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blythewallick

Can Artificial Intelligence Be Creative? | JSTOR Daily - 0 views

  • Machines can write compelling ad copy and solve complex “real life” problems. Should the creative class be worried?
  • Rich breaks down some “abstract” problems into their fundamental parts and shows how, with comprehensive enough data and well-structured enough logical programming, AI could be suited to tackle creative problems. In one case, she offers a “real world” example about a manufacturing company’s new line of products and their plans, goals, and expectations for marketing the new line to a specific city. Weekly Newsletter Get your fix of JSTOR Daily’s best stories in your inbox each Thursday. Privacy Policy   Contact Us You may unsubscribe at any time by clicking on the provided link on any marketing message.
  • almost all problems rely (or ought to rely) on an understanding of the nature of both knowledge and reasoning. Humanists are trying to solve many of these same problems. Thus there is room for a good deal of interaction between artificial intelligence and many disciplines within the humanities.
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  • There is much to be said, however, for art’s ability to evoke emotion based on common experience, sincerity, talent, and unique skill. Rich proves that AI can be used to answer complicated questions. But what we think of as creative work in the humanities is much more often about asking questions than it is about answering them.
runlai_jiang

You Asked About CES 2018. We Answered. - The New York Times - 0 views

  • You Asked About CES 2018. We Answered. By BRIAN X. CHEN At the International Consumer Electronics Show this week in Las Vegas, thousands of tech companies showcased some of the hottest new innovations: artificial intelligence, self-driving car tech, the smart home, voice-controlled accessories, fifth-generation cellular connectivity and more.Curious about the new products and how they will affect your personal technology? Readers asked Brian X. Chen, our lead consumer technology writer who is attending the trade show, their questions about wireless, TV and the Internet of Things. 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  • At the International Consumer Electronics Show this week in Las Vegas, thousands of tech companies showcased some of the hottest new innovations: artificial intelligence, self-driving car tech, the smart home, voice-controlled accessories, fifth-generation cellular connectivity and more.
  • Curious about the new products and how they will affect your personal technology? Readers asked Brian X. Chen, our lead consumer technology writer who attended the trade show, their questions about wireless, TV and the Internet of Things. (In addition,
douglasn89

The Simple Economics of Machine Intelligence - 0 views

  • The year 1995 was heralded as the beginning of the “New Economy.” Digital communication was set to upend markets and change everything. But economists by and large didn’t buy into the hype.
  • Today we are seeing similar hype about machine intelligence. But once again, as economists, we believe some simple rules apply. Technological revolutions tend to involve some important activity becoming cheap, like the cost of communication or finding information. Machine intelligence is, in its essence, a prediction technology, so the economic shift will center around a drop in the cost of prediction.
  • The first effect of machine intelligence will be to lower the cost of goods and services that rely on prediction. This matters because prediction is an input to a host of activities including transportation, agriculture, healthcare, energy manufacturing, and retail.
    • douglasn89
       
      This emphasis on prediction ties into the previous discussion and reading we had which included the idea that humans by nature are poor predictors, so because of that, they have begun to design machines to predict.
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  • As machine intelligence lowers the cost of prediction, we will begin to use it as an input for things for which we never previously did. As a historical example, consider semiconductors, an area of technological advance that caused a significant drop in the cost of a different input: arithmetic. With semiconductors we could calculate cheaply, so activities for which arithmetic was a key input, such as data analysis and accounting, became much cheaper.
  • As machine intelligence improves, the value of human prediction skills will decrease because machine prediction will provide a cheaper and better substitute for human prediction, just as machines did for arithmetic.
  • Using the language of economics, judgment is a complement to prediction and therefore when the cost of prediction falls demand for judgment rises. We’ll want more human judgment.
  • But it yields two key implications: 1) an expanded role of prediction as an input to more goods and services, and 2) a change in the value of other inputs, driven by the extent to which they are complements to or substitutes for prediction. These changes are coming.
    • douglasn89
       
      This article agrees with the readings from Unit 5 Lesson 6 in its prediction of changes.
runlai_jiang

