Data Encoder Quotes

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Mostly something called Sanctuary Moon.” He shook his head, dismissing it. “It’s probably using it to encode data for the company. It can’t be watching it, not in that volume; we’d notice.” I snorted. He underestimated me. Ratthi said, “The one where the colony’s solicitor killed the terraforming supervisor who was the secondary donor for her implanted baby?” Again, I couldn’t help it. I said, “She didn’t kill him, that’s a fucking lie.” Ratthi turned to Mensah. “It’s watching it.
Martha Wells (All Systems Red (The Murderbot Diaries, #1))
Now I existed solely thanks to the quantum paradox, my brain a collection of qubits in quantum superposition, encoding truths and memories, imagination and irrationality in opposing, contradictory states that existed and didn't exist, all at the same time.
Robin Wasserman (Crashed (Cold Awakening, #2))
We no longer live addicted to speech; having lost our senses, now we are going to lose language, too. We will be addicted to data, naturally. Not data that comes from the world, or from language, but encoded data. To know is to inform oneself. Information is becoming our primary and universal addiction.
Michel Serres (The Five Senses: A Philosophy of Mingled Bodies (Athlone Contemporary European Thinkers))
The math-powered applications powering the data economy were based on choices made by fallible human beings. Some of these choices were no doubt made with the best intentions. Nevertheless, many of these models encoded human prejudice, misunderstanding, and bias into the software systems that increasingly managed our lives. Like gods, these mathematical models were opaque, their workings invisible to all but the highest priests in their domain: mathematicians and computer scientists. Their verdicts, even when wrong or harmful, were beyond dispute or appeal. And they tended to punish the poor and the oppressed in our society, while making the rich richer.
Cathy O'Neil (Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy)
There is data [on race and intelligence]. My claim is that it doesn't mean what we think it means. There isn't enough work; there aren't enough people who have done the work – and the definition...I mean, trust me: "heritable" is a serious problem. Because...for example, let's say that there was a belief that people who had a brow ridge, or something, were stupid. And that belief was widespread. And that brow ridge was genetically encoded, and it resulted in people going into the world and facing discrimination in school, let's say, because the brow ridge connoted to the teachers that they were not likely to be intelligent, and therefore they were given simpler lessons; they got dumbtracked or something like that. That would show up as a genetically heritable difference in intelligence between brow-ridged people and non-brow-ridged people. That does not mean that it was encoded in the genome and that it was the brain that was blueprinted...what it means is that some feature that was encoded in the genome caused the environment to interact with the individual in a way that then produced a difference in intellect. [...] It is so early in the study of this stuff, we really don't know. And the taboo nature of those questions is causing a vacuum that is being filled with an artificially pure (and probably not correct) perspective.
Bret Weinstein
For the longest time we believed the world around us was deterministic enough to be understood; that it was just a matter of encoding enough data, and enough processing power, to be able to see the future. That if I do x, and the other person does y, and if I know all the things I need to know about the actors and their actions, I can say that z is the logical outcome…
Jared Shurin (The Big Book of Cyberpunk)
As a thought experiment, von Neumann's analysis was simplicity itself. He was saying that the genetic material of any self-reproducing system, whether natural or artificial, must function very much like a stored program in a computer: on the one hand, it had to serve as live, executable machine code, a kind of algorithm that could be carried out to guide the construction of the system's offspring; on the other hand, it had to serve as passive data, a description that could be duplicated and passed along to the offspring. As a scientific prediction, that same analysis was breathtaking: in 1953, when James Watson and Francis Crick finally determined the molecular structure of DNA, it would fulfill von Neumann's two requirements exactly. As a genetic program, DNA encodes the instructions for making all the enzymes and structural proteins that the cell needs in order to function. And as a repository of genetic data, the DNA double helix unwinds and makes a copy of itself every time the cell divides in two. Nature thus built the dual role of the genetic material into the structure of the DNA molecule itself.
M. Mitchell Waldrop (The Dream Machine: J.C.R. Licklider and the Revolution That Made Computing Personal)
Nevertheless, many of these models encoded human prejudice, misunderstanding, and bias into the software systems that increasingly managed our lives. Like gods, these mathematical models were opaque, their workings invisible to all but the highest priests in their domain: mathematicians and computer scientists. Their verdicts, even when wrong or harmful, were beyond dispute or appeal. And
Cathy O'Neil (Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy)
In 2012, a World Economic Forum analysis found that countries with gender-inflected languages, which have strong ideas of masculine and feminine present in almost every utterance, are the most unequal in terms of gender. 33 But here’s an interesting quirk: countries with genderless languages (such as Hungarian and Finnish) are not the most equal. Instead, that honour belongs to a third group, countries with ‘natural gender languages’ such as English. These languages allow gender to be marked (female teacher, male nurse) but largely don’t encode it into the words themselves. The study authors suggested that if you can’t mark gender in any way you can’t ‘correct’ the hidden bias in a language by emphasising ‘women’s presence in the world’. In short: because men go without saying, it matters when women literally can’t get said at all.
