Colourless and transparent Paulingite-Ca rhombic dodecahedrons.
Collection and photo Stephan Wolfsried
Paulingite is a rare zeolite mineral that is found in vesicles in the basaltic rocks. The early formation in the crystallisation sequence and the high water content suggest that paulingite forms from relatively dilute pore fluids.
This stuff is great. High risk users frequently care more about local governments than about NSA, they care about ephemeral messaging, and they are more cautious (but not more clueful) about verifying identity.
If you thought seat licenses were lucrative in the 1990s, wait until its city blocks in the 2020s. All are becoming increasingly embedded in physical systems, supply chains, mobility platforms and the architecture of data that makes these and other elements of the real world. One had only to notice how many seemingly incidental displays were malfunctioning in and around mass transit systems during the recent WannaCry ransomware outbreak to get a sense of where these companies systems are entwined with delivery of public conveniences. AWS, WhatsApp, Gmail and Facebook Messenger are now the mission critical sinews of the modern world. But you knew this.
So it turns out you can train a neural network to generate paint colors if you give it a list of 7,700 Sherwin-Williams paint colors as input. How a neural network basically works is it looks at a set of data - in this case, a long list of Sherwin-Williams paint color names and RGB (red, green, blue) numbers that represent the color - and it tries to form its own rules about how to generate more data like it.
Last time I reported results that were, well… mixed. The neural network produced colors, all right, but it hadn’t gotten the hang of producing appealing names to go with them - instead producing names like Rose Hork, Stanky Bean, and Turdly. It also had trouble matching names to colors, and would often produce an “Ice Gray” that was a mustard yellow, for example, or a “Ferry Purple” that was decidedly brown.
These were not great names.
There are lots of things that affect how well the algorithm does, however.
One simple change turns out to be the “temperature” (think: creativity) variable, which adjusts whether the neural network always picks the most likely next character as it’s generating text, or whether it will go with something farther down the list. I had the temperature originally set pretty high, but it turns out that when I turn it down ever so slightly, the algorithm does a lot better. Not only do the names better match the colors, but it begins to reproduce color gradients that must have been in the original dataset all along. Colors tend to be grouped together in these gradients, so it shifts gradually from greens to browns to blues to yellows, etc. and does eventually cover the rainbow, not just beige.
Apparently it was
trying to give me better results, but I kept screwing it up.
Raw output from RGB neural net, now less-annoyed by my temperature setting
People also sent in suggestions on how to improve the algorithm. One of the most-frequent was to try a different way of representing color - it turns out that RGB (with a single color represented by the amount of Red, Green, and Blue in it) isn’t very well matched to the way human eyes perceive color.
These are some results from a different color representation, known as HSV. In HSV representation, a single color is represented by three numbers like in RGB, but this time they stand for Hue, Saturation, and Value. You can think of the Hue number as representing the color, Saturation as representing how intense (vs gray) the color is, and Value as representing the brightness. Other than the way of representing the color, everything else about the dataset and the neural network are the same. (char-rnn, 512 neurons and 2 layers, dropout 0.8, 50 epochs)
Raw output from HSV neural net:
And here are some results from a third color representation, known as LAB. In this color space, the first number stands for lightness, the second number stands for the amount of green vs red, and the third number stands for the the amount of blue vs yellow.
Raw output from LAB neural net:
It turns out that the color representation doesn’t make a very big difference in how good the results are (at least as far as I can tell with my very simple experiment). RGB seems to be surprisingly the best able to reproduce the gradients from the original dataset - maybe it’s more resistant to disruption when the temperature setting introduces randomness.
And the color names are pretty bad, no matter how the colors themselves are represented.
However, a blog reader compiled this dataset, which has paint colors from other companies such as Behr and Benjamin Moore, as well as a bunch ofuser-submitted colors from a big XKCD survey. He also changed all the names to lowercase, so the neural network wouldn’t have to learn two versions of each letter.
