Algorithms Book Quotes

There are different types of censorship. There is the outright ban on a book type. Then there are the type where the ones who can give it voice, squash it by burying it under search engine algorithms and under other news, videos or books of their own agenda or publication. A smart consumer should be free to choose what to read and what to believe. That choice on a consumer-oriented website, is really what is best for the consumer. - Strong by Kailin Gow

Don't be fooled by the many books on complexity or by the many complex and arcane algorithms you find in this book or elsewhere. Although there are no textbooks on simplicity, simple systems work and complex don't.

The lack of transparency regarding training data sources and the methods used can be problematic. For example, algorithmic filtering of training data can skew representations in subtle ways. Attempts to remove overt toxicity by keyword filtering can disproportionately exclude positive portrayals of marginalized groups. Responsible data curation requires first acknowledging and then addressing these complex tradeoffs through input from impacted communities.

We've developed algorithms for orgasms, broken it down to a science, I spell out equations on the small of your back, your kisses, the most beautiful calculus I've ever studied. You do fractions and long handed division up my thighs, balance equations between my legs...even my sharp clefts and C-notes can't match our depths...

Fake Math owes its existence to a number of things and people who have inspired and assisted this book on its way into the world.

If we think in term of months, we had probably focus on immediate problems such as the turmoil in the Middle East, the refugee crisis in Europe and the slowing of the Chinese economy. If we think in terms of decades, then global warming, growing inequality and the disruption of the job market loom large. Yet if we take the really grand view of life, all other problems and developments are overshadowed by three interlinked processes: 1.​Science is converging on an all-encompassing dogma, which says that organisms are algorithms and life is data processing. 2.​Intelligence is decoupling from consciousness. 3.​Non-conscious but highly intelligent algorithms may soon know us better than we know ourselves. These three processes raise three key questions, which I hope will stick in your mind long after you have finished this book: 1.​Are organisms really just algorithms, and is life really just data processing? 2.​What’s more valuable – intelligence or consciousness? 3.​What will happen to society, politics and daily life when non-conscious but highly intelligent algorithms know us better than we know ourselves?

Many presume that integrating more advanced automation will directly translate into productivity gains. But research reveals that lower-performing algorithms often elicit greater human effort and diligence. When automation makes obvious mistakes, people stay attentive to compensate. Yet flawless performance prompts blind reliance, causing costly disengagement. Workers overly dependent on accurate automation sleepwalk through responsibilities rather than apply their own judgment.

In his book In This Very Life, the Burmese meditation teacher Sayadaw U Pandita, wrote, "In their quest for happiness, people mistake excitement of the mind for real happiness." We get excited when we hear good news, start a new relationship, or ride a roller coaster. Somewhere in human history, we were conditioned to think that the feeling we get when dopamine fires in our brain equals happiness. Don't forget, this was probably set up so that we would remember where food could be found, not to give us the feeling "you are now fulfilled." To be sure, defining happiness is a tricky business, and very subjective. Scientific definitions of happiness continue to be controversial and hotly debated. The emotion doesn't seem to be something that fits into a survival-of-the-fittest learning algorithm. But we can be reasonably sure that the anticipation of a reward isn't happiness.

When Charles Darwin was trying to decide whether he should propose to his cousin Emma Wedgwood, he got out a pencil and paper and weighed every possible consequence. In favor of marriage he listed children, companionship, and the 'charms of music and female chit-chat.' Against marriage he listed the 'terrible loss of time,' lack of freedom to go where he wished, the burden of visiting relatives, the expense and anxiety provoked by children, the concern that 'perhaps my wife won't like London,' and having less money to spend on books. Weighing one column against the other produced a narrow margin of victory, and at the bottom Darwin scrawled, 'Marry—Marry—Marry Q.E.D.' Quod erat demonstrandum, the mathematical sign-off that Darwin himself restated in English: 'It being proved necessary to Marry.

There is a saturation of books on Amazon due to a sudden get-rich-quick surge in "everyone can be authors" seminars similar to the house flipping ones in the early 2000s which led to the housing bubble and an economic slowdown in the U.S. To distinguish quality books from those get-rich-quick ones, look at the author's track record - worldwide recognition as books that garnered credible awards, authors who speak at book industry events, authors who speak at schools, authors whose books are reference materials and reading sources at school and libraries. Get-rich books have a system to get over 500 reviews quickly, manipulates the Kindle Unlimited algorithm, and encourage collusion in the marketplace to knock out rivals. Be wary of trolls who are utilized to knock down the rankings of rival's books too. Once people have heard there is money to be made as a self-published author, just like house flipping, a cottage industry has risen to take advantage of it and turn book publishing into a get rich scheme, which is a shame for all the book publishers and authors, like me, who had published for the love of books, to write to help society, and for the love of literature. Kailin Gow, Parents and Books

1.​Science is converging on an all-encompassing dogma, which says that organisms are algorithms and life is data processing. 2.​Intelligence is decoupling from consciousness. 3.​Non-conscious but highly intelligent algorithms may soon know us better than we know ourselves. These three processes raise three key questions, which I hope will stick in your mind long after you have finished this book: 1.​Are organisms really just algorithms, and is life really just data processing? 2.​What’s more valuable – intelligence or consciousness? 3.​What will happen to society, politics and daily life when non-conscious but highly intelligent algorithms know us better than we know ourselves?

Not only has volume been ratcheted up but expectations have, too. Quiet success--painting a picture, writing a poem, writing an algorithm--is all well and good, but if you haven't become famous doing it, then did it really matter?

The algorithm seemed to be really good at distinguishing the two rather similar canines; it turned out that it was simply labeling any picture with snow as containing a wolf. An example with more serious implications was described by Janelle Shane in her book You Look Like a Thing and I Love You: an algorithm that was shown pictures of healthy skin and of skin cancer. The algorithm figured out the pattern: if there was a ruler in the photograph, it was cancer.7 If we don’t know why the algorithm is doing what it’s doing, we’re trusting our lives to a ruler detector.

I’ve laid down ten statistical commandments in this book. First, we should learn to stop and notice our emotional reaction to a claim, rather than accepting or rejecting it because of how it makes us feel. Second, we should look for ways to combine the “bird’s eye” statistical perspective with the “worm’s eye” view from personal experience. Third, we should look at the labels on the data we’re being given, and ask if we understand what’s really being described. Fourth, we should look for comparisons and context, putting any claim into perspective. Fifth, we should look behind the statistics at where they came from—and what other data might have vanished into obscurity. Sixth, we should ask who is missing from the data we’re being shown, and whether our conclusions might differ if they were included. Seventh, we should ask tough questions about algorithms and the big datasets that drive them, recognizing that without intelligent openness they cannot be trusted. Eighth, we should pay more attention to the bedrock of official statistics—and the sometimes heroic statisticians who protect it. Ninth, we should look under the surface of any beautiful graph or chart. And tenth, we should keep an open mind, asking how we might be mistaken, and whether the facts have changed.

Over the next three decades, scholars and fans, aided by computational algorithms, will knit together the books of the world into a single networked literature. A reader will be able to generate a social graph of an idea, or a timeline of a concept, or a networked map of influence for any notion in the library. We’ll come to understand that no work, no idea stands alone, but that all good, true, and beautiful things are ecosystems of intertwined parts and related entities, past and present.