Caroline Criado Pérez (Invisible Women: Exposing Data Bias in a World Designed for Men)
The Sumerian writing system did so by combining two types of signs, which were pressed in clay tablets. One type of signs represented numbers. There were signs for 1, 10, 60, 600, 3,600 and 36,000. (The Sumerians used a combination of base-6 and base-10 numeral systems. Their base-6 system bestowed on us several important legacies, such as the division of the day into twenty-four hours and of the circle into 360 degrees.) The other type of signs represented people, animals, merchandise, territories, dates and so forth. By combining both types of signs the Sumerians were able to preserve far more data than any human brain could remember or any DNA chain could encode.
Yuval Noah Harari (Sapiens: A Brief History of Humankind)
Then Volescu said, “Gurathin, you wanted to know how it spends its time. That was what you were originally looking for in the logs. Tell them.” Mensah lifted her brows. “Well?” Gurathin hesitated. “It’s downloaded seven hundred hours of entertainment programming since we landed. Mostly serials. Mostly something called Sanctuary Moon.” He shook his head, dismissing it. “It’s probably using it to encode data for the company. It can’t be watching it, not in that volume; we’d notice.” I snorted. He underestimated me. Ratthi said, “The one where the colony’s solicitor killed the terraforming supervisor who was the secondary donor for her implanted baby?” Again, I couldn’t help it. I said, “She didn’t kill him, that’s a fucking lie.” Ratthi turned to Mensah. “It’s watching it.
Martha Wells (All Systems Red (The Murderbot Diaries, #1))
The third cardinal feature of gene regulation, Monod and Jacob discovered, was that every gene had specific regulatory DNA sequences appended to it that acted like recognition tags. Once a sugar sensing-protein had detected sugar in the environment, it would recognize one such tag and turn the target genes on or off. That was a gene's signal to make more RNA messages and thereby generate the relevant enzyme to digest the sugar. A gene, in short, possessed not just information to encode a protein, but also information about when and where to make that protein. All that data was encrypted in DNA, typically appended to the front of every gene (although regulatory sequences) an also be appended to the ends and middle of genes). The combination of regulatory sequences and the protein-encoding sequence defined a gene.
Siddhartha Mukherjee (The Gene: An Intimate History)
Two observations take us across the finish line. The Second Law ensures that entropy increases throughout the entire process, and so the information hidden within the hard drives, Kindles, old-fashioned paper books, and everything else you packed into the region is less than that hidden in the black hole. From the results of Bekenstein and Hawking, we know that the black hole's hidden information content is given by the area of its event horizon. Moreover, because you were careful not to overspill the original region of space, the black hole's event horizon coincides with the region's boundary, so the black hole's entropy equals the area of this surrounding surface. We thus learn an important lesson. The amount of information contained within a region of space, stored in any objects of any design, is always less than the area of the surface that surrounds the region (measured in square Planck units). This is the conclusion we've been chasing. Notice that although black holes are central to the reasoning, the analysis applies to any region of space, whether or not a black hole is actually present. If you max out a region's storage capacity, you'll create a black hole, but as long as you stay under the limit, no black hole will form. I hasten to add that in any practical sense, the information storage limit is of no concern. Compared with today's rudimentary storage devices, the potential storage capacity on the surface of a spatial region is humongous. A stack of five off-the-shelf terabyte hard drives fits comfortable within a sphere of radius 50 centimeters, whose surface is covered by about 10^70 Planck cells. The surface's storage capacity is thus about 10^70 bits, which is about a billion, trillion, trillion, trillion, trillion terabytes, and so enormously exceeds anything you can buy. No one in Silicon Valley cares much about these theoretical constraints. Yet as a guide to how the universe works, the storage limitations are telling. Think of any region of space, such as the room in which I'm writing or the one in which you're reading. Take a Wheelerian perspective and imagine that whatever happens in the region amounts to information processing-information regarding how things are right now is transformed by the laws of physics into information regarding how they will be in a second or a minute or an hour. Since the physical processes we witness, as well as those by which we're governed, seemingly take place within the region, it's natural to expect that the information those processes carry is also found within the region. But the results just derived suggest an alternative view. For black holes, we found that the link between information and surface area goes beyond mere numerical accounting; there's a concrete sense in which information is stored on their surfaces. Susskind and 'tHooft stressed that the lesson should be general: since the information required to describe physical phenomena within any given region of space can be fully encoded by data on a surface that surrounds the region, then there's reason to think that the surface is where the fundamental physical processes actually happen. Our familiar three-dimensional reality, these bold thinkers suggested, would then be likened to a holographic projection of those distant two-dimensional physical processes. If this line of reasoning is correct, then there are physical processes taking place on some distant surface that, much like a puppeteer pulls strings, are fully linked to the processes taking place in my fingers, arms, and brain as I type these words at my desk. Our experiences here, and that distant reality there, would form the most interlocked of parallel worlds. Phenomena in the two-I'll call them Holographic Parallel Universes-would be so fully joined that their respective evolutions would be as connected as me and my shadow.
