A support-group leader for female survivors of sexual abuse — and someone who had spent many years within a positive-thinking metaphysical church — wrote to me in 2012. She said that she had experienced both sides of the positive-thinking equation, witnessing how survivors could ably use a program of mental therapeutics to rebuild their sense of self, but also observing the kind of burden that affirmative-thinking nostrums could visit upon those recovering from trauma.
“Is there room for a positive-thinking model that doesn’t include blame and single-model definitions of success?” I take the attitude that such a model can exist. But for positive thinking to reach maturity, its followers must take fuller stock of the movement’s flaws, particularly the attachment to a single, all-encompassing theory of life, which is to say, the Law of Attraction, recently popularized in The Secret. While the mind does possess influences that are not yet fully understood, and that are palpably felt by many people, the idea of a mental super-law binds New Thought to a paradigm of extremist self-responsibility, which cannot be defended to its limits.
Glycolysis and the citric acid cycle are at the center of metabolism.
Some 500 metabolic reactions of a typical cell are shown schematically with the reactions of glycolysis and the citric acid cycle in red. Other reactions either lead into these two central pathways-delivering small molecules to be catabolized with production of energy-or they lead outward and thereby supply carbon compounds for the purpose of biosynthesis.
Modern research has become so specialized that our notion of impact is sometimes siloed. A world-class clinician may be rewarded for inventing a new surgery; an AI researcher may get credit for beating the world record on MNIST. When two fields cross, there can sometimes be fear, misunderstanding, or culture clashes. We’re not unique in history. In 1944, the foundations of quantum physics had been laid, including, dramatically, the later detonation of the first atomic bomb. After the war, a generation of physicists turned their attention to biology. In the 1944 book What is Life?, Erwin Schrödinger referred to a sense of noblesse oblige that prevented researchers in disparate fields from collaborating deeply, and “beg[ged] to renounce the noblesse”
After hearing the bomb go off on 23rd and getting flooded with texts on Saturday night, I decided to send a few notes that I was OK and turn off my phone. My partner is Israeli. We’ve been there for two wars and he’s been there through countless bombs. We both knew that getting riled up was of no help to anyone. So we went to sleep. I woke up on Sunday, opened my blinds, and was surprised to see an obscene number of men in black with identical body types, identical haircuts, and identical cars. It looked like the weirdest casting call I’ve ever seen. And no one else. No cars, no people. As always, Twitter had an explanation so we settled into our PJs and realized it was going to be a strange day.
a consortium of scholars called the Cambodian Archaeological Lidar Initiative (CALI), which uses a technology known as lidar to shoot ultraquick pulses of light at the ground from lasers mounted on helicopters. The way they bounce back can show the presence of subtle gradations in the landscape, indicating places where past civilizations altered their environment, even if buried beneath thick vegetation or other obstructions. The soft-spoken, fedora-clad Mr. Mackey, a 14-year veteran of fieldwork here, noted that before lidar’s availability, an accurate ground survey of archaeological features in the Cambodian landscape entailed years or even decades of work.
About a week ago, I put together the Marmot Checker, which is another piece of the puzzle in terms of automating knowledge generation throughput. Briefly, an image is uploaded, processed, and sent to the Google Cloud Vision API to get descriptions of the image; these descriptions are checked against a user-defined list of words, and if there is a match, the image is added to the toadserver. Although the implementation is quite is simple, a few hundred lines more of code and you’d have, say, a smart contract that sends the submitter of matched content some amount of tokens as a function of the match score and/or the users’ reputation. On the whole, this is part a growing set of tools for the scientific community. Already we’re seeing more and more startups building tools to streamline the collaboration workflow process between research laboratories.
I think a more open and participatory conversation on eradicating extreme poverty is critical in this day and age. Extreme poverty sits at the bottom of Maslow’s hierarchy of needs and contributes to massive issues all the way up the pyramid; leading to widespread health epidemics, overpopulation, lack of education, lack of environmental and conservational awareness, increased violence and pollution. We’re all connected, and the more people who live in dignity, the better off we can all be. Do you think cash transfers are a terrible idea? Do you know of a better way?
