Data Scientist Quotes

The next Freud will be a data scientist. The next Marx will be a data scientist. The next Salk might very well be a data scientist.

I sometimes suspect that inside every data scientist is a kid trying to figure out why his childhood dreams didn't come true.

The great difficulty is to get modern audiences to realize that you are preaching Christianity solely and simply because you happen to think it true; they always suppose you are preaching it because you like it or think it good for society or something of that sort. Now a clearly maintained distinction between what the Faith actually says and what you would like it to have said or what you understand or what you personally find helpful or think probable, forces your audience to realize that you are tied to your data just as the scientist is tied by the results of the experiments; that you are not just saying what you like. This immediately helps them realize that what is being discussed is a question about objective fact — not gas about ideals and points of view.

The TV scientist who mutters sadly, "The experiment is a failure; we have failed to achieve what we had hoped for," is suffering mainly from a bad script writer. An experiment is never a failure solely because it fails to achieve predicted results. An experiment is a failure only when it also fails adequately to test the hypothesis in question, when the data it produces don't prove anything one way or another.

Scientists peered into data and concluded that we should all be worried. -Lunar planet

A scientist's data are influenced by the questions she asks, which are steered by her imagination, which is delimited by her senses. The boundaries of our own Umwelt corral our ability to understand the Umwelten of others.

Robots are important also. If I don my pure-scientist hat, I would say just send robots; I'll stay down here and get the data. But nobody's ever given a parade for a robot. Nobody's ever named a high school after a robot. So when I don my public-educator hat, I have to recognize the elements of exploration that excite people. It's not only the discoveries and the beautiful photos that come down from the heavens; it's the vicarious participation in discovery itself.

In the business people with expertise, experience and evidence will make more profitable decisions than people with instinct, intuition and imagination.

A good scientist must remain curious, open-minded, humble, and above all, obedient to the data, and to the facts.

Data scientist (noun): Person who is better at statistics than any software engineer and better at software engineering than any statistician. — Josh Wills

All businesses could use a garden where Data Scientists plant seeds of possibility and water them with collaboration.

Lawyers are filing documents. Judges are signing orders. Scientist and doctors are collating data. Pharaoh’s heart is as hard as ever.

At this point, godless materialists might be cheering. If humans evolved strictly by mutation and natural selection, who needs God to explain us? To this, I reply: I do. The comparison of chimp and human sequences, interesting as it is, does not tell us what it means to be human. In my views, DNA sequence alone, even if accompanied by a vast trove of data on biological function, will never explain certain special human attributes, such as the knowledge of the Moral Law and the universal search for God. Freeing God from the burden of special acts of creation does not remove Him as the source of the things that make humanity special, and of the universe itself. It merely shows us something of how He operates.

Scientists do not collect data randomly and utterly comprehensively. The data they collect are only those that they consider *relevant* to some hypothesis or theory.

the best data scientists tend to be “hard scientists,” particularly physicists, rather than computer science majors.

Genetically speaking, humans are terrible research subjects. We're genetically promiscuous--we mate with anyone we choose--and we don't take kindly to scientists telling us who we should reproduce with. Plus, unlike plans and mice, it takes decades to produce enough offspring to give scientists much meaningful data.

Arno Penzias, the Nobel Prize–winning scientist who codiscovered the cosmic microwave background radiation that provided strong support for the Big Bang in the first place, states, “The best data we have are exactly what I would have predicted, had I nothing to go on but the five Books of Moses, the Psalms, the Bible as a whole.

The newspapers kept stroking my fear. New surveys provided awful statistics on just about everything. Evidence suggested that we were not doing well. Researchers gloomily agreed. Environment psychologists were interviewed. Damage had ‘unwittingly’ been done. There were ‘feared lapses’. There were ‘misconceptions’ about potential. Situations had ‘deteriorated’. Cruelty was on the rise and there was nothing anyone could do about it. The populace was confounded, yet didn’t care. Unpublished studies hinted that we were all paying a price. Scientists peered into data and concluded that we should all be very worried. No one knew what normal behavior was anymore, and some argued that this was a form of virtue. And no one argued back. No one challenged anything. Anxiety was soaking up most people’s days. Everyone had become preoccupied with horror. Madness was fluttering everywhere. There was fifty years of research supporting this data. There were diagrams illustrating all of these problems – circles and hexagons and squares, different sections colored in lime or lilac or gray. Most troubling were the fleeting signs that nothing could transform any of this into something positive. You couldn’t help being both afraid and fascinated. Reading these articles made you feel that the survival of mankind didn’t seem very important in the long run. We were doomed. We deserved it. I was so tired.

I’ve said several times that the brain acts like a scientist. It forms hypotheses through prediction and tests them against the “data” of sensory input. It corrects its predictions by way of prediction error, like a scientist adjusts his or her hypotheses in the face of contrary evidence. When the brain’s predictions match the sensory input, this constitutes a model of the world in that instant, just like a scientist judges that a correct hypothesis is the path to scientific certainty.

A third reason scientists are reluctant to examine paranormal phenomena is that they appear to contradict known physical laws. What is the point of studying the impossible? Only a fool would waste his time. The problem of data in conflict with existing theory cannot be overstated. Arthur Eddington once said you should never believe any experiment until it has been confirmed by theory, but this humorous view has a reality that cannot be discounted.

LeCun made an unexpected prediction about the effects all of this AI and machine learning technology would have on the job market. Despite being a technologist himself, he said that the people with the best chances of coming out ahead in the economy of the future were not programmers and data scientists, but artists and artisans.

Even rational, data-driven scientists could be sent into prolonged states of hysteria when presented with evidence that their favorite foods might be killing them.

Data Scientists should recall innovation often times is not providing fancy algorithms, but rather value to the customer.

Data Scientists should refuse to be defined by someone else's vision of what's possible.

Aways remember: You’re going to die soon enough anyway; even if it’s a hundred years from now, that’s still the blink of a cosmic eye. In the meantime, live like a scientist—even a controversial one with only an ally or two in all the world—and treat life as a grand experiment, blood, sweat, tears and all. Bear in mind that there's no such thing as a failed experiment—only data.

Excel suffers from an image problem. Most people assume that spreadsheet programs such as Excel are intended for accountants, analysts, financiers, scientists, mathematicians, and other geeky types. Creating a spreadsheet, sorting data, using functions, and making charts seems daunting, and best left to the nerds.

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In the November 2006 issue of Science, a report by an international team of scientists studying a vast amount of data gathered between 1950 and 2003 declared that if current trends of fishing and pollution continue, every fishery in the world's oceans will collapse by 2048...The oceans as an ecosystem would completely collapse.

How do we distinguish between the legitimate skepticism of those who scoffed at cold fusion, and the stifling dogma of the seventeenthcentury clergymen who, doubting Galileo's claim that the earth was not the center of the solar system, put him under house arrest for the last eight years of his life? In part, the answer lies in the distinction between skepticism and closed-mindedness. Many scientists who were skeptical about cold fusion nevertheless tried to replicate the reported phenomenon in their own labs; Galileo's critics refused to look at the pertinent data.

In one of history’s great ironies, scientists today know vastly more than their colleagues a century ago, and possess vastly more data-crunching power, but they are much less confident in the prospects for perfect predictability.

