The Progressive Voice “Too Hot” Nurse Punished For Working In Just Underwear Under See Through PPE Grown In COVID-19 Ward #Coronavirus#Corona#nurse#covid19#ppe#russiahttps://nypost.com/2020/05/20/nurse-d… ‘Hot’ nurse disciplined for wearing bra and panties under see-through PPE gown This naughty nurse is going viral. A nurse in Russia was suspended from the hospital where she worked in Tula, 100 miles south of Moscow, after she arrived at her shift in the all-male coronavirus patient wing with no clothing save for her skivvies under her transparent personal protective equipment. The unidentified staffer told her managers at Tula Regional Clinical Hospital that she was “too hot” to wear clothing underneath the head-to-toe vinyl gown, which protected her from contracting COVID-19. The incident was first reported by a local news outlet, the Tula Pressa newspaper. While there were reportedly “no complaints” from her patients, hospital chiefs punished the nearly nude nurse for “non-compliance with the requirements for medical clothing.” The nurse claimed she did not realize that her underwear was showing through the PPE. However, the regional health ministry confirmed that “a disciplinary sanction was applied to the nurse of the infectious diseases department who violated [uniform] requirements,” the Sun reports. They did not elaborate on what exactly the disciplinary measure would be. The hospital administration originally claimed the woman in her 20s had been wearing “lingerie” — but later clarified the two-piece ensemble was possibly a “swimming suit.” The overheated health care worker has yet to make a public statement on the incident. However, readers of the Tula Pressa had plenty to say. “At least someone has a sense of humor in this gloomy, gloomy reality,” said Sergey Ratnikov. Marina Astakhova added, “Well done, she raised the mood of the patients.” And Valery Kapnin asked, “Why punish the nurse? You need to reward her. Seeing this outfit, no one wants to die.” As of Wednesday, there have been approximately 309,000 cases of COVID-19 reported in Russia, and nearly 3,000 deaths. Support The Show & Watch Exclusive Content: https://www.patreon.com/theprogressiv… Donate: https://www.paypal.me/theprogressivev… Listen to the audio podcast format on Itunes: https://itunes.apple.com/us/podcast/t… Purchase Progressive Voice Merch: https://teespring.com/stores/theprogr… Donate On Bitcoin: 1LYYaW9T1pe2r3P5dqXCAgdZFJBYF1TDiU Ethereum: 0x7699AE5E4A4b491Cf2cF3dAc3EE14Adf8c29b438 Follow me on Twitter: https://twitter.com/Progressvoice
Cannabis For Coronavirus? New Research Looks Into It
he Rational National Support the show at http://TheRationalNational.com/Join Donate Directly at http://PayPal.me/daviddoel Tip at https://streamlabs.com/therationalnat… ‘Join’ on YouTube: https://www.youtube.com/channel/UCo9o… Follow David Doel at http://twitter.com/DavidDoel Follow The Rational National at http://twitter.com/TRNshow Follow on Twitch at http://twitch.tv/TheRationalNational === Sources: https://bit.ly/36kOyHchttps://bit.ly/2LMCl4C
Why Creating a COVID-19 Vaccine is taking so Long
By Matt Simon – May 20, 2020 (wired.com)
Developing a vaccine that’s both effective and safe is grueling, methodical work. And once we have one, we’ll need many, many doses too.
THE RACE IS on for a Covid-19 vaccine, but the pace is less hare and more tortoise. And necessarily so: Developing a vaccine that’s both effective and safe is grueling, methodical work.
“When experts optimistically say that they expect a Covid-19 vaccine by the end of 2020, they’re talking about an emergency use authorized vaccine, not a fully-approved one,” says Dr. Seema Yasmin, director of the Stanford Health Communication Initiative.
No one can say for sure when a Covid-19 vaccine might arrive, because vaccine development is broken into several stages, each with a highly variable timeline. “But here’s a comparison,” Yasmin says. “The fastest vaccine we previously developed was for mumps, and that took four years to develop. And typically it takes 10 to 15 years to develop a vaccine. So 12 to 18 months would be record-breaking.”
Now, about those stages. The first is the exploratory phase, in which drug companies tinker with different approaches. With Covid-19, for instance, some drugmakers are trying to develop a nucleotide-based vaccine, which uses the virus’s genetic code instead of its proteins. Typically, the exploratory phase could take two to four years, though new technologies are speeding up the progress. Also, this novel coronavirus is similar to the first SARS virus, so that may give researchers a head start.
Next comes the preclinical stage, in which a vaccine candidate is tested in cell cultures and animals to see if it triggers an immune response. “If there’s no immune response or the vaccine is causing harm to cells, then it’s back to square one, the exploratory stage,” says Yasmin. “The reality is there’s no way to speed up this stage, and it will probably take at least a year.”
