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Contents contributed and discussions participated by Javier E

Javier E

English Is a Dialect With an Army - Ta-Nehisi Coates - The Atlantic - 0 views

  • I am getting some small notion of what it feels like to be white in America. What my classmates are telling me is that the Anglophone world is the international power. It dominates. Thus knowledge is tangibly necessary for them in a way that it is not for me
  • Of course the flip-side of this calculus is that power enables ignorance. Black people know this well.
  • I think this is the seed of the "We don't have any white history month!" syndrome. Through conquest the ways of whiteness become the air.
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  • But once those ways are apprehended by the conquered--as they must be--they are no longer the strict property of the conqueror. On the contrary you find the conquered mixing, cutting, folding, and flipping the ways of the conqueror into something that he barely recognizes and yet finds oddly compelling. And all the while the conquered still enjoys her own private home. She need not be amnesiac, only bilingual
  • . The phrase "code-switching" is overdone, but there is no cultural code from which all white people can "switch" from. It's not even a code. It's just the world. 
  • In the context of France, je suis américan. I am an aspect of the great power.
  • There is no "nigger" for me, no private language, no private way of being all my own. And with that comes a great feeling of weakness and shame.
  • the literature of slavemasters is filled with exasperation over their slaves laughing at invisible jokes.
Javier E

Jennifer Rosoff's death and the Associated Press's sexist reporting of it. - 0 views

  • the minor details that journalists choose to include or exclude from their reporting are one of many subtle ways that oppressive gender norms are perpetuated
  • the fact that totally irrelevant details about Rosoff’s love life and cigarette habit made it into the lede and nut graf of an ostensibly unbiased news article—and that no editor stopped to ask, “Hmm, why is this information here?”—just goes to show how deeply ingrained sexist attitudes can b
Javier E

Chinese Journalist Detained in Beijing, One Day After Human Rights Talk With U.S. - NYT... - 0 views

  • Mr. Xi has indicated that proposed economic reforms would not be accompanied by any significant political relaxation. He has instead repeatedly stressed his loyalty to party traditions and political orthodoxy
  • Chinese state-run news media featured a commentary from the official Xinhua news agency that warned that if China embraced democratic ideas promoted by liberal intellectuals, it would succumb to turmoil worse than that in the Soviet Union after the collapse of Communism.
Javier E

A Star Philosopher Falls, and a Debate Over Sexism Is Set Off - NYTimes.com - 0 views

  • Many credit the blog What Is It Like to Be a Woman in Philosophy?, which in 2010 began posting anonymous stories of harassment, with helping to highlight the issue. “Just about every woman you talk to in philosophy has experienced first- or secondhand some form of sexual harassment that is egregious,” said Gideon Rosen, a philosopher at Princeton. “It’s not just one or two striking anecdotes.”
  • changing the broader culture of philosophy to make it more woman-friendly, many say, is a daunting task — particularly since no one can agree on the root cause of that unfriendliness. Is it straight-up sexual discrimination? The lack of female mentors? The highly technical nature of much contemporary Anglo-American philosophy? The field’s notoriously rough-and-tumble style of argument?
Javier E

Did Zeus Exist? - NYTimes.com - 0 views

  • This set me thinking about why we are so certain that Zeus never existed. Of course, we are in no position to say that he did.  But are we really in a position to say that he didn’t?
  • as this civilization developed the critical tools of historiography and philosophy, Zeus’s reality remained widely unquestioned. 
  • Why did belief in the gods persist in spite of critical challenges? What evidence seemed decisive to the ancient Greeks? 
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  • what the Greeks saw as experiences of divine actions in their lives. ”The greatest evidence for the existence of gods is that piety works . . . the converse is that impiety leads to disaster,
  • There were also rituals, associated with the many cults of specific gods, that for some worshippers “created a sense of contact with the divine. One knows that the gods exist because one feels their presence during the drama of the mysteries or the elation of the choral dance.
  • there were “epiphanies” that could “indicate not merely a visible or audible epiphany (whether in the light of day or through a dream . . .) but also any clear expression of a god’s favor such as weather conditions hampering an enemy, a miraculous escape, or a cure;
  • how can we be so sure that the Greeks lived in the same sort of world as we do?  What decisive reason do we have for thinking that for them divinity was not a widely and deeply experienced fact of life?
  • shouldn’t we hold a merely agnostic position on Zeus and the other Greek gods, taking seriously the possibility that they existed but holding that we have good reason neither to assert nor deny their existence?
  • We may well think that our world contains little or no evidence of the supernatural.  But that is no reason to think the same was true of the Greek world.
  • I’m inclined to say that an atheistic denial of Zeus is ungrounded.  There is no current evidence of his present existence, but to deny that he existed in his Grecian heyday we need to assume that there was no good evidence for his existence available to the ancient Greeks.  We have no reason to make this assumption.
Javier E