Automation promises to execute certain tasks with superhuman speed and precision. But its brittle limitations reveal themselves when the unexpected arises. Studies consistently show that, as overseers, humans make for fickle partners to algorithms. Charged with monitoring for rare failures, boredom and passivity render human supervision unreliable.

Don’t be scared of racist people. Be frightened of ‘racist’ algorithms because they have no conscience and are much more effective.

Explainability is one thing; interpreting it rightly (for the good of society), is another.

But now what was I worth? The books I discovered at the behest of my intellectually superior professors are now coming to me thanks to an algorithm developed to suggest what I should buy based on what other similar shoppers have also bought...

One of the ideas of this book is to give the reader a possibility to develop problem-solving skills using both systems, to solve various nonlinear PDEs in both systems. To achieve equal results in both systems, it is not sufficient simply “to translate” one code to another code. There are numerous examples, where there exists some predefined function in one system and does not exist in another. Therefore, to get equal results in both systems, it is necessary to define new functions knowing the method or algorithm of calculation.

In a way, this book is an attempt to recapture recommendations from recommender systems. We should talk even more about the things we like, experience them together, and build up our own careful collections of likes and dislikes. Not for the sake of fine-tuning an algorithm, but for our collective satisfaction.

When you feel the need to escape your problems, to escape from this world, don't make the mistake of resorting to suicide Don't do it! You will hear the empty advice of many scholars in the matter of life and death, who will tell you, "just do it" there is nothing after this, you will only extinguish the light that surrounds you and become part of nothingness itself, so when you hear these words remember this brief review of suicide: When you leave this body after committing one of the worst acts of cowardice that a human being can carry out, you turn off the light, the sound and the sense of reality, you become nothing waiting for the programmers of this game to pick you up from the darkness, subtly erase your memories and enable your return and I emphasize the word subtle because sometimes the intelligence behind this maneuver or automated mechanism is wrong and send human beings wrongly reset to such an extent, that when they fall to earth and are born again, they begin to experience memories of previous lives, in many cases they perceive themselves of the opposite sex, and science attributes this unexplainable phenomenon to genetic and hormonal factors, but you and I know better! And we quickly identified this trigger as a glitch in the Matrix. Then we said! That a higher intelligence or more advanced civilization throws you back into this game for the purpose of experimenting, growing and developing as an advanced consciousness and due to your toxic and destructive behavior you come back again but in another body and another life, but you are still you, then you will carry with you that mark of suicide and cowardice, until you learn not to leave this experience without having learned the lesson of life, without having experienced and surprised by death naturally or by design of destiny. About this first experience you will find very little material associated with this event on the internet, it seems that the public is more reserved, because they perceive themselves and call themselves "awakened" And that is because the system has total control over the algorithm of fame and fortune even over life and death. Now, according to religion and childish fears, which are part of the system's business to keep you asleep, eyes glued to the cellular device all day, it says the following: If you commit this act of sin, you turn off light, sound and sense of reality, and from that moment you begin to experience pain, fear and suffering on alarming scales, and that means they will come for you, a couple of demons and take you to the center of the earth where the weeping and gnashing of teeth is forever, and in that hell tormented by demons you will spend eternity. About this last experience we will find hundreds of millions of people who claim to have escaped from there! And let me tell you that all were captivated by the same deity, one of dubious origin, that feeds on prayers and energetic events, because it is not of our nature, because it knows very well that we are beings of energy, then this deity or empire of darkness receives from the system its food and the system receives from them power, to rule, to administer, to control, to control, to kill, to exclude, to inhibit, to classify, to imprison, to silence, to infect, to contaminate, to depersonalize. So now that you know the two sides of the same coin, which one will your intelligence lean towards! You decide... Heads or tails? From the book Avatars, the system's masterpiece.

Counterfactuals are the building blocks of moral behavior as well as scientific thought. The ability to reflect on one’s past actions and envision alternative scenarios is the basis of free will and social responsibility. The algorithmization of counterfactuals invites thinking machines to benefit from this ability and participate in this (until now) uniquely human way of thinking about the world.

These three processes raise three key questions, which I hope will stick in your mind long after you have finished this book: 1. Are organisms really just algorithms, and is life really just data processing? 2. What’s more valuable – intelligence or consciousness? 3. What will happen to society, politics and daily life when non-conscious but highly intelligent algorithms know us better than we know ourselves?

To see what happens in the real world when an information cascade takes over, and the bidders have almost nothing but one another’s behavior to estimate an item’s value, look no further than Peter A. Lawrence’s developmental biology text The Making of a Fly, which in April 2011 was selling for $23,698,655.93 (plus $3.99 shipping) on Amazon’s third-party marketplace. How and why had this—admittedly respected—book reached a sale price of more than $23 million? It turns out that two of the sellers were setting their prices algorithmically as constant fractions of each other: one was always setting it to 0.99830 times the competitor’s price, while the competitor was automatically setting their own price to 1.27059 times the other’s. Neither seller apparently thought to set any limit on the resulting numbers, and eventually the process spiraled totally out of control.

What if, by contrast, you are more a user of intangible assets: say, the Amazon warehouse, using the knowledge of the routing algorithm, or Starbucks, using the franchise book? For these firms, the organization and so management would look different. You probably want to have more hierarchies and short-term targets, since you are less worried about information flows form below and more concerned about low performance and stopping influence activities.

The politics of deference focuses on the consequences that are likeliest to show up in the rooms where elites do most of their interacting: classrooms, boardrooms, political parties. As a result, we seem to end up with far more, and more specific, practical advice about how to, say, allocate tasks at a committee meeting than how to keep people alive. Deference as a default political orientation can work counter to marginalized groups' interests. We are surrounded by a discourse that locates attentional injustice in the selection of spokespeople and book lists taken to represent the marginalized, rather than focusing on the actions of the corporations and algorithms that much more powerfully distribute attention. This discourse ultimately participates in the weaponization of attention in the service of marginalization. It directs what little attentional power we can control at symbolic sites of power rather than at the root political issues that explain why everything is so fucked up.

I dare to hope that search engines and social media algorithms will be optimized for truth and social relevance rather than simply showing people what they want to see; that there will be independent, third-party algorithms that rate the veracity of headlines, websites, and news stories in real time, allowing users to more quickly sift through the propaganda-laden garbage and get closer to evidence-based truth; that there will be actual respect for empirically tested data, because in an infinite sea of possible beliefs, evidence is the only life preserver we’ve got.

We are living through a movement from an organic, industrial society to a polymorphous, information system,” wrote Donna Haraway, “from all work to all play, a deadly game.”10 With the growing significance of immaterial labor, and the concomitant increase in cultivation and exploitation of play—creativity, innovation, the new, the singular, flexibility, the supplement—as a productive force, play will become more and more linked to broad social structures of control. Today we are no doubt witnessing the end of play as politically progressive, or even politically neutral.)