Yet like many other human traits that made sense in past ages but cause trouble in the modern age, the knowledge illusion has its downside. The world is becoming ever more complex, and people fail to realise just how ignorant they are of what’s going on. Consequently some who know next to nothing about meteorology or biology nevertheless propose policies regarding climate change and genetically modified crops, while others hold extremely strong views about what should be done in Iraq or Ukraine without being able to locate these countries on a map. People rarely appreciate their ignorance, because they lock themselves inside an echo chamber of like-minded friends and self-confirming newsfeeds, where their beliefs are constantly reinforced and seldom challenged. Providing people with more and better information is unlikely to improve matters. Scientists hope to dispel wrong views by better science education, and pundits hope to sway public opinion on issues such as Obamacare or global warming by presenting the public with accurate facts and expert reports. Such hopes are grounded in a misunderstanding of how humans actually think. Most of our views are shaped by communal groupthink rather than individual rationality, and we hold on to these views out of group loyalty. Bombarding people with facts and exposing their individual ignorance is likely to backfire. Most people don’t like too many facts, and they certainly don’t like to feel stupid. Don’t be so sure that you can convince Tea Party supporters of the truth of global warming by presenting them with sheets of statistical data.

Google gets $59 billion, and you get free search and e-mail. A study published by the Wall Street Journal in advance of Facebook’s initial public offering estimated the value of each long-term Facebook user to be $80.95 to the company. Your friendships were worth sixty-two cents each and your profile page $1,800. A business Web page and its associated ad revenue were worth approximately $3.1 million to the social network. Viewed another way, Facebook’s billion-plus users, each dutifully typing in status updates, detailing his biography, and uploading photograph after photograph, have become the largest unpaid workforce in history. As a result of their free labor, Facebook has a market cap of $182 billion, and its founder, Mark Zuckerberg, has a personal net worth of $33 billion. What did you get out of the deal? As the computer scientist Jaron Lanier reminds us, a company such as Instagram—which Facebook bought in 2012—was not valued at $1 billion because its thirteen employees were so “extraordinary. Instead, its value comes from the millions of users who contribute to the network without being paid for it.” Its inventory is personal data—yours and mine—which it sells over and over again to parties unknown around the world. In short, you’re a cheap date.

Also remember, you are a scientist—it is not your job to be right. It is your job to be thoughtful, careful, and analytical; it is your job to challenge your ideas and to try to falsify your hypotheses; it is your job to be open and honest about the uncertainties in your data and conclusions. But if you are doing cutting-edge work, you are not always going to be right.

The math-powered applications powering the data economy were based on choices made by fallible human beings. Some of these choices were no doubt made with the best intentions. Nevertheless, many of these models encoded human prejudice, misunderstanding, and bias into the software systems that increasingly managed our lives. Like gods, these mathematical models were opaque, their workings invisible to all but the highest priests in their domain: mathematicians and computer scientists. Their verdicts, even when wrong or harmful, were beyond dispute or appeal. And they tended to punish the poor and the oppressed in our society, while making the rich richer.

write whatever comes into your head, as fast as you can type, without reference to outlines, notes, data, books or any other aids. The object is to find out what you would like to say, what all your earlier work on the topic or project has already led you to believe.

it sometimes happens that evidence accumulates across many studies to the point where scientists must change their minds. I’ve seen this happen in my colleagues (and myself) many times,34 and it’s part of the accountability system of science—you’d look foolish clinging to discredited theories. But for nonscientists, there is no such thing as a study you must believe. It’s always possible to question the methods, find an alternative interpretation of the data, or, if all else fails, question the honesty or ideology of the researchers.

This is one of the things I love about science. When the evidence for a slightly older universe was discovered, there were no scientists who sought to suppress it. As soon as the new data were verified, this revision in our understanding was embraced by the whole scientific community. That permanently revolutionary attitude, that openness to change, at the heart of science is what makes it so effective.

This rather disheartening finding tallies with decades of ‘draw a scientist’ data, where participants overwhelmingly draw men (the bias has historically been so extreme that media around the world celebrated as great progress a recent paper which found that 28% of children now draw women).

It is very easy to grow tired at collecting; the period of a low tide is about all men can endure. At first the rocks are bright and every moving animal makes his mark on the attention. The picture is wide and colored and beautiful. But after an hour and a half the attention centers weary, the color fades, and the field is likely to narrow to an individual animal. Here one may observe his own world narrowed down until interest and, with it, observation, flicker and go out. And what if with age this weariness becomes permanent and observation dim out and not recover? Can this be what happens to so many men of science? Enthusiasm, interest, sharpness, dulled with a weariness until finally they retire into easy didacticism? With this weariness, this stultification of attention centers, perhaps there comes the pained and sad memory of what the old excitement was like, and regret might turn to envy of the men who still have it. Then out of the shell of didacticism, such a used-up man might attack the unwearied, and he would have in his hands proper weapons of attack. It does seem certain that to a wearied man an error in a mass of correct data wipes out all the correctness and is a focus for attack; whereas the unwearied man, in his energy and receptivity, might consider the little dross of error a by-product of his effort. These two may balance and produce a purer thing than either in the end. These two may be the stresses which hold up the structure, but it is a sad thing to see the interest in interested men thin out and weaken and die. We have known so many professors who once carried their listeners high on their single enthusiasm, and have seen these same men finally settle back comfortably into lectures prepared years before and never vary them again. Perhaps this is the same narrowing we observe in relation to ourselves and the tide pool—a man looking at reality brings his own limitations to the world. If he has strength and energy of mind the tide pool stretches both ways, digs back to electrons and leaps space into the universe and fights out of the moment into non-conceptual time. Then ecology has a synonym which is ALL.

If you could be any character on The Next Generation, who would you be?" "Easy," Solomon said. "Data. For sure." "That makes sense," Clark said. "You?" "I always liked Wesley Crusher." "What?" Solomon was appalled. "Nobody likes Wesley Crusher." "Why not?" Lisa asked. "Because he's a total Mary Sue," Solomon said. "He's too perfect." "But he's always saving the day," Clark argued. "Like, always." "Exactly. He's just a talking deus ex machina. Everybody on the ship treats him like a dumb kid, then he saves them at the last minute and, every single time, they go right back to treating him like a dumb kid again. Do I need to remind you that the starship Enterprise is full of genius scientists and engineers? Why's this kid who can't get into Starfleet Academy smarter than all of them?" "Good point," Clark said. "He's still my choice, though.

Some research suggests that collecting vast amounts of data simply can’t predict rare events like terrorism. A 2006 paper by Jeff Jonas, an IBM research scientist, and Jim Harper, the director of information policy at the Cato Institute, concluded that terrorism events aren’t common enough to lend themselves to large-scale computer data mining.

Random search for data on ... off-chance is hardly scientific. A questionnaire on 'Intellectual Immoralities' was circulated by a well-known institution. 'Intellectual Immorality No. 4' read: 'Generalizing beyond one's data'. [Wilder Dwight] Bancroft asked whether it would not be more correct to word question no. 4 'Not generalizing beyond one's data.

It could be that the climate alarmists have answers to rebut these critics and their data, but if so, they are not showing it. Rather than engaging in scientific debate, they have evaded it, choosing instead to try to stop dissenting publications and cut off the funding of their opponents. That is not the way to do science. That is the way to do fraud.

Having the data is not the same as having the expertise to look through the data - if it were, everybody with a smartphone would be a doctor or a scientist.

So, just collecting lots of data to SUPPORT a Theory is of limited use: a good Scientist looks for evidence to DISPROVE a Theory.

Contrary to the rules of philosophers of science, who advise testing hypotheses by trying to refute them, people (and scientists, quite often) seek data that are likely to be compatible with the beliefs they currently hold. The confirmatory bias of System 1 favors uncritical acceptance of suggestions and exaggeration of the likelihood of extreme and improbable events. If

Without hypothesis tests, you risk drawing the wrong conclusions and making bad decisions. That can be costly, either in business dollars or for your reputation as an analyst or scientist.