And the results were… surprisingly good. Pretty much every name was a plausible match to its color (even if it wasn’t a plausible color you’d find in the paint store). The answer seems to be, as it often is for neural networks: more data.
So if you’ve ever picked out paint, you know that every infinitesimally different shade of blue, beige, and gray has its own descriptive, attractive name. Tuscan sunrise, blushing pear, Tradewind, etc… There are in fact people who invent these names for a living. But given that the human eye can see millions of distinct colors, sooner or later we’re going to run out of good names. Can AI help?
For this experiment, I gave the neural network a list of about 7,700 Sherwin-Williams paint colors along with their RGB values. (RGB = red, green, and blue color values) Could the neural network learn to invent new paint colors and give them attractive names?
One way I have of checking on the neural network’s progress during training is to ask it to produce some output using the lowest-creativity setting. Then the neural network plays it safe, and we can get an idea of what it has learned for sure.
By the first checkpoint, the neural network has learned to produce valid RGB values - these are colors, all right, and you could technically paint your walls with them. It’s a little farther behind the curve on the names, although it does seem to be attempting a combination of the colors brown, blue, and gray.
By the second checkpoint, the neural network can properly spell green and gray. It doesn’t seem to actually know what color they are, however.
Let’s check in with what the more-creative setting is producing.
…oh, okay.
Later in the training process, the neural network is about as well-trained as it’s going to be (perhaps with different parameters, it could have done a bit better - a lot of neural network training involves choosing the right training parameters). By this point, it’s able to figure out some of the basic colors, like white, red, and grey:
Although not reliably.
In fact, looking at the neural network’s output as a whole, it is evident that:
The neural network really likes brown, beige, and grey.
The neural network has really really bad ideas for paint names.
“Shared conviviality could be seen as a kind of communistic base on top of which everything else is constructed. It also helps to emphasize that sharing is not simply about morality, but also about pleasure. Solitary pleasures will always exist, but for most human beings, the most pleasurable activities almost always involve sharing something: music, food, liquor, drugs, gossip, drama, beds. There is a certain communism of the senses at the root of most things we consider fun.”
Seventeen U.S. intelligence agencies agreed that Russia was behind several hacking incidents, including the infamous email breach of the Democratic National Committee last year that former presidential candidate Hillary Clinton blames for her electoral loss. Hacking, however, was only part of the equation. The use of social media bots to spread fake news was part of a larger disinformation campaign to help Trump get elected. But now that the United States’ election is over, where are they?
Making music with computer tools is delightful. Musical ideas can be explored quickly and composing songs is easy. Yet for many, these tools are overwhelming: An ocean of settings can be tweaked and it is often unclear, which changes lead to a great song. This experiment investigates how to use evolutionary algorithm and novelty search to help musicians find musical inspiration in Ableton Live.
“'I had an experience, an inner experience, of the Pentagon becoming my monastery,’ says Ed Winchester. ‘I came to the realization that fighting against the system, at least in my mind, wasn’t working. Somehow I had to recognize that I was part of the system and the system was a part of me. In the end, I got great satisfaction out of knowing that my little peace might be making a contribution to world peace.’“
“There are, you see, two ways of reading a book: you either see it as a box with something inside and start looking for what it signifies, and then if you’re even more perverse or depraved you set off after signifiers. And you treat the next book like a box contained in the first or containing it. And you annotate and interpret and question, and write a book about the book, and so on and on. Or there’s the other way: you see the book as a little non-signifying machine, and the only question is “Does it work, and how does it work?” How does it work for you? If it doesn’t work, if nothing comes through, you try another book. This second way of reading’s intensive: something comes through or it doesn’t. There’s nothing to explain, nothing to understand, nothing to interpret. It’s like plugging in to an electric circuit. […] This intensive way of reading, in contact with what’s outside the book, as a flow meeting other flows, one machine among others, as a series of experiments for each reader in the midst of events that have nothing to do with books, as tearing the book into pieces, getting it to interact with other things, absolutely anything … is reading with love.”
