Algorithm Best Quotes

The best programs are written so that computing machines can perform them quickly and so that human beings can understand them clearly. A programmer is ideally an essayist who works with traditional aesthetic and literary forms as well as mathematical concepts, to communicate the way that an algorithm works and to convince a reader that the results will be correct.

Don’t always consider all your options. Don’t necessarily go for the outcome that seems best every time. Make a mess on occasion. Travel light. Let things wait. Trust your instincts and don’t think too long. Relax. Toss a coin. Forgive, but don’t forget. To thine own self be true.

Even the best strategy sometimes yields bad results—which is why computer scientists take care to distinguish between “process” and “outcome.” If you followed the best possible process, then you’ve done all you can, and you shouldn’t blame yourself if things didn’t go your way.

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

The issue of finding the best possible answer or achieving maximum efficiency usually arises in industry only after serious performance or legal troubles.

In the long run, optimism is the best prevention for regret.

She wants to tell them that Blue Gamma was more right than it knew: experience isn’t merely the best teacher; it’s the only teacher. If she’s learned anything raising Jax, it’s that there are no shortcuts; if you want to create the common sense that comes from twenty years of being in the world, you need to devote twenty years to the task. You can’t assemble an equivalent collection of heuristics in less time; experience is algorithmically incompressible.

Habits are undeniably useful tools, relieving us of the need to run a complex mental operation every time we’re confronted with a new task or situation. Yet they also relieve us of the need to stay awake to the world: to attend, feel, think, and then act in a deliberate manner. (That is, from freedom rather than compulsion.) If you need to be reminded how completely mental habit blinds us to experience, just take a trip to an unfamiliar country. Suddenly you wake up! And the algorithms of everyday life all but start over, as if from scratch. This is why the various travel metaphors for the psychedelic experience are so apt. The efficiencies of the adult mind, useful as they are, blind us to the present moment. We’re constantly jumping ahead to the next thing. We approach experience much as an artificial intelligence (AI) program does, with our brains continually translating the data of the present into the terms of the past, reaching back in time for the relevant experience, and then using that to make its best guess as to how to predict and navigate the future. One of the things that commends travel, art, nature, work, and certain drugs to us is the way these experiences, at their best, block every mental path forward and back, immersing us in the flow of a present that is literally wonderful—wonder being the by-product of precisely the kind of unencumbered first sight, or virginal noticing, to which the adult brain has closed itself. (It’s so inefficient!) Alas, most of the time I inhabit a near-future tense, my psychic thermostat set to a low simmer of anticipation and, too often, worry. The good thing is I’m seldom surprised. The bad thing is I’m seldom surprised.

Look-Then-Leap Rule: You set a predetermined amount of time for “looking”—that is, exploring your options, gathering data—in which you categorically don’t choose anyone, no matter how impressive. After that point, you enter the “leap” phase, prepared to instantly commit to anyone who outshines the best applicant you saw in the look phase. We

It’s fairly intuitive that never exploring is no way to live. But it’s also worth mentioning that never exploiting can be every bit as bad. In the computer science definition, exploitation actually comes to characterize many of what we consider to be life’s best moments. A family gathering together on the holidays is exploitation. So is a bookworm settling into a reading chair with a hot cup of coffee and a beloved favorite, or a band playing their greatest hits to a crowd of adoring fans, or a couple that has stood the test of time dancing to “their song.

When our expectations are uncertain and the data are noisy, the best bet is to paint with a broad brush

Don’t always consider all your options. Don’t necessarily go for the outcome that seems best every time.

Whoever has the best algorithms and the most data wins. A new type of network effect takes hold: whoever has the most customers accumulates the most data, learns the best models, wins the most new customers, and so on in a virtuous circle (or a vicious one, if you’re the competition).

Unless we have good reason to think otherwise, it seems that our best guide to the future is a mirror image of the past. The nearest thing to clairvoyance is to assume that history repeats itself — backward.

When we talk about decision-making, we usually focus just on the immediate payoff of a single decision—and if you treat every decision as if it were your last, then indeed only exploitation makes sense. But over a lifetime, you’re going to make a lot of decisions. And it’s actually rational to emphasize exploration—the new rather than the best, the exciting rather than the safe, the random rather than the considered—for many of those choices, particularly earlier in life.

For millions upon millions of years, feelings were the best algorithms in the world. Hence in the days of Confucius, of Muhammad or of Stalin, people should have listened to their feelings rather than to the teachings of Confucianism, Islam or communism. Yet

The best means we have for keeping our keys safe is called “zero knowledge,” a method that ensures that any data you try to store externally—say, for instance, on a company’s cloud platform—is encrypted by an algorithm running on your device before it is uploaded, and the key is never shared. In the zero knowledge scheme, the keys are in the users’ hands—and only in the users’ hands. No company, no agency, no enemy can touch them.

Dash concludes that, ultimately, the tech industry doesn’t really exist. It’s just in these organizations’ best interests to be seen as “tech.

Then the algorithm will know best, the algorithm will always be right, and beauty will be in the calculations of the algorithm.

Hiring the right SEO team is the best algorithm update you’ll ever make

it’s crucial to make the right call about whether to use an algorithm or a heuristic in a specific situation. This is why the Google experiment with forty-one shades of blue seems so foreign to me, accustomed as I am to the Apple approach. Google used an A/B test to make a color choice. It used a single predetermined value criterion and defined it like so: The best shade of blue is the one that people clicked most often in the test. This is an algorithm.

Armed with machine learning, a manager becomes a supermanager, a scientist a superscientist, an engineer a superengineer. The future belongs to those who understand at a very deep level how to combine their unique expertise with what algorithms do best.

