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.

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...

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.

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.

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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.

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

out:“I know not with what weapons World War Three will be fought, but World War Four will be fought with sticks and stones.

A demon nearly as powerful as God tricked Eve. But Adam was fooled by an ordinary girl—so who’s the real dummy?

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

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.

Tips on Web Design and Site Marketing Web content is king, which is why we have devoted an entire chapter to it later in this book. It is what draws visitors and ultimately what converts them to customers. So, try to make your web content as engaging as possible. Make sure the content is interactive, unique and educational. Ensure that visitors have the option of plugins while encouraging them to visit as many pages on your site as possible if they want to obtain vital information. The images you use on your website should be both enticing and descriptive in nature. In today’s world, social media is all pervasive. In order to encourage visitors to share your web content, you can include icons of social media platforms on your website. In some select cases, consider integrating social media feeds, like Facebook or Instagram, onto your website so that they can automatically show the latest postings. A "Call-to-Action" can help convert visitors to your site into customers. Always try using a very clear and concise "Call-to-Action" language. Understand what type of conversion you are looking for, and try to provide multiple levels of conversion. For example, a plastic surgeon may provide Schedule an Appointment as a call to action, which will attract only the segment of web visitors who have reached their decision stage. By adding conversion points for visitors who are at earlier stages of their decision making, like signing up for a webcast or your newsletter can help you widen your conversion points and provide inputs to your email marketing. To raise the average amount of time a visitor spends on your website and to minimize the bounce rate, ensure that your website offers a user-friendly and attractive design. This way you will increase the number of links you have on your website and boost its SEO ranking (Tip: While Google’s algorithm is not public, our iterative testing shows that sites with good usability analytics metrics like time on site and bounce rate play favorably in Google’s algorithm, other things remaining constant). Ensure you observe due diligence when designing a website that will enable visitors to navigate in different languages. For example, you may need a lot more space for your menu, as there are languages that use up more space than the English language.

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.

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

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

Our phones and feeds absorb so much of our attention and dominate so many of our preferences that stepping out of their conveniently predetermined paths and choosing an experience not immediately engaging feels somewhat radical. This applies to fashion choices as well as food, which television shows we watch, which books we read, which furniture we buy, where we travel.

Naturally occurring processes are often informally modeled by priority queues. Single people maintain a priority queue of potential dating candidates, mentally if not explicitly. One’s impression on meeting a new person maps directly to an attractiveness or desirability score, which serves as the key field for inserting this new entry into the “little black book” priority queue data structure. Dating is the process of extracting the most desirable person from the data structure (Find-Maximum), spending an evening to evaluate them better, and then reinserting them into the priority queue with a possibly revised score.

My book ordering history is definitely going to get me flagged by some evil government algorithm. Lots and lots of books about Vichy France and the French Resistance, and more books than any civilian could possibly need about spycraft, and fascism.

Software,” as the venture capitalist Marc Andreessen has proclaimed, “is eating the world.” It’s true. You use software nearly every instant you’re awake. There’s the obvious stuff, like your phone, your laptop, email and social networking and video games and Netflix, the way you order taxis and food. But there’s also less-obvious software lurking all around you. Nearly any paper book or pamphlet you touch was designed using software; code inside your car helps manage the braking system; “machine-learning” algorithms at your bank scrutinize your purchasing activity to help spy the moment when a criminal dupes your card and starts fraudulently buying things using your money. And this may sound weirdly obvious, but every single one of those pieces of software was written by a programmer—someone precisely like Ruchi Sanghvi or Mark Zuckerberg. Odds are high the person who originally thought of the product was a coder: Programmers spend their days trying to get computers to do new things, so they’re often very good at understanding the crazy what-ifs that computers make possible. (What if you had a computer take every word you typed and, quietly and constantly and automatically in the background, checked it against a dictionary of common English words? Hello, spell-check!) Sometimes it seems that the software we use just sort of sprang into existence, like grass growing on the lawn. But it didn’t. It was created by someone who wrote out—in code—a long, painstaking set of instructions telling the computer precisely what to do, step-by-step, to get a job done. There’s a sort of priestly class mystery cultivated around the word algorithm, but all they consist of are instructions: Do this, then do this, then do this. News Feed is now an extraordinarily complicated algorithm involving some trained machine learning; but it’s ultimately still just a list of rules. So the rule makers have power. Indeed, these days, the founders of high-tech companies—the ones who determine what products get created, what problems get solved, and what constitutes a “problem” in the first place—are increasingly technologists, the folks who cut their teeth writing endless lines of code and who cobbled together the prototype for their new firm themselves. Programmers are thus among the most quietly influential people on the planet.