Much of this data-centric history still haunts us today. We live in an era that presumes Big Data to be the solution to all our problems. Courses in “data science” are proliferating in our universities, and jobs for “data scientists” are lucrative in the companies that participate in the “data economy.” But I hope with this book to convince you that data are profoundly dumb.

The combination of Bayes and Markov Chain Monte Carlo has been called "arguably the most powerful mechanism ever created for processing data and knowledge." Almost instantaneously MCMC and Gibbs sampling changed statisticians' entire method of attacking problems. In the words of Thomas Kuhn, it was a paradigm shift. MCMC solved real problems, used computer algorithms instead of theorems, and led statisticians and scientists into a worked where "exact" meant "simulated" and repetitive computer operations replaced mathematical equations. It was a quantum leap in statistics.

Contrary to the rules of philosophers of science, who advise testing hypotheses by trying to refute them, people (and scientists, quite often) seek data that are likely to be compatible with the beliefs they currently hold.

The “real” agenda of environmentalists—and the scientists who provided the data on which they relied—was to destroy capitalism and replace it with some sort of worldwide utopian Socialism—or perhaps Communism. That echoed a common right-wing refrain in the early 1990s: that environmental regulation was the slippery slope to Socialism. In 1992, columnist George Will encapsulated this view, saying that environmentalism was a “green tree with red roots.”99

Each of us, actually every animal, is a data scientist. We collect data from our sensors, and then we process the data to get abstract rules to perceive our environment and control our actions in that environment to minimize pain and/or maximize pleasure. We have memory to store those rules in our brains, and then we recall and use them when needed. Learning is lifelong; we forget rules when they no longer apply or revise them when the environment changes. Learning

Realizing the newfound promise of electrification a century ago required four key inputs: fossil fuels to generate it, entrepreneurs to build new businesses around it, electrical engineers to manipulate it, and a supportive government to develop the underlying public infrastructure. Harnessing the power of AI today—the “electricity” of the twenty-first century—requires four analogous inputs: abundant data, hungry entrepreneurs, AI scientists, and an AI-friendly policy environment.

Over a century ago, scientists first began to argue that the patent system and scientific data should be opened up. Back then, it was popular for conservatives to claim that putting geneng into the hands of the public would result in mega-viruses or total species collapse. Open data would be the gateway to a runaway synthetic biology apocalypse. But now we know there has been no one great disaster—only the slow-motion disaster of capitalism converting every living thing and idea into property.

Part Three, that part of formal scientific method called experimentation, is sometimes thought of by romantics as all of science itself because that’s the only part with much visual surface. They see lots of test tubes and bizarre equipment and people running around making discoveries. They do not see the experiment as part of a larger intellectual process and so they often confuse experiments with demonstrations, which look the same. A man conducting a gee-whiz science show with fifty thousand dollars’ worth of Frankenstein equipment is not doing anything scientific if he knows beforehand what the results of his efforts are going to be. A motorcycle mechanic, on the other hand, who honks the horn to see if the battery works is informally conducting a true scientific experiment. He is testing a hypothesis by putting the question to nature. The TV scientist who mutters sadly, “The experiment is a failure; we have failed to achieve what we had hoped for,” is suffering mainly from a bad scriptwriter. An experiment is never a failure solely because it fails to achieve predicted results. An experiment is a failure only when it also fails adequately to test the hypothesis in question, when the data it produces don’t prove anything one way or another.

If you’re a scientist by trade, rethinking is fundamental to your profession. You’re paid to be constantly aware of the limits of your understanding. You’re expected to doubt what you know, be curious about what you don’t know, and update your views based on new data.

Faced with this disparity, Netflix stopped asking people to tell them what they wanted to see in the future and started building a model based on millions of clicks and views from similar customers. The company began greeting its users with suggested lists of films based not on what they claimed to like but on what the data said they were likely to view. The result: customers visited Netflix more frequently and watched more movies. “The algorithms know you better than you know yourself,” says Xavier Amatriain, a former data scientist at Netflix.

Despite all their surface diversity, most jokes and funny incidents have the following logical structure: Typically you lead the listener along a garden path of expectation, slowly building up tension. At the very end, you introduce an unexpected twist that entails a complete reinterpretation of all the preceding data, and moreover, it's critical that the new interpretation, though wholly unexpected, makes as much "sense" of the entire set of facts as did the originally "expected" interpretation. In this regard, jokes have much in common with scientific creativity, with what Thomas Kuhn calls a "paradigm shift" in response to a single "anomaly." (It's probably not coincidence that many of the most creative scientists have a great sense of humor.) Of course, the anomaly in the joke is the traditional punch line and the joke is "funny" only if the listener gets the punch line by seeing in a flash of insight how a completely new interpretation of the same set of facts can incorporate the anomalous ending. The longer and more tortuous the garden path of expectation, the "funnier" the punch line when finally delivered.

[Science] presupposes as data principles that are themselves thoroughly lacking in actual rationality. In so far as the intuitive environing world, purely subjective as it is, is forgotten in the scientific thematic, the working subject is also forgotten, and the scientist is not studied.

The word 'proof' should strictly only be used when we are dealing with deductive inferences.... Popper claimed that scientists only need to use deductive inferences.... So if a scientist is only interested in demonstrating that a given theory is false, she may be able to accomplish her goal without the use of inductive inferences.... When a scientist collects experimental data, her aim might be to show that a particular theory...is false. She will have to resort to inductive reasoning.... So Popper's attempt to show that science can get by without induction does not succeed.

Once I had been diagnosed with a terminal illness, I began to view the world through two perspectives; I was starting to see death as both doctor and patient. As a doctor, I knew not to declare “Cancer is a battle I’m going to win!” or ask “Why me?” (Answer: Why not me?) I knew a lot about medical care, complications, and treatment algorithms. I quickly learned from my oncologist and my own study that stage IV lung cancer today was a disease whose story might be changing, like AIDS in the late 1980s: still a rapidly fatal illness but with emerging therapies that were, for the first time, providing years of life. While being trained as a physician and scientist had helped me process the data and accept the limits of what that data could reveal about my prognosis, it didn’t help me as a patient. It didn’t tell Lucy and me whether we should go ahead and have a child, or what it meant to nurture a new life while mine faded. Nor did it tell me whether to fight for my career, to reclaim the ambitions I had single-mindedly pursued for so long, but without the surety of the time to complete them. Like my own patients, I had to face my mortality and try to understand what made my life worth living—and I needed Emma’s help to do so. Torn between being a doctor and being a patient, delving into medical science and turning back to literature for answers, I struggled, while facing my own death, to rebuild my old life—or perhaps find a new one. —

Philosophers of science have repeatedly demonstrated that more than one theoretical construction can always be placed upon a given collection of data. History of science indicates that, particularly in the early developmental stages of a new paradigm, it is not even very difficult to invent such alternates. But that invention of alternates is just what scientists seldom undertake except during the pre-paradigm stage of their science's development and at very special occasions during its subsequent evolution. So long as the tools a paradigm supplies continue to prove capable of solving the problems it defines, science moves fastest and penetrates most deeply through confident employment of those tools. The reason is clear. As in manufacture so in science-retooling is an extravagance to be reserved for the occasion that demands it. The significance of crises is the indication they provide that an occasion for retooling has arrived.

Humans in general are great at coming up with reasons to maintain their desired beliefs in the face of contradictory data. More intelligent and educated people aren’t necessarily better at critical thinking, but they are likely to be more clever and creative in coming up with such excuses—and scientists are no exception.