Let's Shake Up the Social Sciences - NYTimes.com - 1 views

  • everyone knows that monopoly power is bad for markets, that people are racially biased and that illness is unequally distributed by social class. There are diminishing returns from the continuing study of many such topics. And repeatedly observing these phenomena does not help us fix them.
  • social scientists should devote a small palace guard to settled subjects and redeploy most of their forces to new fields like social neuroscience, behavioral economics, evolutionary psychology and social epigenetics, most of which, not coincidentally, lie at the intersection of the natural and social science
  • It is time to create new social science departments that reflect the breadth and complexity of the problems we face as well as the novelty of 21st-century science. These would include departments of biosocial science, network science, neuroeconomics, behavioral genetics and computational social science.
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  • Nicholas A. Christakis, a physician and sociologist at Yale University, is a co-director of the Yale Institute for Network Science.
Javier E

Interview: Ted Chiang | The Asian American Literary Review - 0 views

  • I think most people’s ideas of science fiction are formed by Hollywood movies, so they think most science fiction is a special effects-driven story revolving around a battle between good and evil
  • I don’t think of that as a science fiction story. You can tell a good-versus-evil story in any time period and in any setting. Setting it in the future and adding robots to it doesn’t make it a science fiction story.
  • I think science fiction is fundamentally a post-industrial revolution form of storytelling. Some literary critics have noted that the good-versus-evil story follows a pattern where the world starts out as a good place, evil intrudes, the heroes fight and eventually defeat evil, and the world goes back to being a good place. Those critics have said that this is fundamentally a conservative storyline because it’s about maintaining the status quo. This is a common story pattern in crime fiction, too—there’s some disruption to the order, but eventually order is restored. Science fiction offers a different kind of story, a story where the world starts out as recognizable and familiar but is disrupted or changed by some new discovery or technology. At the end of the story, the world is changed permanently. The original condition is never restored. And so in this sense, this story pattern is progressive because its underlying message is not that you should maintain the status quo, but that change is inevitable. The consequences of this new discovery or technology—whether they’re positive or negative—are here to stay and we’ll have to deal with them.
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  • There’s also a subset of this progressive story pattern that I’m particularly interested in, and that’s the “conceptual breakthrough” story, where the characters discover something about the nature of the universe which radically expands their understanding of the world.  This is a classic science fiction storyline.
  • one of the cool things about science fiction is that it lets you dramatize the process of scientific discovery, that moment of suddenly understanding something about the universe. That is what scientists find appealing about science, and I enjoy seeing the same thing in science fiction.
  • when you mention myth or mythic structure, yes, I don’t think myths can do that, because in general, myths reflect a pre-industrial view of the world. I don’t know if there is room in mythology for a strong conception of the future, other than an end-of-the-world or Armageddon scenario …
Javier E

Is crime a virus or a beast? How metaphors shape our thoughts and decisions [Repost] : ... - 0 views