But if all goes well, the candidate heads to clinical trials. The experimental vaccine is given to a small group of people, then a bigger group, then an even bigger group, typically in an outbreak area. This series of tests can take years to complete. Some bioethicists have recently proposed that the Covid-19 pandemic is so serious that so-called “challenge trials” should be considered to accelerate the process. In a challenge trial, researchers would intentionally infect the people they vaccinate in a controlled environment to see if the drug is effective. No such studies have been authorized yet.
The next stage is regulatory review, in which the manufacturer submits an application for a license to produce the vaccine. In the US, this typically takes 10 months and is authorized through the Food and Drug Administration, though it’s virtually a given that regulators will speed up the approval of a Covid-19 vaccine.
But we aren’t done yet. Scaling up production—for instance, building manufacturing facilities—can take years and hundreds of millions of dollars. The production of any Covid-19 vaccine will of course be fast-tracked, but drugmakers will still need to produce enough materials to vaccinate billions of people.
To learn more about the race for a Covid-19 vaccine, check out our video with Yasmin above.
WIRED is providing free access to stories about public health and how to protect yourself during the coronavirus pandemic. Sign up for our Coronavirus Update newsletter for the latest updates, and subscribe to support our journalism.
Matt Simon is a science journalist at WIRED, where he covers biology, robotics, cannabis, and the environment. He’s also the author of Plight of the Living Dead: What Real-Life Zombies Reveal About Our World—And Ourselves, and The Wasp That Brainwashed the Caterpillar, which won an Alex Award.
Dizzy, Jesus and Group Dynamics
By Mike Zonta, H.W., M.
In the YouTube video “War on Sensemaking 3, the Infinite Game: Jamie Wheal” (posted on the Bathtub Bulletin), Mr. Wheal says the spirit of the “infinite game” is self-policing, that might doesn’t make right and that “healthy shame” is an undervalued asset. That we can see each as we could be, as we should be, and that we can go back and mend our trauma.
He talks of Dizzy Gillespie and his band. “He was a genius musician and he was utterly ruthless about who could hang with his band,” Wheal says of Gillespie. “If you couldn’t swing, if you couldn’t hit the chord changes, if you couldn’t play at that level, you would be swapped for someone else who could.”
So very exclusive.
Jesus said, whenever two or more of you are gathered in my name [in my nature], there I am also. So Jesus was exclusive in his way as well. If you weren’t gathering in his nature, then you self-excluded yourself from Jesus’s band.
In The Prosperos, we teach something called Group Dynamics. It is an effort to allow people to function from their highest Self in the context of a group discussion. Too often, however, members of a group (whether it’s the Sunday Night Translation Group or The Prosperos as a whole, or Occupy, or the Berniecrats, or you and your significant other) look upon group activity as a chance to finally unload on a captive audience and get the attention and love which you were denied as a child.
At that point you are self-excluding yourself from Dizzy’s and Jesus’s band. The opportunity, however, is always there to think with the group and not about it.
We can’t exclude those who are not ready, willing or able to really practice group dynamics, to be a member of Dizzy’s or Jesus’s band. We can only provide them with the encouragement to mend their trauma and then provide them with the on-going opportunity to gather together in Dizzy’s or Jesus’s name. Gandhi said (as paraphrased by Marianne Williams): “Self-purification must precede political activism.“
In another YouTube video (“Making Sense of Sensemaking: Daniel Schmachtenberger, Jamie Wheal, Jordan Hall”) Wheal speaks of a Quaker gathering where people only speak when they are spoken through.
Now that would be group meeting worthy of Dizzy Gillespie or Jesus Christ.
Clashes with Neptune
Covid Patients Testing Positive After Recovery Aren’t Infectious, Study Shows
By Heesu Lee and Jason Gale
May 18, 2020, Updated on May 19, 2020 (bloomberg.com)
- Result is a positive sign for regions reopening economies
- Findings may also aid in the debate over antibody testing
Recovered Patients May Not Be Contagious
Researchers are finding evidence that patients who test positive for the coronavirus after recovering aren’t capable of transmitting the infection, and could have the antibodies that prevent them from falling sick again.
Scientists from the Korean Centers for Disease Control and Prevention studied 285 Covid-19 survivors who had tested positive for the coronavirus after their illness had apparently resolved, as indicated by a previous negative test result. The so-called re-positive patients weren’t found to have spread any lingering infection, and virus samples collected from them couldn’t be grown in culture, indicating the patients were shedding non-infectious or dead virus particles.
The findings, reported late Monday, are a positive sign for regions looking to open up as more patients recover from the pandemic that has sickened at least 4.8 million people. The emerging evidence from South Korea suggests those who have recovered from Covid-19 present no risk of spreading the coronavirus when physical distancing measures are relaxed.