Like all cults, this new one adeptly represses heresy. . . . In defiance of the Western scientific tradition, which maintains that skepticism of inferences drawn from observation is always in order, and that all accepted conclusions are always subject to review and potential overthrow by new data, the climate catastrophists insist that ‘the science is settled’ (science is never ‘settled’) and that therefore no debate, and no new data, can or should be entertained. Those wishing to advance theories or data contrary to the accepted orthodoxy are not merely wrong, but criminal ‘deniers’ who should be silenced, vilified, and if possible, prosecuted.

One theory I am working on: Big Data just confirms everything the late Leonard Cohen ever said. For example, Leonard Cohen once gave his nephew the following advice for wooing women: “Listen well. Then listen some more. And when you think you are done listening, listen some more.” That seems to be roughly similar to what these scientists found.

I advise you to look for a chance to break away, to find a subject you can make your own. That is where the quickest advances are likely to occur, as measured by discoveries per investigator per year. Therein you have the best chance to become a leader and, as time passes, to gain growing freedom to set your own course. If a subject is already receiving a great deal of attention, if it has a glamorous aura, if its practitioners are prizewinners who receive large grants, stay away from that subject. Listen to the news coming from the hubbub, learn how and why the subject became prominent, but in making your own long-term plans be aware it is already crowded with talented people. You would be a newcomer, a private amid bemedaled first sergeants and generals. Take a subject instead that interests you and looks promising, and where established experts are not yet conspicuously competing with one another, where few if any prizes and academy memberships have been given, and where the annals of research are not yet layered with superfluous data and mathematical models.

This is what it means to create: not to make something out of nothing, but to make order out of chaos. A creative scientist or historian does not make up facts but orders facts; he sees connections between them rather than seeing them as random data. A creative writer does not make up new words but arranges familiar words in patterns which say something fresh to us.

The modern world is drowning in information. We have more data than we can possibly use regarding nearly every picayune matter of society, economics, and politics. Science has contributed to this tsunami of facts and figures, but Riley's reports demonstrated that the tidal wave of minutiae is hardly unique to our time. In every age the challenge has been to move from information to knowledge. And the value of experts lies in their capacity to extract meaning from the reams of facts. Rather than being swamped by raw data, the connoisseur, craftsman, engineer, clinician, or scientist is selectively and self-consciously blind. Knowing what to ignore, recognizing what is extraneous, is the key to deriving pattern, form, and insight.

Even the editors of main journals themselves recognise that peer review may not be the best system ever devised by mankind. Here is what Richard Horton, the editor of The Lancet, has to say on the matter: “The mistake, of course, is to have thought that peer review was any more than a crude means of discovering the acceptability — not the validity — of a new finding. Editors and scientists alike insist on the pivotal importance of peer review. We portray peer review to the public as a quasi-sacred process that helps to make science our most objective truth teller. But we know that the system of peer review is biased, unjust, unaccountable, incomplete, easily fixed, often insulting, usually ignorant, occasionally foolish, and frequently wrong.

The Swedish town of Överkalix has the most comprehensive and oldest birth, death, and crop records in the world. Their records go back generations—a remarkably rich data set. And in analyzing this data set, scientists found some fascinating correlations. There were good and bad years for the crops in Överkalix and some particularly bad years where families were forced to go hungry. But scientists discovered that when children suffered starvation between the ages of nine and twelve, their grandchildren would on average live thirty years longer. Their descendants had far lower rates of diabetes and heart disease. On the other hand, when children were well-fed during those ages, their descendants were at four times the risk for heart attacks and their life expectancy dropped. In some strange way, the trauma of starvation changed descendants’ genes to be more resilient. Healthier. More likely to survive.[5] — Clearly, it wasn’t just my ruthless nurture that had shaped me into who I was, though who knows what kind of rampant methylation savaged my epigenome during my beatings and assaults. Beyond that, every cell in my body is filled with the code of generations of trauma, of death, of birth, of migration, of history that I cannot understand. Just piecemeal moments I collected from Auntie over the years. My family tried to erase this history. But my body remembers. My work ethic. My fear of cockroaches. My hatred for the taste of dirt. These are not random attributes, a spin of the wheel. They were gifted to me with purpose, with necessity. I want to have words for what my bones know. I want to use those gifts when they serve me and understand and forgive them when they do not.

That rarely happens in the other mental modes. In preacher mode, changing our minds is a mark of moral weakness; in scientist mode, it’s a sign of intellectual integrity. In prosecutor mode, allowing ourselves to be persuaded is admitting defeat; in scientist mode, it’s a step toward the truth. In politician mode, we flip-flop in response to carrots and sticks; in scientist mode, we shift in the face of sharper logic and stronger data.

Relativistic twins? When one looks at the paths that Newton and Einstein followed while pursuing their theories of gravity, one is struck by the many similarities: the unexplained data on orbits, the sudden insight about falling objects, the need for a new mathematics, the calculational difficulties, the retroactive agreements, the controversy, the problem-plagued expeditions, and the final triumph and acclaim.. Both men had worked in the same eccentric and lonely way, divorced from other scientists, armed with a great feeling of self-reliance while struggling with new concepts and difficult mathematics, and both produced earth-shaking results. One can't help but wonder if these two greatest of scientists, born 237 years apart, were "relativistically related", conceived as twins in some ethereal plane in a far-off galaxy and sent to earth to solve a matter of some gravity.

The Measure of America, a report of the Social Science Research Council, ranks every state in the United States on its “human development.” Each rank is based on life expectancy, school enrollment, educational degree attainment, and median personal earnings. Out of the 50 states, Louisiana ranked 49th and in overall health ranked last. According to the 2015 National Report Card, Louisiana ranked 48th out of 50 in eighth-grade reading and 49th out of 50 in eighth-grade math. Only eight out of ten Louisianans have graduated from high school, and only 7 percent have graduate or professional degrees. According to the Kids Count Data Book, compiled by the Annie E. Casey Foundation, Louisiana ranked 49th out of 50 states for child well-being. And the problem transcends race; an average black in Maryland lives four years longer, earns twice as much, and is twice as likely to have a college degree as a black in Louisiana. And whites in Louisiana are worse off than whites in Maryland or anywhere else outside Mississippi. Louisiana has suffered many environmental problems too: there are nearly 400 miles of low, flat, subsiding coastline, and the state loses a football field–size patch of wetland every hour. It is threatened by rising sea levels and severe hurricanes, which the world’s top scientists connect to climate change.

Our brain is therefore not simply passively subjected to sensory inputs. From the get-go, it already possesses a set of abstract hypotheses, an accumulated wisdom that emerged through the sift of Darwinian evolution and which it now projects onto the outside world. Not all scientists agree with this idea, but I consider it a central point: the naive empiricist philosophy underlying many of today's artificial neural networks is wrong. It is simply not true that we are born with completely disorganized circuits devoid of any knowledge, which later receive the imprint of their environment. Learning, in man and machine, always starts from a set of a priori hypotheses, which are projected onto the incoming data, and from which the system selects those that are best suited to the current environment. As Jean-Pierre Changeux stated in his best-selling book Neuronal Man (1985), “To learn is to eliminate.