A New Antidote for Noisy Airports: Slower Planes - WSJ - 0 views

  • Urban airports like Boston’s Logan thought they had silenced noise issues with quieter planes. Now complaints pour in from suburbs 10 to 15 miles away because new navigation routes have created relentless noise for some homeowners. Photo: Alamy By Scott McCartney Scott McCartney The Wall Street Journal BiographyScott McCartney @MiddleSeat Scott.McCartney@wsj.com March 7, 2018 8:39 a.m. ET 146 COMMENTS saveSB107507240220
  • It turns out engines aren’t the major culprit anymore. New airplanes are much quieter. It’s the “whoosh” that big airplanes make racing through the air.
  • Computer models suggest slowing departures by 30 knots—about 35 miles an hour—would reduce noise on the ground significantly.
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  • The FAA says it’s impressed and is moving forward with recommendations Boston has made.
  • . A working group is forming to evaluate the main recommendation to slow departing jets to a speed limit of 220 knots during the climb to 10,000 feet, down from 250 knots.
  • New routes put planes over quiet communities. Complaints soared. Phoenix neighborhoods sued the FAA; Chicago neighborhoods are pushing for rotating runway use. Neighborhoods from California to Washington, D.C., are fighting the new procedures that airlines and the FAA insist are vital to future travel.
  • “It’s a concentration problem. It’s a frequency problem. It’s not really a noise problem.”
  • “The flights wake you up. We get a lot of complaints from young families with children,” says Mr. Wright, a data analyst who works from home for a major health-care company.
  • In Boston, an analysis suggested only 54% of the complaints Massport received resulted from noise louder than 45 decibels—about the level of background noise. When it’s relentless, you notice it more.
  • With a 30-knot reduction, noise directly under the flight track would decrease by between 1.5 and 5 decibels and the footprint on the ground would get a lot skinnier, sharply reducing the number of people affected, Mr. Hansman says.
  • The industry trade association Airlines for America has offered cautious support of the Boston recommendations. In a statement, the group said the changes must be safe, work with a variety of aircraft and not reduce the airport’s capacity for takeoffs and landings.
  • Air-traffic controllers will need to delay a departure a bit to put more room between a slower plane and a faster one, or modify its course slightly.
Javier E

Beyond Billboards - The Daily Dish | By Andrew Sullivan - 0 views

  • The Atlantic Home todaysDate();Sunday, December 12, 2010Sunday, December 12, 2010 Go Follow the Atlantic » atlanticPrintlayoutnavigation()Politics Presented ByBack to the Gold Standard? Joshua GreenSenate Dems Lose Vote on 'Don't Ask' RepealMegan Scully & Dan FriedmanA Primary Challenge to Obama? Marc Ambinder Business Presented byif (typeof window.dartOrd == 'undefined') {window.dartOrd = ('000000000' + Math.ceil(Math.random()*1000000000).toString()).slice(-9);}jsProperties = 'TheAtlanticOnline/channel_business;pos=navlogo;sz=88x31,215x64;tile=1';document.write('');if( $(".adNavlogo").html().search("grey.gif") != -1 ){$(".adNavlogo").hide();}Will the Economy Get Jobs for Christmas?Daniel Indiviglio27 Key Facts About US ExportsDerek ThompsonThe Last StimulusDerek Thompson Culture Presented ByThe 10 Biggest Sports Stories of 2010Eleanor Barkhorn and Kevin Fallon al
  • at the force behind all that exists actually intervened in the consciousness of humankind in the form of a man so saturated in godliness that merely being near him healed people of the weight of the world's sins.
Javier E

How To Look Smart, Ctd - The Daily Dish | By Andrew Sullivan - 0 views

  • The Atlantic Home todaysDate();Tuesday, February 8, 2011Tuesday, February 8, 2011 Go Follow the Atlantic » Politics Presented by When Ronald Reagan Endorsed Ron Paul Joshua Green Epitaph for the DLC Marc Ambinder A Hard Time Raising Concerns About Egypt Chris Good Business Presented by Could a Hybrid Mortgage System Work? Daniel Indiviglio Fighting Bias in Academia Megan McArdle The Tech Revolution For Seniors Derek Thompson Culture Presented By 'Tiger Mother' Creates a New World Order James Fallows Justin Bieber: Daydream Believer James Parker <!-- /li
  • these questions tend to overlook the way IQ tests are designed. As a neuropsychologist who has administered hundreds of these measures, I can tell you that their structures reflect a deeply embedded bias toward intelligence as a function of reading skills
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.
tongoscar

A Pattern Recognition Theory of Mind | Praxis - 0 views

  • the pace of improvement in technology would become a runaway phenomenon that would transform all aspects of human civilization.
  • the structure and functioning of the human brain is actually quite simple, a basic unit of cognition repeated millions of times. Therefore,&nbsp;creating an artificial brain will not require simulating the human brain at every level of detail. It will only require reverse engineering this basic repeating unit.
  • our memories are organized in discrete&nbsp;segments. If you try to start mid-segment, you’ll struggle for a bit until your sequential memory kicks in.
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  • your memories are sequential, like symbols on a ticker tape. They are designed to be read in a certain direction and in order.
  • your memories are nested. Every action and thought is made up of smaller actions and thoughts.
  • the cortical column, a basic structure that is repeated throughout the neocortex. Each of the approximately 500,000 cortical columns is about two millimeters high and a half millimeter wide, and contains about 60,000 neurons (for a total of about 30 billion neurons in the neocortex).
  • The human brain has evolved to recognize patterns, perhaps more than any other single function. Our brain is weak at processing logic, remembering facts, and making calculations, but pattern recognition is its deep core capability.
  • The neocortex is&nbsp;an elaborately folded sheath of tissue covering the whole top and front of the brain, making up nearly 80% of its weight.
  • The basic structure and functioning of the human brain is hierarchical. This may not seem intuitive at first. It sounds like how a computer works.
  • For our purposes, the most important thing to understand about the neocortex is that it has an extremely uniform structure.
  • Mountcastle also believed there must be smaller sub-units, but that couldn’t be confirmed until years later. These “mini-columns” are so tightly interwoven it is impossible to distinguish them, but they constitute the fundamental component of the neocortex. Thus, they constitute the fundamental component of human thought.
  • The basic structure of a PR has three parts: the input, the name, and the output.
  • The first part is the input – dendrites coming from other PRs that signal the presence of lower-level patterns
  • The third part is the output – axons emerging from the PR that signal the presence of its designated pattern.
  • When the inputs to a PR cross a certain threshold, it fires. That is, it emits a nerve impulse to the higher-level PRs it connects to. This is essentially the “A” PR shouting “Hey guys! I just saw the letter “A”!” When the PR for “Apple” hears such signals for a, p, p again, l, and e, it fires itself, shouting “Hey guys! I just saw “Apple!” And so on up the hierarchy.
  • “neurons that fire together, wire together,” which emphasizes the plasticity of individual neurons and is known as the Hebbian Theory, may be incorrect.
Javier E

