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I learned something more useful: the existence of non-fiction literature that can teach you anything.

something felt odd. Whenever a conversation revolved around a serious non-fiction book I read, such as ‘Sapiens’ or ‘Thinking Fast and Slow,’ I could never remember much. Turns out, I hadn’t absorbed as much information as I’d believed. Since I couldn’t remember much, I felt as though reading wasn’t an investment in knowledge but mere entertainment.

When I opened up about my struggles, many others confessed they also can’t remember most of what they read, as if forgetting is a character flaw. But it isn’t.

It’s the way we work with books that’s flawed.

there’s a better way to read. Most people rely on techniques like highlighting, rereading, or, worst of all, completely passive reading, which are highly ineffective.

Since I started applying evidence-based learning strategies to reading non-fiction books, many things have changed. I can explain complex ideas during dinner conversations. I can recall interesting concepts and link them in my writing or podcasts. As a result, people come to me for all kinds of advice.

What’s the Architecture of Human Learning and Memory?

Human brains don’t work like recording devices. We don’t absorb information and knowledge by reading sentences.

we store new information in terms of its meaning to our existing memory

we give new information meaning by actively participating in the learning process — we interpret, connect, interrelate, or elaborate

To remember new information, we not only need to know it but also to know how it relates to what we already know.

Learning is dependent on memory processes because previously-stored knowledge functions as a framework in which newly learned information can be linked.”

Human memory works in three stages: acquisition, retention, and retrieval. In the acquisition phase, we link new information to existing knowledge; in the retention phase, we store it, and in the retrieval phase, we get information out of our memory.

Retrieval, the third stage, is cue dependent. This means the more mental links you’re generating during stage one, the acquisition phase, the easier you can access and use your knowledge.

we need to understand that the three phases interrelate

creating durable and flexible access to to-be-learned information is partly a matter of achieving a meaningful encoding of that information and partly a matter of exercising the retrieval process.”

Next, we’ll look at the learning strategies that work best for our brains (elaboration, retrieval, spaced repetition, interleaving, self-testing) and see how we can apply those insights to reading non-fiction books.

The strategies that follow are rooted in research from professors of Psychological & Brain Science around Henry Roediger and Mark McDaniel. Both scientists spent ten years bridging the gap between cognitive psychology and education fields. Harvard University Press published their findings in the book ‘Make It Stick.

#1 Elaboration

“Elaboration is the process of giving new material meaning by expressing it in your own words and connecting it with what you already know.”

Why elaboration works: Elaborative rehearsal encodes information into your long-term memory more effectively. The more details and the stronger you connect new knowledge to what you already know, the better because you’ll be generating more cues. And the more cues they have, the easier you can retrieve your knowledge.

How I apply elaboration: Whenever I read an interesting section, I pause and ask myself about the real-life connection and potential application. The process is invisible, and my inner monologues sound like: “This idea reminds me of…, This insight conflicts with…, I don’t really understand how…, ” etc.

For example, when I learned about A/B testing in ‘The Lean Startup,’ I thought about applying this method to my startup. I added a note on the site stating we should try it in user testing next Wednesday. Thereby the book had an immediate application benefit to my life, and I will always remember how the methodology works.

How you can apply elaboration: Elaborate while you read by asking yourself meta-learning questions like “How does this relate to my life? In which situation will I make use of this knowledge? How does it relate to other insights I have on the topic?”

While pausing and asking yourself these questions, you’re generating important memory cues. If you take some notes, don’t transcribe the author’s words but try to summarize, synthesize, and analyze.

#2 Retrieval

With retrieval, you try to recall something you’ve learned in the past from your memory. While retrieval practice can take many forms — take a test, write an essay, do a multiple-choice test, practice with flashcards

the authors of ‘Make It Stick’ state: “While any kind of retrieval practice generally benefits learning, the implication seems to be that where more cognitive effort is required for retrieval, greater retention results.”

Whatever you settle for, be careful not to copy/paste the words from the author. If you don’t do the brain work yourself, you’ll skip the learning benefits of retrieval

Retrieval strengthens your memory and interrupts forgetting and, as other researchers replicate, as a learning event, the act of retrieving information is considerably more potent than is an additional study opportunity, particularly in terms of facilitating long-term recall.

How I apply retrieval: I retrieve a book’s content from my memory by writing a book summary for every book I want to remember. I ask myself questions like: “How would you summarize the book in three sentences? Which concepts do you want to keep in mind or apply? How does the book relate to what you already know?”

I then publish my summaries on Goodreads or write an article about my favorite insights

How you can apply retrieval: You can come up with your own questions or use mine. If you don’t want to publish your summaries in public, you can write a summary into your journal, start a book club, create a private blog, or initiate a WhatsApp group for sharing book summaries.

a few days after we learn something, forgetting sets in

#3 Spaced Repetition

With spaced repetition, you repeat the same piece of information across increasing intervals.

The harder it feels to recall the information, the stronger the learning effect. “Spaced practice, which allows some forgetting to occur between sessions, strengthens both the learning and the cues and routes for fast retrieval,”

Why it works: It might sound counterintuitive, but forgetting is essential for learning. Spacing out practice might feel less productive than rereading a text because you’ll realize what you forgot. Your brain has to work harder to retrieve your knowledge, which is a good indicator of effective learning.

How I apply spaced repetition: After some weeks, I revisit a book and look at the summary questions (see #2). I try to come up with my answer before I look up my actual summary. I can often only remember a fraction of what I wrote and have to look at the rest.

“Knowledge trapped in books neatly stacked is meaningless and powerless until applied for the betterment of life.”

How you can apply spaced repetition: You can revisit your book summary medium of choice and test yourself on what you remember. What were your action points from the book? Have you applied them? If not, what hindered you?

By testing yourself in varying intervals on your book summaries, you’ll strengthen both learning and cues for fast retrieval.

Why interleaving works: Alternate working on different problems feels more difficult as it, again, facilitates forgetting.

How I apply interleaving: I read different books at the same time.