This is the thinking behind Amazon’s anticipatory shopping patent.43 Instead of customers making their own decisions, Amazon decides for them, sending what they want before they know they want it. It is, as one commentator noticed, one more step towards cutting out human agency altogether.44 Pervasive monitoring devices – smartphones, wearables, voice-enabled speakers and smart meters – allow companies to track and manage consumer behaviour. The Harvard business scholar Shoshana Zuboff quotes an unnamed chief data scientist who explains: ‘The goal of everything we do is to change people’s actual behavior at scale . . . we can capture their behaviours and identify good and bad [ones]. Then we develop “treatments” or “data pellets” that select good behaviours.’45 MIT’s Alex Pentland seems more interested in enhancing machines than human understanding. He celebrates the opportunity to deploy sensors and data in order to increase efficiency

The best method for acquiring scientific data is direct observation. Nothing beats watching a subject in action. But scientist have an easier time gazing at intergalactic quasars than peeking into someone's bedroom. Quasars don't close the curtains out of modesty of suspicion. In contrast, most of us are unwilling to let curious scientists photograph us as we tumble between the sheets. Radio waves may be invisible, but they don't try do deceive curious physicists and they are incapable of self-deception. Humans are guilty of both.

So we had better call upon our lawyers, politicians, philosophers and even poets to turn their attention to this conundrum: how do you regulate the ownership of data? This may well be the most important political question of our era. If we cannot answer this question soon, our sociopolitical system might collapse. People are already sensing the coming cataclysm. Perhaps this is why citizens all over the world are losing faith in the liberal story, which just a decade ago seemed irresistible. How, then, do we go forward from here, and how do we cope with the immense challenges of the biotech and infotech revolutions? Perhaps the very same scientists and entrepreneurs who disrupted the world in the first place could engineer some technological solution? For example, might networked algorithms form the scaffolding for a global human community that could collectively own all the data and oversee the future development of life? As global inequality rises and social tensions increase around the world, perhaps Mark Zuckerberg could call upon his 2 billion friends to join forces and do something together?

At the time of this writing, our knowledge of East Asian population history is relatively limited compared to that of West Eurasia because less than 5 percent of published ancient DNA data comes from East Asia. The difference reflects the fact that ancient DNA technology was invented in Europe, and it is nearly impossible for researchers to export samples from China and Japan because of government restrictions or a preference that studies be led by local scientists. This has meant that these regions have missed out on the first few years of the ancient DNA revolution.

The future for ancient DNA laboratories that I find appealing is based on a model that has emerged among radiocarbon dating laboratories. For example, the Oxford Radiocarbon Accelerator Unit processes large numbers of samples for a fee, and uses this income stream to support a factory that churns out routine dates and produces data more cheaply, efficiently, and at higher quality than would be possible if its scientists limited themselves to their own questions. But its scientists then piggyback on the juggernaut of the radiocarbon dating factory they have built to do cutting-edge science,

The vast majority of scientists deserve our trust. But no matter how you slice it, scientific fraud isn’t rare. Hundreds of scientific papers get retracted every year, and while firm numbers are elusive, something like half of them are retracted due to fraud or other misconduct. Even big-name scientists transgress. Again, it’s unfair to condemn people from the past for failing to meet today’s standards, but historians have noted that Galileo, Newton, Bernoulli, Dalton, Mendel, and more all manipulated experiments and/or fudged data in ways that would have gotten them fired from any self-respecting lab today.

In 2004, a panel of respected scientists—none of whom had worked with vaccines—was convened by the Institute of Medicine of the National Academy of Sciences to review all of the available data. They concluded that there was no evidence that vaccines were associated with the development of autism.23 Since that time, the evidence has become even more robust that there is no link between measles vaccines or thimerosal-containing vaccines and autism. The continuing public “controversy” in the face of the scientific evidence is now considered to be misinformation. And what pervasive misinformation it has been and continues to be.

A scientist must put faith in the experimental data reported by other scientists, and in the institutions that sponsored those scientists, and in the standards by which those scientists received their credentials. A scientist must put faith in the authority of the journals that publish the results of various studies. Finally, but perhaps most fundamentally, a scientist must trust that empirical reality is indeed perceptible and measurable, and that the laws of cause and effect will apply universally. No scientific endeavor can proceed if the experimenter subjects every phenomenon to radical doubt, disqualifying his own observations as well as those of his peers. Polanyi concluded that science proceeds from a trust that is “fiduciary”—a word that derives from the Latin root meaning “faith-based.” Such faith is well placed and well founded, and it enables science to proceed apace; but, nonetheless, it is a species of faith, not an absolutely certain knowledge. “We must now recognize belief once more as the source of all knowledge,…” Polanyi said. “No intelligence, however critical or original, can operate outside such a fiduciary framework.” Secularism’s attempts to replace the authority of religion with a supposed “authority of experience and reason” has proven, in Polanyi’s words, “farcically inadequate

Schopenhauer’s framing kicked the problem of consciousness onto a much larger playing field. The mind, with all of its rational processes, is all very well but the “will,” the thing that gives us our “oomph,” is the key: “The will … again fills the consciousness through wishes, emotions, passions, and cares.”14 Today, the subconscious rumblings of the “will” are still unplumbed; only a few inroads have been made. As I write these words, enthusiasts for the artificial intelligence (AI) agenda, the goal of programming machines to think like humans, have completely avoided and ignored this aspect of mental life. That is why Yale’s David Gelernter, one of the leading computer scientists in the world, says the AI agenda will always fall short, explaining, “As it now exists, the field of AI doesn’t have anything that speaks to emotions and the physical body, so they just refuse to talk about it.” He asserts that the human mind includes feelings, along with data and thoughts, and each particular mind is a product of a particular person’s experiences, emotions, and memories hashed and rehashed over a lifetime: “The mind is in a particular body, and consciousness is the work of the whole body.” Putting it in computer lingo, he declares, “I can run an app on any device, but can I run someone else’s mind on your brain? Obviously not.”15

My gratitude goes as well to the other data scientists I pestered and to the institutions that collect and maintain their data: Karlyn Bowman, Daniel Cox (PRRI), Tamar Epner (Social Progress Index), Christopher Fariss, Chelsea Follett (HumanProgress), Andrew Gelman, Yair Ghitza, April Ingram (Science Heroes), Jill Janocha (Bureau of Labor Statistics), Gayle Kelch (US Fire Administration/FEMA), Alaina Kolosh (National Safety Council), Kalev Leetaru (Global Database of Events, Language, and Tone), Monty Marshall (Polity Project), Bruce Meyer, Branko Milanović (World Bank), Robert Muggah (Homicide Monitor), Pippa Norris (World Values Survey), Thomas Olshanski (US Fire Administration/FEMA), Amy Pearce (Science Heroes), Mark Perry, Therese Pettersson (Uppsala Conflict Data Program), Leandro Prados de la Escosura, Stephen Radelet, Auke Rijpma (OECD Clio Infra), Hannah Ritchie (Our World in Data), Seth Stephens-Davidowitz (Google Trends), James X. Sullivan, Sam Taub (Uppsala Conflict Data Program), Kyla Thomas, Jennifer Truman (Bureau of Justice Statistics), Jean Twenge, Bas van Leeuwen (OECD Clio Infra), Carlos Vilalta, Christian Welzel (World Values Survey), Justin Wolfers, and Billy Woodward (Science Heroes). David Deutsch, Rebecca Newberger Goldstein, Kevin Kelly, John Mueller, Roslyn Pinker, Max Roser, and Bruce Schneier read a draft of the entire manuscript and offered invaluable advice.

It’s easy to raise graduation rates, for example, by lowering standards. Many students struggle with math and science prerequisites and foreign languages. Water down those requirements, and more students will graduate. But if one goal of our educational system is to produce more scientists and technologists for a global economy, how smart is that? It would also be a cinch to pump up the income numbers for graduates. All colleges would have to do is shrink their liberal arts programs, and get rid of education departments and social work departments while they’re at it, since teachers and social workers make less money than engineers, chemists, and computer scientists. But they’re no less valuable to society.