Psychological nativism - Wikipedia - 0 views

  • In the field of psychology, nativism is the view that certain skills or abilities are "native" or hard-wired into the brain at birth. This is in contrast to the "blank slate" or tabula rasa view, which states that the brain has inborn capabilities for learning from the environment but does not contain content such as innate beliefs.
  • Some nativists believe that specific beliefs or preferences are "hard-wired". For example, one might argue that some moral intuitions are innate or that color preferences are innate. A less established argument is that nature supplies the human mind with specialized learning devices. This latter view differs from empiricism only to the extent that the algorithms that translate experience into information may be more complex and specialized in nativist theories than in empiricist theories. However, empiricists largely remain open to the nature of learning algorithms and are by no means restricted to the historical associationist mechanisms of behaviorism.
  • Nativism has a history in philosophy, particularly as a reaction to the straightforward empiricist views of John Locke and David Hume. Hume had given persuasive logical arguments that people cannot infer causality from perceptual input. The most one could hope to infer is that two events happen in succession or simultaneously. One response to this argument involves positing that concepts not supplied by experience, such as causality, must exist prior to any experience and hence must be innate.
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  • The philosopher Immanuel Kant (1724–1804) argued in his Critique of Pure Reason that the human mind knows objects in innate, a priori ways. Kant claimed that humans, from birth, must experience all objects as being successive (time) and juxtaposed (space). His list of inborn categories describes predicates that the mind can attribute to any object in general. Arthur Schopenhauer (1788–1860) agreed with Kant, but reduced the number of innate categories to one—causality—which presupposes the others.
  • Modern nativism is most associated with the work of Jerry Fodor (1935–2017), Noam Chomsky (b. 1928), and Steven Pinker (b. 1954), who argue that humans from birth have certain cognitive modules (specialised genetically inherited psychological abilities) that allow them to learn and acquire certain skills, such as language.
  • For example, children demonstrate a facility for acquiring spoken language but require intensive training to learn to read and write. This poverty of the stimulus observation became a principal component of Chomsky's argument for a "language organ"—a genetically inherited neurological module that confers a somewhat universal understanding of syntax that all neurologically healthy humans are born with, which is fine-tuned by an individual's experience with their native language
  • In The Blank Slate (2002), Pinker similarly cites the linguistic capabilities of children, relative to the amount of direct instruction they receive, as evidence that humans have an inborn facility for speech acquisition (but not for literacy acquisition).
  • A number of other theorists[1][2][3] have disagreed with these claims. Instead, they have outlined alternative theories of how modularization might emerge over the course of development, as a result of a system gradually refining and fine-tuning its responses to environmental stimuli.[4]
  • Many empiricists are now also trying to apply modern learning models and techniques to the question of language acquisition, with marked success.[20] Similarity-based generalization marks another avenue of recent research, which suggests that children may be able to rapidly learn how to use new words by generalizing about the usage of similar words that they already know (see also the distributional hypothesis).[14][21][22][23]
  • The term universal grammar (or UG) is used for the purported innate biological properties of the human brain, whatever exactly they turn out to be, that are responsible for children's successful acquisition of a native language during the first few years of life. The person most strongly associated with the hypothesising of UG is Noam Chomsky, although the idea of Universal Grammar has clear historical antecedents at least as far back as the 1300s, in the form of the Speculative Grammar of Thomas of Erfurt.
  • This evidence is all the more impressive when one considers that most children do not receive reliable corrections for grammatical errors.[9] Indeed, even children who for medical reasons cannot produce speech, and therefore have no possibility of producing an error in the first place, have been found to master both the lexicon and the grammar of their community's language perfectly.[10] The fact that children succeed at language acquisition even when their linguistic input is severely impoverished, as it is when no corrective feedback is available, is related to the argument from the poverty of the stimulus, and is another claim for a central role of UG in child language acquisition.
  • Researchers at Blue Brain discovered a network of about fifty neurons which they believed were building blocks of more complex knowledge but contained basic innate knowledge that could be combined in different more complex ways to give way to acquired knowledge, like memory.[11
  • experience, the tests would bring about very different characteristics for each rat. However, the rats all displayed similar characteristics which suggest that their neuronal circuits must have been established previously to their experiences. The Blue Brain Project research suggests that some of the "building blocks" of knowledge are genetic and present at birth.[11]
  • modern nativist theory makes little in the way of specific falsifiable and testable predictions, and has been compared by some empiricists to a pseudoscience or nefarious brand of "psychological creationism". As influential psychologist Henry L. Roediger&nbsp;III remarked that "Chomsky was and is a rationalist; he had no uses for experimental analyses or data of any sort that pertained to language, and even experimental psycholinguistics was and is of little interest to him".[13]
  • , Chomsky's poverty of the stimulus argument is controversial within linguistics.[14][15][16][17][18][19]
  • Neither the five-year-old nor the adults in the community can easily articulate the principles of the grammar they are following. Experimental evidence shows that infants come equipped with presuppositions that allow them to acquire the rules of their language.[6]
  • Paul Griffiths, in "What is Innateness?", argues that innateness is too confusing a concept to be fruitfully employed as it confuses "empirically dissociated" concepts. In a previous paper, Griffiths argued that innateness specifically confuses these three distinct biological concepts: developmental fixity, species nature, and intended outcome. Developmental fixity refers to how insensitive a trait is to environmental input, species nature reflects what it is to be an organism of a certain kind, and the intended outcome is how an organism is meant to develop.[24]
kirkpatrickry