1) Highlight everything you want to remember

#5 Self-Testing

While reading often falsely tricks us into perceived mastery, testing shows us whether we truly mastered the subject at hand. Self-testing helps you identify knowledge gaps and brings weak areas to the light

“It’s better to solve a problem than to memorize a solution.”

Why it works: Self-testing helps you overcome the illusion of knowledge. “One of the best habits a learner can instill in herself is regular self-quizzing to recalibrate her understanding of what she does and does not know.”

How I apply self-testing: I explain the key lessons from non-fiction books I want to remember to others. Thereby, I test whether I really got the concept. Often, I didn’t

instead of feeling frustrated, cognitive science made me realize that identifying knowledge gaps are a desirable and necessary effect for long-term remembering.

How you can apply self-testing: Teaching your lessons learned from a non-fiction book is a great way to test yourself. Before you explain a topic to somebody, you have to combine several mental tasks: filter relevant information, organize this information, and articulate it using your own vocabulary.

Now that I discovered how to use my Kindle as a learning device, I wouldn’t trade it for a paper book anymore. Here are the four steps it takes to enrich your e-reading experience

How you can apply interleaving: Your brain can handle reading different books simultaneously, and it’s effective to do so. You can start a new book before you finish the one you’re reading. Starting again into a topic you partly forgot feels difficult first, but as you know by now, that’s the effect you want to achieve.

it won’t surprise you that researchers proved highlighting to be ineffective. It’s passive and doesn’t create memory cues.

2) Cut down your highlights in your browser

After you finished reading the book, you want to reduce your highlights to the essential part. Visit your Kindle Notes page to find a list of all your highlights. Using your desktop browser is faster and more convenient than editing your highlights on your e-reading device.

Now, browse through your highlights, delete what you no longer need, and add notes to the ones you really like. By adding notes to the highlights, you’ll connect the new information to your existing knowledge

3) Use software to practice spaced repetitionThis part is the main reason for e-books beating printed books. While you can do all of the above with a little extra time on your physical books, there’s no way to systemize your repetition praxis.

Readwise is the best software to combine spaced repetition with your e-books. It’s an online service that connects to your Kindle account and imports all your Kindle highlights. Then, it creates flashcards of your highlights and allows you to export your highlights to your favorite note-taking app.

Common Learning Myths DebunkedWhile reading and studying evidence-based learning techniques I also came across some things I wrongly believed to be true.

#2 Effective learning should feel easyWe think learning works best when it feels productive. That’s why we continue to use ineffective techniques like rereading or highlighting. But learning works best when it feels hard, or as the authors of ‘Make It Stick’ write: “Learning that’s easy is like writing in sand, here today and gone tomorrow.”

In Conclusion

I developed and adjusted these strategies over two years, and they’re still a work in progress.

Try all of them but don’t force yourself through anything that doesn’t feel right for you. I encourage you to do your own research, add further techniques, and skip what doesn’t serve you

“In the case of good books, the point is not to see how many of them you can get through, but rather how many can get through to you.”— Mortimer J. Adler

How do we reconcile our enjoyment of social media even as we understand that the corporations who control them aren’t always acting in our best interests?

hat was because the underlying economic conditions hadn’t been changed or “disrupted,” to use a favorite Silicon Valley phrase. Google has to serve its shareholders, just like NBCUniversal does. As a result, many of the unappealing aspects of the legacy-media model have simply carried over into a digital age — namely, commercialism, consolidation, and centralization. In fact, the new system is even more dependent on advertising dollars than the one that preceded it, and digital advertising is far more invasive and ubiquitous

the popular narrative — new communications technologies would topple the establishment and empower regular people — didn’t accurately capture reality. Something more complex and predictable was happening. The old-media dinosaurs weren’t dying out, but were adapting to the online environment; meanwhile the new tech titans were coming increasingly to resemble their predecessors

I use lots of products that are created by companies whose business practices I object to and that don’t act in my best interests, or the best interests of workers or the environment — we all do, since that’s part of living under capitalism. That said, I refuse to invest so much in any platform that I can’t quit without remorse

these services aren’t free even if we don’t pay money for them; we pay with our personal data, with our privacy. This feeds into the larger surveillance debate, since government snooping piggybacks on corporate data collection. As I argue in the book, there are also negative cultural consequences (e.g., when advertisers are paying the tab we get more of the kind of culture marketers like to associate themselves with and less of the stuff they don’t) and worrying social costs. For example, the White House and the Federal Trade Commission have both recently warned that the era of “big data” opens new avenues of discrimination and may erode hard-won consumer protections.

I’m resistant to the tendency to place this responsibility solely on the shoulders of users. Gadgets and platforms are designed to be addictive, with every element from color schemes to headlines carefully tested to maximize clickability and engagement. The recent news that Facebook tweaked its algorithms for a week in 2012, showing hundreds of thousands of users only “happy” or “sad” posts in order to study emotional contagion — in other words, to manipulate people’s mental states — is further evidence that these platforms are not neutral. In the end, Facebook wants us to feel the emotion of wanting to visit Facebook frequently

social inequalities that exist in the real world remain meaningful online. What are the particular dangers of discrimination on the Internet?

That it’s invisible or at least harder to track and prove. We haven’t figured out how to deal with the unique ways prejudice plays out over digital channels, and that’s partly because some folks can’t accept the fact that discrimination persists online. (After all, there is no sign on the door that reads Minorities Not Allowed.)

just because the Internet is open doesn’t mean it’s equal; offline hierarchies carry over to the online world and are even amplified there. For the past year or so, there has been a lively discussion taking place about the disproportionate and often outrageous sexual harassment women face simply for entering virtual space and asserting themselves there — research verifies that female Internet users are dramatically more likely to be threatened or stalked than their male counterparts — and yet there is very little agreement about what, if anything, can be done to address the problem.

What steps can we take to encourage better representation of independent and non-commercial media? We need to fund it, first and foremost. As individuals this means paying for the stuff we believe in and want to see thrive. But I don’t think enlightened consumption can get us where we need to go on its own. I’m skeptical of the idea that we can shop our way to a better world. The dominance of commercial media is a social and political problem that demands a collective solution, so I make an argument for state funding and propose a reconceptualization of public media. More generally, I’m struck by the fact that we use these civic-minded metaphors, calling Google Books a “library” or Twitter a “town square” — or even calling social media “social” — but real public options are off the table, at least in the United States. We hand the digital commons over to private corporations at our peril.