The Scientific Revolution proposed a very different formula for knowledge: Knowledge = Empirical Data × Mathematics. If we want to know the answer to some question, we need to gather relevant empirical data, and then use mathematical tools to analyse the data. For example, in order to gauge the true shape of the earth, we can observe the sun, the moon and the planets from various locations across the world. Once we have amassed enough observations, we can use trigonometry to deduce not only the shape of the earth, but also the structure of the entire solar system. In practice, that means that scientists seek knowledge by spending years in observatories, laboratories and research expeditions, gathering more and more empirical data, and sharpening their mathematical tools so they could interpret the data correctly. The scientific formula for knowledge led to astounding breakthroughs in astronomy, physics, medicine and countless other disciplines. But it had one huge drawback: it could not deal with questions of value and meaning. Medieval pundits could determine with absolute certainty that it is wrong to murder and steal, and that the purpose of human life is to do God’s bidding, because scriptures said so. Scientists could not come up with such ethical judgements. No amount of data and no mathematical wizardry can prove that it is wrong to murder. Yet human societies cannot survive without such value judgements.

Wild animals enjoying one another and taking pleasure in their world is so immediate and so real, yet this reality is utterly absent from textbooks and academic papers about animals and ecology. There is a truth revealed here, absurd in its simplicity. This insight is not that science is wrong or bad. On the contrary: science, done well, deepens our intimacy with the world. But there is a danger in an exclusively scientific way of thinking. The forest is turned into a diagram; animals become mere mechanisms; nature's workings become clever graphs. Today's conviviality of squirrels seems a refutation of such narrowness. Nature is not a machine. These animals feel. They are alive; they are our cousins, with the shared experience kinship implies. And they appear to enjoy the sun, a phenomenon that occurs nowhere in the curriculum of modern biology. Sadly, modern science is too often unable or unwilling to visualize or feel what others experience. Certainly science's "objective" gambit can be helpful in understanding parts of nature and in freeing us from some cultural preconceptions. Our modern scientific taste for dispassion when analyzing animal behaviour formed in reaction to the Victorian naturalists and their predecessors who saw all nature as an allegory confirming their cultural values. But a gambit is just an opening move, not a coherent vision of the whole game. Science's objectivity sheds some assumptions but takes on others that, dressed up in academic rigor, can produce hubris and callousness about the world. The danger comes when we confuse the limited scope of our scientific methods with the true scope of the world. It may be useful or expedient to describe nature as a flow diagram or an animal as a machine, but such utility should not be confused with a confirmation that our limited assumptions reflect the shape of the world. Not coincidentally, the hubris of narrowly applied science serves the needs of the industrial economy. Machines are bought, sold, and discarded; joyful cousins are not. Two days ago, on Christmas Eve, the U.S. Forest Service opened to commercial logging three hundred thousand acres of old growth in the Tongass National Forest, more than a billion square-meter mandalas. Arrows moved on a flowchart, graphs of quantified timber shifted. Modern forest science integrated seamlessly with global commodity markets—language and values needed no translation. Scientific models and metaphors of machines are helpful but limited. They cannot tell us all that we need to know. What lies beyond the theories we impose on nature? This year I have tried to put down scientific tools and to listen: to come to nature without a hypothesis, without a scheme for data extraction, without a lesson plan to convey answers to students, without machines or probes. I have glimpsed how rich science is but simultaneously how limited in scope and in spirit. It is unfortunate that the practice of listening generally has no place in the formal training of scientists. In this absence science needlessly fails. We are poorer for this, and possibly more hurtful. What Christmas Eve gifts might a listening culture give its forests? What was the insight that brushed past me as the squirrels basked? It was not to turn away from science. My experience of animals is richer for knowing their stories, and science is a powerful way to deepen this understanding. Rather, I realized that all stories are partly wrapped in fiction—the fiction of simplifying assumptions, of cultural myopia and of storytellers' pride. I learned to revel in the stories but not to mistake them for the bright, ineffable nature of the world.

Even so, putting all exaggerations aside, sound neuroscience really is providing us with an ever richer picture of the brain and its operations, and in some far distant epoch may actually achieve something like a comprehensive survey of what is perhaps the single most complex physical object in the universe. That is all entirely irrelevant to my argument, however. My claim here is that, whatever we may learn about the brain in the future, it will remain in principle impossible to produce any entirely mechanistic account of the conscious mind, for a great many reasons (many of which I shall soon address), and that therefore consciousness is a reality that defeats mechanistic or materialist thinking. For the intuitions of folk psychology are in fact perfectly accurate; they are not merely some theory about the mind that is either corrigible or dispensable. They constitute nothing less than a full and coherent phenomenological description of the life of the mind, and they are absolutely “primordial data,” which cannot be abandoned in favor of some alternative description without producing logical nonsense. Simply said, consciousness as we commonly conceive of it is quite real (as all of us, apart from a few cognitive scientists and philosophers, already know—and they know it too, really). And this presents a problem for materialism, because consciousness as we commonly conceive of it is also almost certainly irreconcilable with a materialist view of reality.

In their book American Grace: How Religion Divides and Unites Us, political scientists Robert Putnam and David Campbell analyzed a variety of data sources to describe how religious and nonreligious Americans differ. Common sense would tell you that the more time and money people give to their religious groups, the less they have left over for everything else. But common sense turns out to be wrong. Putnam and Campbell found that the more frequently people attend religious services, the more generous and charitable they become across the board.58 Of course religious people give a lot to religious charities, but they also give as much as or more than secular folk to secular charities such as the American Cancer Society.59 They spend a lot of time in service to their churches and synagogues, but they also spend more time than secular folk serving in neighborhood and civic associations of all sorts. Putnam and Campbell put their findings bluntly: By many different measures religiously observant Americans are better neighbors and better citizens than secular Americans—they are more generous with their time and money, especially in helping the needy, and they are more active in community life.60 Why are religious people better neighbors and citizens? To find out, Putnam and Campbell included on one of their surveys a long list of questions about religious beliefs (e.g., “Do you believe in hell? Do you agree that we will all be called before God to answer for our sins?”) as well as questions about religious practices (e.g., “How often do you read holy scriptures? How often do you pray?”). These beliefs and practices turned out to matter very little. Whether you believe in hell, whether you pray daily, whether you are a Catholic, Protestant, Jew, or Mormon … none of these things correlated with generosity. The only thing that was reliably and powerfully associated with the moral benefits of religion was how enmeshed people were in relationships with their co-religionists. It’s the friendships and group activities, carried out within a moral matrix that emphasizes selflessness. That’s what brings out the best in people. Putnam and Campbell reject the New Atheist emphasis on belief and reach a conclusion straight out of Durkheim: “It is religious belongingness that matters for neighborliness, not religious believing.”61

Joy and sadness, vivacity and obtuseness are data of introspection, and when we invest landscapes or other people with these states, it is because we have observed in ourselves the coincidence between these internal perceptions and the external signs associated with them by the accidents of our constitution. Perception thus impoverished becomes purely a matter of knowledge, a progressive noting down of qualities and of their most habitual distribution, and the perceiving subject approaches the world as the scientist approaches his experiments. If on the other hand we admit that all these ‘projections’, all these ‘associations’, all these ‘transferences’ are based on some intrinsic characteristic of the object, the ‘human world’ ceases to be a metaphor and becomes once more what it really is, the seat and as it were the homeland of our thoughts.