  • how influential metaphors can be. They can change the way we try to solve big problems like crime. They can shift the sources that we turn to for information. They can polarise our opinions to a far greater extent than, say, our political leanings. And most of all, they do it under our noses
  • In the first report, crime was described as a “wild beast preying on the city” and “lurking in neighbourhoods”. After reading these words, 75% of the students put forward solutions that involved enforcement or punishment,
  • The second report was exactly the same, except it described crime as a “virus infecting the city” and “plaguing” neighbourhoods. After reading this version, only 56% opted for more enforcement, while 44% suggested social reforms.
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  • The metaphors affected how the students saw the problem, and how they proposed to fix it.
  • So metaphors can influence opinions and choices, but how strong are their effects really? At the end of their experiments, Thibodeau and Boroditsky asked the students to state their gender and political affiliation. As you might expect, men and Republicans were more likely to emphasise enforcement, while women and Democrats leant towards social reforms. But these factors only created differences of around 8 to 9 percentage points. The metaphors, on the other hand, created shifts of between 18 to 22 percentage points!
  • it’s virtually impossible to talk about complex issues like crime, the economy, health and so on, without resorting to metaphors.
  • bad metaphors can do a great disservice to the public understanding of science. The idea of the “evolutionary ladder” perpetuates the myth that evolution is about a steady linear march towards complexity.
Javier E

The American Scholar: Picturing a Whole New Language - Jessica Love - 0 views

  • What would a new full-fledged pictorial language—not the hieroglyphics of old, but a language built to suit the social media-mentality of “right here, right now, this is who I am, who I’m with, and what I’m up to”—look like?
Javier E

Wine-tasting: it's junk science | Life and style | The Observer - 0 views

  • google_ad_client = 'ca-guardian_js'; google_ad_channel = 'lifeandstyle'; google_max_num_ads = '3'; // Comments Click here to join the discussion. We can't load the discussion on guardian.co.uk because you don't have JavaScript enabled. if (!!window.postMessage) { jQuery.getScript('http://discussion.guardian.co.uk/embed.js') } else { jQuery('#d2-root').removeClass('hd').html( '' + 'Comments' + 'Click here to join the discussion.We can\'t load the ' + 'discussion on guardian.co.uk ' + 'because your web browser does not support all the features that we ' + 'need. If you cannot upgrade your browser to a newer version, you can ' + 'access the discussion ' + 'here.' ); } Wor
  • Hodgson approached the organisers of the California State Fair wine competition, the oldest contest of its kind in North America, and proposed an experiment for their annual June tasting sessions.Each panel of four judges would be presented with their usual "flight" of samples to sniff, sip and slurp. But some wines would be presented to the panel three times, poured from the same bottle each time. The results would be compiled and analysed to see whether wine testing really is scientific.
  • Results from the first four years of the experiment, published in the Journal of Wine Economics, showed a typical judge's scores varied by plus or minus four points over the three blind tastings. A wine deemed to be a good 90 would be rated as an acceptable 86 by the same judge minutes later and then an excellent 94.