6. You advocate for greater government regulation of the Internet. Why is this important?

I’m for regulating specific things, like Internet access, which is what the fight for net neutrality is ultimately about. We also need stronger privacy protections and restrictions on data gathering, retention, and use, which won’t happen without a fight.

I challenge the techno-libertarian insistence that the government has no productive role to play and that it needs to keep its hands off the Internet for fear that it will be “broken.” The Internet and personal computing as we know them wouldn’t exist without state investment and innovation, so let’s be real.

there’s a pervasive and ill-advised faith that technology will promote competition if left to its own devices (“competition is a click away,” tech executives like to say), but that’s not true for a variety of reasons. The paradox of our current media landscape is this: our devices and consumption patterns are ever more personalized, yet we’re simultaneously connected to this immense, opaque, centralized infrastructure. We’re all dependent on a handful of firms that are effectively monopolies — from Time Warner and Comcast on up to Google and Facebook — and we’re seeing increased vertical integration, with companies acting as both distributors and creators of content. Amazon aspires to be the bookstore, the bookshelf, and the book. Google isn’t just a search engine, a popular browser, and an operating system; it also invests in original content

So it’s not that the Internet needs to be regulated but that these big tech corporations need to be subject to governmental oversight. After all, they are reaching farther and farther into our intimate lives. They’re watching us. Someone should be watching them.

“The Dictator’s Practical Internet Guide to Power Retention,” is a compilation of tips, gleaned from the experiences of leaders in China, Singapore, Russia, Iran, Pakistan, and other countries, that illustrate just how brutal the modern, connected world can be for a tyrant.

Recep Tayyip Erdoğan, recently said that “this thing called social media is a curse on societies.”

n Syria, President Bashar al-Assad has proved canny online. Blackouts have shut down the Internet at various moments in the past two years

The state news agency blamed one blackout, in May, on “a malfunctioning fibre-optic cable,” but it was not lost on many that it was timed near a vote on a U.N. resolution on Syria.

Does it matter if this is a kind of misinformation? What does a social-media company do when a user known to be attacking civilians is blasting out feel-good content?

But she explained that, generally speaking, if a user created content that promoted violence, Instagram would remove it and possibly disable the user. Schumer stressed the importance of the context of the image in making those calls—a caption might make an image threatening, for instance—but also said that “context” is generally limited to content on the site.

And yet, even within that complex framework, what does it mean to follow a man strongly suggested to be a war criminal, to have a virtual shrine to a dictators’ glory that can fit in our pockets?

The results were similarly disturbing when students were tested to determine understanding of basic concepts. More instruction wasn't helping students advance from novice to expert. In fact, the data indicated the opposite: students had more novice-like beliefs after they completed a course than they had when they started.

But in addition, experts have a mental organizational structure that facilitates the retrieval and effective application of their knowledge.

experts have an ability to monitor their own thinking ("metacognition"), at least in their discipline of expertise. They are able to ask themselves, "Do I understand this? How can I check my understanding?"

But that is not what cognitive science tells us. It tells us instead that students need to develop these different ways of thinking by means of extended, focused mental effort.

new ways of thinking are always built on the prior thinking of the individual, so if the educational process is to be successful, it is essential to take that prior thinking into account.

. Everything that constitutes "understanding" science and "thinking scientifically" resides in the long-term memory, which is developed via the construction and assembly of component proteins.

What is elementary, worldly wisdom? Well, the first rule is that you can't really know anything if you just remember isolated facts and try and bang 'em back. If the facts don't hang together on a latticework of theory, you don't have them in a usable form.

"So it makes perfect sense," Wieman writes, "that they are not learning to think like experts, even though they are passing science courses by memorizing facts and problem-solving recipes."

Anything one can do to reduce cognitive load improves learning.

A second way teachers can improve instruction is by recognizing the importance of student beliefs about science

My third example of how teaching and learning can be improved is by implementing the principle that effective teaching consists of engaging students, monitoring their thinking, and providing feedback.

I assign students to groups the first day of class (typically three to four students in adjacent seats) and design each lecture around a series of seven to 10 clicker questions that cover the key learning goals for that day.

The process of critiquing each other's ideas in order to arrive at a consensus also enormously improves both their ability to carry on scientific discourse and to test their own understanding. [Change]

Dr. James attributes the differences to the messiness inherent in free-form handwriting: Not only must we first plan and execute the action in a way that is not required when we have a traceable outline, but we are also likely to produce a result that is highly variable.

That variability may itself be a learning tool. “When a kid produces a messy letter,” Dr. James said, “that might help him learn it.”

printing, cursive writing, and typing on a keyboard are all associated with distinct and separate brain patterns — and each results in a distinct end product.

When the children composed text by hand, they not only consistently produced more words more quickly than they did on a keyboard, but expressed more ideas. And brain imaging in the oldest subjects suggested that the connection between writing and idea generation went even further. When these children were asked to come up with ideas for a composition, the ones with better handwriting exhibited greater neural activation in areas associated with working memory — and increased overall activation in the reading and writing networks.

In alexia, or impaired reading ability, some individuals who are unable to process print can still read cursive, and vice versa — suggesting that the two writing modes activate separate brain networks and engage more cognitive resources than would be the case with a single approach.

in both laboratory settings and real-world classrooms, students learn better when they take notes by hand than when they type on a keyboard. Contrary to earlier studies attributing the difference to the distracting effects of computers, the new research suggests that writing by hand allows the student to process a lecture’s contents and reframe it — a process of reflection and manipulation that can lead to better understanding and memory encoding.

“With handwriting, the very act of putting it down forces you to focus on what’s important,” he said. He added, after pausing to consider, “Maybe it helps you think better.”

Nothing can compete with the shimmering immediacy of now, and not just when seismic events take place, but in our everyday lives. We are sponges and we live in a world where the fire hose is always on.

a study that found that comprehension, concentration and retention all went off a cliff when information was taken in online.

