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.

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

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.

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.

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

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

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

Not too long ago he had to spend countless hours refining and improving his lucrative investment algorithm to keep money flowing into his accounts, but nowadays he was swimming in earnings received by way of inside information from numerous connections he’d acquired on his rise in the financial and business scene. He was reeling in windfall profits. His connections afforded him with gains that most people couldn’t dream of acquiring in their wildest dreams, and the best part was he didn’t need to use an ounce of his brainpower or intellect to make it happen. He’d reached that upper echelon tier where wealth was casually doled out to those who were on the list, on the take. It was akin to having one’s own money-printing machine sitting in their den. A privately owned banking system that printed money out of thin air and accumulated debt from anyone who laid hands on it—no different than the Federal Reserve.

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.

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

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

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.

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.

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.

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.

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.

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.

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.

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.

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

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

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,

There are four key steps in a data science study. First, the data must be processed and prepared for analysis. Next, suitable algorithms are shortlisted based on our study’s requirements. Following which, parameters of the algorithms have to be tuned to optimize results. These finally culminate in the building of models that are then compared to select the best one.

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

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.

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 …

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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.

Reason #1: Downtime Aids Insights Consider the following excerpt from a 2006 paper that appeared in the journal Science: The scientific literature has emphasized the benefits of conscious deliberation in decision making for hundreds of years… The question addressed here is whether this view is justified. We hypothesize that it is not. Lurking in this bland statement is a bold claim. The authors of this study, led by the Dutch psychologist Ap Dijksterhuis, set out to prove that some decisions are better left to your unconscious mind to untangle. In other words, to actively try to work through these decisions will lead to a worse outcome than loading up the relevant information and then moving on to something else while letting the subconscious layers of your mind mull things over. Dijksterhuis’s team isolated this effect by giving subjects the information needed for a complex decision regarding a car purchase. Half the subjects were told to think through the information and then make the best decision. The other half were distracted by easy puzzles after they read the information, and were then put on the spot to make a decision without having had time to consciously deliberate. The distracted group ended up performing better. Observations from experiments such as this one led Dijksterhuis and his collaborators to introduce unconscious thought theory (UTT)—an attempt to understand the different roles conscious and unconscious deliberation play in decision making. At a high level, this theory proposes that for decisions that require the application of strict rules, the conscious mind must be involved. For example, if you need to do a math calculation, only your conscious mind is able to follow the precise arithmetic rules needed for correctness. On the other hand, for decisions that involve large amounts of information and multiple vague, and perhaps even conflicting, constraints, your unconscious mind is well suited to tackle the issue. UTT hypothesizes that this is due to the fact that these regions of your brain have more neuronal bandwidth available, allowing them to move around more information and sift through more potential solutions than your conscious centers of thinking. Your conscious mind, according to this theory, is like a home computer on which you can run carefully written programs that return correct answers to limited problems, whereas your unconscious mind is like Google’s vast data centers, in which statistical algorithms sift through terabytes of unstructured information, teasing out surprising useful solutions to difficult questions. The implication of this line of research is that providing your conscious brain time to rest enables your unconscious mind to take a shift sorting through your most complex professional challenges. A shutdown habit, therefore, is not necessarily reducing the amount of time you’re engaged in productive work, but is instead diversifying the type of work you deploy.

Change happens so rapidly now that the best technique to determine what works is to test, fail, learn, and try again. We walk you through a step-by-step process for how to use experiments by taking advantage of digital exhaust—a vast subject from which we’ve distilled the 30 percent you need to know.

From this we might infer that minimizing our pain and suffering when it comes to sorting is all about minimizing the number of things we have to sort. It’s true: one of the best preventives against the computational difficulty of sock sorting is just doing your laundry more often. Doing laundry three times as frequently, say, could reduce your sorting overhead by a factor of nine. Indeed, if Hillis’s roommate stuck with his peculiar procedure but went thirteen days between washes instead of fourteen, that alone would save him twenty-eight pulls from the hamper.

Just as how unconscious bias can seep into algorithms, so can conscious bias. Conscious bias happens when we know we’re being biased toward a particular person or a group of people. Although this is rare in AI, the threat is always there.

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

The future belongs to those who understand at a very deep level how to combine their unique expertise with what algorithms do best

The lesson of the TCP sawtooth is that in an unpredictable and changing environment, pushing things to the point of failure is indeed sometimes the best (or the only) way to use all the resources to their fullest. What matters is making sure that the response to failure is both sharp and resilient. Under AIMD, every connection that isn’t dropping the ball is accelerated until it is—and then it’s cut in half, and immediately begins accelerating again. And though it would violate almost every norm of current corporate culture, one can imagine a corporation in which, annually, every employee is always either promoted a single step up the org chart or sent part of the way back down.