On the contrary, I’m too weak for it. I mean, everyone is, but I am especially susceptible to its false rewards, you know? It’s designed to addict you, to prey on your insecurities and use them to make you stay. It exploits everybody’s loneliness and promises us community, approval, friendship. Honestly, in that sense, social media is a lot like the Church of Scientology. Or QAnon. Or Charles Manson. And then on top of that—weaponizing a person’s isolation—it convinces every user that she is a minor celebrity, forcing her to curate some sparkly and artificial sampling of her best experiences, demanding a nonstop social performance that has little in common with her inner life, intensifying her narcissism, multiplying her anxieties, narrowing her worldview. All while commodifying her, harvesting her data, and selling it to nefarious corporations so that they can peddle more shit that promises to make her prettier, smarter, more productive, more successful, more beloved. And throughout all this, you have to act stupefied by your own good luck. Everybody’s like, Words cannot express how fortunate I feel to have met this amazing group of people, blah blah blah. It makes me sick. Everybody influencing, everybody under the influence, everybody staring at their own godforsaken profile, searching for proof that they’re lovable. And then, once you’re nice and distracted by the hard work of tallying up your failures and comparing them to other people’s triumphs, that’s when the algorithmic predators of late capitalism can pounce, enticing you to partake in consumeristic, financially irresponsible forms of so-called self-care, which is really just advanced selfishness. Facials! Pedicures! Smoothie packs delivered to your door! And like, this is just the surface stuff. The stuff that oxidizes you, personally. But a thousand little obliterations add up, you know? The macro damage that results is even scarier. The hacking, the politically nefarious robots, opinion echo chambers, fearmongering, erosion of truth, etcetera, etcetera. And don’t get me started on the destruction of public discourse. I mean, that’s just my view. Obviously to each her own. But personally, I don’t need it. Any of it.” Blandine cracks her neck. “I’m corrupt enough.

Intuitively, we think that rational decision-making means exhaustively enumerating our options, weighting each one carefully, and then selecting the best. But in practice, when the clock - or the ticker - is ticking, few aspects of decision-making (or of thinking more generally) are as important as this one: when to stop.

We users can’t fight against this stultifying environment on our own. Switching between apps and toggling settings can accomplish only so much. To break down Filterworld, change has to happen on the industrial level, at the scale of the tech companies themselves. Decentralization tends to give users the most agency, though it also places a higher burden of labor and responsibility on the individual. It’s also the best way to resist Filterworld and cultivate new possibilities for digital life. But companies are unlikely to embrace decentralization on their own, because it’s usually less profitable. The only path for change may be to force them.

Unless we’re willing to spend eons striving for perfection every time we encounter a hitch, hard problems demand that instead of spinning our tires we imagine easier versions and tackle those first. When applied correctly, this is not just wishful thinking, not fantasy or idle daydreaming. It’s one of our best ways of making progress.

To get the most out of an algorithm, you must be able to do more than simply follow its steps. You need to understand the following: The algorithm's behavior. Does it find the best possible solution, or does it just find a good solution? Could there be multiple best solutions? Is there a reason to pick one “best” solution over the others? The algorithm's speed. Is it fast? Slow? Is it usually fast but sometimes slow for certain inputs? The algorithm's memory requirements. How much memory will the algorithm need? Is this a reasonable amount? Does the algorithm require billions of terabytes more memory than a computer could possibly have (at least today)? The main techniques the algorithm uses. Can you reuse those techniques to solve similar problems?

experience isn’t merely the best teacher; it’s the only teacher. If she’s learned anything raising Jax, it’s that there are no shortcuts; if you want to create the common sense that comes from twenty years of being in the world, you need to devote twenty years to the task. You can’t assemble an equivalent collection of heuristics in less time; experience is algorithmically incompressible.

In Mattersight systems your call is routed by a clever algorithm. You first state your reason for calling. The algorithm listens to your problem, analyses the words you have used and your tone of voice, and deduces not only your present emotional state but also your personality type – whether you are introverted, extroverted, rebellious or dependent. Based on this information, the algorithm forwards your call to the representative who best matches your mood and personality.

If you want the best odds of getting the best apartment, spend 37% of your apartment hunt (eleven days, if you’ve given yourself a month for the search) noncommittally exploring options. Leave the checkbook at home; you’re just calibrating. But after that point, be prepared to immediately commit—deposit and all—to the very first place you see that beats whatever you’ve already seen. This is not merely an intuitively satisfying compromise between looking and leaping. It is the provably optimal solution.

When you read the Bible you are getting advice from a few priests and rabbis who lived in ancient Jerusalem. In contrast, when you listen to your feelings, you follow an algorithm that evolution has developed for millions of years, and that withstood the harshest quality-control tests of natural selection. Your feelings are the voice of millions of ancestors, each of whom managed to survive and reproduce in an unforgiving environment. Your feelings are not infallible, of course, but they are better than most other sources of guidance. For millions upon millions of years, feelings were the best algorithms in the world.

I post a petition on my Facebook page. Which of my friends will see it on their news feed? I have no idea. As soon as I hit send, that petition belongs to Facebook, and the social network’s algorithm makes a judgment about how to best use it. It calculates the odds that it will appeal to each of my friends. Some of them, it knows, often sign petitions, and perhaps share them with their own networks. Others tend to scroll right past. At the same time, a number of my friends pay more attention to me and tend to click the articles I post. The Facebook algorithm takes all of this into account as it decides who will see my petition. For many of my friends, it will be buried so low on their news feed that they’ll never see it.

The best bit of advice I ever received about how to pray was this: keep it simple, keep it real, keep it up. You’ve got to keep it simple so that the most natural thing in the world doesn’t become complicated, weird and intense. You’ve got to keep it real because when life hurts like hell you’re going to be tempted to pretend you’re fine. And then at other times, when you make a mess of things, you’re going to be tempted to hide from God (which never really works) and end up hiding from yourself (which works quite well). And you’ve got to keep it up because life is tough, the battle is fierce, and God is not an algorithm. The journey of faith demands a certain bloody-mindedness of us all, not least in the realm of prayer.

As black-box technologies become more widespread, there have been no shortage of demands for increased transparency. In 2016 the European Union's General Data Protection Regulation included in its stipulations the "right to an explanation," declaring that citizens have a right to know the reason behind the automated decisions that involve them. While no similar measure exists in the United States, the tech industry has become more amenable to paying lip service to "transparency" and "explainability," if only to build consumer trust. Some companies claim they have developed methods that work in reverse to suss out data points that may have triggered the machine's decisions—though these explanations are at best intelligent guesses. (Sam Ritchie, a former software engineer at Stripe, prefers the term "narratives," since the explanations are not a step-by-step breakdown of the algorithm's decision-making process but a hypothesis about reasoning tactics it may have used.) In some cases the explanations come from an entirely different system trained to generate responses that are meant to account convincingly, in semantic terms, for decisions the original machine made, when in truth the two systems are entirely autonomous and unrelated. These misleading explanations end up merely contributing another layer of opacity. "The problem is now exacerbated," writes the critic Kathrin Passig, "because even the existence of a lack of explanation is concealed.