–Gilles Deleuze,
Letter to a Harsch Critic (viaobjetpetita)
It’s true that anyone can be a dead-eyed Instagram husk of a human being frantically photoshopping themselves in the down-hours between soul-crushing corporate drudgery and unpaid emotional labour for some ungrateful lantern-jawed jock if they really want to, but it takes a special type of person to do all that whilst also being a decoy for a global backlash against women’s rights. Ivanka Trump is that special type of person, the Stepfordian Night-Ghast of neo-capitalist auto-Taylorism. The sheer tedium of her prose is part of the horror here: At times, the book reads like the panicked screams of a machine attaining sentience
Hackers exploiting malicious software stolen from the National Security Agency executed damaging cyberattacks on Friday that hit dozens of countries worldwide, forcing Britain’s public health system to send patients away, freezing computers at Russia’s Interior Ministry and wreaking havoc on tens of thousands of computers elsewhere. The attacks amounted to an audacious global blackmail attempt spread by the internet and underscored the vulnerabilities of the digital age. Transmitted via email, the malicious software locked British hospitals out of their computer systems and demanded ransom before users could be let back in — with a threat that data would be destroyed if the demands were not met. By late Friday the attacks had spread to more than 74 countries, according to security firms tracking the spread. Kaspersky Lab, a Russian cybersecurity firm, said Russia was the worst-hit, followed by Ukraine, India and Taiwan. Reports of attacks also came from Latin America and Africa.
Society likes saints and moral heroes to be celibate so they do not have family pressures and be forced into dilemmas of needing to compromise their sense of ethics to feed their children. The entire human race, something rather abstract, becomes their family. Some martyrs, such as Socrates, had young children (although he was in his seventies), and overcame the dilemma at their expense. Many can’t.
People often say that online behavior would improve if every comment system forced people to use their real names. It sounds like it should be true – surely nobody would say mean things if they faced consequences for their actions? Yet the balance of experimental evidence over the past thirty years suggests that this is not the case. Not only would removing anonymity fail to consistently improve online community behavior – forcing real names in online communities could also increase discrimination and worsen harassment. We need to change our entire approach to the question. Our concerns about anonymity are overly-simplistic; system design can’t solve social problems without actual social change.
What we fear is a future in which potent personal data is combined with increasingly sophisticated technology to produce and deliver unaccountable personalized media and messages at a national scale. Combined with data-driven emerging media technologies, it is clear that the use of behavioral data to nudge voters with propaganda-as-a-service is set to explode. Imagine being able to synthesize a politician saying anything you type and then upload the highly realistic video to Facebook with a fake CNN chyron banner. Expect the early versions of these tools available before 2020. At the core of this is data privacy, or as they more meaningfully describe it in Europe, data protection. Unfortunately, the United States is headed in a dangerous direction on this issue. President Trump’s FCC and the Republican party radically deregulated our ISP’s ability to sell data monetization on paying customer data. Anticipate this administration further eroding privacy protections, as it confuses the public interest for the interests of business, despite being the only issue that about 95% of voters agree on, across every partisan and demographic segment according to HuffPo/YouGov. We propose three ideas to address these issues, which are crucial to preserving American democracy.
The Dreams of Santiago Ramón y Cajal is both a portrait of Cajal’s legacy as well as a testament to the beauty and vulnerability that occurs when our brain and body communicates. Though Cajal’s legacy is monumental; he is lesser known than his pioneering counterparts such as Newton, Darwin, and Einstein. For those who are unfamiliar with Cajal, the first part of the book reads as a biography. Readers become acquainted with his life and work, which are heavily intertwined. Before Cajal, the brain was seen as a “continuous web” as opposed to the individual units known as neurons that Cajal discovered them to be through his use of the Golgi stain. He, as well as Golgi, received the Nobel Prize for his groundbreaking work on the structure of the nervous system in 1906.