In the process, fit has become a catchall used to justify hiring people who are similar to decision makers and rejecting people who are not.

The concept of fit first gained traction in the 1980s. The original idea was that if companies hired individuals whose personalities and values — and not just their skills — meshed with an organization’s strategy, workers would feel more attached to their jobs, work harder and stay longer.

in many organizations, fit has gone rogue. I saw this firsthand while researching the hiring practices of the country’s top investment banks, management consultancies and law firms. I interviewed 120 decision makers and spent nine months observing

While résumés (and connections) influenced which applicants made it into the interview room, interviewers’ perceptions of fit strongly shaped who walked out with job offers.

Crucially, though, for these gatekeepers, fit was not about a match with organizational values. It was about personal fit. In these time- and team-intensive jobs, professionals at all levels of seniority reported wanting to hire people with whom they enjoyed hanging out and could foresee developing close relationships with

To judge fit, interviewers commonly relied on chemistry. “

Many used the “airport test.” As a managing director at an investment bank put it, “Would I want to be stuck in an airport in Minneapolis in a snowstorm with them?”

interviewers were primarily interested in new hires whose hobbies, hometowns and biographies matched their own. Bonding over rowing college crew, getting certified in scuba, sipping single-malt Scotches in the Highlands or dining at Michelin-starred restaurants was evidence of fit; sharing a love of teamwork or a passion for pleasing clients was not

it has become a common feature of American corporate culture. Employers routinely ask job applicants about their hobbies and what they like to do for fun, while a complementary self-help industry informs white-collar job seekers that chemistry, not qualifications, will win them an offer.

Selection based on personal fit can keep demographic and cultural diversity low

In the elite firms I studied, the types of shared experiences associated with fit typically required large investments of time and money.

Class-biased definitions of fit are one reason investment banks, management consulting firms and law firms are dominated by people from the highest socioeconomic backgrounds

Also, whether the industry is finance, high-tech or fashion, a good fit in most American corporations still tends to be stereotypically masculine.

Perhaps most important, it is easy to mistake rapport for skill. Just as they erroneously believe that they can accurately tell when someone is lying, people tend to be overly confident in their ability to spot talent. Unstructured interviews, which are the most popular hiring tools for American managers and the primary way they judge fit, are notoriously poor predictors of job performance.

Organizations that use cultural fit for competitive advantage tend to favor concrete tools like surveys and structured interviews that systematically test behaviors associated with increased performance and employee retention.

For managers who want to use cultural fit in a more productive way, I have several suggestions.

First, communicate a clear and consistent idea of what the organization’s culture is (and is not) to potential employees. Second, make sure the definition of cultural fit is closely aligned with business goals. Ideally, fit should be based on data-driven analysis of what types of values, traits and behaviors actually predict on-the-job success. Third, create formal procedures like checklists for measuring fit, so that assessment is not left up to the eyes (and extracurriculars) of the beholder.

But cultural fit has become a new form of discrimination that keeps demographic and cultural diversity down

The depressing conclusion of a faculty report: “failing math at all levels affects retention more than any other academic factor.” A national sample of transcripts found mathematics had twice as many F’s and D’s compared as other subjects.

Of all who embark on higher education, only 58 percent end up with bachelor’s degrees. The main impediment to graduation: freshman math.

California’s two university systems, for instance, consider applications only from students who have taken three years of mathematics and in that way exclude many applicants who might excel in fields like art or history. Community college students face an equally prohibitive mathematics wall. A study of two-year schools found that fewer than a quarter of their entrants passed the algebra classes they were required to take.

a definitive analysis by the Georgetown Center on Education and the Workforce forecasts that in the decade ahead a mere 5 percent of entry-level workers will need to be proficient in algebra or above.

“mathematical reasoning in workplaces differs markedly from the algorithms taught in school.” Even in jobs that rely on so-called STEM credentials — science, technology, engineering, math — considerable training occurs after hiring, including the kinds of computations that will be required.

I fully concur that high-tech knowledge is needed to sustain an advanced industrial economy. But we’re deluding ourselves if we believe the solution is largely academic.

Nor will just passing grades suffice. Many colleges seek to raise their status by setting a high mathematics bar. Hence, they look for 700 on the math section of the SAT, a height attained in 2009 by only 9 percent of men and 4 percent of women. And it’s not just Ivy League colleges that do this: at schools like Vanderbilt, Rice and Washington University in St. Louis, applicants had best be legacies or athletes if they have scored less than 700 on their math SATs.

A January 2012 analysis from the Georgetown center found 7.5 percent unemployment for engineering graduates and 8.2 percent among computer scientists.

“Our civilization would collapse without mathematics.” He’s absolutely right.

Quantitative literacy clearly is useful in weighing all manner of public policies

Mathematics is used as a hoop, a badge, a totem to impress outsiders and elevate a profession’s status.

Instead of investing so much of our academic energy in a subject that blocks further attainment for much of our population, I propose that we start thinking about alternatives. Thus mathematics teachers at every level could create exciting courses in what I call “citizen statistics.” This would not be a backdoor version of algebra, as in the Advanced Placement syllabus. Nor would it focus on equations used by scholars when they write for one another. Instead, it would familiarize students with the kinds of numbers that describe and delineate our personal and public lives.

This need not involve dumbing down. Researching the reliability of numbers can be as demanding as geometry.

I hope that mathematics departments can also create courses in the history and philosophy of their discipline, as well as its applications in early cultures. Why not mathematics in art and music — even poetry — along with its role in assorted sciences? The aim would be to treat mathematics as a liberal art, making it as accessible and welcoming as sculpture or ballet

Yes, young people should learn to read and write and do long division, whether they want to or not. But there is no reason to force them to grasp vectorial angles and discontinuous functions. Think of math as a huge boulder we make everyone pull, without assessing what all this pain achieves. So why require it, without alternatives or exceptions? Thus far I haven’t found a compelling answer.

In the structured course, all demographic groups reported completing the readings more frequently and spending more time studying; all groups also achieved higher final grades than did students in the lecture course.

