to have certain desired traits. Within the last few decades, however, modern advances in biotechnology have allowed scientists to directly modify the DNA of microorganisms, crops, and animals.

They're everywhere ...

Everything's Alive/rev II

A blast from the past ... Everything's Alive, a video done by yours truly and good friend, Murry Christensen, was a personal take on tech, AI, education and its relationship to my hometown, Redding, CT. We were pretty spot on in 2007 I must say though I look much younger then without question. :)

The ability to see ... the future

Ada Lovelace, the first true programmer, was extraordinary to the nth degree.

In her notes, which ended up twice as long as the original article, Lovelace drew on different areas of her education. She began by describing how to code instructions onto cards with punched holes, like those used for the Jacquard weaving loom, a device patented in 1804 that used punch cards to automate weaving patterns in fabric.

Having learned embroidery herself, Lovelace was familiar with the repetitive patterns used for handicrafts. Similarly repetitive steps were needed for mathematical calculations. To avoid duplicating cards for repetitive steps, Lovelace used loops, nested loops and conditional testing in her program instructions.

The notes included instructions on how to calculate Bernoulli numbers, which Lovelace knew from her training to be important in the study of mathematics. Her program showed that the Analytical Engine was capable of performing original calculations that had not yet been performed manually. At the same time, Lovelace noted that the machine could only follow instructions and not “originate anything.”

Ada Lovelace created this chart for the individual program steps to calculate Bernoulli numbers.   Photo: Courtesy of Linda Hall Library of Science, Engineering Technology, CC BY-ND

Finally, Lovelace recognized that the numbers manipulated by the Analytical Engine could be seen as other types of symbols, such as musical notes. An accomplished singer and pianist, Lovelace was familiar with musical notation symbols representing aspects of musical performance such as pitch and duration, and she had manipulated logical symbols in her correspondence with De Morgan. It was not a large step for her to realize that the Analytical Engine could process symbols — not just crunch numbers — and even compose music.

The Analytical Engine

Portion of the calculating machine with a printing mechanism of the Analytical Engine, built by Charles Babbage, as displayed at the Science Museum (London)[1]

If the Analytical Engine had been built, it would have been digital, programmable and Turing-complete. It would, however, have been very slow. Luigi Federico Menabrea reported in Sketch of the Analytical Engine: "Mr. Babbage believes he can, by his engine, form the product of two numbers, each containing twenty figures, in three minutes".[49] By comparison the Harvard Mark I could perform the same task in just six seconds. A modern PC can do the same thing in well under a billionth of a second.

The second programmer ...

In 1936, Turing wrote “On Computable Numbers,” in which he reformulated and advanced ideas first put forward by Kurt Gödel in 1931, positing the existence of what would be called “Turing machines” — an abstract computing device that was intended as an aid to exploring the limitations of what could be computed — and demonstrating that such devices could in principle perform any mathematical computation that was represented as an algorithm.

The Rabbit Hole beckons ...

1. The Imitation Game

I PROPOSE to consider the question, ‘Can machines think?’ This should begin with definitions of the meaning of the terms ‘machine’ and ‘think’. The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous. If the meaning of the words ‘machine’ and ‘think’ are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, ‘Can machines think?’ is to be sought in a statistical survey such as a Gallup poll. But this is absurd. Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.

The new form of the problem can be described in terms of a game which we call the ‘imitation game’. It is played with three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart from the other two. The object of the game for the interrogator is to determine which of the other two is the man and which is the woman. He knows them by labels X and Y, and at the end of the game he says either ‘X is A and Y is B’ or ‘X is B and Y is A’. The interrogator is allowed to put questions to A and B thus:

The kicker ...

We now ask the question, ‘What will happen when a machine takes the part of A in this game?’ Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman? These questions replace our original, ‘Can machines think?’ - Alan Turing

Statue of Turing holding an Enigma machine by Stephen Kettle at Bletchley Park, 

commissioned by Sidney Frank, built from half a million pieces of Welsh slate[97]

To be continued ...

I don't know ...

[Photo: Avery Evans/Unsplash]

I don't know, can you tell me what it means? I can honestly say I do this all the time. Interestingly enough, the true creative giants of the world say this also. Think Einstein and "Question everything" as a start point to seeing why I don't know resonates without question.

The other thing most enlightening is the fact people liked to be asked, another notion not readily expressed in the uptight and frantic world in which we all live.

To whit ...