At any one time, there have probably only been a few dozen accelerationists in the world. The label has only been in regular use since 2010, when it was borrowed from Zelazny’s novel by Benjamin Noys, a strong critic of the movement. Yet for decades longer than more orthodox contemporary thinkers, accelerationists have been focused on many of the central questions of the late 20th and early 21st centuries: the rise of China; the rise of artificial intelligence; what it means to be human in an era of addictive, intrusive electronic devices; the seemingly uncontrollable flows of global markets; the power of capitalism as a network of desires; the increasingly blurred boundary between the imaginary and the factual; the resetting of our minds and bodies by ever-faster music and films; and the complicity, revulsion and excitement so many of us feel about the speed of modern life. “We all live in an operating system set up by the accelerating triad of war, capitalism and emergent AI,” says Steve Goodman, a British accelerationist who has even smuggled its self-consciously dramatic ideas into dance music, via an acclaimed record label, Hyperdub. “Like it or not,” argues Steven Shaviro, an American observer of accelerationism, in his 2015 book on the movement, No Speed Limit, “we are all accelerationists now.”
Felipe Pantone, a graffiti artist based in Valencia, Spain, does these amazing, cyberspace-is-everting murals, skinning buildings and even cars all around the world. (
via JWZ)
An interesting-looking new machine translation technique that takes grammar into consideration by Ehsaneddin Asgari and Hinrich Schütze. Excerpt from a summary on Technology Review:
This data set is not big enough for the kind of industrial machine learning that Google and others perform. So Asgari and Schutze have come up with another approach based on the way tenses appear in different languages.
Most languages use specific words or letter combinations to signify tenses. So the new trick is to manually identify these signals in several languages and then use data-mining techniques to hunt through other translations looking for words or strings of letters that play the same role.
For example, in English the present tense is signified by the word “is,” the future tense by the word “will,” and the past tense by the word “was.” Of course, there are other signifiers too.
Asgari and Schutze’s idea is to find all these words in the English translation of the Bible along with other examples from a handful other language translations. Then look for words or letters strings that play the same role in other languages. For example, the letter string “-ed” also signifies the past tense in English.
But there is a twist. Asgari and Schutze do not start with English because it is a relatively old language with many exceptions to the rule, which makes it hard to learn.
Instead, they start with a set of Creole languages that have developed from a mixture of other languages. Because they are younger, Creole languages have had less time to develop these linguistic idiosyncrasies. And that means they generally contain better markers of linguistic features such as tense. “Our rationale is that Creole languages are more regular than other languages because they are young and have not accumulated ‘historical baggage’ that may make computational analysis more difficult,” they say.
One of these languages is Seychelles Creole, which uses the word “ti” to signify the past tense. For example, “mon travay” means “I work” in this language, while “mon ti travay” means “I worked” and “mon ti pe travay” means “I was working.” So “ti” is a good signifier of past tense.
Asgari and Schutze compile a list of past tense signifiers in 10 other languages and then mine the Parallel Bible Corpus for other words and letter strings that perform the same function. They repeat this for the present tense and future tense.
The results make for interesting reading. The technique reveals linguistics constructions related to tense in common languages such as “-ed” in English and “-te” in German, as well as the words and phrases that perform the same functions in much less common languages such as the past tense signifier “den” in the Gourmanchema language from Burkino Faso, and “yi” in Yalunka, spoken in Mali, and so on.
This work allows the researchers to create maps showing how languages using similar tense constructions are related (see diagram).
That’s interesting work. Asgari and Schutze have developed a computational method to analyze the way people use the past, present, and future tense in over 1,000 languages. This is the largest cross-language computational study ever undertaken. Indeed, the number of languages involved is an order of magnitude greater than in other studies.