The following are the key “lenses” through which I view and discuss the ongoing transformation to panarchy. Each of these lenses provide crucial understandings and insights into facets of panarchy, but panarchy itself emerges only as a result of the interactions between all of these elements. Like all complex systems, panarchy itself is an emergent property.
Facial Weaponization Suite protests against biometric facial recognition–and the inequalities these technologies propagate–by making “collective masks” in workshops that are modeled from the aggregated facial data of participants, resulting in amorphous masks that cannot be detected as human faces by biometric facial recognition technologies. The masks are used for public interventions and performances. One mask, the Fag Face Mask, generated from the biometric facial data of many queer men’s faces, is a response to scientific studies that link determining sexual orientation through rapid facial recognition techniques. Another mask explores a tripartite conception of blackness: the inability of biometric technologies to detect dark skin as racist, the favoring of black in militant aesthetics, and black as that which informatically obfuscates. A third mask engages feminism’s relations to concealment and imperceptibility, taking veil legislation in France as a troubling site that oppressively forces visibility. A fourth mask considers biometrics’ deployment as a security technology at the Mexico-US border and the nationalist violence it instigates. These masks intersect with social movements’ use of masking as an opaque tool of collective transformation that refuses dominant forms of political representation.
As humanity continues to excel in going beyond human abilities through technology, the victory comes with a price: American photographer Roland Miller travels to abandoned places once found useful by the space exploration organization NASA and the U.S. Army and collects their remnants as memories.
A couple recent articles about bias in machine language processing. A study from UMass Amherst on improving parsing of African American English using data from twitter:
For the past 30 years, computer science researchers have been teaching their machines to read, for example, assigning back issues of the Wall Street Journal, so computers can learn the English they need to run search engines like Google or mine platforms like Facebook and Twitter for opinions and marketing data.
But using only standard English has left out whole segments of society who use dialects and non-standard varieties of English. […]
To expand NLP and teach computers to recognize words, phrases and language patterns associated with African-American English, the researchers analyzed dialects found on Twitter used by African Americans. They identified these users with U.S. census data and Twitter’s geo-location features to correlate to African-American neighborhoods through a statistical model that assumes a soft correlation between demographics and language.
They validated the model by checking it against knowledge from previous linguistics research, showing that it can successfully figure out patterns of African-American English. Green, a linguist who is an expert in the syntax and language of African-American English, has studied a community in southwest Louisiana for decades. She says there are clear patterns in sound and syntax, how sentences are put together, that characterize this dialect, which is a variety spoken by some, not all, African Americans. It has interesting differences compared to standard American English; for example, “they be in the store” can mean “they are often in the store.”
The researchers also identified “new phenomena that are not well known in the literature, such as abbreviations and acronyms used on Twitter, particularly those used by African-American speakers,” notes Green.
Several blog posts from Rachael Tatman comparing YouTube’s auto-generated captions in “accent challenge” videos across dialects and gender:
I picked videos with accents from Maine (U.S), Georgia (U.S.), California (U.S), Scotland and New Zealand. I picked these locations because they’re pretty far from each other and also have pretty distinct regional accents. […]There’s variation, sure, but in general the recognizer seems to be working best on people from California (which just happens to be where Google is headquartered) and worst on Scottish English. The big surprise for me is how well the recognizer works on New Zealand English, especially compared to Scottish English.
Bret Victor complained on Twitter that technologists were wasting their imaginations, energy and talent on things that wouldn’t matter after climate change reduced the world to a drowned cinder; his followers pushed back and asked what they, as technologists, could do about climate change.
In response, Victor’s produced an incredible, exhaustive, insightful andattainable list of projects that technologists can address in their work to mitigate climate change and build a better world. Even better, Victor’s list is full of potentially profitable ideas that nerds can raise money for (“there are basically only two scenarios for investors as a collective: (a) invest in clean energy immediately; clean energy takes over the $6 trillion global energy market; investors get a nice piece of that; or (b) don’t invest in clean energy immediately; fossil fuels burn past our carbon budget; investors inherit a cinder”).
