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Bill Fulkerson

It's not all Pepes and trollfaces - memes can be a force for good - The Verge - 0 views

www.theverge.com/...d-behavioral-science-nazi-pepe

memes

shared by Bill Fulkerson on 30 Aug 18 - No Cached
  • Bill Fulkerson on 30 Aug 18
     
    "How the 'emotional contagion' of memes makes them the internet's moral conscience By Allie Volpe Aug 27, 2018, 11:30am EDT Illustration by Alex Castro & Keegan Larwin SHARE Newly single, Jason Donahoe was perusing Tinder for the first time since it started integrating users' Instagram feeds. Suddenly, he had an idea: follow the Instagram accounts of some of the women he'd been interested in but didn't match with on the dating service. A few days later, he considered taking it a step further and direct messaging one of the women on Instagram. After all, the new interface of the dating app seemed to encourage users to explore other areas of potential matches' online lives, so why not take the initiative to reach out? Before he had a chance, however, he came across the profile of another woman whose Tinder photo spread featured a meme with Parks and Recreation character Jean-Ralphio Saperstein (Ben Schwartz) leaning into the face of Ben Wyatt (Adam Scott) with the caption: hey I saw you on Tinder but we didn't match so I found your Instagram you're so beautiful you don't need to wear all that makeup ahah I bet you get a lot of creepy dm's but I'm not like all those other guys message me back beautiful btw what's your snap "I was like, 'Oh shit, wow,'" Donahoe says. Seeing his potential jerk move laid out so plainly as a neatly generalized joke, he saw it in a new light. "I knew a) to be aware of that, and b) to cut that shit out … It prompted self-reflection on my part." THE MOST SUCCESSFUL MEMES STRIKE A CULTURAL CHORD AND CAN GUIDE AND EVEN INFLUENCE BEHAVIOR Donahoe says memes have resonated with him particularly when they depict a "worse, extreme version" of himself. For Donahoe, the most successful memes are more than just jokes. They "strike a societal, cultural chord" and can be a potent cocktail for self-reflection as tools that can guide and even influence behavior. In the months leading up to the 2016 US
Steve Bosserman

Will AI replace Humans? - FutureSin - Medium - 0 views

medium.com/...ai-replace-humans-42c19c114cb8

humans robotics artificial intelligence

shared by Steve Bosserman on 26 Apr 18 - No Cached
  • According to the World Economic Forum’s Future of Jobs report, some jobs will be wiped out, others will be in high demand, but all in all, around 5 million jobs will be lost. The real question is then, how many jobs will be made redundant in the 2020s? Many futurists including Google’s Chief Futurist believe this will necessitate a universal human stipend that could become globally ubiquitous as early as the 2030s.
  • AI will optimize many of our systems, but also create new jobs. We don’t know the rate at which it will do this. Research firm Gartner further confirms the hypothesis of AI creating more jobs than it replaces, by predicting that in 2020, AI will create 2.3 million new jobs while eliminating 1.8 million traditional jobs.
  • In an era where it’s being shown we can’t even regulate algorithms, how will we be able to regulate AI and robots that will progressively have a better capacity to self-learn, self-engineer, self-code and self-replicate? This first wave of robots are simply robots capable of performing repetitive tasks, but as human beings become less intelligent trapped in digital immersion, the rate at which robots learn how to learn will exponentially increase.How do humans stay relevant when Big Data enables AI to comb through contextual data as would a supercomputer? Data will no longer be the purvey of human beings, neither medical diagnosis and many other things. To say that AI “augments” human in this respect, is extremely naive and hopelessly optimistic. In many respects, AI completely replaces the need for human beings. This is what I term the automation economy.
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  • If China, Russia and the U.S. are in a race for AI supremacy, the kind of manifestations of AI will be so significant, they could alter the entire future of human civilization.
  • THE EXPONENTIAL THREATFrom drones, to nanobots to 3D-printing, automation could lead to unparalleled changes to how we live and work. In spite of the increase in global GDP, most people’s quality of living is not likely to see the benefit as it will increasingly be funneled into the pockets of the 1%. Capitalism then, favors the development of an AI that’s fundamentally exploitative to the common global citizen.Just as we exchanged our personal data for convenience and the illusion of social connection online, we will barter convenience for a world a global police state where social credit systems and AI decide how much of a “human stipend” (basic income) we receive. Our poverty or the social privilege we are born into, may have a more obscure relationship to a global system where AI monitors every aspect of our lives.Eventually AI will itself be the CEOs, inventors, master engineers and creator of more efficient robots. That’s when we will know that AI has indeed replaced human beings. What will Google’s DeepMind be able to do with the full use of next-gen quantum computing and supercomputers?
  • Artificial Intelligence Will Replace HumansTo argue that AI and robots and 3D-printing and any other significant technology won’t impact and replace many human jobs, is incredibly irresponsible.That’s not to say humans won’t adapt, and even thrive in more creative, social and meaningful work!That AI replacing repetitive tasks is a good thing, can hardly be denied. But will it benefit all globally citizens equally? Will ethics, common sense and collective pragmatism and social inclusion prevail over profiteers?Will younger value systems such as decentralization and sustainable living thrive with the advances of artificial intelligence?Will human beings be able to find sufficient meaning in a life where many of them won’t have a designated occupation to fill their time?These are the question that futurists like me ponder, and you should too.
Steve Bosserman

