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.

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.

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?

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.

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?

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.

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?

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.

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.

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...

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.

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.

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.

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?

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.

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 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.

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.

If your main interest is in the business uses of machine learning, this book can help you in at least six ways: to become a savvier consumer of analytics; to make the most of your data scientists; to avoid the pitfalls that kill so many data-mining projects; to discover what you can automate without the expense of hand-coded software; to reduce the rigidity of your information systems; and to anticipate some of the new technology that’s coming your way. I’ve seen too much time and money wasted trying to solve a problem with the wrong learning algorithm, or misinterpreting what the algorithm said. It doesn’t take much to avoid these fiascoes. In fact, all it takes is to read this book.

Another simple learner, called the nearest-neighbor algorithm, has been used for everything from handwriting recognition to controlling robot hands to recommending books and movies you might like.

Thus one route—arguably the most popular one—to inventing the Master Algorithm is to reverse engineer the brain. Jeff Hawkins took a stab at this in his book On Intelligence. Ray Kurzweil pins his hopes for the Singularity—the rise of artificial intelligence that greatly exceeds the human variety—on doing just that and takes a stab at it himself in his book How to Create a Mind. Nevertheless, this is only one of several possible approaches, as we’ll see.

Physicists and mathematicians are not the only ones who find unexpected connections between disparate fields. In his book Consilience, the distinguished biologist E. O. Wilson makes an impassioned argument for the unity of all knowledge, from science to the humanities.

Nassim Taleb hammered on it forcefully in his book The Black Swan. Some events are simply not predictable. If you’ve only ever seen white swans, you think the probability of ever seeing a black one is zero. The financial meltdown of 2008 was a “black swan.” It’s true that some things are predictable and some aren’t, and the first duty of the machine learner is to distinguish between them.

In a famous passage of his book The Sciences of the Artificial, AI pioneer and Nobel laureate Herbert Simon asked us to consider an ant laboriously making its way home across a beach. The ant’s path is complex, not because the ant itself is complex but because the environment is full of dunelets to climb and pebbles to get around. If we tried to model the ant by programming in every possible path, we’d be doomed. Similarly, in machine learning the complexity is in the data; all the Master Algorithm has to do is assimilate it, so we shouldn’t be surprised if it turns out to be simple. The human hand is simple—four fingers, one opposable thumb—and yet it can make and use an infinite variety of tools. The Master Algorithm is to algorithms what the hand is to pens, swords, screwdrivers, and forks.

Each tribe’s solution to its central problem is a brilliant, hard-won advance. But the true Master Algorithm must solve all five problems, not just one. For example, to cure cancer we need to understand the metabolic networks in the cell: which genes regulate which others, which chemical reactions the resulting proteins control, and how adding a new molecule to the mix would affect the network. It would be silly to try to learn all of this from scratch, ignoring all the knowledge that biologists have painstakingly accumulated over the decades. Symbolists know how to combine this knowledge with data from DNA sequencers, gene expression microarrays, and so on, to produce results that you couldn’t get with either alone. But the knowledge we obtain by inverse deduction is purely qualitative; we need to learn not just who interacts with whom, but how much, and backpropagation can do that. Nevertheless, both inverse deduction and backpropagation would be lost in space without some basic structure on which to hang the interactions and parameters they find, and genetic programming can discover it. At this point, if we had complete knowledge of the metabolism and all the data relevant to a given patient, we could figure out a treatment for her. But in reality the information we have is always very incomplete, and even incorrect in places; we need to make headway despite that, and that’s what probabilistic inference is for. In the hardest cases, the patient’s cancer looks very different from previous ones, and all our learned knowledge fails. Similarity-based algorithms can save the day by seeing analogies between superficially very different situations, zeroing in on their essential similarities and ignoring the rest. In this book we will synthesize a single algorithm will all these capabilities:

Harvard’s Leslie Valiant received the Turing Award, the Nobel Prize of computer science, for inventing this type of analysis, which he describes in his book entitled, appropriately enough, Probably Approximately Correct.