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  • Hodgson's findings have stunned the wine industry. Over the years he has shown again and again that even trained, professional palates are terrible at judging wine."The results are disturbing," says Hodgson from the Fieldbrook Winery in Humboldt County, described by its owner as a rural paradise. "Only about 10% of judges are consistent and those judges who were consistent one year were ordinary the next year."Chance has a great deal to do with the awards that wines win."
  • why are ordinary drinkers and the experts so poor at tasting blind? Part of the answer lies in the sheer complexity of wine.For a drink made by fermenting fruit juice, wine is a remarkably sophisticated chemical cocktail. Dr Bryce Rankine, an Australian wine scientist, identified 27 distinct organic acids in wine, 23 varieties of alcohol in addition to the common ethanol, more than 80 esters and aldehydes, 16 sugars, plus a long list of assorted vitamins and minerals that wouldn't look out of place on the ingredients list of a cereal pack. There are even harmless traces of lead and arsenic that come from the soil.
  • In 2011 Professor Richard Wiseman, a psychologist (and former professional magician) at Hertfordshire University invited 578 people to comment on a range of red and white wines, varying from £3.49 for a claret to £30 for champagne, and tasted blind.People could tell the difference between wines under £5 and those above £10 only 53% of the time for whites and only 47% of the time for reds. Overall they would have been just as a successful flipping a coin to guess.
  • French academic Frédéric Brochet tested the effect of labels in 2001. He presented the same Bordeaux superior wine to 57 volunteers a week apart and in two different bottles – one for a table wine, the other for a grand cru.The tasters were fooled.When tasting a supposedly superior wine, their language was more positive – describing it as complex, balanced, long and woody. When the same wine was presented as plonk, the critics were more likely to use negatives such as weak, light and flat.
  • "People underestimate how clever the olfactory system is at detecting aromas and our brain is at interpreting them," says Hutchinson."The olfactory system has the complexity in terms of its protein receptors to detect all the different aromas, but the brain response isn't always up to it. But I'm a believer that everyone has the same equipment and it comes down to learning how to interpret it." Within eight tastings, most people can learn to detect and name a reasonable range of aromas in wine
  • People struggle with assessing wine because the brain's interpretation of aroma and bouquet is based on far more than the chemicals found in the drink. Temperature plays a big part. Volatiles in wine are more active when wine is warmer. Serve a New World chardonnay too cold and you'll only taste the overpowering oak. Serve a red too warm and the heady boozy qualities will be overpowering.
  • Colour affects our perceptions too. In 2001 Frédérick Brochet of the University of Bordeaux asked 54 wine experts to test two glasses of wine – one red, one white. Using the typical language of tasters, the panel described the red as "jammy' and commented on its crushed red fruit.The critics failed to spot that both wines were from the same bottle. The only difference was that one had been coloured red with a flavourless dye
  • Other environmental factors play a role. A judge's palate is affected by what she or he had earlier, the time of day, their tiredness, their health – even the weather.
  • Robert Hodgson is determined to improve the quality of judging. He has developed a test that will determine whether a judge's assessment of a blind-tasted glass in a medal competition is better than chance. The research will be presented at a conference in Cape Town this year. But the early findings are not promising."So far I've yet to find someone who passes," he says.
Javier E