When Charles Darwin was trying to decide whether he should propose to his cousin Emma Wedgwood, he got out a pencil and paper and weighed every possible consequence. In favor of marriage he listed children, companionship, and the “charms of music & female chit-chat.” Against marriage he listed the “terrible loss of time,” lack of freedom to go where he wished, the burden of visiting relatives, the expense and anxiety provoked by children, the concern that “perhaps my wife won’t like London,” and having less money to spend on books. Weighing one column against the other produced a narrow margin of victory, and at the bottom Darwin scrawled, “Marry—Marry—Marry

Yet if we take the really grand view of life, all other problems and developments are overshadowed by three interlinked processes: 1.​Science is converging on an all-encompassing dogma, which says that organisms are algorithms and life is data processing. 2.​Intelligence is decoupling from consciousness. 3.​Non-conscious but highly intelligent algorithms may soon know us better than we know ourselves. These three processes raise three key questions, which I hope will stick in your mind long after you have finished this book: 1.​Are organisms really just algorithms, and is life really just data processing? 2.​What’s more valuable – intelligence or consciousness? 3.​What will happen to society, politics and daily life when non-conscious but highly intelligent algorithms know us better than we know ourselves?

When I was a kid people used to say one could travel the entire world just by sitting in a library and reading books. Sadly, in the age of billionaire-controlled social media functioning and governing bodies and minds based on carefully engineered algorithms, I don’t believe this is true anymore. The saying should be revised in our times to be ‘one could hate the entire world and see everyone as a villain or an enemy just by browsing through reels and social posts carefully selected to confirm one’s limited knowledge, perspective, and prejudices.’ With that in mind, we need more than ever to master the art of traveling, whether we go near or far. We need to undo the unreasonable, amplified, and exaggerated fear of strangers." [From “Can We Travel Without Being Tourists?” published on CounterPunch on March 15, 2024]

Who among us can predict the future? Who would dare to? The answer to the first question is no one, really, and the answer to the second is everyone, especially every government and business on the planet. This is what that data of ours is used for. Algorithms analyze it for patterns of established behavior in order to extrapolate behaviors to come, a type of digital prophecy that’s only slightly more accurate than analog methods like palm reading. Once you go digging into the actual technical mechanisms by which predictability is calculated, you come to understand that its science is, in fact, anti-scientific, and fatally misnamed: predictability is actually manipulation. A website that tells you that because you liked this book you might also like books by James Clapper or Michael Hayden isn’t offering an educated guess as much as a mechanism of subtle coercion.

Sure, we can hear the reverberating echoes of the Big Bang. Yet that cosmic vibration tells us nothing about what was before the Big Bang, or what was before that, or how or why there was even a bang to be binged at all. This mostly wet ball full of ptarmigans, ponytails, and poverty is floating in space among a billion other balls, and there are galaxies swirling and there is a universe expanding, which itself may actually just be an undulating freckle on the cusp of something we can’t even conceive of, amid an endless soup of ever more unfathomables. And I find such a situation to be utterly, manifestly, psychedelically amazing—and far more spine-tinglingly awe-inspiring than any story I’ve ever read in the Bible, the Quran, the Vedas, the Upanishads, Dianetics, the Doctrine and Covenants, or the Tibetan Book of the Dead. So smell that satchel of tangerines and nimbly hammer a dulcimer or pluck a chicken and listen to your conscience or master a new algorithm or walk to work or hitch a ride. Because we’re here. And we will never, ever know why or exactly how this all comes about. That’s the situation. Deal with it. Accept it. Let the mystery be.

The word “collect” has a very special definition, according to the Department of Defense. It doesn’t mean collect; it means that a person looks at, or analyzes, the data. In 2013, Director of National Intelligence James Clapper likened the NSA’s trove of accumulated data to a library. All those books are stored on the shelves, but very few are actually read. “So the task for us in the interest of preserving security and preserving civil liberties and privacy is to be as precise as we possibly can be when we go in that library and look for the books that we need to open up and actually read.” Think of that friend of yours who has thousands of books in his house. According to this ridiculous definition, the only books he can claim to have collected are the ones he’s read. This is why Clapper asserts he didn’t lie in a Senate hearing when he replied “no” to the question “Does the NSA collect any type of data at all on millions or hundreds of millions of Americans?” From the military’s perspective, it’s not surveillance until a human being looks at the data, even if algorithms developed and implemented by defense personnel or contractors have analyzed it many times over.

overcome. Seth Godin calls these inevitable obstacles The Dip. In his brilliant little book of the same name, he describes the intricacies of knowing when to quit and when to stick—and why it’s so important to learn how to do this effectively. Seth gives a pertinent example of the entrepreneur-wannabe: Do you know an entrepreneur-wannabe who is on his sixth or twelfth new project? He jumps from one to another, and every time he hits an obstacle, he switches to a new, easier, better opportunity. And while he’s a seeker, he’s never going to get anywhere. He never gets anywhere because he’s always switching lines, never able to really run for it. While starting up is thrilling, it’s not until you get through the Dip that your efforts pay off. Countless entrepreneurs have perfected the starting part, but give up long before they finish paying their dues. The sad news is that when you start over, you get very little credit for how long you stood in line with your last great venture.[31] Quitting isn’t always bad, but it needs to be done for the right reasons, and never for the wrong ones. It’s never black and white, but it always comes back to passion. Read The Dip. It will help.

I will give technology three definitions that we will use throughout the book. The first and most basic one is that a technology is a means to fulfill a human purpose. For some technologies-oil refining-the purpose is explicit. For others- the computer-the purpose may be hazy, multiple, and changing. As a means, a technology may be a method or process or device: a particular speech recognition algorithm, or a filtration process in chemical engineering, or a diesel engine. it may be simple: a roller bearing. Or it may be complicated: a wavelength division multiplexer. It may be material: an electrical generator. Or it may be nonmaterial: a digital compression algorithm. Whichever it is, it is always a means to carry out a human purpose. The second definition I will allow is a plural one: technology as an assemblage of practices and components. This covers technologies such as electronics or biotechnology that are collections or toolboxes of individual technologies and practices. Strictly speaking, we should call these bodies of technology. But this plural usage is widespread, so I will allow it here. I will also allow a third meaning. This is technology as the entire collection of devices and engineering practices available to a culture. Here we are back to the Oxford's collection of mechanical arts, or as Webster's puts it, "The totality of the means employed by a people to provide itself with the objects of material culture." We use this collective meaning when we blame "technology" for speeding up our lives, or talk of "technology" as a hope for mankind. Sometimes this meaning shades off into technology as a collective activity, as in "technology is what Silicon Valley is all about." I will allow this too as a variant of technology's collective meaning. The technology thinker Kevin Kelly calls this totality the "technium," and I like this word. But in this book I prefer to simply use "technology" for this because that reflects common use. The reason we need three meanings is that each points to technology in a different sense, a different category, from the others. Each category comes into being differently and evolves differently. A technology-singular-the steam engine-originates as a new concept and develops by modifying its internal parts. A technology-plural-electronics-comes into being by building around certain phenomena and components and develops by changing its parts and practices. And technology-general, the whole collection of all technologies that have ever existed past and present, originates from the use of natural phenomena and builds up organically with new elements forming by combination from old ones.