The results mean health authorities in South Korea will no longer consider people infectious after recovering from the illness. Research last month showed that so-called PCR tests for the coronavirus’s nucleic acid can’t distinguish between dead and viable virus particles, potentially giving the wrong impression that someone who tests positive for the virus remains infectious.
The research may also aid in the debate over antibody tests, which look for markers in the blood that indicate exposure to the novel coronavirus. Experts believe antibodies probably convey some level of protection against the virus, but they don’t have any solid proof yet. Nor do they know how long any immunity may last.
A recent study in Singapore showed that recovered patients from severe acute respiratory syndrome, or SARS, are found to have “significant levels of neutralizing antibodies” nine to 17 years after initial infection, according to researchers including Danielle E. Anderson of Duke-NUS Medical School.
Other scientists have found higher levels of IgM, an antibody that appears in response to exposure to an antigen, in children, according to an article published on medRxiv. That suggests younger populations have the potential to produce a more potent defense against Covid-19. The study has not been certified by peer review.
Revised Protocols
As a result of the findings in the South Korea study, authorities said that under revised protocols, people should no longer be required to test negative for the virus before returning to work or school after they have recovered from their illness and completed their period of isolation.
“Under the new protocols, no additional tests are required for cases that have been discharged from isolation,” the Korean CDC said in a report. The agency said it will now refer to “re-positive” cases as “PCR re-detected after discharge from isolation.”
Some coronavirus patients have tested positive again for the virus up to 82 days after becoming infected. Almost all of the cases for which blood tests were taken had antibodies against the virus.
— With assistance by Peter Pae, and Claire Che
Nietzsche: What Causes Nihilism?
Existentialist Dasein Nietzsche defines nihilism as the devaluation of highest values. Nihilism is “the recognition of the long waste of strength, the agony of the ‘in vain,’ insecurity, the lack of any opportunity to recover and to regain composure.” Nihilism “includes disbelief in any metaphysical world and forbids itself any belief in a true world.” According to Nietzsche, nihilism has various causes; in this video, we examine just a few. ____________ Literature: Nietzsche, Friedrich. The Anti-Christ, Ecce Homo, Twilight of the Idols. Translated by Judith Norman. Cambridge: Cambridge University Press, 2007. Nietzsche, Friedrich. The Will to Power. Edited, with Commentary, by Walter Kaufmann. New York: Random House, Inc., 1968. ____________ Support my channel on Patreon and I can make more videos: https://www.patreon.com/Existentialis… Follow me on Instagram: https://www.instagram.com/johannesabs… Listen to Johannes Absurdus: https://www.youtube.com/channel/UCCua… Also, don’t forget to follow me on facebook: https://www.facebook.com/BoooksAndBoo…
The Nooscope — a visual manifesto of the limits of AI
May 18, 2020 (skynettoday.com)
Introducing the Nooscope, a diagram that shows how Machine Learning works and how it fails
Image credit: On the the invention of metaphors as instrument of knowledge magnification. Emanuele Tesauro, Il canocchiale aristotelico, frontispiece of the 1670 edition, Turin.
This is a shorter version of the text “The Nooscope Manifested: Artificial Intelligence as Instrument of Knowledge Extractivism” by Matteo Pasquinelli and Vladan Joler available at nooscope.ai.
Abstract
The Nooscope is a visual manifesto of the limits of AI, intended as a provocation to both computer science and the humanities. It questions the technical definition of intelligence and the autonomy from society that are implicit in the expression ‘artificial intelligence.’ It is also an attempt to the expose the role of ‘ghost work’ in the logical construction of machine learning.
Some enlightenment regarding the project to mechanise reason
The Nooscope is a cartography of the limits of artificial intelligence, intended as a provocation to both computer science and the humanities. Any map is a partial perspective, a way to provoke debate. Similarly, this map is a manifesto — of AI dissidents. Its main purpose is to challenge the mystifications of artificial intelligence. First, as a technical definition of intelligence and, second, as a political form that would be autonomous from society and the human. In the expression ‘artificial intelligence’ the adjective ‘artificial’ carries the myth of the technology’s autonomy: it hints to caricatural ‘alien minds’ that self-reproduce in silico but, actually, mystifies two processes of proper alienation: the growing geopolitical autonomy of hi-tech companies and the invisibilization of workers’ autonomy worldwide. The modern project to mechanise human reason has clearly mutated, in the 21st century, into a corporate regime of knowledge extractivism and epistemic colonialism. This is unsurprising, since machine learning algorithms are the most powerful algorithms for information compression.