About Face: Emotions and Facial Expressions May Not Be Directly Related | Boston Magazine - 0 views

  • Ekman had traveled the globe with photographs that showed faces experiencing six basic emotions—happiness, sadness, fear, disgust, anger, and surprise. Everywhere he went, from Japan to Brazil to the remotest village of Papua New Guinea, he asked subjects to look at those faces and then to identify the emotions they saw on them. To do so, they had to pick from a set list of options presented to them by Ekman. The results were impressive. Everybody, it turned out, even preliterate Fore tribesmen in New Guinea who’d never seen a foreigner before in their lives, matched the same emotions to the same faces. Darwin, it seemed, had been right.
  • Ekman’s findings energized the previously marginal field of emotion science. Suddenly, researchers had an objective way to measure and compare human emotions—by reading the universal language of feeling written on the face. In the years that followed, Ekman would develop this idea, arguing that each emotion is like a reflex, with its own circuit in the brain and its own unique pattern of effects on the face and the body. He and his peers came to refer to it as the Basic Emotion model—and it had significant practical applications
  • What if he’s wrong?
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  • Barrett is a professor of psychology at Northeastern
  • her research has led her to conclude that each of us constructs them in our own individual ways, from a diversity of sources: our internal sensations, our reactions to the environments we live in, our ever-evolving bodies of experience and learning, our cultures.
  • if Barrett is correct, we’ll need to rethink how we interpret mental illness, how we understand the mind and self, and even what psychology as a whole should become in the 21st century.
  • The problem was the options that Ekman had given his subjects when asking them to identify the emotions shown on the faces they were presented with. Those options, Barrett discovered, had limited the ways in which people allowed themselves to think. Barrett explained the problem to me this way: “I can break that experiment really easily, just by removing the words. I can just show you a face and ask how this person feels. Or I can show you two faces, two scowling faces, and I can say, ‘Do these people feel the same thing?’ And agreement drops into the toilet.”
  • The brain, it turns out, doesn’t consciously process every single piece of information that comes its way. Think of how impossibly distracting the regular act of blinking would be if it did. Instead, it pays attention to what you need to pay attention to, then raids your memory stores to fill in the blanks.
  • emotion isn’t a simple reflex or a bodily state that’s hard-wired into our DNA, and it’s certainly not universally expressed. It’s a contingent act of perception that makes sense of the information coming in from the world around you, how your body is feeling in the moment, and everything you’ve ever been taught to understand as emotion. Culture to culture, person to person even, it’s never quite the same. What’s felt as sadness in one person might as easily be felt as weariness in another, or frustration in someone else.
  • Just as that first picture of the bee actually wasn’t a picture of a bee for me until I taught myself that it was, my emotions aren’t actually emotions until I’ve taught myself to think of them that way. Without that, I have only a meaningless mishmash of information about what I’m feeling.
  • In many quarters, Barrett was angrily attacked for her ideas, and she’s been the subject of criticism ever since. “I think Lisa does a disservice to the actual empirical progress that we’re making,” says Dacher Keltner, a Berkeley psychologist
  • Keltner told me that he himself has coded thousands of facial expressions using Ekman’s system, and the results are strikingly consistent: Certain face-emotion combinations recur regularly, and others never occur. “That tells me, ‘Wow, this approach to distinct emotions has real power,’” he says.
  • Ekman reached the peak of his fame in the years following 2001. That’s the year the American Psychological Association named him one of the most influential psychologists of the 20th century. The next year, Malcolm Gladwell wrote an article about him in the New Yorker, and in 2003 he began working pro bono for the TSA. A year later, riding the updraft of success, he left his university post and started the Paul Ekman Group,
  • a small research team to visit the isolated Himba tribe in Namibia, in southern Africa. The plan was this: The team, led by Maria Gendron, would do a study similar to Ekman’s original cross-cultural one, but without providing any of the special words or context-heavy stories that Ekman had used to guide his subjects’ answers. Barrett’s researchers would simply hand a jumbled pile of different expressions (happy, sad, fearful, angry, disgusted, and neutral) to their subjects, and would ask them to sort them into six piles. If emotional expressions are indeed universal, they reasoned, then the Himba would put all low-browed, tight-lipped expressions into an anger pile, all wrinkled-nose faces into a disgust pile, and so on.
  • It didn’t happen that way. The Himba sorted some of the faces in ways that aligned with Ekman’s theory: smiling faces went into one pile, wide-eyed fearful faces went into another, and affectless faces went mostly into a third. But in the other three piles, the Himba mixed up angry scowls, disgusted grimaces, and sad frowns. Without any suggestive context, of the kind that Ekman had originally provided, they simply didn’t recognize the differences that leap out so naturally to Westerners.
  • “What we’re trying to do,” she told me, “is to just get people to pay attention to the fact that there’s a mountain of evidence that does not support the idea that facial expressions are universally recognized as emotional expressions.” That’s the crucial point, of course, because if we acknowledge that, then the entire edifice that Paul Ekman and others have been constructing for the past half-century comes tumbling down. And all sorts of things that we take for granted today—how we understand ourselves and our relationships with others, how we practice psychology
  • Barrett’s theory is still only in its infancy. But other researchers are beginning to take up her ideas, sometimes in part, sometimes in full, and where the science will take us as it expands is impossible to predict. It’s even possible that Barrett will turn out to be wrong, as she herself acknowledges. “Every scientist has to face that,” she says. Still, if she is right, then perhaps the most important change we’ll need to make is in our own heads. If our emotions are not universal physiological responses but concepts we’ve constructed from various biological signals and stashed memories, then perhaps we can exercise more control over our emotional lives than we’ve assumed.
  • “Every experience you have now is seeding your experience for the future,” Barrett told me. “Knowing that, would you choose to do what you’re doing now?” She paused a beat and looked me in the eye. “Well? Would you? You are the architect of your own experience.”
Javier E

Noam Chomsky on Where Artificial Intelligence Went Wrong - Yarden Katz - The Atlantic - 0 views

