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Bill Fulkerson

Anatomy of an AI System - 1 views

anatomyof.ai

AI amazon

shared by Bill Fulkerson on 14 Sep 18 - No Cached
  • Bill Fulkerson on 14 Sep 18
     
    "With each interaction, Alexa is training to hear better, to interpret more precisely, to trigger actions that map to the user's commands more accurately, and to build a more complete model of their preferences, habits and desires. What is required to make this possible? Put simply: each small moment of convenience - be it answering a question, turning on a light, or playing a song - requires a vast planetary network, fueled by the extraction of non-renewable materials, labor, and data. The scale of resources required is many magnitudes greater than the energy and labor it would take a human to operate a household appliance or flick a switch. A full accounting for these costs is almost impossible, but it is increasingly important that we grasp the scale and scope if we are to understand and govern the technical infrastructures that thread through our lives. III The Salar, the world's largest flat surface, is located in southwest Bolivia at an altitude of 3,656 meters above sea level. It is a high plateau, covered by a few meters of salt crust which are exceptionally rich in lithium, containing 50% to 70% of the world's lithium reserves. 4 The Salar, alongside the neighboring Atacama regions in Chile and Argentina, are major sites for lithium extraction. This soft, silvery metal is currently used to power mobile connected devices, as a crucial material used for the production of lithium-Ion batteries. It is known as 'grey gold.' Smartphone batteries, for example, usually have less than eight grams of this material. 5 Each Tesla car needs approximately seven kilograms of lithium for its battery pack. 6 All these batteries have a limited lifespan, and once consumed they are thrown away as waste. Amazon reminds users that they cannot open up and repair their Echo, because this will void the warranty. The Amazon Echo is wall-powered, and also has a mobile battery base. This also has a limited lifespan and then must be thrown away as waste. According to the Ay
Bill Fulkerson

The Birth of the New American Aristocracy - The Atlantic - 0 views

www.theatlantic.com/...559130

economy meritocracy

shared by Bill Fulkerson on 17 Sep 18 - No Cached
  • Bill Fulkerson on 17 Sep 18
     
    "The meritocratic class has mastered the old trick of consolidating wealth and passing privilege along at the expense of other people's children."
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 Cheap Labor Drives China's A.I. Ambitions - The New York Times - 1 views

www.nytimes.com/...ial-intelligence-labeling.html

artificial intelligence china labor data management machine learning

shared by Steve Bosserman on 26 Nov 18 - No Cached
  • But the ability to tag that data may be China’s true A.I. strength, the only one that the United States may not be able to match. In China, this new industry offers a glimpse of a future that the government has long promised: an economy built on technology rather than manufacturing.
  • “We’re the construction workers in the digital world. Our job is to lay one brick after another,” said Yi Yake, co-founder of a data labeling factory in Jiaxian, a city in central Henan province. “But we play an important role in A.I. Without us, they can’t build the skyscrapers.”
  • While A.I. engines are superfast learners and good at tackling complex calculations, they lack cognitive abilities that even the average 5-year-old possesses. Small children know that a furry brown cocker spaniel and a black Great Dane are both dogs. They can tell a Ford pickup from a Volkswagen Beetle, and yet they know both are cars.A.I. has to be taught. It must digest vast amounts of tagged photos and videos before it realizes that a black cat and a white cat are both cats. This is where the data factories and their workers come in.
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  • “All the artificial intelligence is built on human labor,” Mr. Liang said.
  • “We’re the assembly lines 10 years ago,” said Mr. Yi, the co-founder of the data factory in Henan.
Steve Bosserman

Unintended consequences - Wikipedia - 0 views

en.wikipedia.org/...Unintended_consequences

sociology human behavior

shared by Steve Bosserman on 15 Aug 17 - No Cached
  • Unintended consequences can be grouped into three types: Unexpected benefit: A positive unexpected benefit (also referred to as luck, serendipity or a windfall). Unexpected drawback: An unexpected detriment occurring in addition to the desired effect of the policy (e.g., while irrigation schemes provide people with water for agriculture, they can increase waterborne diseases that have devastating health effects, such as schistosomiasis). Perverse result: A perverse effect contrary to what was originally intended (when an intended solution makes a problem worse). This is sometimes referred to as 'backfire'.
  • Robert K. Merton listed five possible causes of unanticipated consequences in 1936:[13] Ignorance, making it impossible to anticipate everything, thereby leading to incomplete analysis Errors in analysis of the problem or following habits that worked in the past but may not apply to the current situation Immediate interests overriding long-term interests Basic values which may require or prohibit certain actions even if the long-term result might be unfavorable (these long-term consequences may eventually cause changes in basic values) Self-defeating prophecy, or, the fear of some consequence which drives people to find solutions before the problem occurs, thus the non-occurrence of the problem is not anticipated
Steve Bosserman