The purpose of the Nooscope map is to secularize AI from the ideological status of ‘intelligent machine’ to one of knowledge instrument. Rather than evoking legends of alien cognition, it is more reasonable to consider machine learning as an instrument of knowledge magnification that helps to perceive features, patterns, and correlations through vast spaces of data beyond human reach. In the history of science and technology, this is no news: it has already been pursued by optical instruments throughout the histories of astronomy and medicine. In the tradition of science, machine learning is just a Nooscope, an instrument to see and navigate the space of knowledge (from the Greek skopein ‘to examine, look’ and noos ‘knowledge’).
Borrowing the idea from Gottfried Wilhelm Leibniz, the Nooscope diagram applies the analogy of optical media to the structure of all machine learning apparatuses. Discussing the power of his calculus ratiocinator and ‘characteristic numbers’ (the idea to design a numerical universal language to codify and solve all the problems of human reasoning), Leibniz made an analogy with instruments of visual magnification such as the microscope and telescope. He wrote: ‘Once the characteristic numbers are established for most concepts, mankind will then possess a new instrument which will enhance the capabilities of the mind to a far greater extent than optical instruments strengthen the eyes, and will supersede the microscope and telescope to the same extent that reason is superior to eyesight.’ Although the purpose of this text is not to reiterate the opposition between quantitative and qualitative cultures, Leibniz’s credo need not be followed. Controversies cannot be conclusively computed. Machine learning is not the ultimate form of intelligence.
Instruments of measurement and perception always come with inbuilt aberrations. In the same way that the lenses of microscopes and telescopes are never perfectly curvilinear and smooth, the logical lenses of machine learning embody faults and biases. To understand machine learning and register its impact on society is to study the degree by which social data are diffracted and distorted by these lenses. This is generally known as the debate on bias in AI, but the political implications of the logical form of machine learning are deeper. Machine learning is not bringing a new dark age but one of diffracted rationality, in which, as it will be shown, an episteme of causation is replaced by one of automated correlations. More in general, AI is a new regime of truth, scientific proof, social normativity and rationality, which often does take the shape of a statistical hallucination. This diagram manifesto is another way to say that AI, the king of computation (patriarchal fantasy of mechanised knowledge, ‘master algorithm’ and alpha machine) is naked. Here, we are peeping into its black box.
The assembly line of machine learning: Data, Algorithm, Model
The history of AI is a history of experiments, machine failures, academic controversies, epic rivalries around military funding, popularly known as ‘winters of AI.’ Although corporate AI today describes its power with the language of ‘black magic’ and ‘superhuman cognition’, current techniques are still at the experimental stage. AI is now at the same stage as when the steam engine was invented, before the laws of thermodynamics necessary to explain and control its inner workings, had been discovered. Similarly, today, there are efficient neural networks for image recognition, but there is no theory of learning to explain why they work so well and how they fail so badly. Like any invention, the paradigm of machine learning consolidated slowly, in this case through the last half-century. A master algorithm has not appeared overnight. Rather, there has been a gradual construction of a method of computation that still has to find a common language. Manuals of machine learning for students, for instance, do not yet share a common terminology. How to sketch, then, a critical grammar of machine learning that may be concise and accessible, without playing into the paranoid game of defining General Intelligence?
As an instrument of knowledge, machine learning is composed of an object to be observed (training dataset), an instrument of observation (learning algorithm) and a final representation (statistical model). The assemblage of these three elements is proposed here as a spurious and baroque diagram of machine learning, extravagantly termed Nooscope. Staying with the analogy of optical media, the information flow of machine learning is like a light beam that is projected by the training data, compressed by the algorithm and diffracted towards the world by the lens of the statistical model.
The Nooscope diagram aims to illustrate two sides of machine learning at the same time: how it works and how it fails — enumerating its main components, as well as the broad spectrum of errors, limitations, approximations, biases, faults, fallacies and vulnerabilities that are native to its paradigm. This double operation stresses that AI is not a monolithic paradigm of rationality but a spurious architecture made of adapting techniques and tricks. Besides, the limits of AI are not simply technical but are imbricated with human bias. In the Nooscope diagram the essential components of machine learning are represented at the centre, human biases and interventions on the left, and technical biases and limitations on the right. Optical lenses symbolize biases and approximations representing the compression and distortion of the information flow. The total bias of machine learning is represented by the central lens of the statistical model through which the perception of the world is diffracted.
While the social consequences of AI are popularly understood under the issue of bias, the common understanding of technical limitations is known as the black box problem. The black box effect is an actual issue of deep neural networks (which filter information so much that their chain of reasoning cannot be reversed) but has become a generic pretext for the opinion that AI systems are not just inscrutable and opaque, but even ‘alien’ and out of control. The black box effect is part of the nature of any experimental machine at the early stage of development (it has already been noticed that the functioning of the steam engine remained a mystery for some time, even after having been successfully tested). The actual problem is the black box rhetoric, which is closely tied to conspiracy theory sentiments in which AI is an occult power that cannot be studied, known, or politically controlled.