Victor builds on the work of Macarthur Genius winner Saul Griffith, whowrote a still-essential series of posts on the subject for us in 2009. He breaks his suggestions into seven categories: Funding, Producing Energy, Moving Energy, Consuming Energy, Tools for Scientists and Engineers, Media for Understanding Situations and Other.
This is your essential read for the day. If this inspires you, read the late David MacKay’s open access, accessible engineering book Sustainable Energy Without the Hot Air, the single best book about energy and climate I’ve ever read.
Dr Rodrigo Nieto-Gomez is a research professor at the Naval Postgraduate School, studying “criminal entrepreneurship” in drug cartels, who beat Amazon to using drones for delivery by years, use modified potato guns to shoot cocaine and marijuana bundles over border fences, and represent the “true libertarian, Ayn Rand capitalism.”
In a wide-ranging interview with Motherboard, Nieto-Gomez speculates on the future of drug smuggling (flying and submarine drones), and describes the Silicon Valley-like relationship between a Mexican investor class and the smuggler-innovators, who sell a share in future returns in exchange for capital to fund high-risk/high-tech R&D efforts to beat police interdiction.
“In 1999, Dave Eggers managed to get an entire David Foster Wallace short story onto the spine of the third issue of McSweeney’s Quarterly Concern. Just getting the title on there – Another Example of the Porousness of Various Borders (VI): Projected But Not Improbable Transcript of Author’s Parents’ Marriage’s End, 1971 – is quite a feat in itself.”
A bipartisan group of military experts released a statement and two reports today arguing that climate change poses a significant risk to U.S. national security. The statement, released by the bipartisan group
Climate Security Consensus project, argues that the effects of climate change will put a strain on water, food and energy supplies, which can result in “unique and hard-to-predict security risks.”
“The effects of climate change present a strategically-significant risk to U.S. national security and international security,” the statement, signed by a bipartisan group of 25 national security and military leaders including Dr. Geoffrey Kemp, former special assistant to President Reagan for national security affairs and Dov S. Zakheim, former undersecretary of defense under President George W. Bush, said.
The statement argued that the “U.S. must advance a comprehensive policy for addressing this risk.”
Some of the climate change risks to international security include “the likelihood of intra or international conflict, state failure, mass migration, and the creation of additional ungoverned spaces,” the statement added.
Reports from two organizations that specialize in the convergence of climate change and national security released with the statement also described the risks.
The Climate and Security Advisory Group, comprised of forty-two military and national security experts, issued a “briefing book” to advise the next President of their growing concerns and suggest tactics for creating a task force within the government.The book urges the next President to prioritize climate change and assign a cabinet-level leader for climate change and security issues, adding that the Secretary of Homeland Security should develop a “National Adaptation and Resilience Strategy” to deal with climate change and better prepare the nation for extreme weather events.
The second report was prepared by
The Center for Climate and Security’s military expert panel, which includes senior retired flag officers from each of the division of the Armed Services. This panel outlined the effect of sea level rise on coastal military bases and how it could affect military operations.
“In early modern Britain, disbelief in the existence of spirits was tantamount to atheism. The overwhelming majority of people, whether rich or poor, educated or uneducated, believed in the existence of a countless number and variety of invisible supernatural beings. Different types of people were concerned with different types of spirits: for the devout Christian, angels and demons stood centre stage; for the elite magician, spirits originating from classical cosmologies could be equally significant while the uneducated country people placed a greater emphasis on the ‘fairy folk’. Trying to make any hard and fast distinction between categories of spirits in early modern Britain is impossible because supernatural beings were labelled differently, depending on geography, education and religious perspective and definitions overlapped considerably. The term ‘fairy’, for example, is a misleadingly broad generic term which, in the period, covered a wide range of supernatural entities. On a popular level there was often little difference between a fairy and an angel, saint, ghost, or devil. We find the popular link between fairies and angels, for example, expressed in the confession of a cunning man on trial for witchcraft in Aberdeen, in 1598. The magical practitioner, who was identified in the trial records as ‘Andro Man’, claimed that his familiar (described by the interrogators as the Devil) was an angel who, like Tom Reid, served the queen of the fairies. The records state ‘Thow confessis that the Devill, thy maister, quhom thow termes Christsonday, and supponis to be ane engell, and Goddis godsone, albeit he hes a thraw by God, and swyis to the Quene of Elphen, is rasit be the speking of the word Benedicte.’”