Other active-learning courses administer frequent quizzes that oblige students to retrieve knowledge from memory rather than passively read it over in a textbook. Such quizzes have been shown to improve retention of factual material among all kinds of students.

The act of putting one’s own thoughts into words and communicating them to others, research has shown, is a powerful contributor to learning. Active-learning courses regularly provide opportunities for students to talk and debate with one another in a collaborative, low-pressure environment.

researchers from the University of Massachusetts Amherst and Yale University compare a course in physical chemistry taught in traditional lecture style to the same course taught in a “flipped” format, in which lectures were moved online and more time was devoted to in-class problem-solving activities. Exam performance over all was nearly 12 percent higher in the flipped class

even genuine scientists are human and as such susceptible to the allure of offering up new paradigms (as the historian of science Thomas Kuhn put it)

paleontologist Stephen Jay Gould insisted that his conception of “punctuated equilibria” (a kind of Marxist biology that blurred the lines between evolution and revolution), which he developed along with fellow paleontologist Niles Eldredge, upended the traditional Darwinian understanding of how natural selection works.

This notion doesn’t constitute a fundamental departure from plain old evolution by natural selection; it simply italicizes that sometimes the process is comparatively rapid, other times slower.

In addition, they have long had a peculiar perspective on evolution, because of the limitations of the fossil record

Darwin was a pioneering geologist as well as the greatest of all biologists, and his insights were backgrounded by the key concept of uniformitarianism, as advocated by Charles Lyell, his friend and mentor

previously regnant paradigm among geologists had been “catastrophism

fossil record was therefore seen as reflecting the creation and extinction of new species by an array of dramatic and “unnatural” dei ex machina.

Of late, however, uniformitarianism has been on a losing streak. Catastrophism is back, with a bang . . . or a flood, or a burst of extraterrestrial radiation, or an onslaught of unpleasant, previously submerged chemicals

This emphasis on catastrophes is the first of a triad of novelties on which “A New History of Life” is based. The second involves an enhanced role for some common but insufficiently appreciated inorganic molecules, notably carbon dioxide, oxygen and hydrogen sulfide.

Life didn’t so much unfold smoothly over hundreds of millions of years as lurch chaotically in response to diverse crises and opportunities: too much oxygen, too little carbon dioxide, too little oxygen, too much carbon dioxide, too hot, too cold

So far, so good, except that in their eagerness to emphasize what is new and different, the authors teeter on the verge of the same trap as Gould: exaggerating the novelty of their own ideas.

Things begin to unravel when it comes to the third leg of Messrs. Ward and Kirschvink’s purported paradigmatic novelty: a supposed role for ecosystems—rain forests, deserts, rivers, coral reefs, deep-sea vents—as units of evolutionary change

“While the history of life may be populated by species,” they write, “it has been the evolution of ecosystems that has been the most influential factor in arriving at the modern-day assemblage of life. . . . [W]e know that on occasion in the deep past entirely new ecosystems appear, populated by new kinds of life.” True enough, but it is those “new kinds of life,” not whole ecosystems, upon which natural selection acts.

One of the most common popular misconceptions about evolution is that it proceeds “for the good of the species.”

The problem is that smaller, nimbler units are far more likely to reproduce differentially than are larger, clumsier, more heterogeneous ones. Insofar as ecosystems are consequential for evolution—and doubtless they are—it is because, like occasional catastrophes, they provide the immediate environment within which something not-so-new is acted out.

This is natural selection doing its same-old, same-old thing: acting by a statistically potent process of variation combined with selective retention and differential reproduction, a process that necessarily operates within the particular ecosystem that a given lineage occupies.

Another dark psychological secret magicians routinely take advantage of is known as change blindness — the failure to detect changes in consecutive scenes.

Scientists have found a way to induce change blindness, with a machine called a transcranial magnetic stimulator, which uses a magnetic field to disrupt localized brain regions

Such blind spots confirm what many philosophers have long suspected: reality and our perception of it are incommensurate to a far greater degree than is often believed. For all its apparent fidelity, the movie in our heads is a “Rashomon” narrative pieced together from inconsistent and unreliable bits of information. It is, to a certain extent, an illusion.

By the end of World War II, however, American corporations were facing severe talent shortages. Their senior executives were growing old, and a dearth of hiring from the Depression through the war had resulted in a shortfall of able, well-trained managers. Finding people who had the potential to rise quickly through the ranks became an overriding preoccupation of American businesses. They began to devise a formal hiring-and-management system based in part on new studies of human behavior, and in part on military techniques developed during both world wars, when huge mobilization efforts and mass casualties created the need to get the right people into the right roles as efficiently as possible. By the 1950s, it was not unusual for companies to spend days with young applicants for professional jobs, conducting a battery of tests, all with an eye toward corner-office potential.

But companies abandoned their hard-edged practices for another important reason: many of their methods of evaluation turned out not to be very scientific.

this regime, so widespread in corporate America at mid-century, had almost disappeared by 1990. “I think an HR person from the late 1970s would be stunned to see how casually companies hire now,”

Many factors explain the change, he said, and then he ticked off a number of them: Increased job-switching has made it less important and less economical for companies to test so thoroughly. A heightened focus on short-term financial results has led to deep cuts in corporate functions that bear fruit only in the long term. The Civil Rights Act of 1964, which exposed companies to legal liability for discriminatory hiring practices, has made HR departments wary of any broadly applied and clearly scored test that might later be shown to be systematically biased.

about a quarter of the country’s corporations were using similar tests to evaluate managers and junior executives, usually to assess whether they were ready for bigger roles.

He has encouraged the company’s HR executives to think about applying the games to the recruitment and evaluation of all professional workers.