What smart bees can teach humans about collective intelligence - 0 views

theconversation.com/collective-intelligence-110656

collective responsibility bees collective intelligence collective leadership

shared by Steve Bosserman on 08 Feb 19 - No Cached
  • Why do groups of humans sometimes exhibit collective wisdom and at other times madness? Can we reduce the risk of maladaptive herding and at the same time increase the possibility of collective wisdom?
  • Understanding this apparent conflict has been a longstanding problem in social science. The key to this puzzle could be the way that individuals use information from others versus information gained from their own trial-and-error problem solving. If people simply copy others without reference to their own experience, any idea – even a bad one – can spread. So how can social learning improve our decision making? Striking the right balance between copying others and relying on personal experience is key. Yet we still need to know exactly what the right balance is.
  • Our results suggest that we should be more aware of the risk of maladaptive herding when these conditions – large group size and a difficult problem – prevail. We should take account of not just the most popular opinion, but also other minority opinions. In thinking this way, the crowd can avoid maladaptive herding behaviour. This research could inform how collective intelligence is applied to real-world situations, including online shopping and prediction markets.
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  • Stimulating independent thought in individuals may reduce the risk of collective madness. Dividing a group into sub-groups or breaking down a task into small easy steps promotes flexible, yet smart, human “swarm” intelligence. There is much we can learn from the humble bee.
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

Coronavirus Mitigation - 0 views

mitigation online learning school coronavirus

shared by Bill Fulkerson on 14 Mar 20 - No Cached
  • Bill Fulkerson on 14 Mar 20
     
    Numerical results show that school closure alone would have limited benefit in reducing the peak incidence (less than 10% reduction with 8-week school closure for regions in the early phase of the epidemic). When coupled with 25% adults teleworking, 8-week school closure would be enough to delay the peak by almost 2 months with an approximately 40% reduction of the case incidence at the peak. This is critical to reduce the burden on the healthcare system in the weeks of highest demand. Moderate overall reduction of the final attack rate (15%) would also be achieved. Results across regions are qualitatively similar, with differences
Steve Bosserman

It's time to regulate the gig economy | openDemocracy - 0 views

www.opendemocracy.net/...s-time-to-regulate-gig-economy

gig economy sharing economy favors exchange platforms labor unions

shared by Steve Bosserman on 19 Apr 17 - No Cached
  • Although it would seem straightforward that the laws protecting workers should also apply to workers in what is described as the ‘gig economy’ or ‘platform-based work’, there is much debate – and confusion – on this issue. This lack of clarity stems in part from the novelty of platform-based work. There has also been an effort to conceal the nature of platform-based work through buzzwords such as ‘favours’, ‘rides’, and ‘tasks’ as well as the practice common to many platforms of classifying their workers as independent contractors. Platform-based work includes ‘crowdwork’ and ‘work-on-demand via apps’. In crowdwork, workers complete small jobs or tasks through online platforms, such as Amazon Mechanical Turk, Crowdflower, and Clickworker.  In ‘work-on-demand via apps,’ workers perform duties such as providing transport, cleaning, home repairs, or running errands, but the workers learn about these jobs through mobile apps, from companies such as Uber, Taskrabbit, and Handy. The jobs are performed locally.
  • Platforms mediate extensively the transactions they have with their workers, and also between the customers and the workers.  Platforms often fix the price of the service as well as define the terms and conditions of the service, or they allow the clients to define the terms (but not the worker). The platform may define the schedule or the details of the work, including instructing workers to wear uniforms, to use specific tools, or to treat customers in a particular way. Many platforms have performance review systems that allow customers to rate the workers and they use these ratings to limit the ability of lower-rated workers to access jobs, including by excluding workers from their system. The amount of direction and discipline that clients and platforms impose on workers, in many instances amounts to the degree of control that is normally reserved to employers and is normally accompanied by labour protections such as the minimum wage, limits on working time, and contributions to social security. This recent ILO study provides more detailed analysis on these features of platform-based work.
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