Time to call an end to free market supremacy | GulfNews.com - 0 views

  • Time to call an end to free market supremacy Recently published book, ‘Concrete Economics’, advocates a new industrialism that requires some form of activist government
  • If you’re at all concerned about economic policy, this is a book you need to read. It will take you only a couple of hours, and the time will be well spent
  • But despite the inherent limitations of historical analysis, the message of ‘Concrete Economics’ is one that US policymakers need to hear. One reason is that DeLong and Cohen are absolutely right — the American mind has been far too captured by the beguilingly simple and powerful theory of free-market dogma. That theory was oversold, and we need a corrective. We need history.
Javier E

Do Political Experts Know What They're Talking About? | Wired Science | Wired... - 1 views

  • I often joke that every cable news show should be forced to display a disclaimer, streaming in a loop at the bottom of the screen. The disclaimer would read: “These talking heads have been scientifically proven to not know what they are talking about. Their blather is for entertainment purposes only.” The viewer would then be referred to Tetlock’s most famous research project, which began in 1984.
  • He picked a few hundred political experts – people who made their living “commenting or offering advice on political and economic trends” – and began asking them to make predictions about future events. He had a long list of pertinent questions. Would George Bush be re-elected? Would there be a peaceful end to apartheid in South Africa? Would Quebec secede from Canada? Would the dot-com bubble burst? In each case, the pundits were asked to rate the probability of several possible outcomes. Tetlock then interrogated the pundits about their thought process, so that he could better understand how they made up their minds.
  • Most of Tetlock’s questions had three possible answers; the pundits, on average, selected the right answer less than 33 percent of the time. In other words, a dart-throwing chimp would have beaten the vast majority of professionals. These results are summarized in his excellent Expert Political Judgment.
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  • Some experts displayed a top-down style of reasoning: politics as a deductive art. They started with a big-idea premise about human nature, society, or economics and applied it to the specifics of the case. They tended to reach more confident conclusions about the future. And the positions they reached were easier to classify ideologically: that is the Keynesian prediction and that is the free-market fundamentalist prediction and that is the worst-case environmentalist prediction and that is the best case technology-driven growth prediction etc. Other experts displayed a bottom-up style of reasoning: politics as a much messier inductive art. They reached less confident conclusions and they are more likely to draw on a seemingly contradictory mix of ideas in reaching those conclusions (sometimes from the left, sometimes from the right). We called the big-idea experts “hedgehogs” (they know one big thing) and the more eclectic experts “foxes” (they know many, not so big things).
  • The most consistent predictor of consistently more accurate forecasts was “style of reasoning”: experts with the more eclectic, self-critical, and modest cognitive styles tended to outperform the big-idea people (foxes tended to outperform hedgehogs).
  • Lehrer: Can non-experts do anything to encourage a more effective punditocracy?
  • Tetlock: Yes, non-experts can encourage more accountability in the punditocracy. Pundits are remarkably skillful at appearing to go out on a limb in their claims about the future, without actually going out on one. For instance, they often “predict” continued instability and turmoil in the Middle East (predicting the present) but they virtually never get around to telling you exactly what would have to happen to disconfirm their expectations. They are essentially impossible to pin down. If pundits felt that their public credibility hinged on participating in level playing field forecasting exercises in which they must pit their wits against an extremely difficult-to-predict world, I suspect they would be learn, quite quickly, to be more flexible and foxlike in their policy pronouncements.
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kortanekev