1:THE “CRISIS”: Although Chief Judge Bazelon said in 1960 that “we desperately need all the help we can get from modern behavioral scientists”69 in dealing with the criminal law, the cold facts suggest no such desperation or crisis. Since the most reliable long-term crime data are on murder, what was the murder rate at that point? The number of murders committed in the United States in 1960 was less than in 1950, 1940, or 1930—even though the population was growing over those decades and murders in the two new states of Hawaii and Alaska were counted in the national statistics for the first time in 1960.70 The murder rate, in proportion to population, was in 1960 just under half of what it had been in 1934.71 As Judge Bazelon saw the criminal justice system in 1960, the problem was not with “the so-called criminal population”72 but with society, whose “need to punish” was a “primitive urge” that was “highly irrational”73—indeed, a “deep childish fear that with any reduction of punishment, multitudes would run amuck.”74 It was this “vindictiveness,” this “irrationality” of “notions and practices regarding punishment”75 that had to be corrected. The criminal “is like us, only somewhat weaker,” according to Judge Bazelon, and “needs help if he is going to bring out the good in himself and restrain the bad.”76 Society is indeed guilty of “creating this special class of human beings,” by its “social failure” for which “the criminal serves as a scapegoat.”77 Punishment is itself a “dehumanizing process” and a “social branding” which only promotes more crime.78 Since criminals “have a special problem and need special help,” Judge Bazelon argued for “psychiatric treatment” with “new, more sophisticated techniques” and asked: Would it really be the end of the world if all jails were turned into hospitals or rehabilitation centers?79

We are living in a golden age of genetic research, with new technologies permitting the easy collection of genetic data from millions upon millions of people and the rapid development of new statistical methodologies for analyzing it. But it is not enough to just produce new genetic knowledge. As this research leaves the ivory tower and disseminates through the public, it is essential for scientists and the public to grapple with what this research means about human identity and equality. Far too often, however, this essential task of meaning-making is being abdicated to the most extreme and hate-filled voices. As Eric Turkheimer, Dick Nisbett, and I warned: If people with progressive political values, who reject claims of genetic determinism and pseudoscientific racialist speculation, abdicate their responsibility to engage with the science of human abilities and the genetics of human behavior, the field will come to be dominated by those who do not share those values.

is dynamic. “Experts” frequently differ on scientific questions and their opinions can vary in accordance with and demands of politics, power, and financial self-interest. Nearly every lawsuit I have ever litigated pitted highly credentialed experts from opposite sides against each other, with all of them swearing under oath to diametrically antithetical positions based on the same set of facts. Telling people to “trust the experts” is either naive or manipulative—or both. All of Dr. Fauci’s intrusive mandates and his deceptive use of data tended to stoke fear and amplify public desperation for the anticipated arrival of vaccines that would transfer billions of dollars from taxpayers to pharmaceutical executives and shareholders. Some of America’s most accomplished scientists, and the physicians leading the battle against COVID in the trenches, came to believe that Anthony Fauci’s do-or-die obsession with novel mRNA vaccines—and Gilead’s expensive patented antiviral, remdesivir—prompted him to ignore or even suppress effective early treatments, causing hundreds of thousands of unnecessary deaths while also prolonging the pandemic

Judging types are in a hurry to make decisions. Perceiving types are not. This is why science doesn’t make any serious attempt to reach a final theory of everything. It always says, “Let’s do another experiment. And another. And another.” When will the experimentation ever end? When will scientists conclude that they have now collected easily enough data to now draw definitive conclusions? But they don’t want to draw any such conclusions. That’s not how they roll. Their method has no such requirement. That’s why many of them openly say that they do not want a final theory. It will stop them, they say, from “discovering” new things. Judging types like order and structure. They like decisions, conclusions, getting things done and reaching objectives. Perceiving types are doubtful and skeptical about all of that. They frequently refer to judging types as “judgmental”, which is literally perceived as a bad thing, “authoritarian”, “totalitarian”, “fascist”, “Nazi”, and so on. Perceiving types always want to have an open road ahead of them. They never want to actually arrive. Judging types cannot see the point of not wanting to reach your destination.

The beauty of Mars exists in the human mind. without the human presence it is just a concatenation of atoms, no different than any other random speck of matter in the universe. It's we who understand it, and we who give it meaning. All our centuries of looking up at the night sky and watching it wander through the stars. All those nights of watching it through the telescopes, looking at a tiny disk trying to see canals in the albedo changes. All those dumb sci-fi novels with their monsters and maidens and dying civilizations. And all the scientists who studied the data, or got us here. That's what makes Mars Beautiful. Not the basalt and the oxides. Now that we are here, it isn't enough to just hide under ten meters of soil and study the rock. That's science, yes, and needed science too. But science is more than that. Science is part of a larger human enterprise, and that enterprise includes going to the stars, adapting to other planets, adapting them to us. Science is creation. The lack of life here, and the lack of any finding in fifty years of the SETI program, indicates that life is rare, and intelligent life even rarer. And yet the whole meaning of the universe, its beauty, is contained in the consciousness of intelligent life. We are the consciousness of the universe, and our job is to spread that around, to go look at things, to live everywhere we can. It's too dangerous to keep the consciousness of the universe on only one planet, it could be wiped out. And so now we're on two, three if you count the moon. And we can change this one to make it safer to live on. Changing it won't destroy it. Reading its past might get harder, but the beauty of it won't go away. If there are lakes, or forests, or glaciers, how does that diminish Mars beauty? I don't think it does. I think it only enhances it. It adds life, the most beautiful system of all. But nothing life can do will bring Tharsis down, or fill Marineris. Mars will always remain Mars, different from Earth, colder and wilder. But it can be Mars and ours at the same time. And it will be. There is this about the human mind; if it can be done, it will be done. We can transform Mars and build it like you would build a cathedral, as a monument to humanity and the universe both. We can do it, so we will do it. So, we might as well start.

As information processing machines, our ability to process data about the external world begins at the level of sensory perception. Although most of us are rarely aware of it, our sensory receptors are designed to detect information at the energy level. Because everything around us - the air we breathe, even the materials we use to build with, are composed of spinning and vibrating atomic particles, you and I are literally swimming in a turbulent sea of electromagnetic fields. We are part of it. We are enveloped within in, and through our sensory apparatus we experience what is. Each of our sensory systems is made up of a complex cascade of neurons that process the incoming neural code from the level of the receptor to specific areas within the brain. Each group of neurons along the cascade alters or enhances the code, and passes it on to the next set of cells in the system, which further defines and refines the message. By the time the code reaches the outermost portion of our brain, the higher levels of the cerebral cortex, we become conscious of the stimulation. However, if any of the cells along the pathway fail in their ability to function normally, then the final perception is skewed away from normal reality.

Statement on Hamas (October 10th, 2023) When Israel strikes, it's "national security" - when Palestine strikes back, it's "terrorism". Just like over two hundred years ago when native americans resisted their homeland being stolen, it was called "Indian Attack". Or like over a hundred years ago when Indian soldiers in the British Army revolted against the empire, in defense of their homeland, it was called "Sepoy Mutiny". The narrative never changes - when the colonizer terrorizes the world, it's given glorious sounding names like "exploration" and "conquest", but if the oppressed so much as utters a word in resistance, it is branded as attack, mutiny and terrorism - so that, the real terrorists can keep on colonizing as the self-appointed ruler of land, life and morality, without ever being held accountable for violating the rights of what they deem second rate lifeforms, such as the arabs, indians, latinos and so on. After all this, some apes will still only be interested in one stupid question. Do I support Hamas? To which I say this. Until you've spent a lifetime under an oppressive regime, you are not qualified to ask that question. An ape can ask anything its puny brain fancies, but it's up to the human to decide whether the ape is worthy of a response. What do you think, by the way - colonizers can just keep coming as they please, to wipe their filthy feet on us like doormat, and we should do nothing - just stay quiet! For creatures who call themselves civilized, you guys have a weird sense of morality. Yet all these might not get through your thick binary skull, so let me put it to you bluntly. I don't stand with Hamas, I am Hamas, just like, I don't stand with Ukraine, I am Ukraine. Russia stops fighting, war ends - Ukraine stops fighting, Ukraine ends. Israel ends invasion, war ends - Palestine ends resistance, Palestine ends. However, I do have one problem here. Why do civilians have to die, if that is indeed the case - which I have no way of confirming, because news reports are not like reputed scientific data, that a scientist can naively trust. During humankind's gravest conflicts news outlets have always peddled a narrative benefiting the occupier and demonizing the resistance, either consciously or subconsciously. So never go by news reports, particularly on exception circumstances like this. No matter the cause, no civilian must die, that is my one unimpeachable law. But the hard and horrific fact of the matter is, only the occupier can put an end to the death and destruction peacefully - the resistance does not have that luxury.