Knack makes app-based video games, among them Dungeon Scrawl, a quest game requiring the player to navigate a maze and solve puzzles, and Wasabi Waiter, which involves delivering the right sushi to the right customer at an increasingly crowded happy hour. These games aren’t just for play: they’ve been designed by a team of neuroscientists, psychologists, and data scientists to suss out human potential. Play one of them for just 20 minutes, says Guy Halfteck, Knack’s founder, and you’ll generate several megabytes of data, exponentially more than what’s collected by the SAT or a personality test. How long you hesitate before taking every action, the sequence of actions you take, how you solve problems—all of these factors and many more are logged as you play, and then are used to analyze your creativity, your persistence, your capacity to learn quickly from mistakes, your ability to prioritize, and even your social intelligence and personality. The end result, Halfteck says, is a high-resolution portrait of your psyche and intellect, and an assessment of your potential as a leader or an innovator.

When the results came back, Haringa recalled, his heart began to beat a little faster. Without ever seeing the ideas, without meeting or interviewing the people who’d proposed them, without knowing their title or background or academic pedigree, Knack’s algorithm had identified the people whose ideas had panned out. The top 10 percent of the idea generators as predicted by Knack were in fact those who’d gone furthest in the process.

What Knack is doing, Haringa told me, “is almost like a paradigm shift.” It offers a way for his GameChanger unit to avoid wasting time on the 80 people out of 100—nearly all of whom look smart, well-trained, and plausible on paper—whose ideas just aren’t likely to work out.

Aptitude, skills, personal history, psychological stability, discretion, loyalty—companies at the time felt they had a need (and the right) to look into them all. That ambit is expanding once again, and this is undeniably unsettling. Should the ideas of scientists be dismissed because of the way they play a game? Should job candidates be ranked by what their Web habits say about them? Should the “data signature” of natural leaders play a role in promotion? These are all live questions today, and they prompt heavy concerns: that we will cede one of the most subtle and human of skills, the evaluation of the gifts and promise of other people, to machines; that the models will get it wrong; that some people will never get a shot in the new workforce.

scoring distance from work could violate equal-employment-opportunity standards. Marital status? Motherhood? Church membership? “Stuff like that,” Meyerle said, “we just don’t touch”—at least not in the U.S., where the legal environment is strict. Meyerle told me that Evolv has looked into these sorts of factors in its work for clients abroad, and that some of them produce “startling results.”

consider the alternative. A mountain of scholarly literature has shown that the intuitive way we now judge professional potential is rife with snap judgments and hidden biases, rooted in our upbringing or in deep neurological connections that doubtless served us well on the savanna but would seem to have less bearing on the world of work.

We may like to think that society has become more enlightened since those days, and in many ways it has, but our biases are mostly unconscious, and they can run surprisingly deep. Consider race. For a 2004 study called “Are Emily and Greg More Employable Than Lakisha and Jamal?,” the economists Sendhil Mullainathan and Marianne Bertrand put white-sounding names (Emily Walsh, Greg Baker) or black-sounding names (Lakisha Washington, Jamal Jones) on similar fictitious résumés, which they then sent out to a variety of companies in Boston and Chicago. To get the same number of callbacks, they learned, they needed to either send out half again as many résumés with black names as those with white names, or add eight extra years of relevant work experience to the résumés with black names.

a sociologist at Northwestern, spent parts of the three years from 2006 to 2008 interviewing professionals from elite investment banks, consultancies, and law firms about how they recruited, interviewed, and evaluated candidates, and concluded that among the most important factors driving their hiring recommendations were—wait for it—shared leisure interests.

Lacking “reliable predictors of future performance,” Rivera writes, “assessors purposefully used their own experiences as models of merit.” Former college athletes “typically prized participation in varsity sports above all other types of involvement.” People who’d majored in engineering gave engineers a leg up, believing they were better prepared.

the prevailing system of hiring and management in this country involves a level of dysfunction that should be inconceivable in an economy as sophisticated as ours. Recent survey data collected by the Corporate Executive Board, for example, indicate that nearly a quarter of all new hires leave their company within a year of their start date, and that hiring managers wish they’d never extended an offer to one out of every five members on their team

In the late 1990s, as these assessments shifted from paper to digital formats and proliferated, data scientists started doing massive tests of what makes for a successful customer-support technician or salesperson. This has unquestionably improved the quality of the workers at many firms.

In 2010, however, Xerox switched to an online evaluation that incorporates personality testing, cognitive-skill assessment, and multiple-choice questions about how the applicant would handle specific scenarios that he or she might encounter on the job. An algorithm behind the evaluation analyzes the responses, along with factual information gleaned from the candidate’s application, and spits out a color-coded rating: red (poor candidate), yellow (middling), or green (hire away). Those candidates who score best, I learned, tend to exhibit a creative but not overly inquisitive personality, and participate in at least one but not more than four social networks, among many other factors. (Previous experience, one of the few criteria that Xerox had explicitly screened for in the past, turns out to have no bearing on either productivity or retention

When Xerox started using the score in its hiring decisions, the quality of its hires immediately improved. The rate of attrition fell by 20 percent in the initial pilot period, and over time, the number of promotions rose. Xerox still interviews all candidates in person before deciding to hire them, Morse told me, but, she added, “We’re getting to the point where some of our hiring managers don’t even want to interview anymore”

Gone are the days, Ostberg told me, when, say, a small survey of college students would be used to predict the statistical validity of an evaluation tool. “We’ve got a data set of 347,000 actual employees who have gone through these different types of assessments or tools,” he told me, “and now we have performance-outcome data, and we can split those and slice and dice by industry and location.”

Evolv’s tests allow companies to capture data about everybody who applies for work, and everybody who gets hired—a complete data set from which sample bias, long a major vexation for industrial-organization psychologists, simply disappears. The sheer number of observations that this approach makes possible allows Evolv to say with precision which attributes matter more to the success of retail-sales workers (decisiveness, spatial orientation, persuasiveness) or customer-service personnel at call centers (rapport-building)

There are some data that Evolv simply won’t use, out of a concern that the information might lead to systematic bias against whole classes of people

the idea that hiring was a science fell out of favor. But now it’s coming back, thanks to new technologies and methods of analysis that are cheaper, faster, and much-wider-ranging than what we had before

what most excites him are the possibilities that arise from monitoring the entire life cycle of a worker at any given company.

Now the two companies are working together to marry pre-hire assessments to an increasing array of post-hire data: about not only performance and duration of service but also who trained the employees; who has managed them; whether they were promoted to a supervisory role, and how quickly; how they performed in that role; and why they eventually left.