6 Humans With Real "Superpowers" That Science Can't Explain - Collective Evolution - 0 views

  • was responsible for holding a number of sessions to test the validity of psychokinesis (moving objects with the mind). In these sessions, attendees were taught how to initiate their own PK events using various metal objects. Individuals were able to completely bend or contort their metal specimens with no physical force being applied whatsoever
  •  
    great example of something we can never definitively prove or refute because of so many possible variables. how are we to synthesize a full understanding of all of our human inputs: for example, visual paredoilia, confirmation bias, a magic trick, or an actual genetic mutation ?? We do not know if sci-fi will become scientific reality. We do not even know what we don't know Evie K (3/4/17)
Roth johnson

If I Had a Hammer - NYTimes.com - 0 views

  •  
    Interesting article on artificial intelligence. How "first machines" (machines that needed input) are disappearing and "second machines" (machines that can make decisions for themselves) are taking the places of white collar and blue collar jobs.
Javier E

Scholarship and Politics - The Case of Noam Chomsky - NYTimes.com - 0 views

  • (1) The academy is a world of its own, complete with rules, protocols, systems of evaluation, recognized achievements, agreed-on goals, a roster of heroes and a list of tasks yet to be done.
  • (2) Academic work proceeds within the confines of that world, within, that is, a professional, not a public, space, although its performance may be, and often is, public.
  • (3) academic work is only tangentially, not essentially, political; politics may attend the formation of academic units and the selection of academic personnel, but political concerns and pressures have no place in the unfolding of academic argument, except as objects of its distinctive forms of attention
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  • (4) The academic views of a professor are independent of his or her real-world political views; academic disputes don’t track partisan disputes or vice versa; you can’t reason from an academic’s disciplinary views to the positions he or she would take in the public sphere; they are independent variables.
  • The answer given in the first lecture — “What is Language?” — is that we are creatures with language, and that language as a uniquely human biological capacity appeared suddenly and quite late in the evolutionary story, perhaps 75,000 years ago.
  • Chomsky gave three lectures under the general title “What Kind of Creatures are We?”
  • Language, then, does not arise from the social/cultural environment, although the environment provides the stuff or input it works on. That input is “impoverished”; it can’t account for the creativity of language performance, which has its source not in the empirical world, but in an innate ability that is more powerful than the stimuli it utilizes and plays with. It follows that if you want to understand language, you shouldn’t look to linguistic behavior but to the internal mechanism — the Universal Grammar — of which particular linguistic behaviors are a non-exhaustive expression. (The capacity exceeds the empirical resources it might deploy.)
  • In his second lecture (“What Can We Understand?”), Chomsky took up the question of what humans are capable of understanding and his answer, generally, was that we can understand what we can understand, and that means that we can’t understand what is beyond our innate mental capacities
  • This does not mean, he said, that what we can’t understand is not real: “What is mysterious to me is not an argument that it does not exist.” It’s just that while language is powerful and creative, its power and creativity have limits; and since language is thought rather than an addition to or clothing of thought, the limits of language are the limits of what we can fruitfully think about
  • This is as good as it gets. There is “no evolution in our capacity for language.”
  • These assertions are offered as a counter to what Chomsky sees as the over-optimistic Enlightenment belief — common to many empiricist philosophies — that ours is a “limitless explanatory power” and that “we can do anything.”
  • In the third lecture (“What is the Common Good?”) Chomsky turned from the philosophy of mind and language to political philosophy and the question of what constitutes a truly democratic society
  • He likened dogmatic intellectual structures that interfere with free inquiry to coercive political structures that stifle the individual’s creative independence and fail to encourage humanity’s “richest diversity
  • He asserted that any institution marked by domination and hierarchy must rise to the challenge of justifying itself, and if it cannot meet the challenge, it should be dismantled.
  • He contrasted two accounts of democracy: one — associated by him with James Madison — distrusts the “unwashed” populace and puts its faith in representative government where those doing the representing (and the voting and the distributing of goods) constitute a moneyed and propertied elite
  • the other — associated by him with Adam Smith (in one of his moods), J. S. Mill, the 1960s and a tradition of anarchist writing — seeks to expand the franchise and multiply choices in the realms of thought, politics and economics. The impulse of this second, libertarian, strain of democracy, is “to free society from economic or theological guardianship,” and by “theological” Chomsky meant not formal religion as such but any assumed and frozen ideology that blocked inquiry and limited participation. There can’t, in short, be “too much democracy.”
  • It was thought of the highest order performed by a thinker, now 85 years old, who by and large eschewed rhetorical flourishes (he has called his own speaking style “boring” and says he likes it that way) and just did it, where ‘it” was the patient exploration of deep issues that had been explored before him by a succession of predecessors, fully acknowledged, in a conversation that is forever being continued and forever being replenished.
  • Yes, I said to myself, this is what we — those of us who bought a ticket on this particular train — do; we think about problems and puzzles and try to advance the understanding of them; and we do that kind of thinking because its pleasures are, in a strong sense, athletic and provide for us, at least on occasion, the experience of fully realizing whatever capabilities we might have. And we do it in order to have that experience, and to share it with colleagues and students of like mind, and not to make a moral or political point.
  • The term “master class” is a bit overused, but I feel no hesitation in using it here. It was a master class taught by a master, and if someone were to ask me what exactly is it that academics do, I would point to these lectures and say, simply, here it is, the thing itself.
grayton downing