As strangeness becomes the new normal, your past experiences, as well as the past experiences of the whole of humanity, will become less reliable guides. Humans as individuals and humankind as a whole will increasingly have to deal with things nobody ever encountered before, such as super-intelligent machines, engineered bodies, algorithms that can manipulate your emotions with uncanny precision, rapid man-made climate cataclysms and the need to change your profession every decade. What is the right thing to do when confronting a completely unprecedented situation? How should you act when you are flooded by enormous amounts of information and there is absolutely no way you can absorb and analyse it all? How to live in a world where profound uncertainty is not a bug, but a feature? To survive and flourish in such a world, you will need a lot of mental flexibility and great reserves of emotional balance. You will have to repeatedly let go of some of what you know best, and feel at home with the unknown. Unfortunately, teaching kids to embrace the unknown and to keep their mental balance is far more difficult than teaching them an equation in physics or the causes of the First World War. You cannot learn resilience by reading a book or listening to a lecture. The teachers themselves usually lack the mental flexibility that the twenty-first century demands, for they themselves are the product of the old educational system. The Industrial Revolution has bequeathed us the production-line theory of education. In the middle of town there is a large concrete building divided into many identical rooms, each room equipped with rows of desks and chairs. At the sound of a bell, you go to one of these rooms together with thirty other kids who were all born the same year as you. Every hour some grown-up walks in, and starts talking. They are all paid to do so by the government. One of them tells you about the shape of the earth, another tells you about the human past, and a third tells you about the human body. It is easy to laugh at this model, and almost everybody agrees that no matter its past achievements, it is now bankrupt. But so far we haven’t created a viable alternative. Certainly not a scaleable alternative that can be implemented in rural Mexico rather than just in upmarket California suburbs.

The power to include, exclude, and rank is the power to ensure that certain public impressions become permanent, while others remain fleeting.50 How does Amazon decide which books to prioritize in searches?

The Pseudocode Programming Process Have you checked that the prerequisites have been satisfied? Have you defined the problem that the class will solve? Is the high-level design clear enough to give the class and each of its routines a good name? Have you thought about how to test the class and each of its routines? Have you thought about efficiency mainly in terms of stable interfaces and readable implementations or mainly in terms of meeting resource and speed budgets? Have you checked the standard libraries and other code libraries for applicable routines or components? Have you checked reference books for helpful algorithms? Have you designed each routine by using detailed pseudocode? Have you mentally checked the pseudocode? Is it easy to understand? Have you paid attention to warnings that would send you back to design (use of global data, operations that seem better suited to another class or another routine, and so on)? Did you translate the pseudocode to code accurately? Did you apply the PPP recursively, breaking routines into smaller routines when needed? Did you document assumptions as you made them? Did you remove comments that turned out to be redundant? Have you chosen the best of several iterations, rather than merely stopping after your first iteration? Do you thoroughly understand your code? Is it easy to understand?

Additional Resources on Data Types These books are good sources of information about data types: Cormen, H. Thomas, Charles E. Leiserson, Ronald L. Rivest. Introduction to Algorithms. New York, NY: McGraw Hill. 1990. Sedgewick, Robert. Algorithms in C++, Parts I-IV, 3d ed. Boston, MA: Addison-Wesley, 1998. Sedgewick, Robert. Algorithms in C++, Part V, 3d ed. Boston, MA: Addison-Wesley, 2002.

But while powerful businesses, financial institutions, and government agencies hide their actions behind nondisclosure agreements, “proprietary methods,” and gag rules, our own lives are increasingly open books.

when a computer program beats a grandmaster at chess, the two are not using even remotely similar algorithms. The grandmaster can explain why it seemed worth sacrificing the knight for strategic advantage and can write an exciting book on the subject. The program can only prove that the sacrifice does not force a checkmate, and cannot write a book because it has no clue even what the objective of a chess game is. Programming AGI is not the same sort of problem as programming Jeopardy or chess. An AGI is qualitatively, not quantitatively, different from all other computer programs.

Soon after that, Eno briefly joined a group called the Scratch Orchestra, led by the late British avant-garde composer Cornelius Cardew. There was one Cardew piece that would be a formative experience for Eno—a piece known as “Paragraph 7,” part of a larger Cardew masterwork called The Great Learning. Explaining “Paragraph 7” could easily take up a book of its own. “Paragraph 7”’s score is designed to be performed by a group of singers, and it can be done by anyone, trained or untrained. The words are from a text by Confucius, broken up into 24 short chunks, each of which has a number. There are only a few simple rules. The number tells the singer how many times to repeat that chunk of text; an additional number tells each singer how many times to repeat it loudly or softly. Each singer chooses a note with which to sing each chunk—any note—with the caveats to not hit the same note twice in a row, and to try to match notes with a note sung by someone else in the group. Each note is held “for the length of a breath,” and each singer goes through the text at his own pace. Despite the seeming vagueness of the score’s few instructions, the piece sounds very similar—and very beautiful—each time it is performed. It starts out in discord, but rapidly and predictably resolves into a tranquil pool of sound. “Paragraph 7,” and 1960s tape loop pieces like Steve Reich’s “It’s Gonna Rain,” sparked Eno’s fascination with music that wasn’t obsessively organized from the start, but instead grew and mutated in intriguing ways from a limited set of initial constraints. “Paragraph 7” also reinforced Eno’s interest in music compositions that seemed to have the capacity to regulate themselves; the idea of a self-regulating system was at the very heart of cybernetics. Another appealing facet of “Paragraph 7” for Eno was that it was both process and product—an elegant and endlessly beguiling process that yielded a lush, calming result. Some of Cage’s pieces, and other process-driven pieces by other avant-gardists, embraced process to the point of extreme fetishism, and the resulting product could be jarring or painful to listen to. “Paragraph 7,” meanwhile, was easier on the ears—a shimmering cloud of sonics. In an essay titled “Generating and Organizing Variety in the Arts,” published in Studio International in 1976, a 28-year-old Eno connected his interest in “Paragraph 7” to his interest in cybernetics. He attempted to analyze how the design of the score’s few instructions naturally reduced the “variety” of possible inputs, leading to a remarkably consistent output. In the essay, Eno also wrote about algorithms—a cutting-edge concept for an electronic-music composer to be writing about, in an era when typewriters, not computers, were still en vogue. (In 1976, on the other side of the Atlantic, Steve Jobs and Steve Wozniak were busy building a primitive personal computer in a garage that they called the Apple I.) Eno also talked about the related concept of a “heuristic,” using managerial-cybernetics champion Stafford Beer’s definition. “To use Beer’s example: If you wish to tell someone how to reach the top of a mountain that is shrouded in mist, the heuristic ‘keep going up’ will get him there,” Eno wrote. Eno connected Beer’s concept of a “heuristic” to music. Brecht’s Fluxus scores, for instance, could be described as heuristics.