Hebb’s rule, as it has come to be known, is the cornerstone of connectionism. Indeed, the field derives its name from the belief that knowledge is stored in the connections between neurons. Donald Hebb, a Canadian psychologist, stated it this way in his 1949 book The Organization of Behavior: “When an axon of cell A is near enough cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A’s efficiency, as one of the cells firing B, is increased.” It’s often paraphrased as “Neurons that fire together wire together.

There are also books that contain collections of papers or chapters on particular aspects of knowledge discovery—for example, Relational Data Mining edited by Dzeroski and Lavrac [De01]; Mining Graph Data edited by Cook and Holder [CH07]; Data Streams: Models and Algorithms edited by Aggarwal [Agg06]; Next Generation of Data Mining edited by Kargupta, Han, Yu, et al. [KHY+08]; Multimedia Data Mining: A Systematic Introduction to Concepts and Theory edited by Z. Zhang and R. Zhang [ZZ09]; Geographic Data Mining and Knowledge Discovery edited by Miller and Han [MH09]; and Link Mining: Models, Algorithms and Applications edited by Yu, Han, and Faloutsos [YHF10]. There are many tutorial notes on data mining in major databases, data mining, machine learning, statistics, and Web technology conferences.

As the arguments of this book have shown, mathematical understanding is something different from computation and cannot be completely supplanted by it. Computation can supply extremely valuable aid to understanding, but it never supplies actual understanding itself. However, mathematical understanding is often directed towards the finding of algorithmic procedures for solving problems. In this way, algorithmic procedures can take over and leave the mind free to address other issues. A good notation is something of this nature, such as is supplied by the differential calculus, or the ordinary 'decimal' notation for numbers. Once the algorithm for multiplying numbers together has been mastered, for example, the operations can be performed in an entirely mindless algorithmic way, rather than 'understanding' having to be invoked as to why those particular algorithmic rules are being adopted, rather than something else.

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.

Seibel: Are there any books that you think all programmers should read? Norvig: I think there are a lot of choices. I don't think there's only one path. You've got to read some algorithm book. You can't just pick these things out and paste them together. It could be Knuth, or it could be the Cormen, Leiserson, and Rivest. And there are others. Sally Goldman's here now. She has a new book out that's a more practical take on algorithms. I think that's pretty interesting. So you need one of those. You need something on the ideas of abstraction. I like Abelson and Sussman. There are others.

glory, at the Science Museum of London. Charles Babbage was a well-known scientist and inventor of the time. He had spent years working on his Difference Engine, a revolutionary mechanical calculator. Babbage was also known for his extravagant parties, which he called “gatherings of the mind” and hosted for the upper class, the well-known, and the very intelligent.4 Many of the most famous people from Victorian England would be there—from Charles Darwin to Florence Nightingale to Charles Dickens. It was at one of these parties in 1833 that Ada glimpsed Babbage’s half-built Difference Engine. The teenager’s mathematical mind buzzed with possibilities, and Babbage recognized her genius immediately. They became fast friends. The US Department of Defense uses a computer language named Ada in her honor. Babbage sent Ada home with thirty of his lab books filled with notes on his next invention: the Analytic Engine. It would be much faster and more accurate than the Difference Engine, and Ada was thrilled to learn of this more advanced calculating machine. She understood that it could solve even harder, more complex problems and could even make decisions by itself. It was a true “thinking machine.”5 It had memory, a processor, and hardware and software just like computers today—but it was made from cogs and levers, and powered by steam. For months, Ada worked furiously creating algorithms (math instructions) for Babbage’s not-yet-built machine. She wrote countless lines of computations that would instruct the machine in how to solve complex math problems. These algorithms were the world’s first computer program. In 1840, Babbage gave a lecture in Italy about the Analytic Engine, which was written up in French. Ada translated the lecture, adding a set of her own notes to explain how the machine worked and including her own computations for it. These notes took Ada nine months to write and were three times longer than the article itself! Ada had some awesome nicknames. She called herself “the Bride of Science” because of her desire to devote her life to science; Babbage called her “the Enchantress of Numbers” because of her seemingly magical math