  • If you take a look at the progress of science, the sciences are kind of a continuum, but they're broken up into fields. The greatest progress is in the sciences that study the simplest systems. So take, say physics -- greatest progress there. But one of the reasons is that the physicists have an advantage that no other branch of sciences has. If something gets too complicated, they hand it to someone else.
  • If a molecule is too big, you give it to the chemists. The chemists, for them, if the molecule is too big or the system gets too big, you give it to the biologists. And if it gets too big for them, they give it to the psychologists, and finally it ends up in the hands of the literary critic, and so on.
  • neuroscience for the last couple hundred years has been on the wrong track. There's a fairly recent book by a very good cognitive neuroscientist, Randy Gallistel and King, arguing -- in my view, plausibly -- that neuroscience developed kind of enthralled to associationism and related views of the way humans and animals work. And as a result they've been looking for things that have the properties of associationist psychology.
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  • in general what he argues is that if you take a look at animal cognition, human too, it's computational systems. Therefore, you want to look the units of computation. Think about a Turing machine, say, which is the simplest form of computation, you have to find units that have properties like "read", "write" and "address." That's the minimal computational unit, so you got to look in the brain for those. You're never going to find them if you look for strengthening of synaptic connections or field properties, and so on. You've got to start by looking for what's there and what's working and you see that from Marr's highest level.
  • it's basically in the spirit of Marr's analysis. So when you're studying vision, he argues, you first ask what kind of computational tasks is the visual system carrying out. And then you look for an algorithm that might carry out those computations and finally you search for mechanisms of the kind that would make the algorithm work. Otherwise, you may never find anything.
  • "Good Old Fashioned AI," as it's labeled now, made strong use of formalisms in the tradition of Gottlob Frege and Bertrand Russell, mathematical logic for example, or derivatives of it, like nonmonotonic reasoning and so on. It's interesting from a history of science perspective that even very recently, these approaches have been almost wiped out from the mainstream and have been largely replaced -- in the field that calls itself AI now -- by probabilistic and statistical models. My question is, what do you think explains that shift and is it a step in the right direction?
  • AI and robotics got to the point where you could actually do things that were useful, so it turned to the practical applications and somewhat, maybe not abandoned, but put to the side, the more fundamental scientific questions, just caught up in the success of the technology and achieving specific goals.
  • The approximating unanalyzed data kind is sort of a new approach, not totally, there's things like it in the past. It's basically a new approach that has been accelerated by the existence of massive memories, very rapid processing, which enables you to do things like this that you couldn't have done by hand. But I think, myself, that it is leading subjects like computational cognitive science into a direction of maybe some practical applicability... ..in engineering? Chomsky: ...But away from understanding.
  • I was very skeptical about the original work. I thought it was first of all way too optimistic, it was assuming you could achieve things that required real understanding of systems that were barely understood, and you just can't get to that understanding by throwing a complicated machine at it.
  • if success is defined as getting a fair approximation to a mass of chaotic unanalyzed data, then it's way better to do it this way than to do it the way the physicists do, you know, no thought experiments about frictionless planes and so on and so forth. But you won't get the kind of understanding that the sciences have always been aimed at -- what you'll get at is an approximation to what's happening.
  • Suppose you want to predict tomorrow's weather. One way to do it is okay I'll get my statistical priors, if you like, there's a high probability that tomorrow's weather here will be the same as it was yesterday in Cleveland, so I'll stick that in, and where the sun is will have some effect, so I'll stick that in, and you get a bunch of assumptions like that, you run the experiment, you look at it over and over again, you correct it by Bayesian methods, you get better priors. You get a pretty good approximation of what tomorrow's weather is going to be. That's not what meteorologists do -- they want to understand how it's working. And these are just two different concepts of what success means, of what achievement is.
