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Bill Fulkerson

Anatomy of an AI System - 1 views

shared by Bill Fulkerson on 14 Sep 18 - No Cached
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    "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
Steve Bosserman

The wealth of our collective data should belong to all of us | Chris Hughes - 0 views

  • Nearly every moment of our lives, we’re producing data about ourselves that companies profit from. Our smartwatches know when we wake up, Alexa listens to our private conversations, our phones track where we go, Google knows what we email and search, Facebook knows what we share with friends, and our loyalty cards remember what we buy. We share all this data about ourselves because we like the services these companies provide, and business leaders tell us we must to make it possible for those services to be cheap or free.
  • We should not only expect that these companies better protect our data – we should also ensure that everyone creating it shares in the economic value it generates. One person’s data is worth little, but the collection of lots of people’s data is what fuels the insights that companies use to make more money or networks, like Facebook, that marketers are so attracted to. Data isn’t the “new oil”, as some have claimed: it isn’t a non-renewable natural resource that comes from a piece of earth that a lucky property owner controls. We have all pitched in to create a new commonwealth of information about ourselves that is bigger than any single participant, and we should all benefit from it.
  • The value of our data has a lot in common with the value of our labor: a single individual worker, outside of the rarest professions, can be replaced by another with similar skills. But when workers organize to withhold their labor, they have much more power to ensure employers more fairly value it. Just as one worker is an island but organized workers are a force to be reckoned with, the users of digital platforms should organize not only for better protection of our data, but for a new contract that ensures everyone shares in the historic profits we make possible.
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  • A data dividend would be a powerful way to rebalance the American economy, which currently makes it possible for a very small number of people to get rich while everyone else struggles to make ends meet.
  • A data dividend on its own would not be enough to stem growing income inequality, but it would create a universal benefit that would guarantee people benefit from the collective wealth our economy is creating more than they do today. If paired with fairer wages, more progressive taxation, and stricter enforcement of monopoly and monopsony power, it could help us turn the corner and create a country where we take care of one another and ensure that everyone has basic economic security.
Steve Bosserman

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

  • 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.
Bill Fulkerson

Full article: Re-assembling the surveillable refugee body in the era of data-craving - 0 views

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    This article traces the travel of biometric data of Syrian refugees in Jordan through a hastily evolving political economy characterized by a pervasive craving for the extraction, storage and brokering of displacement data. It analyzes iris-enrollment as problematic acts of quasi-citizenship for the displaced requiring the performance of social and economic docility in order to attain identity, cash and service provision. Quasi-objects in the form of digital footprints are fashioned through infrastructures that simultaneously seek to model, yet fail to capture, socioeconomic existence in displacement contexts. Discourses of anti-fraud, donor dictates, upward accountability and strategies of financial inclusion of 'the unbanked', facilitate the marketization of the creation of data-doubles in laboratories of displacement and loopholes for externalization. Driven by increasingly blurred lines between technological, humanitarian and financial interests, this development has transformative effects on both those displaced, and on a humanitarian sector tasked with safeguarding their rights.
Steve Bosserman

I am a data factory (and so are you) - 0 views

  • Data is no less a form of common property than oil or soil or copper. We make data together, and we make it meaningful together, but its value is currently captured by the companies that own it. We find ourselves in the position of a colonized country, our resources extracted to fill faraway pockets. Wealth that belongs to the many — wealth that could help feed, educate, house and heal people — is used to enrich the few. The solution is to take up the template of resource nationalism, and nationalize our data reserves.
  • Emphasising time well spent means creating a Facebook that prioritises data-rich personal interactions that Facebook can use to make a more engaging platform. Rather than spending a lot of time doing things that Facebook doesn’t find valuable – such as watching viral videos – you can spend a bit less time, but spend it doing things that Facebook does find valuable. In other words, “time well spent” means Facebook can monetise more efficiently. It can prioritise the intensity of data extraction over its extensiveness. This is a wise business move, disguised as a concession to critics. Shifting to this model not only sidesteps concerns about tech addiction – it also acknowledges certain basic limits to Facebook’s current growth model. There are only so many hours in the day. Facebook can’t keep prioritising total time spent – it has to extract more value from less time.
  • But let’s assume that our vast data collective is secure, well managed, and put to purely democratic ends. The shift of data ownership from the private to the public sector may well succeed in reducing the economic power of Silicon Valley, but what it would also do is reinforce and indeed institutionalize Silicon Valley’s computationalist ideology, with its foundational, Taylorist belief that, at a personal and collective level, humanity can and should be optimized through better programming. The ethos and incentives of constant surveillance would become even more deeply embedded in our lives, as we take on the roles of both the watched and the watcher. Consumer, track thyself! And, even with such a shift in ownership, we’d still confront the fraught issues of design, manipulation, and agency.
Steve Bosserman

