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Bill Fulkerson

Research on seawater surface tension becomes international guideline - 0 views

phys.org/...n-international-guideline.html

tension oceans

shared by Bill Fulkerson on 10 Jul 20 - No Cached
  • Bill Fulkerson on 10 Jul 20
     
    The property of water that enables a bug to skim the surface of a pond or keeps a carefully placed paperclip floating on the top of a cup of water is known as surface tension. Understanding the surface tension of water is important in a wide range of applications including heat transfer, desalination, and oceanography. Although much is known about the surface tension of fresh water, very little has been known about the surface tension of seawater-until recently.
Steve Bosserman

There is no difference between computer art and human art | Aeon Ideas - 0 views

aeon.co/...computer-art-it-s-all-just-art

artificial intelligence computer art man versus machine

shared by Steve Bosserman on 20 Jul 16 - No Cached
  • In industry, there is blunt-force algorithmic tension – ‘Efficiency, capitalism, commerce!’ versus ‘Robots are stealing our jobs!’ But for algorithmic art, the tension is subtler. Only 4 per cent of the work done in the United States economy requires ‘creativity at a median human level’, according to the consulting firm McKinsey and Company. So for computer art – which tries explicitly to zoom into this small piece of that vocational pie – it’s a question not of efficiency or equity, but of trust. Art requires emotional and phrenic investments, with the promised return of a shared slice of the human experience. When we view computer art, the pestering, creepy worry is: who’s on the other end of the line? Is it human? We might, then, worry that it’s not art at all.
  • But the honest-to-God truth, at the end of all of this, is that this whole notion is in some way a put-on: a distinction without a difference. ‘Computer art’ doesn’t really exist in an any more provocative sense than ‘paint art’ or ‘piano art’ does. The algorithmic software was written by a human, after all, using theories thought up by a human, using a computer built by a human, using specs written by a human, using materials gathered by a human, at a company staffed by humans, using tools built by a human, and so on. Computer art is human art – a subset rather than a distinction. It’s safe to release the tension.
Bill Fulkerson

What You Should Know About Megaprojects and Why: An Overview by Bent Flyvbjerg :: SSRN - 0 views

papers.ssrn.com/...papers.cfm

mega-projects gradient optimization

shared by Bill Fulkerson on 21 Aug 18 - No Cached
  • Bill Fulkerson on 21 Aug 18
     
    "his paper takes stock of megaproject management, an emerging and hugely costly field of study. First, it answers the question of how large megaprojects are by measuring them in the units mega, giga, and tera, concluding we are presently entering a new "tera era" of trillion-dollar projects. Second, total global megaproject spending is assessed, at USD 6-9 trillion annually, or 8 percent of total global GDP, which denotes the biggest investment boom in human history. Third, four "sublimes" - political, technological, economic, and aesthetic - are identified to explain the increased size and frequency of megaprojects. Fourth, the "iron law of megaprojects" is laid out and documented: Over budget, over time, over and over again. Moreover, the "break-fix model" of megaproject management is introduced as an explanation of the iron law. Fifth, Albert O. Hirschman's theory of the Hiding Hand is revisited and critiqued as unfounded and corrupting for megaproject thinking in both the academy and policy. Sixth, it is shown how megaprojects are systematically subject to "survival of the unfittest," explaining why the worst projects get built instead of the best. Finally, it is argued that the conventional way of managing megaprojects has reached a "tension point," where tradition is challenged and reform is emerging. "
Bill Fulkerson

A New Theorem Maps Out the Limits of Quantum Physics | Quanta Magazine - 0 views

www.quantamagazine.org/ts-of-quantum-physics-20201203

physics quantum limits

shared by Bill Fulkerson on 08 Dec 20 - No Cached
  • Bill Fulkerson on 08 Dec 20
     
    The result highlights a fundamental tension: Either the rules of quantum mechanics don't always apply, or at least one basic assumption about reality must be wrong.
Steve Bosserman

Marcy Wheeler: On "Fake News" | naked capitalism - 0 views

www.nakedcapitalism.com/...marcy-wheeler-fake-news.html

fake news neoliberalism democracy

shared by Steve Bosserman on 16 Jan 17 - No Cached
  • First, underlying most of this argument is an argument about what happens when you subject the telling of true stories to certain conditions of capitalism. There is often a tension in this process, as capitalism may make “news” (and therefore full participation in democracy) available to more people, but to popularize that news, businesses do things that taint the elite’s idealized notion of what true story telling in a democracy should be
  • Finally, one reason there is such a panic about “fake news” is because the western ideology of neoliberalism has failed. It has led to increased authoritarianism, decreased qualify of life in developed countries (but not parts of Africa and other developing nations), and it has led to serial destabilizing wars along with the refugee crises that further destabilize Europe. It has failed in the same way that communism failed before it, but the elites backing it haven’t figured this out yet.
Steve Bosserman