Changing what we think is always a sticky process, especially when it comes to religion. When new information becomes available, we cringe under an orthodox mindset, particularly when we challenge ideas and beliefs that have been “set in stone” for decades. Thomas Kuhn coined the term paradigm shift to represent this often-painful transition to a new way of thinking in science. He argued that “normal science” represented a consensus of thought among scientists when certain precepts were taken as truths during a given period. He believed that when new information emerges, old ideas clash with new ones, causing a crisis. Once the basic truths are challenged, the crisis ends in either revolution (where the information provides new understanding) or dismissal (where the information is rejected as unsound). The information age that we live in today has likely surprised all of us as members of the LDS Church at one time or another as we encounter new ideas that revise or even contradict our previous understanding of various aspects of Church history and teachings. This experience is similar to that of the Copernican Revolution, which Kuhn uses as one of his primary examples to illustrate how a paradigm shift works. Using similar instruments and comparable celestial data as those before them, Copernicus and others revolutionized the heavens by describing the earth as orbiting the sun (heliocentric) rather than the sun as orbiting the earth (geocentric). Because the geocentric model was so ingrained in the popular (and scientific!) understanding, the new, heliocentric idea was almost impossible to grasp. Paradigm shifts also occur in religion and particularly within Mormonism. One major difference between Kuhn’s theory of paradigm shift and the changes that occur within Mormonism lies in the fact that Mormonism privileges personal revelation, which is something that cannot be institutionally implemented or decreed (unlike a scientific law). Regular members have varying degrees of religious experience, knowledge, and understanding dependent upon many factors (but, importantly, not “faithfulness” or “worthiness,” or so forth). When members are faced with new information, the experience of processing that information may occur only privately. As such, different members can have distinct experiences with and reactions to the new information they receive. This short preface uses the example of seer stones to examine the idea of how new information enters into the lives of average Mormons. We have all seen or know of friends or family who experience a crisis of faith upon learning new information about the Church, its members, and our history. Perhaps there are those reading who have undergone this difficult and unsettling experience. Anyone who has felt overwhelmed at the continual emergence of new information understands the gravity of these massive paradigm shifts and the potentially significant impact they can have on our lives. By looking at just one example, this preface will provide a helpful way to think about new information and how to deal with it when it arrives.

Similarly, the computers used to run the software on the ground for the mission were borrowed from a previous mission. These machines were so out of date that Bowman had to shop on eBay to find replacement parts to get the machines working. As systems have gone obsolete, JPL no longer uses the software, but Bowman told me that the people on her team continue to use software built by JPL in the 1990s, because they are familiar with it. She said, “Instead of upgrading to the next thing we decided that it was working just fine for us and we would stay on the platform.” They have developed so much over such a long period of time with the old software that they don’t want to switch to a newer system. They must adapt to using these outdated systems for the latest scientific work. Working within these constraints may seem limiting. However, building tools with specific constraints—from outdated technologies and low bitrate radio antennas—can enlighten us. For example, as scientists started to explore what they could learn from the wait times while communicating with deep space probes, they discovered that the time lag was extraordinarily useful information. Wait times, they realized, constitute an essential component for locating a probe in space, calculating its trajectory, and accurately locating a target like Pluto in space. There is no GPS for spacecraft (they aren’t on the globe, after all), so scientists had to find a way to locate the spacecraft in the vast expanse. Before 1960, the location of planets and objects in deep space was established through astronomical observation, placing an object like Pluto against a background of stars to determine its position.15 In 1961, an experiment at the Goldstone Deep Space Communications Complex in California used radar to more accurately define an “astronomical unit” and help measure distances in space much more accurately.16 NASA used this new data as part of creating the trajectories for missions in the following years. Using the data from radio signals across a wide range of missions over the decades, the Deep Space Network maintained an ongoing database that helped further refine the definition of an astronomical unit—a kind of longitudinal study of space distances that now allows missions like New Horizons to create accurate flight trajectories. The Deep Space Network continued to find inventive ways of using the time lag of radio waves to locate objects in space, ultimately finding that certain ways of waiting for a downlink signal from the spacecraft were less accurate than others. It turned to using the antennas from multiple locations, such as Goldstone in California and the antennas in Canberra, Australia, or Madrid, Spain, to time how long the signal took to hit these different locations on Earth. The time it takes to receive these signals from the spacecraft works as a way to locate the probes as they are journeying to their destination. Latency—or the different time lag of receiving radio signals on different locations of Earth—is the key way that deep space objects are located as they journey through space. This discovery was made possible during the wait times for communicating with these craft alongside the decades of data gathered from each space mission. Without the constraint of waiting, the notion of using time as a locating feature wouldn’t have been possible.

To give you a sense of the sheer volume of unprocessed information that comes up the spinal cord into the thalamus, let’s consider just one aspect: vision, since many of our memories are encoded this way. There are roughly 130 million cells in the eye’s retina, called cones and rods; they process and record 100 million bits of information from the landscape at any time. This vast amount of data is then collected and sent down the optic nerve, which transports 9 million bits of information per second, and on to the thalamus. From there, the information reaches the occipital lobe, at the very back of the brain. This visual cortex, in turn, begins the arduous process of analyzing this mountain of data. The visual cortex consists of several patches at the back of the brain, each of which is designed for a specific task. They are labeled V1 to V8. Remarkably, the area called V1 is like a screen; it actually creates a pattern on the back of your brain very similar in shape and form to the original image. This image bears a striking resemblance to the original, except that the very center of your eye, the fovea, occupies a much larger area in V1 (since the fovea has the highest concentration of neurons). The image cast on V1 is therefore not a perfect replica of the landscape but is distorted, with the central region of the image taking up most of the space. Besides V1, other areas of the occipital lobe process different aspects of the image, including: •  Stereo vision. These neurons compare the images coming in from each eye. This is done in area V2. •  Distance. These neurons calculate the distance to an object, using shadows and other information from both eyes. This is done in area V3. •  Colors are processed in area V4. •  Motion. Different circuits can pick out different classes of motion, including straight-line, spiral, and expanding motion. This is done in area V5. More than thirty different neural circuits involved with vision have been identified, but there are probably many more. From the occipital lobe, the information is sent to the prefrontal cortex, where you finally “see” the image and form your short-term memory. The information is then sent to the hippocampus, which processes it and stores it for up to twenty-four hours. The memory is then chopped up and scattered among the various cortices. The point here is that vision, which we think happens effortlessly, requires billions of neurons firing in sequence, transmitting millions of bits of information per second. And remember that we have signals from five sense organs, plus emotions associated with each image. All this information is processed by the hippocampus to create a simple memory of an image. At present, no machine can match the sophistication of this process, so replicating it presents an enormous challenge for scientists who want to create an artificial hippocampus for the human brain.