It is best to be the CEO; it is satisfactory to be an early employee, maybe the fifth or sixth or perhaps the tenth. Alternately, one may become an engineer devising precious algorithms in the cloisters of Google and its like. Otherwise, one becomes a mere employee. A coder of websites at Facebook is no one in particular. A manager at Microsoft is no one. A person (think woman) working in customer relations is a particular type of no one, banished to the bottom, as always, for having spoken directly to a non-technical human being. All these and others are ways for strivers to fall by the wayside — as the startup culture sees it — while their betters race ahead of them. Those left behind may see themselves as ordinary, even failures.

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value, I can do three things,” he says. “I can improve the algorithm itself, make it more sophisticated. I can throw more and better data at the algorithm so that the existing code produces better results. And I can change the speed of experimentation to get more results faster. “We focused on data and speed, not on a better algorithm.” Candela describes this decision as “dramatic” and “hard.” Computer scientists, especially academic-minded ones, are rewarded for inventing new algorithms or improving existing ones. A better statistical model is the goal. Getting cited in a journal is validation. Wowing your peers gives you cred. It requires a shift in thinking to get those engineers to focus on business impact before optimal statistical model. He thinks many companies are making the mistake of structuring their efforts around building the best algorithms, or hiring developers who claim to have the best algorithms, because that’s how many AI developers think.

Trump and his alt-right supporters take pleasure in pushing the buttons of the politically correct, but it only works because the buttons are there to be pushed—students and activists claiming the right to not hear things that upset them, and to shout down ideas that offend them. Intolerance particularly flourishes online, where measured speech is punished by not getting clicked on, invisible Facebook and Google algorithms steer you toward content you agree with, and nonconforming voices stay silent for fear of being flamed or trolled or unfriended. The result is a silo in which, whatever side you’re on, you feel absolutely right to hate what you hate. And here is another way in which the essay differs from superficially similar kinds of subjective speech. The essay’s roots are in literature, and literature at its best—the work of Alice Munro, for example—invites you to ask whether you might be somewhat wrong, maybe even entirely wrong, and to imagine why someone else might hate you.

Well, it was a kind of back-to-front program. It’s funny how many of the best ideas are just an old idea back-to-front. You see there have already been several programs written that help you to arrive at decisions by properly ordering and analysing all the relevant facts so that they then point naturally towards the right decision. The drawback with these is that the decision which all the properly ordered and analysed facts point to is not necessarily the one you want.’ ‘Yeeeess...’ said Reg’s voice from the kitchen. ‘Well, Gordon’s great insight was to design a program which allowed you to specify in advance what decision you wished it to reach, and only then to give it all the facts. The program’s task, which it was able to accomplish with consummate ease, was simply to construct a plausible series of logical-sounding steps to connect the premises with the conclusion. ‘And I have to say that it worked brilliantly. Gordon was able to buy himself a Porsche almost immediately despite being completely broke and a hopeless driver. Even his bank manager was unable to find fault with his reasoning. Even when Gordon wrote it off three weeks later.’ ‘Heavens. And did the program sell very well?’ ‘No. We never sold a single copy.’ ‘You astonish me. It sounds like a real winner to me.’ ‘It was,’ said Richard hesitantly. ‘The entire project was bought up, lock, stock and barrel, by the Pentagon. The deal put WayForward on a very sound financial foundation. Its moral foundation, on the other hand, is not something I would want to trust my weight to. I’ve recently been analysing a lot of the arguments put forward in favour of the Star Wars project, and if you know what you’re looking for, the pattern of the algorithms is very clear. ‘So much so, in fact, that looking at Pentagon policies over the last couple of years I think I can be fairly sure that the US Navy is using version 2.00 of the program, while the Air Force for some reason only has the beta-test version of 1.5. Odd, that.

Imagine you're sitting having dinner in a restaurant. At some point during the meal, your companion leans over and whispers that they've spotted Lady Gaga eating at the table opposite. Before having a look for yourself, you'll no doubt have some sense of how much you believe your friends theory. You'll take into account all of your prior knowledge: perhaps the quality of the establishment, the distance you are from Gaga's home in Malibu, your friend's eyesight. That sort of thing. If pushed, it's a belief that you could put a number on. A probability of sorts. As you turn to look at the woman, you'll automatically use each piece of evidence in front of you to update your belief in your friend's hypothesis Perhaps the platinum-blonde hair is consistent with what you would expect from Gaga, so your belief goes up. But the fact that she's sitting on her own with no bodyguards isn't, so your belief goes down. The point is, each new observations adds to your overall assessment. This is all Bayes' theorem does: offers a systematic way to update your belief in a hypothesis on the basis of the evidence. It accepts that you can't ever be completely certain about the theory you are considering, but allows you to make a best guess from the information available. So, once you realize the woman at the table opposite is wearing a dress made of meat -- a fashion choice that you're unlikely to chance up on in the non-Gaga population -- that might be enough to tip your belief over the threshold and lead you to conclude that it is indeed Lady Gaga in the restaurant. But Bayes' theorem isn't just an equation for the way humans already make decisions. It's much more important that that. To quote Sharon Bertsch McGrayne, author of The Theory That Would Not Die: 'Bayes runs counter to the deeply held conviction that modern science requires objectivity and precision. By providing a mechanism to measure your belief in something, Bayes allows you to draw sensible conclusions from sketchy observations, from messy, incomplete and approximate data -- even from ignorance.

We live in a time of such great disunity, as the bitter fight over this nomination both in the Senate and among the public clearly demonstrates. It is not merely a case of different groups having different opinions. It is a case of people bearing extreme ill will toward those who disagree with them. In our intense focus on our differences, we have forgotten the common values that bind us together as Americans. When some of our best minds are seeking to develop ever more sophisticated algorithms designed to link us to websites that only reinforce and cater to our views, we can only expect our differences to intensify. This would have alarmed the drafters of our Constitution, who were acutely aware that different values and interests could prevent Americans from becoming and remaining a single people. Indeed, of the six objectives they invoked in the preamble to the Constitution, the one that they put first was the formation of “a more perfect Union.” Their vision of “a more perfect Union” does not exist today, and if anything, we appear to be moving farther away from it.