Ignorance may be bliss, but saying “I don’t know” out loud is often hard to do. It can be especially difficult if you’re in a position of leadership and your team comes to you for answers. However, saying “I don’t know” can actually be empowering if you reframe the concept, says Lizette Warner, author of Power, Poise, and Presence: A New Approach to Authentic Leadership.

“Any statement that starts with ‘I don’t know’ means that you’re open to knowing and it leads to discovery,” she says. “‘I don’t know’ is the first path to wisdom. It’s what leads to innovation.”

This is not a big deal yet it is as possibility is the watchword here. Creativity is the art of asking questions and saying I don't know. The key here is, "but I'll find out", something Einstein did without question. :)

30

Russian author Boris Pasternak in the study of his home near Moscow. (Jerry Cooke via Getty Images)

Every once in a while, yours truly discovers a gem. This is one of them. :)

Two foundational stories stick in my own mind. I will tell the second one next week, but I will start with an event that took place in Moscow in 1937.

That year’s annual Soviet writers’ congress took place in the worst time of the purges. At the major show trials in Moscow, people were confessing to things they could not possibly have done. Both the journalist Malcolm Muggeridge and the author Fitzroy Maclean, who observed these events, were credited with the line “I can believe everything except the facts.”

The people who got public show trials were comparatively lucky: at least the absurdity of the excuse for their murder was made public. Other people disappeared all the time with nothing heard of them again.

It was a dangerous time to be a private citizen, but an even worse time to be a public one. So the writers’ congress that year included a lot of very dull, regime-prescribed speeches praising the virtues of Leninist–Stalinism, Stalinist–Leninism, and so on. It was the sort of occasion to which all artists were subjected through that era: a ritual of forced humiliation. A way of getting everyone to collude in the world of lies. 

Boris Pasternak was one of the most famous writers in the country. Though he had not yet completed Doctor Zhivago—the novel that would make him internationally famous—in that hall, that year, everybody knew him. And Pasternak faced a challenge. He could not speak, and he couldn’t not speak. Stalin’s secret police chief Lavrentiy Beria and his men were literally standing by the side of the stage. If Pasternak spoke, he could be disappeared. If he didn’t speak, he could be disappeared. So he stayed silent. It was on the third and final day of the conference that the writer’s friends persuaded him that the silence was madness. He had to speak. So finally, Pasternak got up to the lectern.

Everybody knew who he was, of course. He was tall, and strikingly handsome. As he pulled himself up to his full height he said not a word but a number. The number was “30.” As he said it, all two thousand writers in the hall got to their feet, and—with Pasternak—began to recite.

Thirty is the number of the Shakespeare sonnet beginning, “When to the sessions of sweet silent thought I summon up remembrance of things past.” Today the line may be best known for giving C. K. Scott Moncrieff the title for his English translation of Proust’s masterpiece about lost time. But the Russian writers in that hall all knew Pasternak had himself done a translation of the sonnet into Russian. It was already a classic. Those who know his translation say it is as beautiful as the original.

Sonnet 30: When to the sessions of sweet silent thought

When to the sessions of sweet silent thought

I summon up remembrance of things past,

I sigh the lack of many a thing I sought,

And with old woes new wail my dear time’s waste:

Then can I drown an eye, unus’d to flow,

For precious friends hid in death’s dateless night,

And weep afresh love’s long since cancell’d woe,

And moan th’ expense of many a vanish’d sight;

Then can I grieve at grievances foregone,

And heavily from woe to woe tell o’er

The sad account of fore-bemoaned moan,

Which I new pay as if not paid before.

But if the while I think on thee, dear friend,

All losses are restor’d, and sorrows end.

Our politicians @ work ...

Our politicians @ work ...

Channeling ELIZA

 Illustration by Erik Carter

They're everywhere, variations of ChatGPT's whereby financial types license Open AI's tech in order to, you guessed it, make money even though many of these entities know nothing about said tech but ... if Microsoft invests 10 billion, why not us?