The history of AI as the automation of perception
The need to demystify AI (at least from the technical point of view) is understood in the corporate world too. Head of Facebook AI and godfather of convolutional neural networks Yann LeCun reiterates that current AI systems are not sophisticated versions of cognition, but rather, of perception. Similarly, the Nooscope diagram exposes the skeleton of the AI black box and shows that AI is not a thinking automaton but an algorithm that performs pattern recognition. The notion of pattern recognition contains issues that must be elaborated upon. What is a pattern, by the way? Is a pattern uniquely a visual entity? What does it mean to read social behaviours as patterns? Is pattern recognition an exhaustive definition of intelligence? Most likely not. To clarify these issues, it would be good to undertake a brief archaeology of AI.
The archetype machine for pattern recognition is Frank Rosenblatt’s Perceptron. Invented in 1957 at Cornell Aeronautical Laboratory in Buffalo, New York, its name is a shorthand for ‘Perceiving and Recognizing Automaton.’ Given a visual matrix of 20×20 photoreceptors, the Perceptron can learn how to recognise simple letters. A visual pattern is recorded as an impression on a network of artificial neurons that are firing up in concert with the repetition of similar images and activating one single output neuron. The output neuron fires 1=true, if a given image is recognised, or 0=false, if a given image is not recognised.
Rosenblatt’s Perceptron was the first algorithm that paved the way to machine learning in the contemporary sense. At a time when ‘computer science’ had not yet been adopted as definition, the field was called ‘computational geometry’ and specifically ‘connectionism’ by Rosenblatt himself. The business of these neural networks, however, was to calculate a statistical inference. What a neural network computes, is not an exact pattern but the statistical distribution of a pattern. Just scraping the surface of the anthropomorphic marketing of AI, one finds another technical and cultural object that needs examination: the statistical model. What is the statistical model in machine learning? How is it calculated? What is the relationship between a statistical model and human cognition? These are crucial issues to clarify. In terms of the work of demystification that needs to be done (also to evaporate some naïve questions), it would be good to reformulate the trite question ‘Can a machine think?’ into the theoretically sounder questions ‘Can a statistical model think?’, ‘Can a statistical model develop consciousness?’, et cetera.
The learning algorithm: compressing the world into a statistical model
The algorithms of AI are often evoked as alchemic formulas, capable of distilling ‘alien’ forms of intelligence. But what do the algorithms of machine learning really do? Few people, including the followers of AGI (Artificial General Intelligence), bother to ask this question. Algorithm is the name of a process, whereby a machine performs a calculation. The product of such machine processes is a statistical model (more accurately termed an ‘algorithmic statistical model’). In the developer community, the term ‘algorithm’ is increasingly replaced with ‘model.’ This terminological confusion arises from the fact that the statistical model does not exist separately from the algorithm: somehow, the statistical model exists inside the algorithm under the form of distributed memory across its parameters. For the same reason, it is essentially impossible to visualise an algorithmic statistical model, as is done with simple mathematical functions. Still, the challenge is worthwhile.
Statistical models have always influenced culture and politics. They did not just emerge with machine learning: machine learning is just a new way to automate the technique of statistical modelling. When Greta Thunberg warns ‘Listen to science.’ what she really means, being a good student of mathematics, is ‘Listen to the statistical models of climate science.’ No statistical models, no climate science: no climate science, no climate activism. Climate science is indeed a good example to start with, in order to understand statistical models. Global warming has been calculated by first collecting a vast dataset of temperatures from Earth’s surface each day of the year, and second, by applying a mathematical model that plots the curve of temperature variations in the past and projects the same pattern into the future. Climate models are historical artefacts that are tested and debated within the scientific community, and today, also beyond. Machine learning models, on the contrary, are opaque and inaccessible to community debate. Given the degree of myth-making and social bias around its mathematical constructs, AI has indeed inaugurated the age of statistical science fiction. Nooscope is the projector of this large statistical cinema.
All models are wrong, but some are useful
‘All models are wrong, but some are useful’ — the canonical dictum of the British statistician George Box has long encapsulated the logical limitations of statistics and machine learning. This maxim, however, is often used to legitimise the bias of corporate and state AI. Computer scientists argue that human cognition reflects the capacity to abstract and approximate patterns. So what’s the problem with machines being approximate, and doing the same? Within this argument, it is rhetorically repeated that ‘the map is not the territory’. This sounds reasonable. But what should be contested is that AI is a heavily compressed and distorted map of the territory and that this map, like many forms of automation, is not open to community negotiation. AI is a map of the territory without community access and community consent.