I mean, everyone is, but I am especially susceptible to its false rewards, you know? It’s designed to addict you, to prey on your insecurities and use them to make you stay. It exploits everybody’s loneliness and promises us community, approval, friendship. Honestly, in that sense, social media is a lot like the Church of Scientology. Or QAnon. Or Charles Manson. And then on top of that—weaponizing a person’s isolation—it convinces every user that she is a minor celebrity, forcing her to curate some sparkly and artificial sampling of her best experiences, demanding a nonstop social performance that has little in common with her inner life, intensifying her narcissism, multiplying her anxieties, narrowing her worldview. All while commodifying her, harvesting her data, and selling it to nefarious corporations so that they can peddle more shit that promises to make her prettier, smarter, more productive, more successful, more beloved. And throughout all this, you have to act stupefied by your own good luck. Everybody’s like, Words cannot express how fortunate I feel to have met this amazing group of people, blah blah blah. It makes me sick. Everybody influencing, everybody under the influence, everybody staring at their own godforsaken profile, searching for proof that they’re lovable. And then, once you’re nice and distracted by the hard work of tallying up your failures and comparing them to other people’s triumphs, that’s when the algorithmic predators of late capitalism can pounce, enticing you to partake in consumeristic, financially irresponsible forms of so-called self-care, which is really just advanced selfishness. Facials! Pedicures! Smoothie packs delivered to your door! And like, this is just the surface stuff. The stuff that oxidizes you, personally. But a thousand little obliterations add up, you know? The macro damage that results is even scarier. The hacking, the politically nefarious robots, opinion echo chambers, fearmongering, erosion of truth, etcetera, etcetera. And don’t get me started on the destruction of public discourse. I mean, that’s just my view. Obviously to each her own. But personally, I don’t need it. Any of it.” Blandine cracks her neck. “I’m corrupt enough.

what was good for survival and reproduction in the African savannah a million years ago does not necessarily make for responsible behavior on twenty-first-century motorways. Distracted, angry, and anxious human drivers kill more than a million people in traffic accidents every year. We can send all our philosophers, prophets, and priests to preach ethics to these drivers, but on the road, mammalian emotions and savannah instincts will still take over. Consequently, seminarians in a rush will ignore people in distress, and drivers in a crisis will run over hapless pedestrians. This disjunction between the seminary and the road is one of the biggest practical problems in ethics. Immanuel Kant, John Stuart Mill, and John Rawls can sit in some cozy university hall and discuss theoretical ethical problems for days—but would their conclusions actually be implemented by stressed-out drivers caught in a split-second emergency? Perhaps Michael Schumacher—the Formula One champion who is sometimes hailed as the best driver in history—had the ability to think about philosophy while racing a car, but most of us aren’t Schumacher. Computer algorithms, however, have not been shaped by natural selection, and they have neither emotions nor gut instincts. Therefore in moments of crisis they could follow ethical guidelines much better than humans—provided we find a way to code ethics in precise numbers and statistics. If we could teach Kant, Mill, and Rawls to write code, they would be able to program the self-driving car in their cozy laboratory and be certain that the car would follow their commandments on the highway. In effect, every car would be driven by Michael Schumacher and Immanuel Kant rolled into one.

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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.

I don't have social media" "Oh right." He rolls his eyes. "Too good for all that." She shakes her head. "Not at all. On the contrary, I'm too weak for it. I mean, everyone is, but I am especially susceptible to its false rewards, you know? It's designed to addict you, to prey on your insecurities and use them to make you stay. It exploits everybody's loneliness and promises us a community, approval, friendship. Honestly, in that sense, social media is a lot like the Church of Scientology. Or QAnon. Or Charles Manson. And then on top of that - weaponizing a person's isolation - it convinces every user that she is a minor celebrity, forcing her to curate some sparkly and artificial sampling of her best experiences, demanding a nonstop social performance that has little in common with her inner life, intensifying her narcissism, multiplying her anxieties, narrowing her worldview. All while commodifying her, harvesting her data, and selling it to nefarious corporations so that they can peddle more shit that promises to make her prettier, smarter, more productive, more successful, more beloved. And throughout all this, you have to act stupefied by your own good luck. Everybody's like 'words cannot express how fortunate I feel to have met this amazing group of people,' blah blah blah. It makes me sick. Everybody's influencing, everybody under the influence, everybody staring at their own godforsaken profile, searching for proof that they're lovable. And then, once you're nice and distracted by the hard work of tallying up your failures and comparing them to other people's triumphs, that's when the algorithmic predators of late capitalism can pounce, enticing you to partake in consumeristic, financially irresponsible forms of so-called self-care, which is really just advanced selfishness. Facials! Pedicures! Smoothie packs delivered to your door! And like, this is just the surface stuff. The stuff that oxidizes you, personally. But a thousand little obliterations add up, you know? The macro damage that results is even scarier. The hacking, the politically nefarious robots, opinion echo chambers, fearmongering, erosion of truth, etcetera, etcetera. And don't get m e started on the destruction of public discourse. I mean, that's just my view. Obviously to each her own. But personally, I don't need it. Any of it." Blandine cracks her neck. "I'm corrupt enough.

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If the combination of mindless, profit-seeking algorithms, dedicated geopolitical adversaries, and corrupt US opportunists over the past few years has taught us anything, it is that serious applied thinking is a form of critical infrastructure. The best hackers are masters of applied thinking, and we cannot afford to ignore them.

Sir, is there an algorithm for this? Because if we could get some kind of best-practice flowchart that we could study when we’re not here, I think it would help us a lot.

In the long run, optimism is the best prevention for regret.

Rather than being signs of moral or psychological degeneracy, restlessness and doubtfulness actually turn out to be part of the best strategy for scenarios where second chances are possible.

Some economists used to model people as rational agents, idealized decision makers who always choose whatever action is optimal in pursuit of their goal, but this is obviously unrealistic. In practice, these agents have what Nobel laureate and AI pioneer Herbert Simon termed “bounded rationality” because they have limited resources: the rationality of their decisions is limited by their available information, their available time to think and their available hardware with which to think. This means that when Darwinian evolution is optimizing an organism to attain a goal, the best it can do is implement an approximate algorithm that works reasonably well in the restricted context where the agent typically finds itself.

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.