Language Makes the Invisible Visible | The Scientist Magazine® - 0 views

  • Language helps the human brain perceive obscured objects,
  • While some scientists have argued that vision is independent from outside factors, such as sounds or the brain’s accumulated knowledge, the study indicates that language influences perception at its most basic level.
  • “I think [the study] makes a really important contribution to the field of visual perception and cognition in general,” said Michael Spivey,
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  • tested the effects of language on perception by either saying or not saying a word and showing study participants either an obscured image that matched the word, an obscured image that did not match the word, or no image at all. Images ranged from kangaroos to bananas to laundry baskets. The researchers then asked the participants if they had perceived any objects and, if so, ascertained what they had seen.
  • participants were more likely to perceive an image if they had been given an accurate verbal cue first than if they had been given no cue or an incorrect one. With an accurate cue, they identified the object correctly around 50 percent and 80 percent of the time
  • &nbsp;By using continuous flash suppression, Lupyan has “done the best job yet of showing where the interaction happening is in perception,” Spivey said.
  • Lupyan said that his work could help researchers discern whether people who speak different languages perceive the world differently. For instance, if two people spoke different languages that either did or did not have words for a certain color or texture, the person lacking language to describe the color or texture might be less likely to perceive it.
  • “More and more what the field is finding is that any cognitive or perceptual capacity you find interesting is probably richly connected with other ones.” “The visual system—and perception in general—uses all the information it can get to make sense of the inputs,” said Lupyan. “Vision is not just about the photons hitting the eye.”
  • “The visual system—and perception in general—uses all the information it can get to make sense of the inputs,” said Lupyan
Javier E

The Fall of Facebook - The Atlantic - 0 views

  • Alexis C. Madrigal Nov 17 2014, 7:59 PM ET Social networking is not, it turns out, winner take all. In the past, one might have imagined that switching between Facebook and “some other network” would be difficult, but the smartphone interface makes it easy to be on a dozen networks. All messages come to the same place—the phone’s notifications screen—so what matters is what your friends are doing, not which apps they’re using.
  • if I were to put money on an area in which Facebook might be unable to dominate in the future, it would be apps that take advantage of physical proximity. Something radically new could arise on that front, whether it’s an evolution of Yik Yak
  • The Social Machine, predicts that text will be a less and less important part of our asynchronous communications mix. Instead, she foresees a “very fluid interface” that would mix text with voice, video, sensor outputs (location, say, or vital signs), and who knows what else
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  • the forthcoming Apple Watch seems like a step toward the future Donath envisions. Users will be able to send animated smiley faces, drawings, voice snippets, and even their live heartbeats, which will be tapped out on the receiver’s wrist.
  • A simple but rich messaging platform—perhaps with specialized hardware—could replace the omnibus social network for most purposes. “I think we’re shifting in a weird way to one-on-one conversations on social networks and in messaging apps,” says Shani Hilton, the executive editor for news at BuzzFeed, the viral-media site. “People don’t want to perform their lives publicly in the same way that they wanted to five years ago.”
  • Facebook is built around a trade-off that it has asked users to make: Give us all your personal information, post all your pictures, tag all your friends, and so on, forever. In return, we’ll optimize your social life. But this output is only as good as the input. And it turns out that, when scaled up, creating this input—making yourself legible enough to the Facebook machine that your posts are deemed “relevant” and worthy of being displayed to your mom and your friends—is exhausting labor.
  • These new apps, then, are arguments that we can still have an Internet that is weird, and private. That we can still have social networks without the social network. And that we can still have friends on the Internet without “friending” them.
  • A Brief History of Information Gatekeepers 1871: Western Union controls 90 percent of U.S. telegraph traffic. 1947: 97 percent of the country’s radio stations are affiliated with one of four national networks. 1969: Viewership for the three nightly network newscasts hits an all-time high, with 50 percent of all American homes tuning in. 1997: About half of all American homes with Internet access get it through America Online. 2002: Microsoft Internet Explorer captures 97 percent of the worldwide browser market. 2014: Amazon sells 63 percent of all books bought online—and 40 percent of books overall.
Javier E