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.

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

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

skills to analyze and use the data, as well as those who can create the algorithms required for machine learning. Secondly, while the technology is still emerging, there are many ongoing developments. It’s clear that AI is a long way from how we might

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This dynamic is even stronger for digital goods, which can be produced almost for free. Once Amazon has formatted an e-book for sale, selling new copies of it doesn’t take any additional paper, ink, or labor—so it sells for a nearly infinite multiple of its marginal cost. As a result, the close relationship between marginal cost, price, and consumers’ willingness to pay has been weakened. In the case of services whose marginal cost is low enough that they can be free to consumers altogether, that relationship breaks down completely. Once Google has designed its search algorithms and built its server farms, providing a user with one additional search costs almost nothing.

Books on the “best seller” lists do not always contain the best information relevant for you! Only reading books on the best seller lists or purchasing the “popular” products means replacing social media algorithms for society promoted algorithms. Always dig deeper, if you wish to get closer to the truth!

the development of sophisticated algorithms has its pros and cons. On the plus side, it becomes easier to discover content you actually want to watch. On the negative side, it makes it easier to become stuck in a never-ending loop, watching one video after the other. In a sense, instead of using the internet to find information or communicate with loved ones, the internet has become the one using you. It does so by hijacking your focus and making you unproductive and, as a result, restless.

One of the people leading the field in algorithmic auditing is Cathy O’Neil, the author of Weapons of Math Destruction. Her book is one of the catalysts for the entire movement for algorithmic accountability. O’Neil’s consulting company, O’Neil Risk Consulting & Algorithmic Auditing (ORCAA), does bespoke auditing to help companies and organizations manage and audit their algorithmic risks. I have had the good fortune to consult with ORCAA. When ORCAA considers an algorithm, they start by asking two questions: What does it mean for this algorithm to work? How could this algorithm fail, and for whom? One thing ORCAA does is what’s called an internal audit, which means they ask these questions directly of companies and other organizations, focusing on algorithms as they are used in specific contexts.

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

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

This effect is called Fooled by Randomness.

and even Saturn can not impress the S & P500 index.

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.

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.

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

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. And besides the need for accidental connections, there’s the fact that some things, clearly, are beyond the wisdom of crowds—sometimes speed and volume should bend to make way for theory and meaning. Sometimes we do still need to quiet down the rancor of mass opinion and ask a few select voices to speak up. And doing so in past generations has never been such a problem as it is for us. They never dealt with such a glut of information or such a horde of folk eager to misrepresent it.

In this book we will be focusing on two types of machine learning algorithms: decision trees and random forests. However, there are many different types of algorithms used in machine learning, such as neural networks, naive bayes, and k-means clustering.

In total, the book covers nearly 40 essential algorithms.

The Connection Algorithm is the great idea that keeps you up at night. It’s the hobby you can’t ignore. It’s the conference you’ve always wanted to attend. It’s the blog post that changed your life. It’s the investor who funded your project. It’s curiosity, courage, failure, and success. In a word, the Connection Algorithm is a mindset, and this book will teach you how to harness it and use it to your advantage. If you build this mindset into your life, it will accelerate your personal growth and naturally lead you to forge relationships with highly connected, successful people. It will also open your eyes to a new lifestyle, freeing you from the shackles of the 9-5 desk job. If this sounds too good to be true, it should. The doubt of the crowd affords opportunity to the few, which is precisely why the Connection Algorithm works.

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.

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.

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.

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.

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]

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.

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.