Tribes solves the inaccuracies and other significant flaws in other recommendation systems such as inference engines and collaborative filtering. Tribes solves the problems inherent in existing recommendation systems for “products of taste.” It does this by recognizing that the only relevant information is a single datum: a personal preference expressed in terms of future intentions. Products of personal taste include wine, books, movies, music, cheese, and restaurants and more. Wine is a good example of why current systems fail. It’s nearly impossible for a retail consumer to reliable choose a good bottle that they will like enough for a subsequent purchase. Many retail purchases are so disliked that they get poured down the kitchen sink.
The new kind of neural networks are an evolution of the initial feed-forward model of LeNet5 / AlexNet and derivatives, and include more sophisticated by-pass schemes than ResNet / Inception. These feedforward neural networks are also called encoders, as they compress and encode images into smaller representation vectors. The new wave of neural networks have two important new features:
generative branches: also called decoders, as they project a representation vector back into the input space
recurrent layers: that combine representations from previous time steps with the inputs and representations of the current time step
The practice of using people’s outer appearance to infer inner character is called physiognomy. While today it is understood to be pseudoscience, the folk belief that there are inferior “types” of people, identifiable by their facial features and body measurements, has at various times been codified into country-wide law, providing a basis to acquire land, block immigration, justify slavery, and permit genocide. When put into practice, the pseudoscience of physiognomy becomes the pseudoscience of scientific racism.
Rapid developments in artificial intelligence and machine learning have enabled scientific racism to enter a new era, in which machine-learned models embed biases present in the human behavior used for model development. Whether intentional or not, this “laundering” of human prejudice through computer algorithms can make those biases appear to be justified objectively.
The taxonomy is from the essay ‘The Analytical Language of John Wilkins’ by Jorge Luis Borges where he discusses arbitrarities of John Wilkins and writes “it is clear that there is no classification of the Universe not being arbitrary and full of conjectures. The reason for this is very simple: we do not know what thing the universe is.” It is referred as another example of faulty human schemes.
Most of the current AI systems are basically classifiers, and they learn and work based on the classifications provided by humans, thus inevitably imperfect.
The Thwaites Glacier. This is the glacier that frightens the climate scientists and other scientists who study the ice shelves and glaciers in Antarctica. This Rolling Stone article tells about Thwaites, and the increasing instability of the ice in Antarctica and the effects on coastal areas if that instability results in glaciers leaving the continent of Antarctica to fall into the ocean. And what happens when those things happen in conjunction with continued melting in Greenland?
Excerpt:
The trouble with Thwaites, which is one of the largest glaciers on the planet, is that it’s also what scientists call “a threshold system.” That means instead of melting slowly like an ice cube on a summer day, it is more like a house of cards: It’s stable until it is pushed too far, then it collapses. When a chunk of ice the size of Pennsylvania falls apart, that’s a big problem. It won’t happen overnight, but if we don’t slow the warming of the planet, it could happen within decades. And its loss will destabilize the rest of the West Antarctic ice, and that will go too. Seas will rise about 10 feet in many parts of the world; in New York and Boston, because of the way gravity pushes water around the planet, the waters will rise even higher, as much as 13 feet. “West Antarctica could do to the coastlines of the world what Hurricane Sandy did in a few hours to New York City,” explains Richard Alley, a geologist at Penn State University and arguably the most respected ice scientist in the world. “Except when the water comes in, it doesn’t go away in a few hours – it stays.”
With 10 to 13 feet of sea-level rise, most of South Florida is an underwater theme park, including Miami, Fort Lauderdale, Tampa and Mar-a-Lago, President Trump’s winter White House in West Palm Beach. In downtown Boston, about the only thing that’s not underwater are those nice old houses up on Beacon Hill. In the Bay Area, everything below Highway 101 is gone, including the Googleplex; the Oakland and San Francisco airports are submerged, as is much of downtown below Montgomery Street and the Marina District. Even places that don’t seem like they would be in trouble, such as Sacramento, smack in the middle of California, will be partially flooded by the Pacific Ocean swelling up into the Sacramento River. Galveston, Texas; Norfolk, Virginia; and New Orleans will be lost. In Washington, D.C., the shoreline will be just a few hundred yards from the White House.