Brian Greene (The Hidden Reality: Parallel Universes and the Deep Laws of the Cosmos)
The last refuge of the Self, perhaps, is “physical continuity.” Despite the body’s mercurial nature, it feels like a badge of identity we have carried since the time of our earliest childhood memories. A thought experiment dreamed up in the 1980s by British philosopher Derek Parfit illustrates how important—yet deceiving—this sense of physical continuity is to us.15 He invites us to imagine a future in which the limitations of conventional space travel—of transporting the frail human body to another planet at relatively slow speeds—have been solved by beaming radio waves encoding all the data needed to assemble the passenger to their chosen destination. You step into a machine resembling a photo booth, called a teletransporter, which logs every atom in your body then sends the information at the speed of light to a replicator on Mars, say. This rebuilds your body atom by atom using local stocks of carbon, oxygen, hydrogen, and so on. Unfortunately, the high energies needed to scan your body with the required precision vaporize it—but that’s okay because the replicator on Mars faithfully reproduces the structure of your brain nerve by nerve, synapse by synapse. You step into the teletransporter, press the green button, and an instant later materialize on Mars and can continue your existence where you left off. The person who steps out of the machine at the other end not only looks just like you, but etched into his or her brain are all your personality traits and memories, right down to the memory of eating breakfast that morning and your last thought before you pressed the green button. If you are a fan of Star Trek, you may be perfectly happy to use this new mode of space travel, since this is more or less what the USS Enterprise’s transporter does when it beams its crew down to alien planets and back up again. But now Parfit asks us to imagine that a few years after you first use the teletransporter comes the announcement that it has been upgraded in such a way that your original body can be scanned without destroying it. You decide to give it a go. You pay the fare, step into the booth, and press the button. Nothing seems to happen, apart from a slight tingling sensation, but you wait patiently and sure enough, forty-five minutes later, an image of your new self pops up on the video link and you spend the next few minutes having a surreal conversation with yourself on Mars. Then comes some bad news. A technician cheerfully informs you that there have been some teething problems with the upgraded teletransporter. The scanning process has irreparably damaged your internal organs, so whereas your replica on Mars is absolutely fine and will carry on your life where you left off, this body here on Earth will die within a few hours. Would you care to accompany her to the mortuary? Now how do you feel? There is no difference in outcome between this scenario and what happened in the old scanner—there will still be one surviving “you”—but now it somehow feels as though it’s the real you facing the horror of imminent annihilation. Parfit nevertheless uses this thought experiment to argue that the only criterion that can rationally be used to judge whether a person has survived is not the physical continuity of a body but “psychological continuity”—having the same memories and personality traits as the most recent version of yourself. Buddhists
James Kingsland (Siddhartha's Brain: Unlocking the Ancient Science of Enlightenment)
To give you a sense of the sheer volume of unprocessed information that comes up the spinal cord into the thalamus, let’s consider just one aspect: vision, since many of our memories are encoded this way. There are roughly 130 million cells in the eye’s retina, called cones and rods; they process and record 100 million bits of information from the landscape at any time. This vast amount of data is then collected and sent down the optic nerve, which transports 9 million bits of information per second, and on to the thalamus. From there, the information reaches the occipital lobe, at the very back of the brain. This visual cortex, in turn, begins the arduous process of analyzing this mountain of data. The visual cortex consists of several patches at the back of the brain, each of which is designed for a specific task. They are labeled V1 to V8. Remarkably, the area called V1 is like a screen; it actually creates a pattern on the back of your brain very similar in shape and form to the original image. This image bears a striking resemblance to the original, except that the very center of your eye, the fovea, occupies a much larger area in V1 (since the fovea has the highest concentration of