It is important to recall that the ‘intelligence’ of machine learning is not driven by exact formulas of mathematical analysis, but by algorithms of brute force approximation. The shape of the correlation function between input x and output y is calculated algorithmically, step by step, through tiresome mechanical processes of gradual adjustment (like gradient descent, for instance) that are equivalent to the differential calculus of Leibniz and Newton. Neural networks are said to be among the most efficient algorithms because these differential methods can approximate the shape of any function given enough layers of neurons and abundant computing resources. Brute-force gradual approximation of a function is the core feature of today’s AI, and only from this perspective can one understand its potentialities and limitations — particularly its escalating carbon footprint (the training of deep neural networks requires exorbitant amounts of energy because of gradient descent and similar training algorithms that operate on the basis of continuous infinitesimal adjustments).
Machine learning classification and prediction are becoming ubiquitous techniques that constitute new forms of surveillance and governance. Some apparatuses, such as self-driving vehicles and industrial robots, can be an integration of both modalities. A self-driving vehicle is trained to recognise different objects on the road (people, cars, obstacles, signs) and predict future actions based on decisions that a human driver has taken in similar circumstances. Even if recognising an obstacle on a road seems to be a neutral gesture (it’s not), identifying a human being according to categories of gender, race and class (and in the recent COVID-19 pandemic as sick or immune), as state institutions are increasingly doing, is the gesture of a new disciplinary regime. The hubris of automated classification has caused the revival of reactionary Lombrosian techniques that were thought to have been consigned to history, techniques such as Automatic Gender Recognition (AGR), ‘a subfield of facial recognition that aims to algorithmically identify the gender of individuals from photographs or videos.’
Labour in the age of AI
The natures of the ‘input’ and ‘output’ of machine learning have to be clarified. AI troubles are not only about information bias but also labour. AI is not just a control apparatus, but also a productive one. As just mentioned, an invisible workforce is involved in each step of its assembly line (dataset composition, algorithm supervision, model evaluation, etc.). Pipelines of endless tasks innervate from the Global North into the Global South; crowdsourced platforms of workers from Venezuela, Brazil and Italy, for instance, are crucial in order to teach German self-driving cars ‘how to see.’ Against the idea of alien intelligence at work, it must be stressed that in the whole computing process of AI the human worker has never left the loop, or put more accurately, has never left the assembly line. Mary Gray and Siddharth Suri coined the term ‘ghost work’ for the invisible labour that makes AI appear artificially autonomous. Automation is a myth; because machines, including AI, constantly call for human help, some authors have suggested replacing ‘automation’ with the more accurate term heteromation. Heteromation means that the familiar narrative of AI as perpetuum mobile is possible only thanks to a reserve army of workers.
Yet there is a more profound way in which labour constitutes AI. The information source of machine learning (whatever its name: input data, training data or just data) is always a representation of human skills, activities and behaviours, social production at large. All training datasets are, implicitly, a diagram of the division of human labour that AI has to analyse and automate. Datasets for image recognition, for instance, record the visual labour that drivers, guards, and supervisors usually perform during their tasks. Even scientific datasets rely on scientific labour, experiment planning, laboratory organisation, and analytical observation. The information flow of AI has to be understood as an apparatus designed to extract ‘analytical intelligence’ from the most diverse forms of labour and to transfer such intelligence into a machine (obviously including, within the definition of labour, extended forms of social, cultural and scientific production). In short, the origin of machine intelligence is the division of labour and its main purpose is the automation of labour.
Historians of computation have already stressed the early steps of machine intelligence in the 19^th^ century project of mechanizing the division of mental labour, specifically the task of hand calculation. The enterprise of computation has since then been a combination of surveillance and disciplining of labour, of optimal calculation of surplus-value, and planning of collective behaviours. Computation was established by and still enforces a regime of visibility and intelligibility, not just of logical reasoning. The genealogy of AI as an apparatus of power is confirmed today by its widespread employment in technologies of identification and prediction, yet the core anomaly which always remains to be computed is the disorganisation of labour.
As a technology of automation, AI will have a tremendous impact on the job market. If Deep Learning has a 1% error rate in image recognition, for example, it means that roughly 99% of routine work based on visual tasks (e.g. airport security) can be potentially replaced (legal restrictions and trade union opposition permitting). The impact of AI on labour is well described (from the perspective of workers, finally) within a paper from the European Trade Union Institute, which highlights ‘seven essential dimensions that future regulation should address in order to protect workers: 1) safeguarding worker privacy and data protection; 2) addressing surveillance, tracking and monitoring; 3) making the purpose of AI algorithms transparent; 4) ensuring the exercise of the ‘right to explanation’ regarding decisions made by algorithms or machine learning models; 5) preserving the security and safety of workers in human-machine interactions; 6) boosting workers’ autonomy in human–machine interactions; 7) enabling workers to become AI literate.’