Into the rabbit hole applies as often said in BRT, we know not how AI works thanks to physics and the reality of realtime. Additionally, why ELIZA? Read on brave reader to learn why ELIZA matters. :)

ELIZA is an early natural language processing computer program created from 1964 to 1966[1] at the MIT Artificial Intelligence Laboratory by Joseph Weizenbaum.[2][3] Created to demonstrate the superficiality of communication between humans and machines, Eliza simulated conversation by using a "pattern matching" and substitution methodology that gave users an illusion of understanding on the part of the program, but had no built in framework for contextualizing events.[4][5][6] Directives on how to interact were provided by "scripts", written originally[7] in MAD-Slip, which allowed ELIZA to process user inputs and engage in discourse following the rules and directions of the script. The most famous script, DOCTOR, simulated a psychotherapist of the Rogerian school (in which the therapist often reflects back the patient's words to the patient),[8][9][10] and used rules, dictated in the script, to respond with non-directional questions to user inputs. As such, ELIZA was one of the first chatterbots and one of the first programs capable of attempting the Turing test.[11]

A conversation between a human and ELIZA's DOCTOR script

Segue to 2023 to see how ELIZA's descendent deals with conversation. Disquieting says it all.

Between Christmas and New Year’s, my family took a six-hour drive to Vermont. I drove; my wife and two children sat in the back seat. Our children are five and two—too old to be hypnotized by a rattle or a fidget spinner, too young to entertain themselves—so a six-hour drive amounted to an hour of napping, an hour of free association and sing-alongs, and four hours of desperation. 

My wife took out her phone and opened ChatGPT, a chatbot that “interacts in a conversational way.” She typed in the prompt, basically word for word, and, within seconds, ChatGPT spat out a story. We didn’t need to tell it the names of the Teenage Mutant Ninja Turtles, or which weapons they used, or how they felt about anchovies on their pizza. More impressive, we didn’t need to tell it what a story was, or what kind of conflict a child might find narratively satisfying.

To date, ChatGPT is not connected to the net butGoogle's Bard is.

The interface is about as simple as it gets: words in, words out. You type in any prompt that comes to mind, press a button that looks like a little paper airplane, and then watch the blinking cursor as ChatGPT responds with its own words—words that often seem eerily human, words that may include characteristic hedges (“It’s important to note that . . .”) or glimmers of shocking novelty or laughable self-owns, but words that, in almost every case, have never been combined in that particular order before. (The graphic design, especially the cursor, seems almost intended to create the illusion that there is a homunculus somewhere, a ghost in the machine typing back to you.) There is a robust and long-standing debate about whether the large-language approach can ever achieve true A.G.I., or artificial general intelligence; but whatever the bots are doing already has been more than enough to capture the public’s imagination. I’ve heard ChatGPT described, sometimes by the same person, as a miracle, a parlor trick, and a harbinger of dystopia. And this demo is just the public tip of a private iceberg. 

Luckily, unlike every other technological transformation in human history, this one will only serve to delight people and meet their needs, with no major externalities or downside risks or moral hazards. Kidding! The opposite of that. If the A.I. revolution ends up having even a fraction of the impact that Altman is predicting, then it will cause a good amount of creative disruption, including, for starters, the rapid reorganization of the entire global economy. And that’s not even the scary part. The stated reason for the existence of OpenAI is that its founders, among them Altman and Elon Musk, believed artificial intelligence to be the greatest existential risk to humanity, a risk that they could only mitigate, they claimed, by developing a benign version of the technology themselves. “OpenAI was born of Musk’s conviction that an A.I. could wipe us out by accident,” my colleague Tad Friend wrote, in a Profile of Altman published in 2016.

It's all different now ...

Me too. :)

Me too. :)

Running low on ammo ...

Final capping at the Iowa Army Ammunition Plant assembly line.

Photographer: Lyndon French/The New York Times/Redux

The World’s War Machine Is Running Low on Ammunition

The industry that keeps on killing needs more ammo, sounds like Emmanuel Goldstein/1984 whereby War, it will be seen, not only accomplishes the necessary destruction, but accomplishes it in a psychologically acceptable way.

And this ...

"The primary aim of modern warfare (in accordance with the principles of doublethink, this aim is simultaneously recognized and not recognized by the directing brains of the Inner Party) is to use up the products of the machine without raising the general standard of living." 

Any questions?

 

Emmanuel Goldstein (John Boswall) appears in a telescreen 

during a Two Minutes’ Hate program, 

in the film Nineteen-Eighty-Four (1984) 

directed by Michael Radford.

The Blade of Time | No. 4300

The Blade of Time

The Blade of Time: 

infinitely thin, forever separating the future from the past. - RM

“enshittification.”