neurons). The image cast on V1 is therefore not a perfect replica of the landscape but is distorted, with the central region of the image taking up most of the space. Besides V1, other areas of the occipital lobe process different aspects of the image, including: •  Stereo vision. These neurons compare the images coming in from each eye. This is done in area V2. •  Distance. These neurons calculate the distance to an object, using shadows and other information from both eyes. This is done in area V3. •  Colors are processed in area V4. •  Motion. Different circuits can pick out different classes of motion, including straight-line, spiral, and expanding motion. This is done in area V5. More than thirty different neural circuits involved with vision have been identified, but there are probably many more. From the occipital lobe, the information is sent to the prefrontal cortex, where you finally “see” the image and form your short-term memory. The information is then sent to the hippocampus, which processes it and stores it for up to twenty-four hours. The memory is then chopped up and scattered among the various cortices. The point here is that vision, which we think happens effortlessly, requires billions of neurons firing in sequence, transmitting millions of bits of information per second. And remember that we have signals from five sense organs, plus emotions associated with each image. All this information is processed by the hippocampus to create a simple memory of an image. At present, no machine can match the sophistication of this process, so replicating it presents an enormous challenge for scientists who want to create an artificial hippocampus for the human brain.
Michio Kaku (The Future of the Mind: The Scientific Quest to Understand, Enhance, and Empower the Mind)
Personal Thinking Blockchains More speculatively for the farther future, the notion of blockchain technology as the automated accounting ledger, the quantized-level tracking device, could be extensible to yet another category of record keeping and administration. There could be “personal thinking chains” as a life-logging storage and backup mechanism. The concept is “blockchain technology + in vivo personal connectome” to encode and make useful in a standardized compressed data format all of a person’s thinking. The data could be captured via intracortical recordings, consumer EEGs, brain/computer interfaces, cognitive nanorobots, and other methodologies. Thus, thinking could be instantiated in a blockchain — and really all of an individual’s subjective experience, possibly eventually consciousness, especially if it’s more precisely defined. After they’re on the blockchain, the various components could be administered and transacted — for example, in the case of a post-stroke memory restoration. Just as there has not been a good model with the appropriate privacy and reward systems that the blockchain offers for the public sharing of health data and quantified-self-tracking data, likewise there has not been a model or means of sharing mental performance data. In the case of mental performance data, there is even more stigma attached to sharing personal data, but these kinds of “life-streaming + blockchain technology” models could facilitate a number of ways to share data privately, safely, and remuneratively. As mentioned, in the vein of life logging, there could be personal thinking blockchains to capture and safely encode all of an individual’s mental performance, emotions, and subjective experiences onto the blockchain, at minimum for backup and to pass on to one’s heirs as a historical record. Personal mindfile blockchains could be like a next generation of Fitbit or Apple’s iHealth on the iPhone 6, which now automatically captures 200+ health metrics and sends them to the cloud for data aggregation and imputation into actionable recommendations. Similarly, personal thinking blockchains could be easily and securely recorded (assuming all of the usual privacy concerns with blockchain technology are addressed) and mental performance recommendations made to individuals through services such as Siri or Amazon’s Alexa voice assistant, perhaps piped seamlessly through personal brain/computer interfaces and delivered as both conscious and unconscious suggestions. Again perhaps speculatively verging on science fiction, ultimately the whole of a society’s history might include not just a public records and document repository, and an Internet archive of all digital activity, but also the mindfiles of individuals. Mindfiles could include the recording of every “transaction” in the sense of capturing every thought and emotion of every entity, human and machine, encoding and archiving this activity into life-logging blockchains.