What begins with an online screening test for entry-level workers ends with the transformation of nearly every aspect of hiring, performance assessment, and management.

I turned to Sandy Pentland, the director of the Human Dynamics Laboratory at MIT. In recent years, Pentland has pioneered the use of specialized electronic “badges” that transmit data about employees’ interactions as they go about their days. The badges capture all sorts of information about formal and informal conversations: their length; the tone of voice and gestures of the people involved; how much those people talk, listen, and interrupt; the degree to which they demonstrate empathy and extroversion; and more. Each badge generates about 100 data points a minute.

he tried the badges out on about 2,500 people, in 21 different organizations, and learned a number of interesting lessons. About a third of team performance, he discovered, can usually be predicted merely by the number of face-to-face exchanges among team members. (Too many is as much of a problem as too few.) Using data gathered by the badges, he was able to predict which teams would win a business-plan contest, and which workers would (rightly) say they’d had a “productive” or “creative” day. Not only that, but he claimed that his researchers had discovered the “data signature” of natural leaders, whom he called “charismatic connectors” and all of whom, he reported, circulate actively, give their time democratically to others, engage in brief but energetic conversations, and listen at least as much as they talk.

His group is developing apps to allow team members to view their own metrics more or less in real time, so that they can see, relative to the benchmarks of highly successful employees, whether they’re getting out of their offices enough, or listening enough, or spending enough time with people outside their own team.

Torrents of data are routinely collected by American companies and now sit on corporate servers, or in the cloud, awaiting analysis. Bloomberg reportedly logs every keystroke of every employee, along with their comings and goings in the office. The Las Vegas casino Harrah’s tracks the smiles of the card dealers and waitstaff on the floor (its analytics team has quantified the impact of smiling on customer satisfaction). E‑mail, of course, presents an especially rich vein to be mined for insights about our productivity, our treatment of co-workers, our willingness to collaborate or lend a hand, our patterns of written language, and what those patterns reveal about our intelligence, social skills, and behavior.

people analytics will ultimately have a vastly larger impact on the economy than the algorithms that now trade on Wall Street or figure out which ads to show us. He reminded me that we’ve witnessed this kind of transformation before in the history of management science. Near the turn of the 20th century, both Frederick Taylor and Henry Ford famously paced the factory floor with stopwatches, to improve worker efficiency.

“The quantities of data that those earlier generations were working with,” he said, “were infinitesimal compared to what’s available now. There’s been a real sea change in the past five years, where the quantities have just grown so large—petabytes, exabytes, zetta—that you start to be able to do things you never could before.”

People analytics will unquestionably provide many workers with more options and more power. Gild, for example, helps companies find undervalued software programmers, working indirectly to raise those people’s pay. Other companies are doing similar work. One called Entelo, for instance, specializes in using algorithms to identify potentially unhappy programmers who might be receptive to a phone cal

He sees it not only as a boon to a business’s productivity and overall health but also as an important new tool that individual employees can use for self-improvement: a sort of radically expanded The 7 Habits of Highly Effective People, custom-written for each of us, or at least each type of job, in the workforce.

the most exotic development in people analytics today is the creation of algorithms to assess the potential of all workers, across all companies, all the time.

The way Gild arrives at these scores is not simple. The company’s algorithms begin by scouring the Web for any and all open-source code, and for the coders who wrote it. They evaluate the code for its simplicity, elegance, documentation, and several other factors, including the frequency with which it’s been adopted by other programmers. For code that was written for paid projects, they look at completion times and other measures of productivity. Then they look at questions and answers on social forums such as Stack Overflow, a popular destination for programmers seeking advice on challenging projects. They consider how popular a given coder’s advice is, and how widely that advice ranges.

The algorithms go further still. They assess the way coders use language on social networks from LinkedIn to Twitter; the company has determined that certain phrases and words used in association with one another can distinguish expert programmers from less skilled ones. Gild knows these phrases and words are associated with good coding because it can correlate them with its evaluation of open-source code, and with the language and online behavior of programmers in good positions at prestigious companies.

having made those correlations, Gild can then score programmers who haven’t written open-source code at all, by analyzing the host of clues embedded in their online histories. They’re not all obvious, or easy to explain. Vivienne Ming, Gild’s chief scientist, told me that one solid predictor of strong coding is an affinity for a particular Japanese manga site.

Gild’s CEO, Sheeroy Desai, told me he believes his company’s approach can be applied to any occupation characterized by large, active online communities, where people post and cite individual work, ask and answer professional questions, and get feedback on projects. Graphic design is one field that the company is now looking at, and many scientific, technical, and engineering roles might also fit the bill. Regardless of their occupation, most people leave “data exhaust” in their wake, a kind of digital aura that can reveal a lot about a potential hire.

professionally relevant personality traits can be judged effectively merely by scanning Facebook feeds and photos. LinkedIn, of course, captures an enormous amount of professional data and network information, across just about every profession. A controversial start-up called Klout has made its mission the measurement and public scoring of people’s online social influence.

Mullainathan expressed amazement at how little most creative and professional workers (himself included) know about what makes them effective or ineffective in the office. Most of us can’t even say with any certainty how long we’ve spent gathering information for a given project, or our pattern of information-gathering, never mind know which parts of the pattern should be reinforced, and which jettisoned. As Mullainathan put it, we don’t know our own “production function.”

Over time, better job-matching technologies are likely to begin serving people directly, helping them see more clearly which jobs might suit them and which companies could use their skills. In the future, Gild plans to let programmers see their own profiles and take skills challenges to try to improve their scores. It intends to show them its estimates of their market value, too, and to recommend coursework that might allow them to raise their scores even more. Not least, it plans to make accessible the scores of typical hires at specific companies, so that software engineers can better see the profile they’d need to land a particular job

Knack, for its part, is making some of its video games available to anyone with a smartphone, so people can get a better sense of their strengths, and of the fields in which their strengths would be most valued. (Palo Alto High School recently adopted the games to help students assess careers.) Ultimately, the company hopes to act as matchmaker between a large network of people who play its games (or have ever played its games) and a widening roster of corporate clients, each with its own specific profile for any given type of job.