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

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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.

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.

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

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Excellence in Statistics: Rigor Statisticians are specialists in coming to conclusions beyond your data safely—they are your best protection against fooling yourself in an uncertain world. To them, inferring something sloppily is a greater sin than leaving your mind a blank slate, so expect a good statistician to put the brakes on your exuberance. They care deeply about whether the methods applied are right for the problem and they agonize over which inferences are valid from the information at hand. The result? A perspective that helps leaders make important decisions in a risk-controlled manner. In other words, they use data to minimize the chance that you’ll come to an unwise conclusion. Excellence in Machine Learning: Performance You might be an applied machine-learning/AI engineer if your response to “I bet you couldn’t build a model that passes testing at 99.99999% accuracy” is “Watch me.” With the coding chops to build both prototypes and production systems that work and the stubborn resilience to fail every hour for several years if that’s what it takes, machine-learning specialists know that they won’t find the perfect solution in a textbook. Instead, they’ll be engaged in a marathon of trial and error. Having great intuition for how long it’ll take them to try each new option is a huge plus and is more valuable than an intimate knowledge of how the algorithms work (though it’s nice to have both). Performance means more than clearing a metric—it also means reliable, scalable, and easy-to-maintain models that perform well in production. Engineering excellence is a must. The result? A system that automates a tricky task well enough to pass your statistician’s strict testing bar and deliver the audacious performance a business leader demands. Wide Versus Deep What the previous two roles have in common is that they both provide high-effort solutions to specific problems. If the problems they tackle aren’t worth solving, you end up wasting their time and your money. A frequent lament among business leaders is, “Our data science group is useless.” And the problem usually lies in an absence of analytics expertise. Statisticians and machine-learning engineers are narrow-and-deep workers—the shape of a rabbit hole, incidentally—so it’s really important to point them at problems that deserve the effort. If your experts are carefully solving the wrong problems, your investment in data science will suffer low returns. To ensure that you can make good use of narrow-and-deep experts, you either need to be sure you already have the right problem or you need a wide-and-shallow approach to finding one.

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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.

In 2011, Insurance giant Allstate, with forty of the best actuaries and data scientists money could buy, wanted to see if its claims algorithm could be improved upon, so it ran a contest on Kaggle.

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

Isolate complexity. Complexity in all forms—complicated algorithms, large data sets, intricate communications protocols, and so on—is prone to errors. If an error does occur, it will be easier to find if it isn't spread through the code but is localized within a class. Changes arising from fixing the error won't affect other code because only one class will have to be fixed—other code won't be touched. If you find a better, simpler, or more reliable algorithm, it will be easier to replace the old algorithm if it has been isolated into a class. During development, it will be easier to try several designs and keep the one that works best.

The brain is the best algorithm,

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.

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]

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.

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

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 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.

Correlations made by big data are likely to reinforce negative bias. Because big data often relies on historical data or at least the status quo, it can easily reproduce discrimination against disadvantaged racial and ethnic minorities. The propensity models used in many algorithms can bake in a bias against someone who lived in the zip code of a low-income neighborhood at any point in his or her life. If an algorithm used by human resources companies queries your social graph and positively weighs candidates with the most existing connections to a workforce, it makes it more difficult to break in in the first place. In effect, these algorithms can hide bias behind a curtain of code. Big data is, by its nature, soulless and uncreative. It nudges us this way and that for reasons we are not meant to understand. It strips us of our privacy and puts our mistakes, secrets, and scandals on public display. It reinforces stereotypes and historical bias. And it is largely unregulated because we need it for economic growth and because efforts to try to regulate it have tended not to work; the technologies are too far-reaching and are not built to recognize the national boundaries of our world’s 196 sovereign nation-states. Yet would it be best to try to shut down these technologies entirely if we could? No. Big data simultaneously helps solve global challenges while creating an entirely new set of challenges. It’s our best chance at feeding 9 billion people, and it will help solve the problem of linguistic division that is so old its explanation dates back to the Old Testament and the Tower of Babel. Big data technologies will enable us to discover cancerous cells at 1 percent the size of what can be detected using today’s technologies, saving tens of millions of lives. The best approach to big data might be one put forward by the Obama campaign’s chief technology officer, Michael Slaby, who said, “There’s going to be a constant mix between your qualitative experience and your quantitative experience. And at times, they’re going to be at odds with each other, and at times they’re going to be in line. And I think it’s all about the blend. It’s kind of like you have a mixing board, and you have to turn one up sometimes, and turn down the other. And you never want to be just one or the other, because if it’s just one, then you lose some of the soul.” Slaby has made an impressive career out of developing big data tools, but even he recognizes that these tools work best when governed by human judgment. The choices we make about how we manage data will be as important as the decisions about managing land during the agricultural age and managing industry during the industrial age. We have a short window of time—just a few years, I think—before a set of norms set in that will be nearly impossible to reverse. Let’s hope humans accept the responsibility for making these decisions and don’t leave it to the machines.