A 63% failure rate, when following the best possible strategy, is a sobering fact. Even when we act optimally in the secretary problem, we will still fail most of the time—that is, we won’t end up with the single best applicant in the pool.

Love is like organized crime. It changes the structure of the marriage game so that the equilibrium becomes the outcome that works best for everybody.

I anticipate diagnostic AI will exceed all but the best doctors in the next twenty years. This trend will be felt first in fields like radiology, where computer-vision algorithms are already more accurate than good radiologists for certain types of MRI and CT scans. In the story “Contactless Love,” we see that by 2041 radiologists’ jobs will be mostly taken over by AI. Alongside radiology, we will also see AI excel in pathology and diagnostic ophthalmology. Diagnostic AI for general practitioners will emerge later, one disease at a time, gradually covering all diagnoses. Because human lives are at stake, AI will first serve as a tool within doctors’ disposal or will be deployed only in situations where a human doctor is unavailable. But over time, when trained on more data, AI will become so good that most doctors will be routinely rubber-stamping AI diagnoses, while the human doctors themselves are transformed into something akin to compassionate caregivers and medical communicators.

The shortcomings of the system are best understood as the result of taking this ocean of data, and the decision points produced by our algorithms, as a near enough substitute for perfect certainty. My own best results are often due to pretending I know relatively little, and acting accordingly, though it’s easier said than done. Far easier.

When you wash your clothes, they have to pass through the washer and the dryer in sequence, and different loads will take different amounts of time in each. A heavily soiled load might take longer to wash but the usual time to dry; a large load might take longer to dry but the usual time to wash. So, Johnson asked, if you have several loads of laundry to do on the same day, what’s the best way to do them? His answer was that you should begin by finding the single step that takes the least amount of time—the load that will wash or dry the quickest. If that shortest step involves the washer, plan to do that load first. If it involves the dryer, plan to do it last. Repeat this process for the remaining loads, working from the two ends of the schedule toward the middle.

Maybe instead we want to minimize the number of foods that spoil. Here a strategy called Moore’s Algorithm gives us our best plan. Moore’s Algorithm says that we start out just like with Earliest Due Date—by scheduling out our produce in order of spoilage date, earliest first, one item at a time. However, as soon as it looks like we won’t get to eating the next item in time, we pause, look back over the meals we’ve already planned, and throw out the biggest item (that is, the one that would take the most days to consume). For instance, that might mean forgoing the watermelon that would take a half dozen servings to eat; not even attempting it will mean getting to everything that follows a lot sooner. We then repeat this pattern, laying out the foods by spoilage date and tossing the largest already scheduled item any time we fall behind. Once everything that remains can be eaten in order of spoilage date without anything spoiling, we’ve got our plan.

The success of Upper Confidence Bound algorithms offers a formal justification for the benefit of the doubt. Following the advice of these algorithms, you should be excited to meet new people and try new things—to assume the best about them, in the absence of evidence to the contrary. In the long run, optimism is the best prevention for regret.

A rule that tends to give the right answer, but is not guaranteed to, is called a heuristic. It is often more practical to use a heuristic than an algorithm: for instance, there are many effective heuristics for the traveling salesman problem—procedures that will provide an almost optimal route very quickly. In fact these heuristics usually do find the best route, although they are not absolutely guaranteed to do so. A real-life traveling salesman would presumably be happier with a good, fast heuristic than with a slow algorithm.

In a search space like that of the traveling salesman problem, where nearby points are likely to have similar scores, it is usually better to use a procedure that searches a path through the space by traveling from point to nearby point. Just as the best method for finding a peak in a hilly landscape is to walk uphill, the equivalent heuristic is to choose the best of nearby solutions found in the search space. In the traveling salesman problem, for example, the computer might vary the best-known solution by exchanging the order of two of the cities in the itinerary. If this variation leads to a more efficient tour, then it is accepted as a superior solution (a step uphill); otherwise, it is rejected and a new variation is tried. This method of search will wander through the space, always traveling in an uphill direction, until it reaches the top of a hill. At this point, the solution cannot be improved by exchanging any pair of cities. The weakness of this method, which is called hill climbing, is that although you thereby reach the top of a hill, it is not necessarily the highest hill in the landscape. Hill climbing is a heuristic, not an algorithm.

Minimizing maximum lateness (for serving customers in a coffee shop) or the sum of completion times (for rapidly shortening your to-do list) both cross the line into intractability if some tasks can’t be started until a particular time. But they return to having efficient solutions once preemption is allowed. In both cases, the classic strategies—Earliest Due Date and Shortest Processing Time, respectively—remain the best, with a fairly straightforward modification. When a task’s starting time comes, compare that task to the one currently under way. If you’re working by Earliest Due Date and the new task is due even sooner than the current one, switch gears; otherwise stay the course. Likewise, if you’re working by Shortest Processing Time, and the new task can be finished faster than the current one, pause to take care of it first; otherwise, continue with what you were doing.

the weighted version of Shortest Processing Time is a pretty good candidate for best general-purpose scheduling strategy in the face of uncertainty. It offers a simple prescription for time management: each time a new piece of work comes in, divide its importance by the amount of time it will take to complete. If that figure is higher than for the task you’re currently doing, switch to the new one;

In a world ruled by algorithms, the best investment we can make is in educating people about AI.

This is all Bayes’ theorem does: offers a systematic way to update your belief in a hypothesis on the basis of the evidence.30 It accepts that you can’t ever be completely certain about the theory you’re considering, but allows you to make a best guess from the information available.

we’re put off by the banal, but also hate the radically unfamiliar. The very best films sit in a narrow sweet spot between ‘new’ and ‘not too new’.

In my view, the best algorithms are the ones that take the human into account at every stage. The ones that recognize our habit of over-trusting the output of a machine, while embracing their own flaws and wearing their uncertainty proudly front and centre.