–Emma Wilby,
Cunning Folk and Familiar Spirits - shamanistic visionary traditions in Early Modern British witchcraft and magic (viaophidiansabbat)
The Korean Demilitarized Zone (DMZ): the beauty of Earth when humans aren’t allowed. Three minute video, worth it. Story from the Center for Biological Diversity:
The Korean Demilitarized Zone (or “DMZ”), at the epicenter of major geopolitical tensions, is a deadly no-man’s land that stretches 160 miles across the Korean peninsula – and now it’s a de facto nature reserve, often considered one of the best-preserved temperate habitats in the world.
It’s an important refuge for two of the planet’s most endangered birds – white-naped and red-crowned cranes – as well as Asiatic black bears and, according to some accounts, extremely rare Korean tigers.
Video footage by South Korean videographer Wanho Lim.
Google are now using their reCaptcha technique of outsourcing image-tagging to humans, a process known as ‘human-based computation’, to assist in the development of driverless cars.
Tim O'Reilly writes about the reality that more and more of our lives – including whether you end up seeing this very sentence! – is in the hands of “black boxes” – algorithmic decision-makers whose inner workings are a secret from the people they effect.
O'Reilly proposes four tests to determine whether a black box is trustable:
1. Its creators have made clear what outcome they are seeking, and it is possible for external observers to verify that outcome.
2. Success is measurable.
3. The goals of the algorithm’s creators are aligned with the goals of the algorithm’s consumers.
4. Does the algorithm lead its creators and its users to make better longer term decisions?
O'Reilly goes on to test these assumptions against some of the existing black boxes that we trust every day, like aviation autopilot systems, and shows that this is a very good framework for evaluating algorithmic systems.
But I have three important quibbles with O'Reilly’s framing. The first is absolutely foundational: the reason that these algorithms are black boxes is that the people who devise them argue that releasing details of their models will weaken the models’ security. This is nonsense.
For example, Facebook’s tweaked its algorithm to downrank “clickbait” stories. Adam Mosseri, Facebook’s VP of product management told Techcrunch, “Facebook won’t be publicly publishing the multi-page document of guidelines for defining clickbait because ‘a big part of this is actually spam, and if you expose exactly what we’re doing and how we’re doing it, they reverse engineer it and figure out how to get around it.’”
There’s a name for this in security circles: “Security through obscurity.” It is as thoroughly discredited an idea as is possible. As far back as the 19th century, security experts have decried the idea that robust systems can rely on secrecy as their first line of defense against compromise.
The reason the algorithms O'Reilly discusses are black boxes is because the people who deploy them believe in security-through-obscurity. Allowing our lives to be manipulated in secrecy because of an unfounded, superstitious belief is as crazy as putting astrologers in charge of monetary policy, no-fly lists, hiring decisions, and parole and sentencing recommendations.
So there’s that: the best way to figure out whether we can trust a black box is the smash it open, demand that it be exposed to the disinfecting power of sunshine, and give no quarter to the ideologically bankrupt security-through-obscurity court astrologers of Facebook, Google, and the TSA.
Then there’s the second issue, which is important whether or not we can see inside the black box: what data was used to train the model? Or, in traditional scientific/statistical terms, what was the sampling methodology?
Garbage in, garbage out is a principle as old as computer science, and sampling bias is a problem that’s as old as the study of statistics. Algorithms are often deployed to replace biased systems with empirical ones: for example, predictive policing algorithms tell the cops where to look for crime, supposedly replacing racially biased stop-and-frisk with data-driven systems of automated suspicion.