  • if you get more and more data, and better and better statistics, you can get a better and better approximation to some immense corpus of text, like everything in The Wall Street Journal archives -- but you learn nothing about the language.
  • the right approach, is to try to see if you can understand what the fundamental principles are that deal with the core properties, and recognize that in the actual usage, there's going to be a thousand other variables intervening -- kind of like what's happening outside the window, and you'll sort of tack those on later on if you want better approximations, that's a different approach.
  • take a concrete example of a new field in neuroscience, called Connectomics, where the goal is to find the wiring diagram of very complex organisms, find the connectivity of all the neurons in say human cerebral cortex, or mouse cortex. This approach was criticized by Sidney Brenner, who in many ways is [historically] one of the originators of the approach. Advocates of this field don't stop to ask if the wiring diagram is the right level of abstraction -- maybe it's no
  • if you went to MIT in the 1960s, or now, it's completely different. No matter what engineering field you're in, you learn the same basic science and mathematics. And then maybe you learn a little bit about how to apply it. But that's a very different approach. And it resulted maybe from the fact that really for the first time in history, the basic sciences, like physics, had something really to tell engineers. And besides, technologies began to change very fast, so not very much point in learning the technologies of today if it's going to be different 10 years from now. So you have to learn the fundamental science that's going to be applicable to whatever comes along next. And the same thing pretty much happened in medicine.
  • that's the kind of transition from something like an art, that you learn how to practice -- an analog would be trying to match some data that you don't understand, in some fashion, maybe building something that will work -- to science, what happened in the modern period, roughly Galilean science.
  • it turns out that there actually are neural circuits which are reacting to particular kinds of rhythm, which happen to show up in language, like syllable length and so on. And there's some evidence that that's one of the first things that the infant brain is seeking -- rhythmic structures. And going back to Gallistel and Marr, its got some computational system inside which is saying "okay, here's what I do with these things" and say, by nine months, the typical infant has rejected -- eliminated from its repertoire -- the phonetic distinctions that aren't used in its own language.
  • people like Shimon Ullman discovered some pretty remarkable things like the rigidity principle. You're not going to find that by statistical analysis of data. But he did find it by carefully designed experiments. Then you look for the neurophysiology, and see if you can find something there that carries out these computations. I think it's the same in language, the same in studying our arithmetical capacity, planning, almost anything you look at. Just trying to deal with the unanalyzed chaotic data is unlikely to get you anywhere, just like as it wouldn't have gotten Galileo anywhere.
  • with regard to cognitive science, we're kind of pre-Galilean, just beginning to open up the subject
  • You can invent a world -- I don't think it's our world -- but you can invent a world in which nothing happens except random changes in objects and selection on the basis of external forces. I don't think that's the way our world works, I don't think it's the way any biologist thinks it is. There are all kind of ways in which natural law imposes channels within which selection can take place, and some things can happen and other things don't happen. Plenty of things that go on in the biology in organisms aren't like this. So take the first step, meiosis. Why do cells split into spheres and not cubes? It's not random mutation and natural selection; it's a law of physics. There's no reason to think that laws of physics stop there, they work all the way through. Well, they constrain the biology, sure. Chomsky: Okay, well then it's not just random mutation and selection. It's random mutation, selection, and everything that matters, like laws of physics.
  • What I think is valuable is the history of science. I think we learn a lot of things from the history of science that can be very valuable to the emerging sciences. Particularly when we realize that in say, the emerging cognitive sciences, we really are in a kind of pre-Galilean stage. We don't know wh
  • at we're looking for anymore than Galileo did, and there's a lot to learn from that.
Javier E