Final checklist To significantly increase the quantity and quality of ideas that you generate, reading this book isn’t enough. You need to make principles from this book a part of your own habits. Below you will find the 7 most fundamental principles of creating successful business ideas. Write them down on a sheet of paper and hang it near the desk where you work or near your bed. Over the next 3 weeks, think for at least 15-30 minutes per day about ideas using these principles. These can be ideas that will help you improve your business, achieve your dreams or make your life more interesting. I promise you that by the end of these 3 weeks you will notice a significant jump in your creative performance. 1. Collect raw materials. Ideas are combinations or modifications of other ideas. The more you know the ideas of other people and the more life experiences you expose yourself to, the more creative raw materials you have. The more creative raw materials you have, the more combinations your subconscious mind will be able to make and the more likely you are to create new valuable and interesting ideas. 2. Set the task for the subconscious mind. Your subconscious mind is a powerful thinking mechanism, but it remains idle if you haven’t given it a task. Once you begin giving your subconscious questions to think about regularly, you will notice how the quantity and quality of your ideas will skyrocket. 3. Separate analyzing and generating ideas. When you are analyzing ideas, your analytical brain blocks your superfast creative brain from thinking. To let the creative brain do its work, separate the processes of analyzing and generating ideas. 4. Think and rest. The most effective thinking algorithm is the following: think about a problem for an extensive period of time, forget about the problem and rest, occasionally think about the problem for few minutes and forget about it again. The incubation period when you don’t think about the problem is essential for your subconscious mind to process millions of thoughts and combinations of ideas, however to give it a task you need to think for some time about the problem consciously. 5. Generate many ideas. In creative thinking, quantity equals quality. You can’t generate one great idea. However, you can generate many ideas and select one or several great ideas out of them. 6. Have fun. Your subconscious mind thinks most effectively when you have fun. When you are serious, you are very unlikely to create really creative and valuable ideas. 7. Believe and desire. Believe that you will generate great ideas and have a burning desire to generate them. If you do, great ideas will come to you in abundance and sooner or later the problem will be solved. Once you have made these 7 principles a part of your own creative habits, glance through the book again and practice other principles and techniques. In a year’s time of practicing generating ideas regularly, you will become a world-class creative thinker. The skill of creating ideas will make your business successful and your life an adventure. I wish you good luck in creating successful ideas and in achieving all your dreams in business.

significance of the key, as opposed to the algorithm, is an enduring principle of cryptography. It was definitively stated in 1883 by the Dutch linguist Auguste Kerckhoffs von Nieuwenhof in his book La Cryptographie militaire: “Kerckhoffs’ Principle: The security of a cryptosystem must not depend on keeping secret the crypto-algorithm. The security depends only on keeping secret the key.

You will read over and over again in this book how important it is to do your homework. To prepare. To practice. To be disciplined. To be smart. To make smart trading moves. You will not win every round against algorithms and HFT, but you can win some of the rounds, and you can profit. You must be able to identify the different algorithmic programs so that you can trade against them. This takes some experience, good mentoring, and practice.

This book is about how to be a cat. How can you remain autonomous in a world where you are under constant surveillance and are constantly prodded by algorithms

This book doesn’t address problems related to family dynamics, to untenable pressures placed on young people, especially young women (please read Sherry Turkle on those topics), the way scammers can use social media to abuse you, the way social media algorithms might discriminate against you for racist or other horrible reasons (please read Cathy O’Neil on that topic), or the way your loss of privacy can bite you personally and harm society in surprising ways.

As a commutational array, The Sirisys array is a combination of algorithms and humans (called UIL for Users in the Loop). Humans form a functional aspect of the algorithms as well as running the algorithms. Each individual interacting with the Sirisys array is considered a User in the Loop.

So let me say what already should be obvious: 1,000 Books to Read Before You Die is neither comprehensive nor authoritative, even if a good number of the titles assembled here would be on most lists of essential reading. It is meant to be an invitation to a conversation—even a merry argument—about the books and authors that are missing as well as the books and authors included, because the question of what to read next is the best prelude to even more important ones, like who to be, and how to live. Such faith in reading’s power, and the learning and imagination it nourishes, is something I’ve been lucky enough to take for granted as both fact and freedom; it’s something I fear may be forgotten in the great amnesia of our in-the-moment newsfeeds and algorithmically defined identities, which hide from our view the complexity of feelings and ideas that books demand we quietly, and determinedly, engage. To get lost in a story, or even a study, is inherently to acknowledge the voice of another, to broaden one’s perspective beyond the confines of one’s own understanding. A good book is the opposite of a selfie; the right book at the right time can expand our lives in the way love does, making us more thoughtful, more generous, more brave, more alert to the world’s wonders and more pained by its inequities, more wise, more kind. In the metaphorical bookshop you are about to enter, I hope you’ll discover a few to add to those you already cherish. Happy reading.

This book is about putting you in the position to get there. It’s for those of us who understand that competition has intensified in all industries, further pushing for participation in more digital ecosystems and making digital transformation a key priority for company boards across all industries.

There is discrimination, and the opportunities are not equal to everyone. Most countries are blocked from using several crucial features on Google, Amazon, Shopify, AliExpress, and many more platforms that the "internet millionaires" use to get all of their wealth. They are not smarter than you! They simply have access to markets that are blocked to you! When you try to compete inside their markets, the domain owners alter the algorithms to favor people in that geolocation and put them and their products in front of your. I have been stopped from uploading books for no other reason than being in east Europe. People don't believe these stories are true because they don't want to believe they are living in such a world. It's like the story of the Native Americans, who were offered blankets contaminated with diseases to kill them. Now you are being offered a blanket of illusions that gives you lies. And when you say the truth, they call it a conspiracy and hate speech.

Math is alive in the crystalline vertices, and algorithms are parts of a higher intelligence/consciousness.

The second way to know if you are on the right track is by experiencing synchronicities. Synchronicities show the algorithmic nature of our reality

Alice: "Before the comparison with the threshold, I apply a Fisher transformation. This gives the curve a Gaussian distribution with sharp and well defined oscillations, so we get less false signals." Bob: "I have no idea what you're talking about.

In the physical world, you can randomize your vegetables by joining a Community-Supported Agriculture farm, which will deliver a box of produce to you every week. As we saw earlier, a CSA subscription does potentially pose a scheduling problem, but being sent fruits and vegetables you wouldn’t normally buy is a great way to get knocked out of a local maximum in your recipe rotation. Likewise, book-, wine-, and chocolate-of-the-month clubs are a way to get exposed to intellectual, oenophilic, and gustatory possibilities that you might never have encountered otherwise.

readable little book by Jonathan Regenstein (Regenstein, 2018) called Reproducible Finance with R.

As I’ve said throughout this book, networked products tend to start from humble beginnings—rather than big splashy launches—and YouTube was no different. Jawed’s first video is a good example. Steve described the earliest days of content and how it grew: In the earliest days, there was very little content to organize. Getting to the first 1,000 videos was the hardest part of YouTube’s life, and we were just focused on that. Organizing the videos was an afterthought—we just had a list of recent videos that had been uploaded, and you could just browse through those. We had the idea that everyone who uploaded a video would share it with, say, 10 people, and then 5 of them would actually view it, and then at least one would upload another video. After we built some key features—video embedding and real-time transcoding—it started to work.75 In other words, the early days was just about solving the Cold Start Problem, not designing the fancy recommendations algorithms that YouTube is now known for. And even once there were more videos, the attempt at discoverability focused on relatively basic curation—just showing popular videos in different categories and countries. Steve described this to me: Once we got a lot more videos, we had to redesign YouTube to make it easier to discover the best videos. At first, we had a page on YouTube to see just the top 100 videos overall, sorted by day, week, or month. Eventually it was broken out by country. The homepage was the only place where YouTube as a company would have control of things, since we would choose the 10 videos. These were often documentaries, or semi-professionally produced content so that people—particularly advertisers—who came to the YouTube front page would think we had great content. Eventually it made sense to create a categorization system for videos, but in the early years everything was grouped in with each other. Even while the numbers of videos was rapidly growing, so too were all the other forms of content on the site. YouTube wasn’t just the videos, it was also the comments left by viewers: Early in we saw that there were 100x more viewers than creators. Every social product at that time had comments, so we added them to YouTube, which was a way for the viewers to participate, too. It seems naive now, but we were just thinking about raw growth at that time—the raw number of videos, the raw number of comments—so we didn’t think much about the quality. We weren’t thinking about fake news or anything like that. The thought was, just get as many comments as possible out there, and the more controversial the better! Keep in mind that the vast majority of videos had zero comments, so getting feedback for our creators usually made the experience better for them. Of course now we know that once you get to a certain level of engagement, you need a different solution over time.