But predictive policing training data comes from earlier, human-judgment-driven stop-and-frisk projects. If the cops only make black kids turn out their pockets, then all the drugs, guns and contraband they find will be in the pockets of black kids. Feed this data to a machine learning model and ask it where the future guns, drugs and contraband will be found, and it will dutifully send the police out to harass more black kids. The algorithm isn’t racist, but its training data is.
There’s a final issue, which is that algorithms have to have their models tweaked based on measurements of success. It’s not enough to merely measure success: the errors in the algorithm’s predictions also have to be fed back to it, to correct the model. That’s the difference between Amazon’s sales-optimization and automated hiring systems. Amazon’s systems predict ways of improving sales, which the company tries: the failures are used to change the model to improve it. But automated hiring systems blackball some applicants and advance others, and the companies that makes these systems don’t track whether the excluded people go on to be great employees somewhere else, or whether the recommended hires end up stealing from the company or alienating its customers.
I like O'Reilly’s framework for evaluating black boxes, but I think we need to go farther.
Meal-replacement drinks were made popular by US firm Soylent in the past few years. Founded in 2013 by Rob Rhinehart, the company was shipping 30,000 “meals” a month a year later and Rhinehart told Bloomberg in January this year that sales were up 300 per cent. Soylent is now valued at more than US$100 million.
Its success has seen similar start-ups springing up around the world. India’s SupermealX, Australia’s Aussielent and British-based Huel all claim to offer nutritionally complete drinks.
Shao Wei, who was working as a programmer in Hangzhou, was also intrigued by the idea. As a start-up worker, he had been looking for healthy meal options for those who had little time away from their computers. In 2014, he quit his job and set up his own meal-substitute brand, Ruffood. Its Chinese name – ruo fan in pinyin – means “like rice“.
n. [mass noun] a form of dance music of the 1960s and 1940s, presented in the 1960s and 1940s, and then in the 1960s, consisting of monochrome convocations and formal drugs.
[count noun] a person who is characterized by a high price of the form of the mononomic process of the character.
the process of transporting or recording a computer system and then making a straight line into a signal to decide the tendency to decide the first passage of the operation.
v. [with obj.] remove the mononomite of (an article or process): the first tax can be mononomiched of single drugs.
make (something) a monoconficial tendency: a moconal railway landing was mononophed from the building.
mononophist n. mononophilia n. mononophilia n. mononophilia n.
A George Washington University researcher has identified a 6,200-year-old indigo-blue fabric from Huaca, Peru, making it one of the oldest-known cotton textiles in the world and the oldest known textile decorated with indigo blue.
The discovery marks the earliest use of indigo as a dye, a technically challenging color to produce. According to Jeffrey Splitstoser, lead author of a paper on the discovery and assistant research professor of anthropology at the George Washington University, the finding speaks to the sophisticated textile technology ancient Andean people developed 6,200 years ago.
“Some of the world’s most significant technological achievements were developed first in the New World,” said Dr. Splitstoser. “Many people, however, remain mostly unaware of the important technological contributions made by Native Americans, perhaps because so many of these technologies were replaced by European systems during the conquest. Read more.
“Numbers do not seem to work well with regard to deep time. Any number above a couple of thousand years—fifty thousand, fifty million—will with nearly equal effect awe the imagination.”
A set of six #abstract #photographs An original A4 sized Abstract #drawing unique to each edition (Randomly Chosen) White ink on Noir paper. Signed and dated by the artist. Photographs, shot on an OLYMPUS EM-10 micro-four-thirds camera and a RI by Irving Paul Pereira (via http://flic.kr/p/LdxnHs )
On July 17, 130 million cubic yards of ice and rock suddenly let go from a glacier in Tibet, hurtling down six-tenths of a mile and killing nine herders along with 350 sheep and 110 yaks. Scientists were baffled. Now, by examining satellite images before and after the event, they think it is an example of a rare glacial surge, when a glacier moves at 10 to 100 times its normal speed. Some researchers believe that climate change at high elevations can trigger such surges.