I don’t have social media.” “Oh, right.” He rolls his eyes. “Too good for all that.” She shakes her head. “Not at all. On the contrary, I’m too weak for it. I mean, everyone is, but I am especially susceptible to its false rewards, you know? It’s designed to addict you, to prey on your insecurities and use them to make you stay. It exploits everybody’s loneliness and promises us community, approval, friendship. Honestly, in that sense, social media is a lot like the Church of Scientology. Or QAnon. Or Charles Manson. And then on top of that—weaponizing a person’s isolation—it convinces every user that she is a minor celebrity, forcing her to curate some sparkly and artificial sampling of her best experiences, demanding a nonstop social performance that has little in common with her inner life, intensifying her narcissism, multiplying her anxieties, narrowing her worldview. All while commodifying her, harvesting her data, and selling it to nefarious corporations so that they can peddle more shit that promises to make her prettier, smarter, more productive, more successful, more beloved. And throughout all this, you have to act stupefied by your own good luck. Everybody’s like, Words cannot express how fortunate I feel to have met this amazing group of people, blah blah blah. It makes me sick. Everybody influencing, everybody under the influence, everybody staring at their own godforsaken profile, searching for proof that they’re lovable. And then, once you’re nice and distracted by the hard work of tallying up your failures and comparing them to other people’s triumphs, that’s when the algorithmic predators of late capitalism can pounce, enticing you to partake in consumeristic, financially irresponsible forms of so-called self-care, which is really just advanced selfishness. Facials! Pedicures! Smoothie packs delivered to your door! And like, this is just the surface stuff. The stuff that oxidizes you, personally. But a thousand little obliterations add up, you know? The macro damage that results is even scarier. The hacking, the politically nefarious robots, opinion echo chambers, fearmongering, erosion of truth, etcetera, etcetera. And don’t get me started on the destruction of public discourse. I mean, that’s just my view. Obviously to each her own. But personally, I don’t need it. Any of it.” Blandine cracks her neck. “I’m corrupt enough.

MILF Token: What Is It and What Are the Prospects? Why MILF symbols? Whoever had actually the intense suggestion of producing a MILF token has actually located a cutting-edge means of touching into 2 distinctive yet similarly eye-catching streams. On the one hand, here's a fresh cryptocurrency including distinctively collectible characters, with evidence of possession saved in a blockchain. On the various other hand, when it concerns those characters, it likewise ventures a fixation among several songs in the very early 21st-century: fully grown, sexually knowledgeable ladies looking for daring times with their suitors. Any kind of speculator wanting to explore the idea behind these extravagant as well as attractive characters can conveniently acquaint themselves with a few of the very best sites concentrating on dating MILFs. These systems provide an algorithm-based solution, where brand-new consumers can surely join, as well as the details offered throughout this enrollment procedure - inspirations, kind of MILF they are brought in to, and so on. - can surely be as compared to the information they currently carry submit. This way, the liaison can surely be easily organized without the individual enquiring also needing to make up a candid message. The computer system software application will certainly give a shortlist of ideal dating prospects. Comparable character-driven symbols MILF symbols are top on from formerly prominent characters that have actually gripped the focus of crypto investors, such as CryptoPunks. These were a collection of 10,000 characters, each distinct, that exposed evidence of possession on the Ethereum blockchain. MILF symbols operate similarly. Due to the fact that no 2 characters are alike, each token can surely ended up being the authorities residential building of a solitary proprietor on this blockchain. Those 10,000 CryptoPunk symbols were quickly purchased, immediately providing the specific characters boosted worth. The presumption is that the MILF symbols will certainly go similarly, so any individual wanting to obtain their practical a certain MILF personality will certainly need to buy this through the market-place that's likewise installed in the Ethereum blockchain. Presently, the most affordable offered rate for MILF symbols is $0.00004078, standing for a 0.61% increase over the previous 24-hour. Shade coding Generally, these characters will certainly have actually a condition when they show up in the crypto markets. Where the CrytoPunks are worried, a blue history suggested that punk was except sale, neither exist energetic quotes. Punks that were offered offer for sale would certainly have actually a red history. Those with an energetic quote would certainly have actually a purple history. MILFs have actually built such a solid track record for desirability, their incorporation as

Overcrowding works in a different way for creators than for viewers. For creators, the problem becomes—how do you stand out? How do you get your videos watched? This is particularly acute for new creators, who face a “rich get richer” phenomenon. Across many categories of networked products, when early users join a network and start producing value, algorithms naturally reward them—and this is a good thing. When they do a good job, perhaps they earn five-star ratings, or they quickly gain lots of followers. Perhaps they get featured, or are ranked highly in popularity lists. This helps consumers find what they want, quickly, but the downside is that the already popular just get more popular. Eventually, the problem becomes, how does a new member of the network break in? If everyone else has millions of followers, or thousands of five-star reviews, it can be hard. Eugene Wei, former CTO of Hulu and noted product thinker, writes about the “Old Money” in the context of social networks, arguing that established networks are harder for new users to break into: Some networks reward those who gain a lot of followers early on with so much added exposure that they continue to gain more followers than other users, regardless of whether they’ve earned it through the quality of their posts. One hypothesis on why social networks tend to lose heat at scale is that this type of old money can’t be cleared out, and new money loses the incentive to play the game. It’s not that the existence of old money or old social capital dooms a social network to inevitable stagnation, but a social network should continue to prioritize distribution for the best content, whatever the definition of quality, regardless of the vintage of user producing it. Otherwise a form of social capital inequality sets in, and in the virtual world, where exit costs are much lower than in the real world, new users can easily leave for a new network where their work is more properly rewarded and where status mobility is higher.75 This is true for social networks and also true for marketplaces, app stores, and other networked products as well. Ratings systems, reviews, followers, advertising systems all reinforce this, giving the most established members of a network dominance over everyone else. High-quality users hogging all of the attention is the good version of the problem, but the bad version is much more problematic: What happens, particularly for social products, when the most controversial and opinionated users are rewarded with positive feedback loops? Or when purveyors of low-quality apps in a developer platform—like the Apple AppStore’s initial proliferation of fart apps—are downloaded by users and ranked highly in charts? Ultimately, these loops need to be broken; otherwise your network may go in a direction you don’t want.

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.

This is all Bayes' theorem does: offers a systematic way to update your belief in a hypothesis on the basis of the evidence. It accepts that you can't ever be completely certain about the theory you're considering, but allows you to make a best guess from the information available

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!