Ultimately, the Nooscope manifests for a novel Machinery Question in the age of AI. The Machinery Question was a debate that sparked in England during the industrial revolution, when the response to the employment of machines and workers’ subsequent technological unemployment was a social campaign for more education about machines, that took the form of the Mechanics’ Institute Movement. Today an Intelligent Machinery Question is needed to develop more collective intelligence about ‘machine intelligence,’ more public education instead of ‘learning machines’ and their regime of knowledge extractivism (which reinforces old colonial routes, just by looking at the network map of crowdsourcing platforms today). Also in the Global North, this colonial relationship between corporate AI and the production of knowledge as a common good has to be brought to the fore. The Nooscope’s purpose is to expose the hidden room of the corporate Mechanical Turk and to illuminate the invisible labour of knowledge that makes machine intelligence appear ideologically alive.
Bio: Jerry Stiller
From Wikipedia, the free encyclopedia
| Jerry Stiller | |
|---|---|
| Stiller in 2005 | |
| Born | Gerald Isaac Stiller June 8, 1927 Brooklyn, New York, U.S. |
| Died | May 11, 2020 (aged 92) Manhattan, New York, U.S. |
| Alma mater | Syracuse University |
| Occupation | Actor, voice actor, comedian |
| Years active | 1954–2016 |
| Spouse(s) | Anne Meara (m. 1954; died 2015) |
| Children | Amy Stiller Ben Stiller |
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Gerald Isaac Stiller (June 8, 1927 – May 11, 2020) was an American comedian, actor, and author. He spent many years as part of the comedy duo Stiller and Meara with his wife, Anne Meara, to whom he was married for over 60 years until her death in 2015. Stiller saw a late-career resurgence starting in 1993, playing George Costanza‘s father Frank on the sitcom Seinfeld, a part which earned him an Emmy nomination. The year Seinfeld went off the air, Stiller began his role as the eccentric Arthur Spooner on the CBS comedy series The King of Queens, another role which garnered him widespread acclaim.[1]
Stiller was the father of actor Ben Stiller, and the father and son appeared together in films such as Zoolander, Heavyweights, Hot Pursuit, The Heartbreak Kid, and Zoolander 2. He also performed voice-over work for television and films including The Lion King 1½ and Planes: Fire and Rescue. In his later career, Stiller became known for playing grumpy and eccentric characters who were nevertheless beloved.[2][3]
Early life
The eldest of four children,[4] Stiller was born at Unity Hospital in Brooklyn, New York, to Bella (née Citron; 1902–1954) and William Stiller (1896–1999), a taxi and bus driver.[5] His family was Jewish. His paternal grandparents immigrated from Galicia (southeast Poland and western Ukraine), and his mother was born in Frampol, Poland.[6] He lived in the Williamsburg and East New York neighborhoods before his family moved to the Lower East Side,[7] where he attended Seward Park High School,[8] where he played Adolf Hitler in a school production.[9]
Upon his return from service in the U.S. Army during World War II,[10][11] Stiller attended Syracuse University, earning a bachelor’s degree in Speech and Drama in 1950.[12] He also studied drama at HB Studio in Greenwich Village.[13] In the 1953 Phoenix Theater production of Coriolanus (produced by John Houseman), Stiller, along with Gene Saks and Jack Klugman, formed (as told by Houseman in the 1980 memoir Front and Center) “the best trio of Shakespearian clowns that I have ever seen on any stage”.[14]
Also in 1953, Stiller met actress-comedienne Anne Meara, and they married in 1954. Until Stiller suggested it, Meara had never thought of doing comedy. “Jerry started us being a comedy team,” she said. “He always thought I would be a great comedy partner.”[15] They joined the Chicago improvisational company The Compass Players (which later became The Second City) and, after leaving, began performing together. In 1961 they were performing in nightclubs in New York City and by the following year were considered a “national phenomenon”, said the New York Times.[15]
Stiller and Meara
The comedy team Stiller and Meara, composed of Stiller and his wife, Anne Meara, was successful throughout the 1960s, with numerous appearances on television variety programs, primarily on The Ed Sullivan Show.[16] In 1970, they broke up the live act before it broke up their marriage. They subsequently forged a career in radio commercials, notably the campaign for Blue Nun wine. They also starred in their own syndicated five-minute sketch comedy show on radio, Take Five with Stiller and Meara, from 1977 to 1978.[17]
From 1979 to 1982, Stiller and Meara hosted HBO Sneak Previews, a half-hour show produced monthly on which they described the movies and programs to be featured in the coming month.[5] They also did some comedy sketches between show discussions. The duo had their own 1986 TV sitcom, The Stiller and Meara Show, in which Stiller played the deputy mayor of New York City and Meara portrayed his wife, a TV commercial actress.