Photo by Nathana Rebouças on Unsplash

Tech is a petri dish for monopolies. If a platform become popular, it dominates. Think Windows in the 90's when Microsoft was king of the world with the only thing saving us rubes from remaining forever trapped by a crappy operating system was the fact Gates didn't understand the ramifications of the web. Now it's the web and tech behemoths like Google, Amazon, Apple and Facebook, along with good old Microsoft, ruling the roost thanks to open source and the ubiquity of the net but something's amiss as ethics, truth and competency are becoming increasingly scarce as we move further into the 21st century.

Each tool that the internet gives us seems to provide us with more information and fewer reasons to trust that information.

Take two of the backbones of the modern internet, Google and Amazon. I use Google so many times a day that I’m afraid to quantify it. It helps me find the weather, sports standings, pictures to post on my social media accounts, and a million other things. Amazon is similarly miraculous. It can deliver pretty much anything from vitamins to a dining room table to my house in 48 hours.

But these services have also gotten worse in ways that make it difficult to trust the seemingly miraculous things I get from them. Most people seem to think that Google is getting worse — it’s gone from seeming like “magic” to being cluttered with ads and SEO-optimized garbage. The company that is supposed to make it easier for us to find information is now profiting from making it harder for us to find good information. And Amazon, once a wondrous, straightforward shopping experience, is so cluttered with counterfeit crap, fake reviews, and shady sellers that I’m once again tempted to jump in the car and head out to Target.

Cory Doctorow has unforgettably coined this process “enshittification.” He argues that tech platforms are useful to users — at first. But once they’ve captured a user base and crushed their competition, they begin squeezing every last cent out of their users, which makes the platform gradually worse.

Remember, it's all about the money, always has been, always will be. 

10 days & counting ...

Occam's Razor yet again ...

 

An artist's concept of a supermassive black hole, complete with a fiery accretion disk.

(Image credit: NASA/JPL-Caltech)

Black holes, the possible connect of quantum mechanics and relativity, might also be the dark energy engines causing the universe to expand ever faster, has been a mystery frustrating researchers to no end until now.

Supermassive black holes could be the engines driving the expansion of the universe, according to research that proposes a solution to "one of the biggest problems in cosmology." 

By comparing supermassive black holes across nine billion years of cosmic history, astronomers have discovered a clue that the ravenous behemoths lurking at the hearts of most large galaxies may be the source of dark energy — the mysterious force that makes up 68% of the known universe(opens in new tab) and causes its accelerating expansion. The researchers published their findings Feb 2. and Feb 15. in two papers in The Astrophysical Journal and The Astrophysical Journal Letters.

"If the theory holds, then this is going to revolutionise the whole of cosmology, because at last we've got a solution for the origin of dark energy that's been perplexing cosmologists and theoretical physicists for more than 20 years," co-author Chris Pearson(opens in new tab), an astrophysicist at the Rutherford Appleton Laboratory (RAL) in the U.K. said in a statement.

Solving the mystery at last?

Over the last century, astronomers discovered that the universe was expanding at an ever faster pace. This was surprising given that, acting on its own, gravity should be expected to slowly crumple the cosmos together in an event known as the Big Crunch. To explain the discrepancy, scientists proposed that something powerful enough to counteract gravity must exist, and was pushing everything in the universe further apart. They named that something dark energy.

But for dark energy to reverse a cosmic collapse, it would have to be present in such enormous quantities that it makes up the vast majority of the universe. Yet, until now, it has been nowhere to be seen.

Now, the new studies have seemingly found a clue as to how the hidden phenomenon works. Both teams compared the masses of black holes at the centers of two sets of galaxies. One group was young and remote, with light that arrived to us from nine billion years in the past, while a nearer and older group sits only some millions of light-years away. The astronomers found that throughout the universe giant black holes had ballooned to be seven to 20 times larger than they once were — a monstrous growth that couldn't be explained simply by black holes devouring stars or colliding and combining with each other.

Instead, the researchers propose that black holes are growing in lockstep with the universe — overcoming the star-crushing, light-capturing forces at their cores with a hypothetical type of dark energy called vacuum energy that makes them expand ever outwards. And, somehow, they drag the entire fabric of the cosmos out with them.

Occam's Razor applies yet again ... AKA  the principle of parsimony or the law of parsimony/Less is more - Dizzy Gillispie

Part of a page from John Duns Scotus's book Commentaria oxoniensia ad IV libros magistri Sententiarus, showing the words: "Pluralitas non est ponenda sine necessitate", i.e., "Plurality is not to be posited without necessity"