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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

six reasons why email is the best: My company AppSumo generates $65 million a year in total transactions. And you know what? Nearly 50 percent of that comes from email. This percentage has been consistent for more than ten years. Don’t believe me? I have 120,000 Twitter followers, 750,000 YouTube subscribers, and 150,000 TikTok fans—and I would give them all up for my 100,000 email subscribers. Why? Every time I send an email, 40,000 people open it and consume my content. I’m not hoping the platform gods will allow me to reach them. On the other platforms, anywhere between 100 and 1 million people pay attention to my content, but it’s not consistent or in my control. I know what you’re saying: “C’mon, Noah, email is dead.” Now ask yourself, when was the last time you checked your email? Exactly. Email is used obsessively by over 4 billion people! It’s the largest way of communicating at scale that exists today. Eighty-nine percent of people check it EVERY DAY! Social media decides who and how many people you’re seen by. One tweak to the algorithm, and you’re toast. Remember the digital publisher LittleThings? Yeah, no one else does, either. They closed after they lost 75 percent of their 20,000,000 monthly visitors when Facebook changed its algorithm in 2018. CEO Joe Speiser says it killed his business and he lost $100 million. You own your email list. Forever. If AppSumo shuts down tomorrow, my insurance policy, my sweet sweet baby, my beloved, my email list comes with me and makes anything I do after so much easier. Because it’s mine. It also doesn’t cost you significant money to grow your list or to communicate with your list, whereas Facebook or Google ads consistently cost money.

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Rules for Building High-Performance Code We’ve got the following rules for creating high-performance software: Know where you’re going (understand the objective of the software). Make a big map (have an overall program design firmly in mind, so the various parts of the program and the data structures work well together). Make lots of little maps (design an algorithm for each separate part of the overall design). Know the territory (understand exactly how the computer carries out each task). Know when it matters (identify the portions of your programs where performance matters, and don’t waste your time optimizing the rest). Always consider the alternatives (don’t get stuck on a single approach; odds are there’s a better way, if you’re clever and inventive enough). Know how to turn on the juice (optimize the code as best you know how when it does matter).

The minute I dropped out I could stop taking the required classes that didn’t interest me, and begin dropping in on the ones that looked interesting. It wasn’t all romantic. I didn’t have a dorm room, so I slept on the floor in friends’ rooms, I returned coke bottles for the 5¢ deposits to buy food with, and I would walk the seven miles across town every Sunday night to get one good meal a week at the Hare Krishna temple. I loved it. And much of what I stumbled into by following my curiosity and intuition turned out to be priceless later on. Let me give you one example: Reed College at that time offered perhaps the best calligraphy instruction in the country. Throughout the campus every poster, every label on every drawer, was beautifully hand calligraphed. Because I had dropped out and didn’t have to take the normal classes, I decided to take a calligraphy class to learn how to do this. I learned about serif and san serif typefaces, about varying the amount of space between different letter combinations, about what makes great typography great. It was beautiful, historical, artistically subtle in a way that science can’t capture, and I found it fascinating. None of this had even a hope of any practical application in my life. But ten years later, when we were designing the first Macintosh computer, it all came back to me. And we designed it all into the Mac. It was the first computer with beautiful typography. If I had never dropped in on that single course in college, the Mac would have never had multiple typefaces or proportionally spaced fonts. And since Windows just copied the Mac, it’s likely that no personal computer would have them. If I had never dropped out, I would have never dropped in on this calligraphy class, and personal computers might not have the wonderful typography that they do. Of course it was impossible to connect the dots looking forward when I was in college. But it was very, very clear looking backwards ten years later. Again, you can’t connect the dots looking forward; you can only connect them looking backwards. So you have to trust that the dots will somehow connect in your future. You have to trust in something—your gut, destiny, life, karma, whatever. This approach has never let me down, and it has made all the difference in my life. The narrator of this story is Steve Jobs, the legendary CEO of Apple. The story was part of his famous Stanford commencement speech in 2005.[23] It’s a perfect illustration of how passion and purpose drive success, not the crossing of an imaginary finish line in the future. Forget the finish line. It doesn’t exist. Instead, look for passion and purpose directly in front of you. The dots will connect later, I promise—and so does Steve.

To do truly meaningful work, you need to get serious, focus, and go all in. Floyd Mayweather Junior is the best pound-for-pound boxer in the world. As of this writing, he is also the highest paid athlete in the world. His motto? Hard Work, Dedication. His team chants the motto as he trains. One group yells, “Hard work!” and the other responds, “Dedication!” The chants get louder and faster as Mayweather increases the speed and intensity of his workout. Mayweather knows the value of these words, and the impact they have on success. He lives by them. He endures grueling training sessions, 2-3 times per day. He often trains late into the night. He doesn’t smoke or drink alcohol—ever. Floyd Mayweather is no joke. He’s the real deal. And that’s why he’s such a big deal. He lives to box. It’s what he loves to do. His hard work and dedication have paid off, literally. Some people question Mayweather’s morals, or ridicule him for his arrogance, but it’s hard to argue with his unparalleled achievements in boxing and the relentless dedication that backs it all up. The best in the world are the best because they work their asses off doing what they were born to do. They make sacrifices. They keep grinding—and they don’t stop.[36]

Algorithmic profits Algorithmic marketing is allowing companies to do things they couldn’t do before, and some early signs show it can deliver big value, especially in financial or information services. In North America, Amazon.com grew 30 to 40 percent, quarter after quarter, throughout the United States’ 2008-2012 recession, while other major retailers shrank or went out of business. From 2006 to 2010, Amazon spent 5.6 percent of its sales revenue on IT, while rivals Target and Best Buy spent 1.3% and 0.5%, respectively. That investment and focus has yielded increasingly sophisticated recommendation engines that deliver over 35 percent of all sales, an automated e-mail/customer service systems (90 percent are automated, versus 44 percent for the average retailer) that are a key component of its best-in-class customer satisfaction, and dynamic pricing systems that crawl the Web and react to competitor pricing and stock levels by altering prices on Amazon.com, in some cases every 15 seconds.

And it’s actually rational to emphasize exploration—the new rather than the best, the exciting rather than the safe, the random rather than the considered—for many of those choices, particularly earlier in life.

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.