Resurgence
Seinfeld
Late in his career, Stiller earned the part of the short-tempered Frank Costanza, father of George Costanza, on the sitcom Seinfeld, a role which Stiller played from 1993 until 1998.[18] Stiller’s character as initially envisioned was a “meek” and “Thurberesque” character that required him to wear a bald cap. After a couple days of rehearsal Stiller realized the character wasn’t working and asked Seinfeld co-creator Larry David if he could perform the character in a different way, which was more in line with his final characterization on the show.[19][20] For his portrayal of Frank, Stiller gained widespread critical and popular acclaim, including being nominated for an Emmy for Outstanding Guest Actor in a Comedy Series in 1997 and winning an American Comedy Award for Funniest Male Guest Appearance in a TV Series in 1998.[18][21]
The King of Queens
After Seinfeld ended, Stiller had planned on retiring. However, Kevin James asked him to join the cast of The King of Queens. James, who played the leading role of Doug Heffernan, had told Stiller that he needed him in order to have a successful show. Stiller agreed and played the role of Arthur Spooner, the father of Carrie Heffernan, from 1998 until 2007. Stiller said that this role tested his acting ability more than any other had, and that, before being a part of The King of Queens, he only saw himself as a “decent actor.”[22]
Other appearances
Stiller in 2006
Stiller played himself in filmed skits opening and closing Canadian rock band Rush‘s 30th Anniversary Tour concerts in 2004. These appearances are seen on the band’s DVD R30: 30th Anniversary World Tour, released in 2005. Stiller later appeared in cameos for in-concert films for the band’s 2007–08 Snakes & Arrows Tour. Stiller appeared on Dick Clark’s $10,000 Pyramid show in the 1970s, and footage of the appearance was edited into an episode of The King of Queens to assist the storyline about his character being a contestant on the show, and, after losing, being bitter about the experience, as he never received his parting gift, a lifetime supply of Rice-a-Roni.[23] He also made several appearances on the game show, Tattletales, with his wife, Anne Meara.
In the late 1990s, Stiller appeared in a series of Nike television commercials as the ghost of deceased Green Bay Packers head coach Vince Lombardi. He also appeared in various motion pictures, most notably Zoolander (2001) and Secret of the Andes (1999). On February 9, 2007, Stiller and Meara were honored with a joint star on the Hollywood Walk of Fame. On October 28, 2010, the couple appeared on an episode of The Daily Show with Jon Stewart. Stiller voiced the announcer on the children’s educational show Crashbox. Starting in October 2010, Stiller and Meara began starring in Stiller & Meara, a Yahoo web series from Red Hour Digital in which they discussed current topics. Each episode was about two minutes long.[24][25] Stiller also worked as a spokesman for Xfinity.
Author
Stiller wrote the foreword to the 2005 book Festivus: The Holiday for the Rest of Us (ISBN 0-446-69674-9) by Allen Salkin. The book discussed Festivus, the fictional holiday promulgated by Stiller’s Seinfeld character Frank Costanza.[26]
Stiller also authored a memoir titled Married to Laughter: A Love Story Featuring Anne Meara, which was published by Simon & Schuster (ISBN 0-684-86903-9).[27]
Personal life
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Stiller’s son, Ben
Stiller was married to Anne Meara for over 60 years, from 1954 until her death on May 23, 2015.[28] The two met in an agent’s office. Meara was upset about an interaction with the casting agent, so Stiller took her out for coffee—all he could afford—and they remained together ever since. Maera was Irish Catholic and converted to Judaism before the couple’s two children were born.[9] Their son is actor-comedian Ben Stiller (born 1965) and their daughter is actress Amy Stiller (born 1961).[29] He has two grandchildren through Ben.
Death
Stiller died from natural causes at his home on the Upper West Side of Manhattan on May 11, 2020, less than a month before his 93rd birthday. His death was announced by his son, Ben Stiller.[30][31] Many actors Stiller worked with, including Seinfeld castmates Jerry Seinfeld, Julia Louis-Dreyfus and Jason Alexander, and King of Queens castmates Kevin James and Leah Remini, paid tributes to him on social media.[32]
Can we edit memories?
Amy Milton|TEDxCambridgeUniversity
Trauma and PTSD rewire your brain — especially your memory — and can unearth destructive emotional responses when stirred. Could we eliminate these triggers without erasing the memories themselves? Enter neurologist Amy Milton’s mind-blowing, memory-editing clinical research poised to defuse the damaging effects of painful remembered experiences and offer a potential path toward better mental health.
This talk was presented to a local audience at TEDxCambridgeUniversity, an independent event. TED’s editors chose to feature it for you.
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