On the same meditation retreat that led me to see a weed that lacked essence-of-weed, I also had an interesting encounter with a reptile. I was walking through the woods and, looking down, saw a lizard frozen in its tracks, presumably by the sight of me. As I watched it look around nervously and calculate its next move, my first thought was that this lizard’s behavior was governed by a relatively simple algorithm: see large creature, freeze; if creature approaches, run. But then I realized that, though my own behavioral algorithms are much more complicated than that, there could well be a being so intelligent that, to it, I look as simple-minded as the lizard looks to me. The more I thought about it, the more that lizard and I seemed to have in common. We were both thrown into a world we didn’t choose, under the guidance of behavioral algorithms we didn’t choose, and were trying to make the best of the situation. I felt a kind of kinship with the lizard that I’d never felt with a lizard.” Excerpt From: Robert Wright. “Why Buddhism is True.” iBooks.

I heard reiteration of the following claim: Complex theories do not work; simple algorithms do. One of the goals of this book is to show that, at least in the problems of statistical inference, this is not true. I would like to demonstrate that in the area of science a good old principle is valid: Nothing is more practical than a good theory.

Religious leaders preach to the poor and downtrodden and enslaved, telling them that they deserve the kingdom of heaven—basically, an open “fuck you” to the corrupt elites of the day. It’s a message that’s easy to get behind. Today, appealing to the hopeless is easier than ever before. All you need is a social media account: start posting extreme and crazy shit, and let the algorithm do the rest. The crazier and more extreme your posts, the more attention you’ll garner, and the more the hopeless will flock to you like flies to cow shit. It’s not hard at all.

At some point, I began to think of this as an activist book disguised as a self-help book. I’m not sure that it’s fully either. But as much as I hope this book has something to offer you, I also hope it has something to contribute to activism, mostly by providing a rest stop for those on their way to fight the good fight. I hope that the figure of “doing nothing” in opposition to a productivity-obsessed environment can help restore individuals who can then help restore communities, human and beyond. And most of all, I hope it can help people find ways of connecting that are substantive, sustaining, and absolutely unprofitable to corporations, whose metrics and algorithms have never belonged in the conversations we have about our thoughts, our feelings, and our survival.

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.

Our senses seem to deceive us into thinking that we live in the material world. Our world is not based on objectively existing particles of matter but it is based on waves of potentiality, that is pure information. Our world is informational. Think of it as an observer-centric virtual reality. Your consciousness is, rather, an optimized meta-algorithmic data stream, a sequence of conscious instants.

A somewhat provocative example of the interconnections between the gaming industry and finance. A technologist working for a large London hedge fund hinted this to me in interview. Trained in computer science and engineering, this interviewee first worked as a network programmer for large online multiplayer games. His greatest challenge was the fact that the Internet is not instantaneous: when a player sends a command to execute in action, it takes time for the signal to reach the computer server and interact with the commands of other players. For the game to be realistic, such delays have to be taken into account when rendering reality on the screen. The challenge for the network programmer is to make these asymmetries as invisible as possible so that the game seem 'equitable to everyone.' The problem is similar in finance, where the physical distance from the stock exchange's matching engines matters tremendously, requiring a similar solution to the problem of latency: simulating the most likely state of the order book on the firm's computers in order to estimate the most advantageous strategies or the firm's trading algorithms. Gaming and finance are linked not through an institutional imperative of culture or capital - or even a strategy, as such - but rather through the more mundane and lowly problems of how to fairly manage latency and connectivity.

Being positive is the only practical way to live." (from my father Stephen)

Algorithms, step-by-step processes, are another approach.

Look for shortcuts via existing design patterns, tools, or clever algorithms. Consider whether you can reframe the problem.

premature optimization, where you tweak or perfect code or algorithms (optimize) too early (prematurely).

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.