In the days following the Rutog avalanche, cracks occurred in nearby glaciers. Temperatures in the Tibetan plateau have risen 0.4 degrees Celsius per decade, twice the global average. One-tenth of the permafrost has melted in just the past decade. Rapidly melting glaciers have added to the number of lakes by 14 percent since 1970 and 80 percent of existing lakes have grown, flooding towns and pastures. In addition, precipitation in the area has increased 12 percent since 1960.
I expected particular types of experiences, as did my volunteers. We thought that mystical unitive enlightenment-like states would predominate. These are states are imageless, content-free, ego-dissolving states of being merged with a powerful but undifferentiated source of being. Instead, these types of experiences were very rare. Rather, volunteers described entering into a world of intensely saturated light, buzzing and morphing, full of “things” — all manner of objects, and oftentimes sentient beings who were awaiting them and interacted with them. Perhaps if I had used another compound for my studies with more unitive properties, such as 5-methoxy-DMT, my expectations would have been met more consistently. But, I studied DMT and this is what we found.
Consider the following as a rule. Whenever you have nonlinearity, the average doesn’t matter anymore. Hence:
The more nonlinearity in the response, the less informational the average.
For instance, your benefit from drinking water would be linear if ten glasses of water were ten times as good as one single glass. If that is not the case, then necessarily the average water consumption matters less than something else that we will call “unevenness”, or volatility, or inequality in consumption. Say your average daily consumption needs to be one liter a day and I gave you ten liters one day and none for the remaining nine days, for an average of one liter a day. Odds are you won’t survive. You want your quantity of water to be as evenly distributed as possible. Within the day, you do not need to consume the same amount water every minute, but at the scale of the day, you want maximal evenness.
From an informational standpoint, someone who tells you “We will supply you with one liter of water liter day on average” is not conveying much information at all; there needs to be a second dimension, the variations around such an average. You are quite certain that you will die of thirst if his average comes from a cluster of a hundred liters every hundred days.
The ‘Terror of War’ case, then, is the tip of the iceberg: a rare visible instance that points to a much larger mass of unseen automated and semi-automated decisions. The concern is that most of these ‘weak AI’ systems are making decisions that don’t garner such attention. They are embedded at the back-end of systems, working at the seams of multiple data sets, with no consumer-facing interface. Their operations are mainly unknown, unseen, and with impacts that are take enormous effort to detect.
“Would it be an exaggeration to say that in the unconscious there is necessarily less cruelty and terror, and of a different type, than in the consciousness of an heir, a soldier, or a Chief of State? The unconscious has its horrors, but they are not anthropomorphic. It is not the slumber of reason that engenders monsters, but vigilant and insomniac rationality.”
–
Deleuze and Guattari,
Anti-Oedipus, 112
Thinking about this in relation to Trump and the problem of labelling - or rather, psychopathologizing - him as a ‘narcissist’, and other terms up to ‘raving lunatic’ (which, rightly, one cannot say). The idea that what’s wrong with Trump is somehow
internal to him, and not related to the economic and political system which produced him (
quafree-speaker, ’parrhesiast’) and which he ostensibly rails against, seems like a dumb mistake for anyone on the left to make.
Central to Deleuze and Guattari’s text, an “introduction to the nonfascist life” in Foucault’s preface, is the question from Wilhelm Reich, “How could the masses be made to desire their own repression?” To a degree, that’s the same question being asked about Brexit, and Trump (Clinton’s “basket of deplorables” comment is both an example of the accusation of racism being turned around into a sign of superiority, and a bald statement of fact). The potency of ideas around ‘sovereignty’ and ‘Make America Great Again’ is not simply an appeal to base libido but the circularity of ‘psychic repression’ with ‘social repression’- although Reich “did not succeed in determining the insertion of desire into the economic infrastructure itself, the insertion of the drives into social production […] Better to depart in search of the Orgone, he said to himself, in search of the vital and cosmic element of desire, than to continue being a psychoanalyst under such conditions.”