Noam Chomsky on Where Artificial Intelligence Went Wrong - Yarden Katz - The Atlantic - 1 views

  • Skinner's approach stressed the historical associations between a stimulus and the animal's response -- an approach easily framed as a kind of empirical statistical analysis, predicting the future as a function of the past.
  • Chomsky's conception of language, on the other hand, stressed the complexity of internal representations, encoded in the genome, and their maturation in light of the right data into a sophisticated computational system, one that cannot be usefully broken down into a set of associations.
  • Chomsky acknowledged that the statistical approach might have practical value, just as in the example of a useful search engine, and is enabled by the advent of fast computers capable of processing massive data. But as far as a science goes, Chomsky would argue it is inadequate, or more harshly, kind of shallow
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  • David Marr, a neuroscientist colleague of Chomsky's at MIT, defined a general framework for studying complex biological systems (like the brain) in his influential book Vision,
  • a complex biological system can be understood at three distinct levels. The first level ("computational level") describes the input and output to the system, which define the task the system is performing. In the case of the visual system, the input might be the image projected on our retina and the output might our brain's identification of the objects present in the image we had observed. The second level ("algorithmic level") describes the procedure by which an input is converted to an output, i.e. how the image on our retina can be processed to achieve the task described by the computational level. Finally, the third level ("implementation level") describes how our own biological hardware of cells implements the procedure described by the algorithmic level.
  • The emphasis here is on the internal structure of the system that enables it to perform a task, rather than on external association between past behavior of the system and the environment. The goal is to dig into the "black box" that drives the system and describe its inner workings, much like how a computer scientist would explain how a cleverly designed piece of software works and how it can be executed on a desktop computer.
  • As written today, the history of cognitive science is a story of the unequivocal triumph of an essentially Chomskyian approach over Skinner's behaviorist paradigm -- an achievement commonly referred to as the "cognitive revolution,"
  • While this may be a relatively accurate depiction in cognitive science and psychology, behaviorist thinking is far from dead in related disciplines. Behaviorist experimental paradigms and associationist explanations for animal behavior are used routinely by neuroscientists
  • Chomsky critiqued the field of AI for adopting an approach reminiscent of behaviorism, except in more modern, computationally sophisticated form. Chomsky argued that the field's heavy use of statistical techniques to pick regularities in masses of data is unlikely to yield the explanatory insight that science ought to offer. For Chomsky, the "new AI" -- focused on using statistical learning techniques to better mine and predict data -- is unlikely to yield general principles about the nature of intelligent beings or about cognition.
  • Behaviorist principles of associations could not explain the richness of linguistic knowledge, our endlessly creative use of it, or how quickly children acquire it with only minimal and imperfect exposure to language presented by their environment.
  • it has been argued in my view rather plausibly, though neuroscientists don't like it -- that neuroscience for the last couple hundred years has been on the wrong track.
  • Implicit in this endeavor is the assumption that with enough sophisticated statistical tools and a large enough collection of data, signals of interest can be weeded it out from the noise in large and poorly understood biological systems.
  • Brenner, a contemporary of Chomsky who also participated in the same symposium on AI, was equally skeptical about new systems approaches to understanding the brain. When describing an up-and-coming systems approach to mapping brain circuits called Connectomics, which seeks to map the wiring of all neurons in the brain (i.e. diagramming which nerve cells are connected to others), Brenner called it a "form of insanity."
  • These debates raise an old and general question in the philosophy of science: What makes a satisfying scientific theory or explanation, and how ought success be defined for science?
  • Ever since Isaiah Berlin's famous essay, it has become a favorite pastime of academics to place various thinkers and scientists on the "Hedgehog-Fox" continuum: the Hedgehog, a meticulous and specialized worker, driven by incremental progress in a clearly defined field versus the Fox, a flashier, ideas-driven thinker who jumps from question to question, ignoring field boundaries and applying his or her skills where they seem applicable.
  • Chomsky's work has had tremendous influence on a variety of fields outside his own, including computer science and philosophy, and he has not shied away from discussing and critiquing the influence of these ideas, making him a particularly interesting person to interview.
  • If you take a look at the progress of science, the sciences are kind of a continuum, but they're broken up into fields. The greatest progress is in the sciences that study the simplest systems. So take, say physics -- greatest progress there. But one of the reasons is that the physicists have an advantage that no other branch of sciences has. If something gets too complicated, they hand it to someone else.
  • If a molecule is too big, you give it to the chemists. The chemists, for them, if the molecule is too big or the system gets too big, you give it to the biologists. And if it gets too big for them, they give it to the psychologists, and finally it ends up in the hands of the literary critic, and so on.
  • An unlikely pair, systems biology and artificial intelligence both face the same fundamental task of reverse-engineering a highly complex system whose inner workings are largely a mystery
  • neuroscience developed kind of enthralled to associationism and related views of the way humans and animals work. And as a result they've been looking for things that have the properties of associationist psychology.
Javier E

Partially Examined Life Ep. 77: Santayana on Beauty | The Partially Examined Life Philo... - 0 views

  • The poet and philosopher Santayana thought that while aesthetic appreciation is an immediate experience–we don’t “infer” the beauty of something by recognizing some natural qualities that it has–we can nonetheless analyze the experience after the fact to uncover a number of grounds on which we might appreciate something.
  • He divides these into areas of matter (e.g. the pretty color or texture), form (the relations between perceived parts), and expression (what external to the work itself does it bring to mind?) and ends up being able to distinguish high art (form-centric) from more savage forms (centered on matter or expression) while distinguishing real appreciation (which can include any of the three elements) from mere pretension (when you don’t really have an immediate experience at all but merely recognize that you’re supposed to think that this is good).
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