Paul Graham is the founder of Y Combinator, one of the most successful and sought-after startup accelerators in the tech world. Graham has invested in several blockbuster companies, including AirBNB and Dropbox, both of which are valued in the billions at the time of this writing. After investing in hundreds of companies and considering thousands more, Paul Graham has perfected the art of identifying promising startups. His methods may surprise you. In an interview, Graham highlighted two key strategies: Favoring people over product Favoring determination over intelligence What’s most essential for a successful startup? Graham: The founders. We’ve learned in the six years of doing Y Combinator to look at the founders—not the business ideas—because the earlier you invest, the more you’re investing in the people. When Bill Gates was starting Microsoft, the idea that he had then involved a small-time microcomputer called the Altair. That didn’t seem very promising, so you had to see that this 19-year-old kid was going places. What do you look for? Graham: Determination. When we started, we thought we were looking for smart people, but it turned out that intelligence was not as important as we expected. If you imagine someone with 100 percent determination and 100 percent intelligence, you can discard a lot of intelligence before they stop succeeding. But if you start discarding determination, you very quickly get an ineffectual and perpetual grad student.[74] Your intelligence doesn’t matter as much as you think it does. If you’re reading this book, you’re probably more than capable. Your ideas don’t matter much, either. What matters most—by far, is your perseverance. Stop worrying about your mental aptitude. Stop worrying about the viability of the project you’re considering. Stop worrying about all the other big decisions keeping you up at night. Instead, focus on relentlessly grinding away at your passion until something incredible happens. Your potential output is governed by your mindset, not your mind itself.

faster. As I was closing this book, Walmart announced that to upgrade its ability to compete in e-commerce with Amazon—which still does eight times Walmart’s sales online—it was buying Jet, a year-old Internet retail startup. The Economist reported on August 13, 2016, that Jet’s appeal to Walmart was its “real-time pricing algorithm, which tempts customers with lower prices if they add more items to their basket. The algorithm also identifies which of Jet’s vendors is closest to the consumer, helping to minimize shipping costs and allowing them to offer discounts. Walmart plans to integrate the software with its own.” It turns out that “under a second” was just too damned slow.

The brightest moments of human discovery are those unplanned and random instants when you thumb through a strange book in a foreign library or talk auto maintenance with a neuroanatomist. We need our searches to include cross-wiring and dumb accidents, too, not just algorithmic surety.

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. It’s possible that a similar mechanism was in play during the enigmatic and controversial stock market “flash crash” of May 6, 2010, when, in a matter of minutes, the price of several seemingly random companies in the S&P 500 rose to more than $100,000 a share, while others dropped precipitously—sometimes to $0.01 a share. Almost $1 trillion of value instantaneously went up in smoke.

For pretty much my whole life, I thought I was living to better myself, to create the best life possible. About a year ago, that mindset changed. I now believe I’m here to create the best world possible. This shift from me to everyone is what altered my entire understanding of passion, and my purpose. Ben Horowitz is one of my digital mentors (meaning I follow his blog). I find him very insightful. Whenever he says (or writes about) anything, I inevitably start nodding my head until my neck is sore. Here’s an excerpt from the commencement speech he gave at Columbia, his alma mater: “Following your passion is a very me centered view of the world, and as you go through life, what you’ll find is that what you take out of the world over time—be it…money, cars, stuff, accolades—is much less important than what you put into the world. And so my recommendation would be to follow your contribution. Find the thing that you’re great at, put that into the world, contribute to others, help the world be better. That is the thing to follow." Most of the time, if you follow your contribution, it’s either already a passion, or likely to become one. Doing something you’re good at is intoxicating, as is contributing to the world. Writing and launching The Connection Algorithm was a full year of hard work. It was the result of countless hours of reflection, deeply philosophical thinking, and brutal honesty. Throughout the entire process, I felt driven, passionate, and motivated. At first, I thought this was because I was doing it on my own. But I’ve come to realize it was something else—something far more profound. Shortly after the book was released, I began receiving emails from people who had read the book and been deeply impacted by it. A highschooler in Miami. An entrepreneur in Amsterdam. A small business owner in the midwest. People were also leaving reviews on Amazon—people I didn’t know, saying the book helped them live a better life. And on my Kindle, I could see passages that people were highlighting. People weren’t just reading my book, they were taking notes on useful things to remember. The craft of writing has been unbelievably fulfilling for me. And so I’m continuing the pursuit. My motivation is no longer to make a buck, or “win at life.” Rather, I’m working to improve the world. I think of myself as an inventor, creating a new piece of art for the world to discover. When you make the world better, you get rewarded. So find your craft, and then determine the best contribution you can make with it.