The true power of Alpha’s little tool, however, lay in a machine learning algorithm the device would insert into the nodes. It was capable of identifying backdoors into servers using impossibly small amounts of data embedded in the communication traffic.

isolate little snippets of sound called phonemes, and they associate multiple keywords with each one. You end up with a database of little digital fragments of sound, each one distinctive. When you want to make the voice speak, you feed another script into the cloning system, and the algorithms do a keyword lookup and

This book is a compilation of interesting ideas that have strongly influenced my thoughts and I want to share them in a compressed form. That ideas can change your worldview and bring inspiration and the excitement of discovering something new. The emphasis is not on the technology because it is constantly changing. It is much more difficult to change the accompanying circumstances that affect the way technological solutions are realized. The chef did not invent salt, pepper and other spices. He just chooses good ingredients and uses them skilfully, so others can enjoy his art. If I’ve been successful, the book creates a new perspective for which the selection of ingredients is important, as well as the way they are smoothly and efficiently arranged together. In the first part of the book, we follow the natural flow needed to create the stimulating environment necessary for the survival of a modern company. It begins with challenges that corporations are facing, changes they are, more or less successfully, trying to make, and the culture they are trying to establish. After that, we discuss how to be creative, as well as what to look for in the innovation process. The book continues with a chapter that talks about importance of inclusion and purpose. This idea of inclusion – across ages, genders, geographies, cultures, sexual orientation, and all the other areas in which new ways of thinking can manifest – is essential for solving new problems as well as integral in finding new solutions to old problems. Purpose motivates people for reaching their full potential. This is The second and third parts of the book describes the areas that are important to support what is expressed in the first part. A flexible organization is based on IT alignment with business strategy. As a result of acceleration in the rate of innovation and technological changes, markets evolve rapidly, products’ life cycles get shorter and innovation becomes the main source of competitive advantage. Business Process Management (BPM) goes from task-based automation, to process-based automation, so automating a number of tasks in a process, and then to functional automation across multiple processes andeven moves towards automation at the business ecosystem level. Analytics brought us information and insight; AI turns that insight into superhuman knowledge and real-time action, unleashing new business models, new ways to build, dream, and experience the world, and new geniuses to advance humanity faster than ever before. Companies and industries are transforming our everyday experiences and the services we depend upon, from self-driving cars, to healthcare, to personal assistants. It is a central tenet for the disruptive changes of the 4th Industrial Revolution; a revolution that will likely challenge our ideas about what it means to be a human and just might be more transformative than any other industrial revolution we have seen yet. Another important disruptor is the blockchain - a distributed decentralized digital ledger of transactions with the promise of liberating information and making the economy more democratic. You no longer need to trust anyone but an algorithm. It brings reliability, transparency, and security to all manner of data exchanges: financial transactions, contractual and legal agreements, changes of ownership, and certifications. A quantum computer can simulate efficiently any physical process that occurs in Nature. Potential (long-term) applications include pharmaceuticals, solar power collection, efficient power transmission, catalysts for nitrogen fixation, carbon capture, etc. Perhaps we can build quantum algorithms for improving computational tasks within artificial intelligence, including sub-fields like machine learning. Perhaps a quantum deep learning network can be trained more efficiently, e.g. using a smaller training set. This is still in conceptual research domain.

Sure, WFO allows to use different price curves for training and for testing. However, you will usually choose your strategy just because of their positive WFO test. This means that the 'out-of-sample' price data in the test period are not so out of sample anymore, since they were used for selecting the best strategy.

In a very real sense all of us, and all of what surrounds us, are pieces inside an ongoing four-billion-year-old game. It’s not a game with a perfectly prescribed set of rules. Instead it’s a game governed by rules that emerge from the game itself. One might say that the very object of the game is to produce its own rules. The ultimate players of the game are also hard to spot. They are even more ancient, and have no discernable physical form—they are the root properties and predispositions of the universe; they are everywhere, yet nowhere in particular.

It is a "known fact" that 95% of all private traders lose all their money in the first 12 months.

It is a "known fact" that 95% of all private traders lose all their money in the first 12 months. Not true - at least not with completely random trading and one trade per day. You can see from the profit distribution that only about 55% lose money at all (the sum of the red bars with negative profit), while 45% end their trading period with a profit. Of course, they won't attribute their success to the bell curve, but to their trading skills.

The profit distribution of real traders is not a Gaussian, but a Lévy distribution. It has a smaller peak and fatter tails. That means the losers lose more, and the winners take more than in a random-trading situation.

As a result, private traders have a slightly higher annual loss rate of about 65%, but certainly not 95%, not even beginners.

When analyzing robot strategies, one can notice such a martingale system from telltale peaks in the lot size. For this reason, robots or signal providers often increase not the number of lots, but the number of trades, which is less suspicious.

But we hope a merciful margin call saved them early.

He glanced that way, and a small hand waving a book appeared over the top of a garment rack. "Time of Unutterable Algorithms." The hand disappeared, then reappeared. It looked empty at first, but then, as Meddy moved her wrist, Milo caught a slight flash from one knuckle. "Ring of Wildest Abandon." Then Meddy's head and shoulders appeared as she climbed up and leaned over the top of the rack. With her other arm, she brandished a carved walking stick. "Eglantine's Patent Blackthorn Wishing Stick, guaranteed to offer considered advice before granting requests. What about you?" Milo laughed. He held up the red case. " Slywhisker's Crimson Casket of Relics, including the Ocher Pages of Invisible Wards, the Ever-Sharp Inscriber of Rose-colored Destinies, and the Flask of Winds and Voids" Meddy whistled. "You don't mess around." "I learned from the best.