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

  • 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

About - Catalog - 0 views

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    "We are developing next generation technology to store digital information in DNA molecules. Our vision is to fit the information content of entire data centers in the palm of your hand. We have proven our approach to encoding data in DNA and are in the process of scaling up our platform. CATALOG technology will make it economically attractive to use DNA as a medium for long-term archival of data."
Bill Fulkerson

Expanded ENCODE delivers invaluable genomic encyclopedia - 0 views

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    In the flagship article, The ENCODE Project Consortium et al.5 provide a bird's-eye view of the updated encyclopedia, which contains newly added data sets from 6,000 experiments, performed on around 1,300 samples. By integrating these data sets, the consortium has created an online registry of candidate CREs. Most are classified as promoters or enhancers - CREs respectively located at or some distance from the genomic site at which transcription of a gene begins. The consortium tracked the activity of each candidate CRE, along with the proteins that bind to it in many different samples from various tissues. They used chromatin-looping data to link enhancers to genes that they might regulate. This online registry marks a true milestone, turning an overwhelming amount of genomic information into a searchable, filterable and retrievable encyclopedia of DNA elements, which is freely accessible at https://screen.encodeproject.org.
Steve Bosserman

How Cheap Labor Drives China's A.I. Ambitions - The New York Times - 1 views

  • 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.
Bill Fulkerson

The Doctor Behind the Disputed Covid Data - 0 views

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    Dr. Sapan Desai, who supplied the data for two prominent and later retracted studies, is said to have a history of cutting corners and misrepresenting information in pursuit of his ambitions.
Bill Fulkerson

Questionnaire data analysis using information geometry | Scientific Reports - 0 views

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    The analysis of questionnaires often involves representing the high-dimensional responses in a low-dimensional space (e.g., PCA, MCA, or t-SNE). However questionnaire data often contains categorical variables and common statistical model assumptions rarely hold. Here we present a non-parametric approach based on Fisher Information which obtains a low-dimensional embedding of a statistical manifold (SM). The SM has deep connections with parametric statistical models and the theory of phase transitions in statistical physics. Firstly we simulate questionnaire responses based on a non-linear SM and validate our method compared to other methods. Secondly we apply our method to two empirical datasets containing largely categorical variables: an anthropological survey of rice farmers in Bali and a cohort study on health inequality in Amsterdam. Compare to previous analysis and known anthropological knowledge we conclude that our method best discriminates between different behaviours, paving the way to dimension reduction as effective as for continuous data.
Steve Bosserman

Social Media's Globe-Shaking Power - The New York Times - 0 views

  • For people who like an orderly, predictable world, this is the scariest thing about Facebook; not that it may be full of lies (a problem that could potentially be fixed), but that its scope gives it real power to change history in bold, unpredictable ways.
  • One is the ubiquity of Facebook, which has reached a truly epic scale. Last month the company reported that about 1.8 billion people now log on to the service every month. Because social networks feed off the various permutations of interactions among people, they become strikingly more powerful as they grow. With about a quarter of the world’s population now on Facebook, the possibilities are staggering.
  • Thanks to the internet, now each person with once-maligned views can see that he’s not alone. And when these people find one another, they can do things — create memes, publications and entire online worlds that bolster their worldview, and then break into the mainstream.
Steve Bosserman

Batteries That Make Use of Solar Power, Even in the Dark - The New York Times - 0 views

  • Amid all this disruption, Britain and other countries have created a smorgasbord of incentives to power providers to keep the lights from going off. Neil Hutchings, director of power systems and storage at Anesco, the small British company that supplied Mr. Beatty’s battery, said there were no fewer than 14 ways that it could make money. “The real secret is how to pick out the best combination,” he said. Independent journalism.More essential than ever. Subscribe to the Times While he said the batteries, which are imported from China, were improving, the real key was in the electronic controls that allowed them to react almost instantaneously to the needs of the grid.
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