When I began my reporting for this story, I was worried that people analytics, if it worked at all, would only widen the divergent arcs of our professional lives, further gilding the path of the meritocratic elite from cradle to grave, and shutting out some workers more definitively. But I now believe the opposite is likely to happen, and that we’re headed toward a labor market that’s fairer to people at every stage of their careers

For decades, as we’ve assessed people’s potential in the professional workforce, the most important piece of data—the one that launches careers or keeps them grounded—has been educational background: typically, whether and where people went to college, and how they did there. Over the past couple of generations, colleges and universities have become the gatekeepers to a prosperous life. A degree has become a signal of intelligence and conscientiousness, one that grows stronger the more selective the school and the higher a student’s GPA, that is easily understood by employers, and that, until the advent of people analytics, was probably unrivaled in its predictive powers.

the limitations of that signal—the way it degrades with age, its overall imprecision, its many inherent biases, its extraordinary cost—are obvious. “Academic environments are artificial environments,” Laszlo Bock, Google’s senior vice president of people operations, told The New York Times in June. “People who succeed there are sort of finely trained, they’re conditioned to succeed in that environment,” which is often quite different from the workplace.

because one’s college history is such a crucial signal in our labor market, perfectly able people who simply couldn’t sit still in a classroom at the age of 16, or who didn’t have their act together at 18, or who chose not to go to graduate school at 22, routinely get left behind for good. That such early factors so profoundly affect career arcs and hiring decisions made two or three decades later is, on its face, absurd.

I spoke with managers at a lot of companies who are using advanced analytics to reevaluate and reshape their hiring, and nearly all of them told me that their research is leading them toward pools of candidates who didn’t attend college—for tech jobs, for high-end sales positions, for some managerial roles. In some limited cases, this is because their analytics revealed no benefit whatsoever to hiring people with college degrees; in other cases, and more often, it’s because they revealed signals that function far better than college history,

Google, too, is hiring a growing number of nongraduates. Many of the people I talked with reported that when it comes to high-paying and fast-track jobs, they’re reducing their preference for Ivy Leaguers and graduates of other highly selective schools.

This process is just beginning. Online courses are proliferating, and so are online markets that involve crowd-sourcing. Both arenas offer new opportunities for workers to build skills and showcase competence. Neither produces the kind of instantly recognizable signals of potential that a degree from a selective college, or a first job at a prestigious firm, might. That’s a problem for traditional hiring managers, because sifting through lots of small signals is so difficult and time-consuming.

all of these new developments raise philosophical questions. As professional performance becomes easier to measure and see, will we become slaves to our own status and potential, ever-focused on the metrics that tell us how and whether we are measuring up? Will too much knowledge about our limitations hinder achievement and stifle our dreams? All I can offer in response to these questions, ironically, is my own gut sense, which leads me to feel cautiously optimistic.

Google’s understanding of the promise of analytics is probably better than anybody else’s, and the company has been changing its hiring and management practices as a result of its ongoing analyses. (Brainteasers are no longer used in interviews, because they do not correlate with job success; GPA is not considered for anyone more than two years out of school, for the same reason—the list goes on.) But for all of Google’s technological enthusiasm, these same practices are still deeply human. A real, live person looks at every résumé the company receives. Hiring decisions are made by committee and are based in no small part on opinions formed during structured interviews.

The fact that the American space agency still uses PowerPoint should not be surprising. Despite the backlash it inspired in the press, and the bile that it raised in billionaires, and the red alert it caused within the military, the corporate-presentation juggernaut rolls on. The program has more monthly users than ever before, according to Shawn Villaron, Microsoft’s vice president of product for PowerPoint—well into the hundreds of millions. If anything, its use cases have proliferated. During lockdown, people threw PowerPoint parties on Zoom. Kids now make PowerPoint presentations for their parents when they want to get a puppy or quit soccer or attend a Niall Horan meet and greet. If PowerPoint is evil, then evil rules the world.

it’s tempting to entertain counterfactuals and wonder how things might have played out if Tufte and the rest of us had worried about social media back in 2003 instead of presentation software. Perhaps a timely pamphlet on The Cognitive Style of Friendster or a Wired headline asserting that “LinkedIn Is Evil” would have changed the course of history. If the social-media backlash of the past few years had been present from the start, maybe Facebook would never have grown into the behemoth it is now, and the country would never have become so hopelessly divided.

it could be that nothing whatsoever would have changed. No matter what their timing, and regardless of their aptness, concerns about new media rarely seem to make a difference. Objections get steamrolled. The new technology takes over. And years later, when we look back and think, How strange that we were so perturbed, the effects of that technology may well be invisible.

To beat confirmation bias, it’s vital for brands to face reality when measuring the success of their campaigns and gauging customer brand perception.

As a marketer, it can be easy to fall victim to simply measuring customer behavioural data (like click-through rates, ad engagement, unsubscribes, etc.). However, the best marketing teams lean on an outside-in perspective to improve customer experiences and create a better brand reputation.

Additionally, marketing teams would be wise to connect with potential customers who did not convert. They will be most able to point out the pain points in a marketing campaign that are dissuading buyers. It’s just as important to ask consumers why they didn’t interact with a brand as it is to ask why they do.

Obtaining these insights allows marketers to gauge specifics into how their competitors are beating them out, and conversely, how they can improve their product and boost customer retention.

At the end of the day, customers want to know that their feedback is valued and has the power to drive change. Companies that choose to neglect customer feedback are prime examples of the dangers of confirmation bias.

It doesn’t matter how marketing teams or their company define success, it’s up to the customer.

For example, in industries like video tech and security, where the community is extremely tight knit, marketing teams must have a deep understanding of their audience’s business needs to have any chance of selling to them. Brands that understand consumer perception and needs, will be able to personalize messages to their target audience and create a more positive customer experience. By gauging and adapting to direct feedback, marketing teams can avoid the dangers of confirmation bias, and make wholesale changes that will turn customers into brand champions.