Are You Creditworthy? The Algorithm Will Decide. - 0 views

undark.org/...redit-scoring-machine-learning

algorithms discrimination bias credit scoring

shared by Steve Bosserman on 12 May 18 - No Cached
  • The decisions made by algorithmic credit scoring applications are not only said to be more accurate in predicting risk than traditional scoring methods; its champions argue they are also fairer because the algorithm is unswayed by the racial, gender, and socioeconomic biases that have skewed access to credit in the past.
  • Algorithmic credit scores might seem futuristic, but these practices do have roots in credit scoring practices of yore. Early credit agencies, for example, hired human reporters to dig into their customers’ credit histories. The reports were largely compiled from local gossip and colored by the speculations of the predominantly white, male middle class reporters. Remarks about race and class, asides about housekeeping, and speculations about sexual orientation all abounded.
  • By 1935, whole neighborhoods in the U.S. were classified according to their credit characteristics. A map from that year of Greater Atlanta comes color-coded in shades of blue (desirable), yellow (definitely declining) and red (hazardous). The legend recalls a time when an individual’s chances of receiving a mortgage were shaped by their geographic status.
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  • These systems are fast becoming the norm. The Chinese Government is now close to launching its own algorithmic “Social Credit System” for its 1.4 billion citizens, a metric that uses online data to rate trustworthiness. As these systems become pervasive, and scores come to stand for individual worth, determining access to finance, services, and basic freedoms, the stakes of one bad decision are that much higher. This is to say nothing of the legitimacy of using such algorithmic proxies in the first place. While it might seem obvious to call for greater transparency in these systems, with machine learning and massive datasets it’s extremely difficult to locate bias. Even if we could peer inside the black box, we probably wouldn’t find a clause in the code instructing the system to discriminate against the poor, or people of color, or even people who play too many video games. More important than understanding how these scores get calculated is giving users meaningful opportunities to dispute and contest adverse decisions that are made about them by the algorithm.
Bill Fulkerson

Even if you build it, the poor can't come: against supply-side - Mark R Reiff | Aeon Ideas - 0 views

aeon.co/...-cant-come-against-supply-side

supply demand economics poor

shared by Bill Fulkerson on 05 Jun 18 - No Cached
  • Recent history has shown that we can’t be sure that economic expansion alone will solve our wider economic problems. Almost all of the benefits of economic growth during the past 30 years or so have accrued to the rich, and mostly to the super-rich. Real income for most people has been stagnant or even declined. The new jobs that have been created are mostly temporary, low-wage, no-benefit jobs. Permanent, good-wage jobs with benefits have continued to disappear. Rather than giving money to the rich in these circumstances and hoping that it trickles down to the rest of us, as the supply-siders suggest, it would be better to give money to the poor and middle-class, as the Keynesians suggest. The Keynesian approach, after all, has worked many times in the past. Indeed, it’s how the West emerged from the Great Depression. But most importantly, if for some reason it doesn’t work, at least we will have made the right people better off.
Bill Fulkerson

Line of defense: Scientists report surprising evolutionary shift in snakes - 0 views

phys.org/...ntists-evolutionary-shift.html

snakes evolutionary defense

shared by Bill Fulkerson on 25 Feb 20 - No Cached
  • Bill Fulkerson on 25 Feb 20
     
    "This is the first documented case of a vertebrate predator switching from a vertebrate prey to an invertebrate prey for the selective advantage of getting the same chemical class of defensive toxin,"
Bill Fulkerson

Why the US is so vulnerable to coronavirus outbreak | Financial Times - 0 views

www.ft.com/...02-5f39-11ea-b0ab-339c2307bcd4

coronavirus vulnerability usa

shared by Bill Fulkerson on 08 Mar 20 - No Cached
  • Bill Fulkerson on 08 Mar 20
     
    Please use the sharing tools found via the share button at the top or side of articles. Copying articles to share with others is a breach of FT.com T&Cs and Copyright Policy. Email licensing@ft.com to buy additional rights. Subscribers may share up to 10 or 20 articles per month using the gift article service. More information can be found at https://www.ft.com/tour. https://www.ft.com/content/00017d02-5f39-11ea-b0ab-339c2307bcd4?sharetype=blocked Public health officials and academics are concerned that a mix of high numbers of uninsured people, a lack of paid sick leave and a political class that has downplayed the threat could mean it spreads more quickly than in other countries.
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