Next Stop: 100,000 Dead? - 0 views

  • A model is not a report sent back from the future. It's an exercise in taking what we know, what we think we know, and what we have no idea about, making some educated guesses about how those three pieces will interact, and coming up with a probabilistic set of possible future outcomes.
  • Models change as new data comes in (adding to the "stuff we know" inputs) and the universe of the other two inputs ("stuff we think we know" and "stuff we have no idea about") change.
marleen_ueberall

Does Democracy Need Truth?: A Conversation with the Historian Sophia Rosenfeld | The Ne... - 0 views

  • Does Democracy Need Truth?: A Conversation with the Historian Sophia Rosenfeld | The New Yorker
  • Ever since Donald Trump announced his Presidential candidacy, in June of 2015, there has been considerable concern about whether his allergy to truth is endangering American democracy
  • the relationship between truth and democracy was fraught for centuries before the time of Twitter and Trump.
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  • One, it’s a story about how democracy itself is always based on uncertain notions of truth, in moral terms and in epistemological terms. The other is a story about a continual conflict between a kind of expert truth and a more populist, everyday, common-sense truth that supposedly stems not from experts but the wisdom of the crowd.
  • Democracy insists on the idea that truth both matters and that nobody gets to say definitively what it is. That’s a tension that’s built into democracy from the beginning, and it’s not solvable but is, in fact, intrinsic to democracy.
  • We don’t want to have one definitive source of truth. Part of the reason ideas evolve and culture changes is that we’re constantly debating what is an accurate rendition of reality in some form.
  • Can we accept evolution as a set truth or not? They have not exploded to the point where they’ve destabilized our political or social life, but they’ve been a controversial question for over a hundred years. That’s a public contest that, actually, democracy’s pretty good for. You know, you contest things in court, you contest things in universities, you contest things in the public sphere.
  • I think it’s important that there be a contest about what is true and also about, How do you know what’s true? Where does your information come from? I would say, largely, science has won. That is, that the mainstream educational institutions, the National Institutes of Health, et cetera, all accept that evolution is as close as we’re going to get to truth.
  • One says that experts often make [bad] decisions because there’s been no popular input on them—not just because they don’t know enough but because they haven’t actually taken account of popular knowledge.
  • The most common example involves things like the World Bank coming up with a plan about water use in some part of the world without studying how people actually think and use water, simply imagining a kind of technocratic solution with no local input, and it turns out to be totally ineffective because it runs contrary to cultural norms and everyday life. There’s every chance that experts alone get things wrong.
  • Social media and the Internet more broadly have clearly had a rather revolutionary effect on not just what we take to be true but how truths circulate, what we believe, how we know anything.
  • new technology causes certain kinds of panics about truth. The Internet is particularly important because of its reach and because of the algorithmic way in which it promotes what’s popular rather than what’s true. It creates a culture of untruth, probably, that other forms of publishing can’t easily.
  • I actually approve of fact-checking, even if I think it’s often not very effective, because it doesn’t persuade people who aren’t already inclined to want to look at fact-checking. And I don’t think it’s much of a substitute for real politics
  • I don’t think facts are pure in any sense. You know, if I give you something like an unemployment rate, it implies all kinds of interpretative work already about what is work and who should be looking for it and how old you should be when you’re working.
  • It’s important that that’s part of democracy, too—questioning received wisdom. If somebody says that’s how it is, it’s correct to think, Is that really how it is? Do I have enough information to be sure that’s how it is?
  • Conspiracy theories, the complex ones that arise from the bottom, tend to involve seeing through official truths and often seeing how the rich and powerful have pulled the wool over people’s eyes, that what looked like this turned out to be that because there was a kind of subterfuge going on from above.
  • Whereas, the climate-change one, which we know has been sort of promoted by the Koch brothers and others in business interest groups, as you say, didn’t start really organically as much as it became a kind of position of industry that then took on a life of its own because it got mixed in with a whole bunch of other assumptions, whether it was about political norms, government overreach, guns.
krystalxu

7 Great Theories About Language Learning by Brilliant Thinkers | FluentU Language Learn... - 0 views

  • philosophers in Ancient Greece and 16th century France were concerned about are largely still relevant today.
  • In the nature versus nurture debate, Plato tended to side with nature, believing that&nbsp;knowledge was&nbsp;innate.
  • what we already know, using our innate abilities to come to an understanding of the particularities of a specific language. If Locke is right, then we must focus our attention on sensory input, gaining as much external input as possible.
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  • &nbsp;all behavior is no more than a response to external stimuli and there’s no innate programming within a human being to learn a language at birth.
  • understood to be the result of the&nbsp;universal elements that structure all languages.
Javier E

For Chat-Based AI, We Are All Once Again Tech Companies' Guinea Pigs - WSJ - 0 views