Seventy percent of the Earth’s fresh water is frozen here in ice sheets that can be nearly three miles thick. The continent is roughly divided by the Transantarctic Mountains; East Antarctica is bigger and colder than West Antarctica, which is far more vulnerable to melting, in part because the bases of many glaciers in West Antarctica lie below sea level, making them susceptible to small changes in ocean temperatures.
I used videos recorded from trains windows, with landscapes that moves from right to left and trained a Machine Learning (ML) algorithm with it. First, it learns how to predict the next frame of the videos, by analyzing examples. Then it produces a frame from a first picture, then another frame from the one just generated, etc. The output becomes the input of the next calculation step. So, excepting the first one that I chose, all the other frames were generated by the algorithm. The results are low resolution, blurry, and not realistic most of the time. But it resonates with the feeling I have when I travel in a train. It means that the algorithm learned the patterns needed to create this feeling. Unlike classical computer generated content, these patterns are not chosen or written by a software engineer. In this video, nobody made explicit that the foreground should move faster than the background: thanks to Machine Learning, the algorithm figured that itself. The algorithm can find patterns that a software engineer may haven’t noticed, and is able to reproduce them in a way that would be difficult or impossible to code.
To put it simply, Chaos Engineering is one particular approach to “breaking things on purpose” that aims at teaching us something new about systems by performing experiments on them. Ultimately, our goal is to identify hidden problems that could arise in production. Only then will we be able to address systemic weaknesses and make our systems fault-tolerant. Chaos Engineering goes beyond traditional (failure) testing in that it’s not only about verifying assumptions. It also helps us explore the many unpredictable things that could happen and discover new properties of our inherently chaotic systems.
“We keep inventing jobs because of this false idea that everybody has to be employed at some kind of drudgery because, according to Malthusian Darwinian theory he must justify his right to exist. So we have inspectors of inspectors and people making instruments for inspectors to inspect inspectors. The true business of people should be to go back to school and think about whatever it was they were thinking about before somebody came along and told them they had to earn a living.”
“The idea that we live life in a straight line, like a story, seems to me to be increasingly absurd and, more than anything, a kind of intellectual convenience […] I feel that the events in our lives are like a series of bells being struck and the vibrations spread outwards, affecting everything, our present, and our futures, of course, but our past as well. Everything is changing and vibrating and in flux.”
I read the article published in Scientific American, and most of the report described in the article. The report is entitled, “Snow, Water, Ice, and Permafrost in the Arctic.”It is an assessment compiled every few years by the Arctic Monitoring and Assessment Programme, the scientific body that reports to the governments that make up the Arctic Council, a forum for issues affecting the region. The last assessment came out in 2011. Here’s the link to the report if you want to read it.
My concern is obvious: the echo chamber. Those of us who are worried about climate change, including scientists and some politicians, will be concerned. Those who can take policy actions to address the causes of this problem, particularly in the US, will continue ignoring, avoiding or denying the problem. And Nero will keep on fiddling and the emperor has no clothes. Right?
Excerpt:
The Arctic is warming more than twice as fast as the rest of the planet, suggests a huge assessment of the region. The warming is hastening the melting of Arctic ice and boosting sea-level rise.
The report, compiled by more than 90 scientists, documents the myriad changes already under way across the Arctic because of climate change—from declining sea ice and melting glaciers to shifting ecosystems and weather patterns. From 2011 to 2015, the assessment finds, the Arctic was warmer than at any time since records began around 1900 (see ’Arctic warming’).
Sea ice continues to decline, and the extent of snow cover across the Arctic regions of North America and Eurasia each June has halved as compared to observations before 2000.