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.

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.

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!

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

In his 1993 book Technopoly, Neil Postman distilled the main tenets of Taylor’s system of scientific management. Taylorism, he wrote, is founded on six assumptions: “that the primary, if not the only, goal of human labor and thought is efficiency; that technical calculation is in all respects superior to human judgment; that in fact human judgment cannot be trusted, because it is plagued by laxity, ambiguity, and unnecessary complexity; that subjectivity is an obstacle to clear thinking; that what cannot be measured either does not exist or is of no value; and that the affairs of citizens are best guided and conducted by experts.”11 What’s remarkable is how well Postman’s summary encapsulates Google’s own intellectual ethic. Only one tweak is required to bring it up to date. Google doesn’t believe that the affairs of citizens are best guided by experts. It believes that those affairs are best guided by software algorithms—which is exactly what Taylor would have believed had powerful digital computers been around in his day.

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Silicon and Sapiens (The Sonnet) Once upon a time, I put down my soldering iron and picked up the keyboard, for I couldn't afford to sustain my passion for electronics any more. But now that I look back, It was for the best. The world has plenty tech genius, what it lacks is reformer scientist. My inside awareness of machine intricacies has been an aid to my neuroscience. In a world torn between mind and machine, I bridge the shores of silicon and sapiens. Biologists often diss the potential of machine, just like gadgeteers are oblivious to life. Life is a cosmic miracle, machines are a human one, and with added purpose, machines could be the mightiest defense of life.

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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.

The current world record holder as the fastest speedcuber is Feliks Zemdegs of Australia. He solved a 3 x 3 Rubik’s Cube at an amazing 4.737 seconds

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

The key to Amazon is its increasingly digital operating model. Amazon’s operating philosophy centers on digitizing the best understanding of operational excellence through the broad-based application of artificial intelligence and machine learning, advanced robotics, and the instantiation of as much know-how as possible into software.

[...] [A] world increasingly reliant on and in thrall to data. Big Data. Which in turn is panned for Big Truths by Big Algorithms, using Big Computers. But when your big data is corrupted by big silences, the truths you get are half-truths, at best. And often, for women, they aren't true at all.

Imagine two Facebook feeds. One is full of updates, news, and videos that make you feel calm and happy. The other is full of updates, news, and videos that make you feel angry and outraged. Which one does the algorithm select? The algorithm is neutral about the question of whether it wants you to be calm or angry. That’s not its concern. It only cares about one thing: Will you keep scrolling? Unfortunately, there’s a quirk of human behavior. On average, we will stare at something negative and outrageous for a lot longer than we will stare at something positive and calm. You will stare at a car crash longer than you will stare at a person handing out flowers by the side of the road, even though the flowers will give you a lot more pleasure than the mangled bodies in a crash. Scientists have been proving this effect in different contexts for a long time—if they showed you a photo of a crowd, and some of the people in it were happy, and some angry, you would instinctively pick out the angry faces first. Even ten-week-old babies respond differently to angry faces. This has been known about in psychology for years and is based on a broad body of evidence. It’s called “negativity bias.” There is growing evidence that this natural human quirk has a huge effect online. On YouTube, what are the words that you should put into the title of your video, if you want to get picked up by the algorithm? They are—according to the best site monitoring YouTube trends—words such as “hates,” “obliterates,” “slams,” “destroys.” A major study at New York University found that for every word of moral outrage you add to a tweet, your retweet rate will go up by 20 percent on average, and the words that will increase your retweet rate most are “attack,” “bad,” and “blame.” A study by the Pew Research Center found that if you fill your Facebook posts with “indignant disagreement,” you’ll double your likes and shares. So an algorithm that prioritizes keeping you glued to the screen will—unintentionally but inevitably—prioritize outraging and angering you. If it’s more enraging, it’s more engaging.

Third, CA then took what they had learned from these algorithms and turned around and used platforms such as Twitter, Facebook, Pandora (music streaming), and YouTube to find out where the people they wished to target spent the most interactive time. Where was the best place to reach each person?

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