  • The companies touting new chat-based artificial-intelligence systems are running a massive experiment—and we are the test subjects.
  • In this experiment, Microsoft, MSFT -2.18% OpenAI and others are rolling out on the internet an alien intelligence that no one really understands, which has been granted the ability to influence our assessment of what’s true in the world.&nbsp;
  • Companies have been cautious in the past about unleashing this technology on the world. In 2019, OpenAI decided not to release an earlier version of the underlying model that powers both&nbsp;ChatGPT and the new Bing because the company’s leaders deemed it too dangerous to do so, they said at the time.
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  • Microsoft leaders felt “enormous urgency” for it to be the company to bring this technology to market, because others around the world are working on similar tech but might not have the resources or inclination to build it as responsibly, says Sarah Bird, a leader on Microsoft’s responsible AI team.
  • One common starting point for such models is what is essentially a download or “scrape” of most of the internet. In the past, these language models were used to try to understand text, but the new generation of them, part of the revolution in “generative” AI, uses those same models to create texts by trying to guess, one word at a time, the most likely word to come next in any given sequence.
  • Wide-scale testing gives Microsoft and OpenAI a big competitive edge by enabling them to gather huge amounts of data about how people actually use such chatbots. Both the prompts users input into their systems, and the results their AIs spit out, can then be fed back into a complicated system—which includes human content moderators paid by the companies—to improve it.
  • , being first to market with a chat-based AI gives these companies a huge initial lead over companies that have been slower to release their own chat-based AIs, such as Google.
  • rarely has an experiment like Microsoft and OpenAI’s been rolled out so quickly, and at such a broad scale.
  • Among those who build and study these kinds of AIs, Mr. Altman’s case for experimenting on the global public has inspired responses ranging from raised eyebrows to condemnation.
  • The fact that we’re all guinea pigs in this experiment doesn’t mean it shouldn’t be conducted, says Nathan Lambert, a research scientist at the AI startup Huggingface.
  • “I would kind of be happier with Microsoft doing this experiment than a startup, because Microsoft will at least address these issues when the press cycle gets really bad,” says Dr. Lambert. “I think there are going to be a lot of harms from this kind of AI, and it’s better people know they are coming,” he adds.
  • Others, particularly those who study and advocate for the concept of “ethical AI” or “responsible AI,” argue that the global experiment Microsoft and OpenAI are conducting is downright dangerous
  • Celeste Kidd, a professor of psychology at University of California, Berkeley, studies how people acquire knowledge
  • Her research has shown that people learning about new things have a narrow window in which they form a lasting opinion. Seeing misinformation during this critical initial period of exposure to a new concept—such as the kind of misinformation that chat-based AIs can confidently dispense—can do lasting harm, she says.
  • Dr. Kidd likens OpenAI’s experimentation with AI to exposing the public to possibly dangerous chemicals. “Imagine you put something carcinogenic in the drinking water and you were like, ‘We’ll see if it’s carcinogenic.’ After, you can’t take it back—people have cancer now,”
  • Part of the challenge with AI chatbots is that they can sometimes simply make things up. Numerous examples of this tendency have been documented by users of both ChatGPT and OpenA
  • These models also tend to be riddled with biases that may not be immediately apparent to users. For example, they can express opinions gleaned from the internet as if they were verified facts
  • When millions are exposed to these biases across billions of interactions, this AI has the potential to refashion humanity’s views, at a global scale, says Dr. Kidd.
  • OpenAI has talked publicly about the problems with these systems, and how it is trying to address them. In a recent blog post, the company said that in the future, users might be able to select AIs whose “values” align with their own.
  • “We believe that AI should be a useful tool for individual people, and thus customizable by each user up to limits defined by society,” the post said.
  • Eliminating made-up information and bias from chat-based search engines is impossible given the current state of the technology, says Mark Riedl, a professor at Georgia Institute of Technology who studies artificial intelligence
  • He believes the release of these technologies to the public by Microsoft and OpenAI is premature. “We are putting out products that are still being actively researched at this moment,” he adds.&nbsp;
  • in other areas of human endeavor—from new drugs and new modes of transportation to advertising and broadcast media—we have standards for what can and cannot be unleashed on the public. No such standards exist for AI, says Dr. Riedl.
  • To modify these AIs so that they produce outputs that humans find both useful and not-offensive, engineers often use a process called “reinforcement learning through human feedback.
  • that’s a fancy way of saying that humans provide input to the raw AI algorithm, often by simply saying which of its potential responses to a query are better—and also which are not acceptable at all.
  • Microsoft’s and OpenAI’s globe-spanning experiments on millions of people are yielding a fire hose of data for both companies. User-entered prompts and the AI-generated results are fed back through a network of paid human AI trainers to further fine-tune the models,
  • Huggingface’s Dr. Lambert says that any company, including his own, that doesn’t have this river of real-world usage data helping it improve its AI is at a huge disadvantage
  • In chatbots, in some autonomous-driving systems, in the unaccountable AIs that decide what we see on social media, and now, in the latest applications of AI, again and again we are the guinea pigs on which tech companies are testing new technology.
  • It may be the case that there is no other way to roll out this latest iteration of AI—which is already showing promise in some areas—at scale. But we should always be asking, at times like these: At what price?
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