Motivated by my research and examples such as Feynman, I decided that focusing my attention on a bottom-up understanding of my own field’s most difficult results would be a good first step toward revitalizing my career capital stores. To initiate these efforts, I chose a paper that was well cited in my research niche, but that was also considered obtuse and hard to follow. The paper focused on only a single result—the analysis of an algorithm that offers the best-known solution to a well-known problem. Many people have cited this result, but few have understood the details that support it. I decided that mastering this notorious paper would prove a perfect introduction to my new regime of self-enforced deliberate practice. Here

your total amount of regret will probably never stop increasing, even if you pick the best possible strategy

The intuitive standard for rational decision making is carefully considering all available options and taking the best one. At first glance, computers look like the paragons of this approach, grinding their way through complex computations for as long as it takes to get perfect answers. But as we've seen, that is an outdated picture of what computers do; it's a luxury afforded by an easy problem. In the hard cases, the best algorithms are all about doing what makes the most sense in the least amount of time, which by no means giving careful consideration to every factor and pursuing every computation to the end. Life is just too complicated for that.

There’s a strong impulse in our culture to run away from these little corners. We’re told that society’s winners will be the thinkers who network, collaborate, create, and strategize in concert with others. Our kids are taught to study in groups, to execute projects as teams. Our workplaces have been stripped of walls so that the organization functions as a unit. The big tech companies also propel us to join the crowd—they provide us with the trending topics and their algorithms suggest that we read the same articles, tweets, and posts as the rest of the world. There’s no doubting the creative power of conversation, the intellectual potential of humbly learning from our peers, the necessity of groups working together to solve problems. Yet none of this should replace contemplation, moments of isolation, where the mind can follow its own course to its own conclusions. We read in our little corners, our beds and tubs and dens, because we have a sense that these are the places where we can think best. I have spent my life searching for an alternative. I will read in the café and on the subway, making a diligent, wholehearted effort to focus the mind. But it never entirely works. My mind can’t shake its awareness of the humans in the room.

Even voices in the proud New York Times newsroom now cede that Facebook, not the Old Gray Lady itself, now drives the national conversation with the horsepower of its search traffic and algorithms providing traditional media its best chance to be seen. “Measured by web traffic, ad revenue and influence over the way the rest of the media makes money, Facebook has grown into the most powerful force in the news industry,” wrote Times media columnist Farhad Manjoo

In order to elicit the best performance from multicore computers, we need to design algorithms with parallelism in mind

Don’t always consider all your options. Don’t necessarily go for the outcome that seems best every time. Make a mess on occasion. Travel light. Let things wait. Trust your instincts and don’t think too long. Relax. Toss a coin. Forgive, but

This is because computer science has traditionally been all about thinking deterministically, but machine learning requires thinking statistically. If a rule for, say, labeling e-mails as spam is 99 percent accurate, that does not mean it’s buggy; it may be the best you can do and good enough to be useful. This difference in thinking is a large part of why Microsoft has had a lot more trouble catching up with Google than it did with Netscape. At the end of the day, a browser is just a standard piece of software, but a search engine requires a different mind-set.

The first eye-opener came in the 1970s, when DARPA, the Pentagon’s research arm, organized the first large-scale speech recognition project. To everyone’s surprise, a simple sequential learner of the type Chomsky derided handily beat a sophisticated knowledge-based system. Learners like it are now used in just about every speech recognizer, including Siri. Fred Jelinek, head of the speech group at IBM, famously quipped that “every time I fire a linguist, the recognizer’s performance goes up.” Stuck in the knowledge-engineering mire, computational linguistics had a near-death experience in the late 1980s. Since then, learning-based methods have swept the field, to the point where it’s hard to find a paper devoid of learning in a computational linguistics conference. Statistical parsers analyze language with accuracy close to that of humans, where hand-coded ones lagged far behind. Machine translation, spelling correction, part-of-speech tagging, word sense disambiguation, question answering, dialogue, summarization: the best systems in these areas all use learning. Watson, the Jeopardy! computer champion, would not have been possible without it.

For the hardest problems—the ones we really want to solve but haven’t been able to, like curing cancer—pure nature-inspired approaches are probably too uninformed to succeed, even given massive amounts of data. We can in principle learn a complete model of a cell’s metabolic networks by a combination of structure search, with or without crossover, and parameter learning via backpropagation, but there are too many bad local optima to get stuck in. We need to reason with larger chunks, assembling and reassembling them as needed and using inverse deduction to fill in the gaps. And we need our learning to be guided by the goal of optimally diagnosing cancer and finding the best drugs to cure it. Optimal learning is the Bayesians’ central goal, and they are in no doubt that they’ve figured out how to reach it. This way, please …

This procedure focuses on decisively resolving the question of which treatment is better, rather than on providing the best treatment to each patient in the trial itself.

The brain is the best algorithm,

thought itself is best understood as planning; even higher forms of thought, such as philosophy, the epitome of disembodied speculation, proceed, they argue, by hijacking algorithms originally developed to help us plan movements.

Meaning, Wahram thought, that right now somewhere in the system there could be machines in human form, escaped into the crowd, doing their best to stay free, perhaps, when any X-ray machine or other surveillance device would reveal what they were—out there hiding, trying to accomplish the goals they had been given, perhaps, or new ones they might choose for themselves, according to some self-invented algorithm of survival. Damaged, dangerous, detached from any other consciousness, solitary and afraid—in other words, just like everyone else.

The aim of the next nine sections will be to present careful arguments to show that none of the loopholes (a), (b), and (c) can provide a plausible way to evade the contradiction of the robot. Accordingly, it, and we also, are driven to the unpalatable (d), if we are still insistent that mathematical understanding can be reduced to computation. I am sure that those concerned with artificial intelligence would find (d) to be as unpalatable as I find it to be. It provides perhaps a conceivable standpoint-essentially the A/D suggestion, referred to at the end of 1.3, whereby divine intervention is required for the implanting of an unknowable algorithm into each of our computer brains (by 'the best programmer in the business'). In any case, the conclusion 'unknowable'-for the very mechanisms that are ultimately responsible for our intelligence-would not be a very happy conclusion for those hoping actually to construct a genuinely artificially intelligent robot! It would not be a particularly happy conclusion, either, for those of us who hope to understand, in principle and in a scientific way, how human intelligence has actually arisen, in accordance with comprehensible scientific laws, such as those of physics, chemistry, biology, and natural selection-irrespective of any desire to reproduce such intelligence in a robot device. In my own opinion, such a pessimistic conclusion is not warranted, for the very reason that 'scientific comprehensibility' is a very different thing from 'computability'. The conclusion should be not that the underlying laws are incomprehensible, but that they are non-computable.