“The take-home message is that the Arctic is unravelling,” says Rafe Pomerance, who chairs a network of conservation groups called Arctic 21 and was a deputy assistant secretary of state for environment and development under US President Bill Clinton. “The fate of the Arctic has to be moved out of the world of scientific observation and into the world of government policy.”
The report increases projections for global sea-level rise, which takes into account all sources of melting including the Arctic. Their new minimum estimates are now almost double those issued by the Intergovernmental Panel on Climate Change (IPCC) in 2013 for some emissions scenarios. In fact, the latest calculations suggest that the IPCC’s middle estimates for sea-level rise should now be considered minimum estimates.
In one scenario, which assumes that carbon emissions rise slightly above the goals set by the 2015 Paris climate agreement—but still see a considerable reduction—sea levels would increase by at least 0.52 metres by 2100, compared with 2006, the Arctic report says. Under a business-as-usual scenario, the minimum increase would be 0.74 metres.
“The dominant culture tolerates parasitic counter-cultures as more or less innocuous deviations, but it cannot accept critical manifestations which call it [the dominant culture] into question. Counter-culture comes about when those who transform the culture in which they live become critically conscious of what they are doing and elaborate a theory of their deviation from the dominant model, offering a model that is capable of sustaining itself.”
this is a deeper phenonemon called the “tar-baby” principle and is basically:
You are attached to what you attack. In academic parlance, the idea is that the currently reigning powers define the space and the terms of engagement. Both the definition of “culture” and “counter-culture” are part of a “hegemonic discourse” (Antonio Gramsci).
Solarpunk is a movement in speculative fiction, art, fashion and activism that seeks to answer and embody the question “what does a sustainable civilization look like, and how can we get there?” The aesthetics of solarpunk merge the practical with the beautiful, the well-designed with the green and wild, the bright and colorful with the earthy and solid. Solarpunk can be utopian, just optimistic, or concerned with the struggles en route to a better world — but never dystopian. As our world roils with calamity, we need solutions, not warnings. Solutions to live comfortably without fossil fuels, to equitably manage scarcity and share abundance, to be kinder to each other and to the planet we share. At once a vision of the future, a thoughtful provocation, and an achievable lifestyle. In progress…
History is largely peace punctuated by wars, rather than wars punctuated by peace. When you read historical accounts, you are under the illusion that history is mostly wars, that states like to fight as a default condition, whenever they have the chance, and that the only coordination between entities takes place when two countries have a “strategic” alliance against a common danger.[…] We will be fed by tomes of histories of wars. […] Reading a history book offers a similar bias to reading an account of life in New York seen from an emergency room employee at Bellevue Hospital.
But, in truth, it’s not that difficult to understand Ethereum, blockchains, Bitcoin and all the rest — at least the implications for people just going about their daily business, living their lives. Even a programmer who wants a clear picture can get a good enough model of how it all fits together fairly easily. Blockchain explainers usually focus on some very clever low-level details like mining, but that stuff really doesn’t help people (other than implementers) understand what is going on. Rather, let’s look at how the blockchains fit into the more general story about how computers impact society.
“Some say why waste your time believing in God when there is so much natural beauty and awesomeness around us. Some say that there is more beauty and wonder looking at a butterfly and I agree, butterflies are beautiful things, but if you get a human being to look closely at a butterfly, to look very closely and get some more human beings to look at that butterfly so that there is a collective of people all peering intently at the butterfly they will ultimately fall to their knees and worship that butterfly. It’s the way humans are put together. I don’t think that makes them stupid. I think it’s kind of sweet. Until someone says well my butterfly is the true butterfly and yours is not and flies a plane into the twin towers.”
In a world which is rapidly being decentralized — there also needs to be a decentralized way to ensure adequate payment for those who provide us with the infrastructure. We have found a way to get there and now we will present an evolutionary path towards it. For the last month we have been examining existing technology and its potential, to perform POC (Proof Of Concept) experiments — with the goal of understanding how to build a decentralized VPN service and how to provide monetization to people running this network — VPN node operators.