If you’re not sure where your passion lies, ask yourself what you end up doing when you have nothing to do. Where does your mind go? What websites do you visit? Which articles and books do you read? What television shows do you watch? Which activities naturally draw your attention? Your passion is right in front of you: it’s how you spend your idle time. People say you shouldn’t make your passion your work. Bullshit. If you want to avoid ZombieLand, you must make your passion your work.[18]

Someday, you may even turn to Google, which has read all your emails and internet searches and looked at your bank account and DNA and sugar levels and blood pressure and heart rate, to determine who you should date. But this is also where liberalism collapses, on the day that the algorithms know you better than you know yourself.

Robots and computers are replacing human hands in factories, hotels, and fast food restaurants. Self-driving cars are eventually going to start replacing taxi drivers and chauffeurs. Take for example what has already happened to bank clerks and travel agents, once jobs that were all protected from automation, which are now endangered species. Stock traders are also being replaced by computer algorithms, which can react and make decisions so much faster than humans can.

Teachers are being replaced by interactive algorithms that can teach students on a far more customized level, such as the ones being developed by companies like Mindojo. Doctors are under attack from the job replacing algorithms, such as IBM’s Jeopardy game show winning Watson computer, which is now being groomed as a medical diagnosis machine. And unlike with spending years training just one doctor at a time, every technical challenge that is beaten while training Watson will ultimately produce an infinite number of well trained doctor “machines.” Algorithms are already being appointed to fill seats on company boards, such as in May 2014 when a Hong Kong venture-capital firm, Deep Knowledge Ventures, appointed the algorithm VITAL to its board. VITAL studies vast amounts of data then gets to vote on whether the firm makes an investment in a specific company or not.

the algorithms might actually come to own businesses instead of just managing them, and humans could end up working for and paying rent to algorithms. Before you say this could never happen, remember that we’ve already made it legal for entities such as Toyota to own land and money, sue and be sued in court, and so on. So what will people do in this kind of a future?

Many new jobs are likely to appear in the 21st century. The crucial problem will be creating new jobs that humans can perform better than algorithms. The tech bonanza will probably make it feasible to feed and support the useless masses effortlessly. But what will keep them all occupied so they don’t go crazy? One solution might be drugs and computer games, or 3D virtual-reality worlds. Some experts warn that the AI might just decide to exterminate humankind to avoid a revolt. Of course, we don’t really know what the human mind might come up with in the future. And those algorithms might end up saving our lives, too.

The future is created not by backward looking masses but by forward thinking innovators. In the early 21st century, the train of progress is once again leaving the station, and the author believes this will probably be the last train to ever leave the station of humanity. In order to get on board, you have to understand 21st century technology, in particular biotech and computer algorithms. The main products of the 21st century will be bodies, brains and minds. When genetic engineering and artificial intelligence reveal their full potential, democracy and free markets will become obsolete too.

In this book we will synthesize a single algorithm will all these capabilities: Our quest will take us across the territory of each of the five tribes.

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.

After 12 years of searching for you in every book about life after death and angels among us, seeking a connection to you and your world through mediums, trying to decipher the algorithm of numbers that seemed to tell a story and form some sort of a timeline....I realised that searching for answers to mysteries I will never be able to solve would only leave me empty and aching for you for the rest of my life. Instead I am owning my grief, releasing my pain and I stopped pretending that the ache in my soul will go away, it will always be there and has become a part of me.

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.

But we must also think seriously about addressing the concerns scientifically—about what it might mean to encode ethical principles directly into the design of the algorithms that are increasingly woven into our daily lives. This book is about the emerging science of ethical algorithm design, which tries to do exactly that.

To make informed decisions, we need to be able to understand the consequences of deploying certain kinds of algorithms, and the costs associated with constraining them in various ways. And that is what this book is about.

All you need is a social media account: start posting extreme and crazy shit, and let the algorithm do the rest.

Over time,[*] the Google algorithm created something that had never previously existed: a consensus about the shared understanding of everything.

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

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.

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.

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.

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.

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.

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

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

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

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.