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...

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?

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.

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?

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.

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.

If one believes their own prophet’s supernatural claims without evidence, on what basis does one doubt the others?

When reality becomes too painful, truth is willingly sacrificed to blissful ignorance.

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

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.

Sitting down to read a book today is an act of rebellion. A rebellion against Big Algorithm. Against the Short Attention Span illuminati, who if have their way, will make every book a banned book.

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...

Absolute morality is an illusion and is reflected in the ever-changing morality of the religions we create. Sometimes religions even serve to subvert our instinctual empathy and guilt by telling us that something clearly immoral is the will of God.

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 a new item of technology is introduced as an option that an individual can accept or not as he chooses, it does not necessarily remain optional. In many cases the new technology changes society in such a way that people eventually find themselves forced to use it.

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.

but the entirety of human existence. Artificial intelligence is, after all, a mirroring and mimicry machine: we feed in the cumulative words, ideas, and images that our species has managed to amass (and digitize) over its history and these programs mirror back to us something that feels uncannily lifelike. A golem world. “I’d rather see an ad for cute shoes that I am going to like than see ads for a bunch of ugly stuff I don’t want,” one student said in an early class. In our discussions, we came to call this the “cute shoes problem” because it encapsulates one of the main reasons why surveillance capitalism and the AI revolution were able to sneak up on us with so little debate. Many of us do appreciate a certain level of automated customization, especially algorithms that suggest music, books, and people who might interest us. And at first, the stakes seemed low: Is it really a big deal if we see ads and suggestions based on our interests and tastes? Or if chatbots help clear our email backlogs? Yet now we find ourselves neck-deep in a system where, as with my own real-life doppelganger, the stakes are distinctly higher. Personal data, extracted without full knowledge or understanding, is sold to third parties and can influence everything from what loans we are eligible for to what job postings we see—to whether our jobs are replaced by deep learning bots that have gotten shockingly good at impersonating us. And those helpful recommendations and eerie impersonations come from the same

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.

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.

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

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.

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.

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