It is not the slumber of reason that engenders monsters, but vigilant and insomniac rationality. This is the other problem with the Trump-analysis - that we’ve departed from a supposed past in which politics was conducted by rational debate, not emotion and traditional positions, and that somehow ‘data’ should have made things uncontroversial. What if our problem is indeed an
excess of rationality of a certain kind - economic? That’s certainly the position of André Gorz; and in
An American Utopia, Fredric Jameson argues that we need to revolt against an idea of ‘efficiency’ determining our political and social lives. I haven’t read it, but the metaphor of sleep brings to mind Jonathan Crary’s
24/7: Late Capitalism and the Ends of Sleep.
Trump, unfortunately, is not a nightmare from which we are trying to awake - but a day-state which we cannot shake off.
“Today, Homo sapiens is faced with a rapid modification of his environment, a transformation for which he is the involuntary collective agent. I am not implying that our species is threatened with extinction or that the “end of the world” is approaching. I am not preaching millenarianism. Rather, I would like to point out an alternative. Either we cross a new threshold, enter a new stage of hominization, by inventing some human attribute that is as essential as language but operates at a much higher level, or we continue to “communicate” through the media and think within the context of separate institutions, which contribute to the suffocation and division of intelligence. In the latter case we will no longer be confronted only by the problems of power and survival. But if we are committed to the process of collective intelligence, we will gradually create the technologies, sign systems, forms of social organization and regulation that enable us to think as a group, concentrate our intellectual and spiritual forces, and negotiate practical real-time solutions to the complex problems we must inevitably confront. We will gradually learn … to collectively invent ourselves as a species.”
“The great merit of the capitalist system, it has been said, is that it succeeds in using the nastiest motives of nasty people for the ultimate benefit of society.”
“Bad philosophy cannot easily be countered by good philosophy – argument and explanation – because it holds itself immune. But it can be countered by progress. People want to understand the world, no matter how loudly they may deny that. And progress makes bad philosophy harder to believe.”
–Deutsch, David.
The Beginning of Infinity: Explanations that Transform the World. London: Allen Lane, 2011. (viacarvalhais)
Report of the Annexation of Switzerland as a Border by the K.R.E.V KonungaRikena Elgaland-Vargaland, Zürich main station, 14.05.2016
With Adrian Notz, Leif Elggren, Carl Michael von Hausswolff, Dave Phillips as special guest and the people of Zürich and beyond.
Imagine if we had a day each year when we all went around asking cancer patients if they were “okay”, yet didn’t fund practical medical help for them or give them any hope? The idea is laughable, yet the analogy is real. I’m not against awareness raising per se. In many fields it does a lot of good and changes lives for the better. And most of those who work in these fields are passionate about their cause and want to help. Awareness raising without action plans for those whose awareness has been raised is cruel. Unconscionable. And utterly disgraceful.
This post presents WaveNet, a deep generative model of raw audio waveforms. We show that WaveNets are able to generate speech which mimics any human voice and which sounds more natural than the best existing Text-to-Speech systems, reducing the gap with human performance by over 50%. We also demonstrate that the same network can be used to synthesize other audio signals such as music, and present some striking samples of automatically generated piano pieces.
Facebook’s Mission Statement states that your objective is to “make the world more open and connected”. In reality you are doing this in a totally superficial sense.
If you will not distinguish between child pornography and documentary photographs from a war, this will simply promote stupidity and fail to bring human beings closer to each other.
To pretend that it is possible to create common, global rules for what may and what may not be published, only throws dust into peoples’ eyes.
Building and maintaining a n-to-n communications platform for over a billion *daily* active users across multiple access platforms *is* difficult and *is* hard and you’ve done it and congratulations, that was lots of work and effort. You - and your Valley compatriots - talk excitedly and breathlessly about solving Hard Problems and Disrupting Things, but in other areas - other areas that are *also* legitimate hard problems like content moderation and community moderation and abuse (which isn’t even a new thing!) - do not appear to interest you. They appear to interest you to such a little degree that it looks like you’ve given up *compared to* the effort that’s put into other hard problems.
You can’t have it both ways. You can’t use rhetoric to say that your people - not just engineers - are the best and the brightest working to solve humanity’s problems without also including the asterisk that says “Actually, *not all hard problems*. Not all difficult problems. Just some. Just the engineering ones, for example."