What does home mean if your bed is on the pavements of Paris? | Aeon Essays - 0 views

aeon.co/...d-is-on-the-pavements-of-paris

homelessness anthropology paris home

shared by Steve Bosserman on 29 Nov 18 - No Cached
  • The American anthropologist Edward Fischer, paraphrasing Aristotle, said that the good life is ‘a life worth living’, or a journey towards ‘a fulfilled life’. It has to do with happiness but is not limited to it; it’s often – perhaps counterintuitively – linked to commitment and sacrifice, to the work of becoming a particular person. The French philosopher Michel Foucault in 1982 described these practices as technologies of the self. According to Foucault, the self is ‘not given to us … we have to create ourselves as a work of art’. My informants on the streets of Paris were striving – in their own ways – towards being better selves. I came to understand the activities, processes and routines that they engaged in – begging, making a shelter, accessing temporary housing, etc – as practices of the self geared towards a better life, as practices of homemaking on the street, as practices of hope.
  • Aside from François, others I met on the streets of Paris – such as Sabal from India, and Alex from Kosovo – talked about their engagement in such practices of hope. Following them through soup kitchens, drop-in centres, government institutions and homeless shelters, I observed two main ways in which they attempted to push for a better life. Both of them were connected to the idea of home: my informants in Paris were longing to find and go back to a homeland, often one from the past, while on a daily basis they were struggling to construct a home in order to survive. That was what a better life looked like for them.
  • Home, according to the Australian social scientist Shelley Mallett, is always suspended between the ideal and the real. It relates to ‘the activity performed by, with or in person’s things and places. Home is lived in the tension between the given and the chosen, then and now.’ While Sabal’s India was part of the ideal, what Alex was dealing with was closer to the ‘real’ side of this distinction. His home-making efforts were a continuous process of daily activities.
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  • For many of the homeless people I’ve met in both London and Paris, home was connected to a place they departed from and have a desire to return to – a place that carried what the English sociologist Liz Kenyon calls a right to return and a sense of one’s origin. Sara Ahmed’s 1999 study of migrants’ writing, particularly Asian women living in Britain, supports this view of home as something in the longer-term future. The British-Australian scholar wrote that home is often a destination, somewhere to travel to: ‘the space which is most like home, which is most comfortable and familiar, is not the space of inhabitance – I am here – but the very space in which one finds the self as almost, but not quite, at home. In such a space, the subject has a destination, an itinerary, indeed a future, but in having such as destination, has not yet arrived.’ Home is, in this sense, not about the present – and surely not a place of passive suffering – but about one’s hopes, about making home an imagined place where one has not yet arrived.
  • Home is exactly such a process, involving the material and the imaginative, social connections and mundane acts. Routines, habits and rhythms – often as simple as regularly visiting certain neighbourhoods, shelters and food kitchens – are important parts of this process, and are deeply connected to a temporal as well as spatial order. This focus on order is best expressed in the classical analysis of home by the English anthropologist Mary Douglas:[Home] is always a localisable idea. Home is located in space but it is not necessarily a fixed space. It does not need bricks and mortar, it can be a wagon, a caravan, a board, or a tent. It need not be a large space, but space there must be, for home starts by bringing some space under control.
  • François, who introduced me to the labour of begging, found something close to home in his daily practices. His home was fashioned by coming face to face with the city around him. These narratives show how far removed these people are from a state of passive suffering. Yes, there were moments of idleness and, for some, long phases of pain. But most of the people I met sleeping rough – independent of age, gender, tenure on the street and level of addiction – were striving, in their way, towards a better life: first on, and hopefully off, the street.
Steve Bosserman