Many AI researchers today claim that their systems are cognitively inspired (in particular inspired by the popular System 1/System 2 distinction introduced by Daniel Kahneman in his dual-process theory) just because their decision-making mechanisms couple both fast routines and slow decision-making strategies. This is a clear example (one of the many in the field) of the misconceptions that have been raised by the shallow ascription of labels coming from the cognitive vocabulary to the behavior and/or design of such systems. Unfortunately, it is not sufficient to just implement “fast” and “slow” mechanisms in an artificial system to claim any kind of cognitive inspiration or of cognitive plausibility. To make one of these claims, in fact, one should build and integrate algorithms in a way that is much more constrained with respect to such a generic and shallow description of how an intelligent system (natural or artificial) works (note: the book Daniel Kahneman - Thinking, Fast and Slow (2011) was written for a popular audience and therefore contains obvious oversimplifications of the dual-process theory of reasoning. Unfortunately, many people in AI have considered the book as a scientific publication ignoring the actual scientific papers laying down the theory). For example, one should consider "how” such fast or slow mechanisms are built, how they interact between them (both within the System 1/System 2 components and between them), how they evolve over time (e.g. System 2 mechanisms can be “automatized” and become System 1 routines) etc. In Cognitive Design for Artificial Minds, the distinction between these “shallow” and “constrained” systems is made clear by introducing the “functional” and “structural” design approaches and by exploring the different explanatory roles that such design perspectives put in place.

PUTTING IT ALL TOGETHER I’ve explained a lot of concepts in this chapter, so I want to recap it all into something a little more tangible. Step #1: The first step is to figure out what type of show you want to have. If you’re a writer, then you should start a blog. If you like video, then you should start a vlog on one of the video platforms. Lastly, if you like audio, then you should start a podcast. Step #2: Your show will be you documenting the process of achieving the same goal that your audience will be striving for. As you’re documenting your process, you’ll be testing your material and paying attention to the things that people respond to. If you commit to publishing your show every day for a year, you’ll have the ability to test your material and find your voice, and your dream customers will be able to find you. Step #3: You’ll leverage your Dream 100 by interviewing them on your show. This will give you the ability to build relationships with them, give them a platform, give you the ability to promote their episode on your show to their audience, and get access to their friends and followers. Step #4: Even though this is your own show, you’re renting time on someone else’s network. It’s important that you don’t forget it and that you focus on converting it into traffic that you own. Figure 7.11: As you create your own show, focus on converting traffic that you earn and control into traffic that you own. And with that, I will close out Section One of this book. So far, we’ve covered a lot of core principles to traffic. We: Identified exactly who your dream client is. Discovered exactly where they are congregating. Talked about how to work your way into those audiences (traffic that you earn) and how you buy your way into those audiences (traffic that you control). Learned how to take all the traffic that you earn and all the traffic that you buy and turn it all into traffic that you own (building your list). Discussed how to plug that list into a follow-up funnel so you can move them through your value ladder. Prepared to infiltrate your Dream 100, find your voice, and build your following by creating your own show. In the next section, we’ll shift our focus to mastering the pattern to get traffic from any advertising networks (like Instagram, Facebook, Google, and YouTube) and how to understand their algorithms so you can get unlimited traffic and leads pouring into your funnels.

The reality check described here can determine that a strategy won't work. But the opposite is not true: it cannot with the same certainty determine that it will work.

An advise call generates trade rules in C code; Detrend = SHUFFLE tests a strategy with a random price curve; NumTotalCycles can generate statistics from multiple simulation runs; plotHistogram plots histograms.

For instance, static variables keep their value between function calls;

An option is not a financial asset. It is a simple contract that gives its owner the right to buy (call option) or sell (put option) a certain amount (the multiplier, usually 100) of a stock, a future, or some other asset (the underlying) at a fixed price (the strike) at or before a fixed date (the expiry). Better read the previous sentence twice.

It is also usual to trade not only single options, but combinations (combos) of different options types. Thus there are innumerable possibilities for options strategies.

If the underlying price (the spot price) of a call option lies above the strike, the option is in the money; otherwise it’s out of the money.

American style options can be exercised anytime, European style options only at the expiry date.

Due to the premium, options can still produce a profit to their seller even if the underlying moves a bit in the wrong direction. That's the seller advantage.

But options are often bought as an insurance against unwanted price moves of the underlying.

Depending on length and resolution it can be a couple hundred dollars. Options data selling is apparently even more lucrative than options selling.

As for algorithms, they are not predictions, nor some kind of magic, nor reading stars and horoscopes, nor any of the superstitions, but rather they are something else, more scientific than all our sciences. Just give them the basic information, and once they have identified what is required, follow it, they put into your hands the identity that it does not know about itself, in the form of graphs on the imaginary timeline, its feelings, attitudes, psychological fluctuations, thoughts, everything about it, with astonishing accuracy, as well as general expectations. They have predicted everything accurately, not just human emotions, though these are the most serious things that they have presented. Before they could connect them in the form of supercomputer systems, they were giving results, in a primitive way, as if they were a magic crystal from the centuries of darkness, they soon discovered that magic does not exist in them, but rather they are a crystal made of a huge number of tiny optical fibers, made of a material that there is no equal to it on earth. Somehow, they kept inside them all the cosmic events, everything, from the motions of galaxies, and explosions, to the flapping of the wings of a butterfly, linked together by non-mathematical equations, something we do not know, incomprehensible symbols, they could not decipher, but they were able to interpret their sequence of results as algorithms.

Like a shepherd and sheep, its principle is simple, redirection towards the obligatory path, and speaking of Ozcan, he is the most proficient in this game. Watch the professionals do it in the reorientation of functional organizations. There is no need to recruit them all, it is enough for them to do what a shepherd does with a flock of sheep; blocking the roads in front of them, putting a dog in one place, standing and waving his stick in another place, to force them to take the path he wants, towards the barn. And if you spoke to one of them, it would swear to you that it is going the way it wants, which it chose with its full will, or chosen for them by their leader at the forefront of the herd, who knows the secrets of the ways, believing that they go the way they want. He decided that he should play the game according to its laws since they are sheep, so do not try to address them or convince them, but rather direct them to where you want. He did not know anything about deterministic algorithms at the time, his decision was based on his innate, something inside him. He succeeded, however, by making a butterfly flutter, far away. Some straying out of the Shepherd’s path, then another artificial flutter associated with the first to accelerate the process, and then a third, and a fourth, then the chaos ensued, and the hurricanes blew up all the inevitable of Alpha Headquarters. A butterfly fluttered where no one was watching, he studied and planned it carefully. Words by a revolutionary Palestinian poet, Mahmoud Darwish, summarized the whole story… Throw a stone into the stagnant water, rivers will break out Ring your bells in the kingdom of silence and sing your anthem And let the wall of fear break into dust like pottery