What you’re doing right now - with your inflexible process that’s designed to be efficient and work at scale without critically being able to deal *at scale* with nuance and context (which, I’d say, is your difficult problem and a challenge you should *relish* - how do you deal with nuance at scale in a positive manner?!) smacks of algorithmic and system-reductionism.
It is tempting to make every fiasco at Facebook about the power (and the abuse of power) of the algorithm. The "napalm girl” controversy does not neatly fit that storyline. A little-known team of humans at Facebook decided to remove the iconic photo from the site this week.
That move revealed, in a klutzy way, just how much the company is struggling internally to exercise the most basic editorial judgment, despite claims by senior leadership that the system is working.
As in the case of Ut’s picture, the decision over whether or not to publicly share photographs like the two East Liverpool ones ought to be in the hands of highly trained photo editors, people who not only have the knowledge to understand the “news value” of the photographs, but who have also wrestled with the different underlying ethical problems.
However much any editor’s decisions might be flawed at times, at the very least we can be certain that they have thought about the underlying problems, that, in other words, we’re looking at the end result of an educated process (regardless of whether or not we end up agreeing with it or not). The world of Facebook does away with this.
When Edward Snowden flew to Hong Kong with thumb-drives full of damning US government documents, he assumed his freedom was forfeit: he didn’t even make an escape plan.
But after the explosive revelations of mass, illegal US spying, people around the world determined that they would save Snowden from the fate of Chelsea Manning: years of torture, decades of imprisonment. Among them was a Canadian human rights lawyer, Robert Tibbo, who had represented many of the teeming masses of refugees crammed into Hong Kong’s asylum-seeker ghetto. Tibbo and his clients shuttled Snowden from shanty to shack to cramped apartment for days, hiding him in plain sight in Kowloon’s Lai Chi Kok district among Vietnamese, Indonesian, Filipino, African and Sri Lankan asylum seekers who endure years of grinding poverty in their bid to make new lives away from their home countries.
Canada’s National Post conducted a long, wide-ranging interview with Tibbo about Snowden’s unlikely escape, filling in the blanks with information from Wikileaks volunteers, other lawyers, Laura Poitras, and Snowden himself. The tale of the vulnerable people who selflessly hid Snowden is the main meat of the story, but perhaps more salient – given the oft-repeated smear that Snowden was a Russian spy – is the story of how Snowden ended up in Moscow.
Twine is a tool that lets you make point-and-click games that run in a web browser—what a lot of people refer to as “choose your own adventure” or CYOA games. It’s pretty easy to make a game, which means that the Twine community is fairly big and diverse.
There are a lot of tools that you can use to do information architecture and to sketch out processes. Visio, PowerPoint, Keynote, or Omnigraffle, for example. In the programming world, some people use UML tools to draw pictures of how a program should operate, and then turn that into code, and a new breed of product prototyping apps are blurring the line between design and code, too. But it has always bummed me out that when you draw a picture on a computer it is, for the most part, just a picture. Why doesn’t the computer make sense of those boxes and arrows for you? Why is it so hard to turn a picture of a web product into a little, functional website?
This is a huge topic — why are most digital documents not presented as dynamic programs? (One good recent exploration of the subject is Bret Victor’s “Up and Down the Ladder of Abstraction.”) And in some ways the Twine interface is a very honest testing and prototyping environment, because it is so good at modeling choices (as in, choose your own adventure).
Last year I turned off all my notifications. I stopped booking meetings. I started living asynchronously. Now instead of being interrupted throughout the day—or rushing from one meeting to the next—I sit down and get work done. I work a lot. I communicate with hundreds of people a day. I collaborate extensively. But I do so on my own terms, at my own tempo. You can live more asynchronously, too. I’ll explain the benefits. I’ll show you how.
What does “causality” mean, and how can you represent it mathematically? How can you encode causal assumptions, and what bearing do they have on data analysis? These types of questions are at the core of the practice of data science, but deep knowledge about them is surprisingly uncommon.