High score, low pay: why the gig economy loves gamification | Business | The Guardian - 0 views

www.theguardian.com/...ivers-ride-hailing-gig-economy

gamification gig economy taxi service incentive productivity profit

shared by Steve Bosserman on 23 Nov 18 - No Cached
  • Simply defined, gamification is the use of game elements – point-scoring, levels, competition with others, measurable evidence of accomplishment, ratings and rules of play – in non-game contexts. Games deliver an instantaneous, visceral experience of success and reward, and they are increasingly used in the workplace to promote emotional engagement with the work process, to increase workers’ psychological investment in completing otherwise uninspiring tasks, and to influence, or “nudge”, workers’ behaviour.
  • According to Burawoy, production at Allied was deliberately organised by management to encourage workers to play the game. When work took the form of a game, Burawoy observed, something interesting happened: workers’ primary source of conflict was no longer with the boss. Instead, tensions were dispersed between workers (the scheduling man, the truckers, the inspectors), between operators and their machines, and between operators and their own physical limitations (their stamina, precision of movement, focus). The battle to beat the quota also transformed a monotonous, soul-crushing job into an exciting outlet for workers to exercise their creativity, speed and skill. Workers attached notions of status and prestige to their output, and the game presented them with a series of choices throughout the day, affording them a sense of relative autonomy and control. It tapped into a worker’s desire for self-determination and self-expression. Then, it directed that desire towards the production of profit for their employer.
  • Former Google “design ethicist” Tristan Harris has also described how the “pull-to-refresh” mechanism used in most social media feeds mimics the clever architecture of a slot machine: users never know when they are going to experience gratification – a dozen new likes or retweets – but they know that gratification will eventually come. This unpredictability is addictive: behavioural psychologists have long understood that gambling uses variable reinforcement schedules – unpredictable intervals of uncertainty, anticipation and feedback – to condition players into playing just one more round.
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  • Gaming the game, Burawoy observed, allowed workers to assert some limited control over the labour process, and to “make out” as a result. In turn, that win had the effect of reproducing the players’ commitment to playing, and their consent to the rules of the game. When players were unsuccessful, their dissatisfaction was directed at the game’s obstacles, not at the capitalist class, which sets the rules. The inbuilt antagonism between the player and the game replaces, in the mind of the worker, the deeper antagonism between boss and worker. Learning how to operate cleverly within the game’s parameters becomes the only imaginable option. And now there is another layer interposed between labour and capital: the algorithm.
Steve Bosserman

How We Made AI As Racist and Sexist As Humans - 0 views

thewalrus.ca/as-racist-and-sexist-as-humans

artificial intelligence algorithms sexism data mining racism

shared by Steve Bosserman on 25 May 18 - No Cached
  • Artificial intelligence may have cracked the code on certain tasks that typically require human smarts, but in order to learn, these algorithms need vast quantities of data that humans have produced. They hoover up that information, rummage around in search of commonalities and correlations, and then offer a classification or prediction (whether that lesion is cancerous, whether you’ll default on your loan) based on the patterns they detect. Yet they’re only as clever as the data they’re trained on, which means that our limitations—our biases, our blind spots, our inattention—become theirs as well.
  • The majority of AI systems used in commercial applications—the ones that mediate our access to services like jobs, credit, and loans— are proprietary, their algorithms and training data kept hidden from public view. That makes it exceptionally difficult for an individual to interrogate the decisions of a machine or to know when an algorithm, trained on historical examples checkered by human bias, is stacked against them. And forget about trying to prove that AI systems may be violating human rights legislation.
  • Data is essential to the operation of an AI system. And the more complicated the system—the more layers in the neural nets, to translate speech or identify faces or calculate the likelihood someone defaults on a loan—the more data must be collected.
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  • But not everyone will be equally represented in that data.
  • And sometimes, even when ample data exists, those who build the training sets don’t take deliberate measures to ensure its diversity
  • The power of the system is its “ability to recognize that correlations occur between gender and professions,” says Kathryn Hume. “The downside is that there’s no intentionality behind the system—it’s just math picking up on correlations. It doesn’t know this is a sensitive issue.” There’s a tension between the futuristic and the archaic at play in this technology. AI is evolving much more rapidly than the data it has to work with, so it’s destined not just to reflect and replicate biases but also to prolong and reinforce them.
  • Accordingly, groups that have been the target of systemic discrimination by institutions that include police forces and courts don’t fare any better when judgment is handed over to a machine.
  • A growing field of research, in fact, now looks to apply algorithmic solutions to the problems of algorithmic bias.
  • Still, algorithmic interventions only do so much; addressing bias also demands diversity in the programmers who are training machines in the first place.
  • A growing awareness of algorithmic bias isn’t only a chance to intervene in our approaches to building AI systems. It’s an opportunity to interrogate why the data we’ve created looks like this and what prejudices continue to shape a society that allows these patterns in the data to emerge.
  • Of course, there’s another solution, elegant in its simplicity and fundamentally fair: get better data.
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