The fact of zero He added nonstop: Did you know that zero was not used throughout human history! Until 781 A.D, when it was first embodied and used in arithmetic equations by the Arab scholar Al-Khwarizmi, the founder of algebra. Algorithms took their name from him, and they are algorithmic arithmetic equations that you have to follow as they are and you will inevitably get the result, the inevitable result. And before that, across tens and perhaps hundreds of thousands of years, humans refused to deal with zero. While the first reference to it was in the Sumerian civilization, where inscriptions were found three thousand years ago in Iraq, in which the Sumerians indicated the existence of something before the one, they refused to deal with it, define it and give it any value or effect, they refused to consider it a number. All these civilizations, some of which we are still unable to decipher many of their codes, such as the Pharaonic civilization that refused to deal with zero! We see them as smart enough to build the pyramids with their miraculous geometry and to calculate the orbits of stars and planets with extreme accuracy, but they are very stupid for not defining zero in a way that they can deal with, and use it in arithmetic operations, how strange this really is! But in fact, they did not ignore it, but gave it its true value, and refused to build their civilizations on an unknown and unknown illusion, and on a wrong arithmetical frame of reference. Throughout their history, humans have looked at zero as the unknown, they refused to define it and include it in their calculations and equations, not because it has no effect, but because its true effect is unknown, and remaining unknown is better than giving it a false effect. Like the wrong frame of reference, if you rely on it, you will inevitably get a wrong result, and you will fall into the inevitability of error, and if you ignore it, your chance of getting it right remains. Throughout their history, humans have preferred to ignore zero, not knowing its true impact, while we simply decided to deal with it, and even rely on it. Today we build all our ideas, our civilization, our software, mathematics, physics, everything, on the basis that 1 + 0 equals one, because we need to find the effect of zero so that our equations succeed, and our lives succeed with, but what if 1 + 0 equals infinity?! Why did we ignore the zero in summation, and did not ignore it in multiplication?! 1×0 equals zero, why not one? What is the reason? He answered himself: There is no inevitable reason, we are not forced. Humans have lived throughout their ages without zero, and it did not mean anything to them. Even when we were unable to devise any result that fits our theorems for the quotient of one by zero, then we admitted and said unknown, and ignored it, but we ignored the logic that a thousand pieces of evidence may not prove me right, and one proof that proves me wrong. Not doing our math tables in the case of division, blowing them up completely, and with that, we decided to go ahead and built everything on that foundation. We have separated the arithmetic tables in detail at our will, to fit our calculations, and somehow separate the whole universe around us to fit these tables, despite their obvious flaws. And if we decide that the result of one multiplied by zero is one instead of zero, and we reconstruct the whole world on this basis, what will happen? He answered himself: Nothing, we will also succeed, the world, our software, our thoughts, our dealings, and everything around us will be reset according to the new arithmetic tables. After a few hundred years, humans will no longer be able to understand that one multiplied by zero equals zero, but that it must be one because everything is built on this basis.

OptimalF is the already mentioned optimal investment factor that Zorro has calculated from the balance curve of the backtest, using the Ralph Vince algorithm.

the square root method always keeps the same distance from the account balance to the maximum expected drawdown.

If your capital has doubled, you can only pay out about 60% of the profit. The remaining 40% must remain on the account as a buffer to compensate future drawdowns.

With high leverage systems, increase your investment only proportionally to the square root of the capital growth.

Strategy parameters must be trained individually for any asset. It is rare that multiple assets share the same parameters.

The book, All I Really Need to Know I Learned in Kindergarten, was written in 1986 by a minister, Robert Fulghum, and it’s full of simple-sounding life advice, like “share everything,” “play fair,” and “clean up after your own mess.” Chen believes that these skills—the elementary, pre-literate skills of treating other people well, acting ethically, and behaving in prosocial ways, all of which I consider “analog ethics”—are badly needed for an age in which our value will come from our ability to relate to other people. He writes: While I know that we’ll need to layer on top of that foundation a set of practical and technical know-how, I agree with [Fulghum] that a foundation rich in EQ [emotional quotient] and compassion and imagination and creativity is the perfect springboard to prepare people—the doctors with the best bedside manner, the sales reps solving my actual problems, crisis counselors who really understand when we’re in crisis—for a machine-learning powered future in which humans and algorithms are better together. Research has indicated that teaching analog ethics can be effective. One 2015 study that tracked children from kindergarten through young adulthood found that people who had developed strong prosocial, noncognitive skills—traits like positivity, empathy, and regulating one’s own emotions—were more likely to be successful as adults. Another study in 2017 found that kids who participated in “social-emotional” learning programs were more likely to graduate from college, were arrested

Step by Step… Can you write out your ideal business step by step Here is a business I am setting up for a client. She wants to shipping start her own shipping company… One she will need a US partner to collect and transfer packages to her in Jamaica. She will also need one in China. I have two contacts. One has a warehouse in Florida The other has two in China. Chinese connect makes goods available within 3 weeks, she has to tell her customers four. The US connect makes it within 3-5 days. She has to tell them within a week… Next she will need a website where her customers can login and track their packages. This will come with individual dashboards. She will need an interface and warehouse management software and logistics APIs. She will also need an automated email set up (journey) to send emails to her customers without her or her agents needing to do that. Without this Saas she would have to hire someone to reply to messages and emails about , someone to call and track, use usps and FedEx tracking numbers to track and reply back to customers. She also needs a beta ApI to allow her warehouse guy to update the CRM with information about her customers packages… Key nodes such as - Intransit to destinations Held at customs Clearance In transit to store Pick up available etc… These will come in as email notifications Fully automated. Everything will be connected using Webhooks… entire system. Saas she might need to use a combination of GOhighlevel, Workiz and To run this as a System as as Service. Each platform can work together using webhooks. Gohighlevel as a Saas is $500 a month Workiz is $200 dollars She can use Odoo which is open source alternative as a CRM And Clickup as Management. This is how a conversational business plan looks. You can see it. You can research it. You can confirm that it’s plausible. It doesn’t sound like pipedreams. It sounds workable to credit companies /banks and investors. It sounds doable to a BDO Client. I also sound as if I know what I am doing. Not a lot of technical language. A confused prospective business investor or banker don’t want to use a dictionary to figure out everything… They want to see the vision as clear as day. You basically need to do to them what I did to you when you joined my programme. It must sound plausible. All businesses is a game of wit. Every deal that is signed benefits both party. Whether initially or in the long term. Those are the sub-tenets of business. Every board meeting or meeting with regulatory boards, banks, credit facilities, municipalities is a game of convincing people to see your thing through… Everyone does Algorithm is simple. People want you to solve their problems with speed and efficiency. Speed is very important and automation. Progress, business and production are tied to ego… that’s why people love seh oh dem start a business or dem have dem online business and nah sell one rass thing. Cause a lot of people think being successful and looking successful are one and the same thing until they meet someone like me or people who done the work… Don’t rush it… you are young and you have time. There are infact certain little nuances Weh yuh only ago learn through experience. Experience and reflection. One of the drawbacks of wanting to run your business by yourself with you and your family members is that you guys will have to be reliant on yourself for feedback which is not alw

Find a price curve anomaly. Decide for a market inefficiency to exploit – or discover a new one. The best known inefficiencies are listed in the next chapter. Think about which price curve anomaly this effect could produce (an anomaly is any systematic deviation from randomness). Describe it with a quantitative formula or at least a qualitative criteria. You’ll need that for the next step.

Meehl’s book, called Clinical versus Statistical Prediction, had shown that psychoanalysts who tried to predict what would become of their neurotic patients fared poorly compared to simple algorithms.

You can now create the user interface of your trading system. Determine which parameters you want to change in real time, and which ones only at start of the system.

such as the Cold Blood Index described on the Financial Hacker blog.

Unleash the potential of Facebook's vast landscape. From algorithms to engagement, our eBook guides you through the art of turning 'likes' into thriving profits.

Unleash the potential of Facebook's vast landscape. From algorithms to engagement, the eBook guides you through the art of turning 'likes' into thriving profits.

That is not to say that there’s any change in their individual physics, but rather that the physics of these building blocks is not enough by itself to tell you all their properties and behaviors when they’re part of you.