Melanie Swan (Blockchain: Blueprint for a New Economy)
The rate of time flow perceived by an observer in the simulated universe is completely independent of the rate at which a computer runs the simulation, a point emphasized in Greg Egan's science-fiction novel Permutation City. Moreover, as we discussed in the last chapter and as stressed by Einstein, it's arguably more natural to view our Universe not from the frog perspective as a three-dimensional space where things happen, but from the bird perspective as a four-dimensional spacetime that merely is. There should therefore be no need for the computer to compute anything at all-it could simply store all the four-dimensional data, that is, encode all properties of the mathematical structure that is our Universe. Individual time slices could then be read out sequentially if desired, and the "simulated" world should still feel as real to its inhabitants as in the case where only three-dimensional data is stored and evolved. In conclusion: the role of the simulating computer isn't to compute the history of our Universe, but to specify it. How specify it? The way in which the data are stored (the type of computer, the data format, etc.) should be irrelevant, so the extent to which the inhabitants of the simulated universe perceive themselves as real should be independent of whatever method is used for data compression. The physical laws that we've discovered provide great means of data compression, since they make it sufficient to store the initial data at some time together with the equations and a program computing the future from these initial data. As emphasized on pages 340-344, the initial data might be extremely simple: popular initial states from quantum field theory with intimidating names such as the Hawking-Hartle wavefunction or the inflationary Bunch-Davies vacuum have very low algorithmic complexity, since they can be defined in brief physics papers, yet simulating their time evolution would simulate not merely one universe like ours, but a vast decohering collection of parallel ones. It's therefore plausible that our Universe (and even the whole Level III multiverse) could be simulated by quite a short computer program.
Max Tegmark (Our Mathematical Universe: My Quest for the Ultimate Nature of Reality)
Minsky was an ardent supporter of the Cyc project, the most notorious failure in the history of AI. The goal of Cyc was to solve AI by entering into a computer all the necessary knowledge. When the project began in the 1980s, its leader, Doug Lenat, confidently predicted success within a decade. Thirty years later, Cyc continues to grow without end in sight, and commonsense reasoning still eludes it. Ironically, Lenat has belatedly embraced populating Cyc by mining the web, not because Cyc can read, but because there’s no other way. Even if by some miracle we managed to finish coding up all the necessary pieces, our troubles would be just beginning. Over the years, a number of research groups have attempted to build complete intelligent agents by putting together algorithms for vision, speech recognition, language understanding, reasoning, planning, navigation, manipulation, and so on. Without a unifying framework, these attempts soon hit an insurmountable wall of complexity: too many moving parts, too many interactions, too many bugs for poor human software engineers to cope with. Knowledge engineers believe AI is just an engineering problem, but we have not yet reached the point where engineering can take us the rest of the way. In 1962, when Kennedy gave his famous moon-shot speech, going to the moon was an engineering problem. In 1662, it wasn’t, and that’s closer to where AI is today. In industry, there’s no sign that knowledge engineering will ever be able to compete with machine learning outside of a few niche areas. Why pay experts to slowly and painfully encode knowledge into a form computers can understand, when you can extract it from data at a fraction of the cost? What about all the things the experts don’t know but you can discover from data? And when data is not available, the cost of knowledge engineering seldom exceeds the benefit. Imagine if farmers had to engineer each cornstalk in turn, instead of sowing the seeds and letting them grow: we would all starve.
Pedro Domingos (The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World)
Data is compressed by different compression techniques (e.g. dictionary encoding, run length encoding, sparse encoding, cluster encoding, indirect encoding) in SAP HANA Column store. When main memory limit is reached in SAP HANA, the whole database objects (table, view,etc.) that are not used will be unloaded from the main memory and saved into the disk.
Krishna Rungta (Learn HANA in 1 Day: Definitive Guide to Learn SAP HANA for Beginners)
By the time I began my Ph.D., the field of artificial intelligence had forked into two camps: the “rule-based” approach and the “neural networks” approach. Researchers in the rule-based camp (also sometimes called “symbolic systems” or “expert systems”) attempted to teach computers to think by encoding a series of logical rules: If X, then Y. This approach worked well for simple and well-defined games (“toy problems”) but fell apart when the universe of possible choices or moves expanded. To make the software more applicable to real-world problems, the rule-based camp tried interviewing experts in the problems being tackled and then coding their wisdom into the program’s decision-making (hence the “expert systems” moniker). The “neural networks” camp, however, took a different approach. Instead of trying to teach the computer the rules that had been mastered by a human brain, these practitioners tried to reconstruct the human brain itself. Given that the tangled webs of neurons in animal brains were the only thing capable of intelligence as we knew it, these researchers figured they’d go straight to the source. This approach mimics the brain’s underlying architecture, constructing layers of artificial neurons that can receive and transmit information in a structure akin to our networks of biological neurons. Unlike the rule-based approach, builders of neural networks generally do not give the networks rules to follow in making decisions. They simply feed lots and lots of examples of a given phenomenon—pictures, chess games, sounds—into the neural networks and let the networks themselves identify patterns within the data. In other words, the less human interference, the better.
Kai-Fu Lee (AI Superpowers: China, Silicon Valley, and the New World Order)
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SAM
A smart contract is code that can create and transform arbitrary data or tokens on top of the blockchain to which it belongs. Powerfully, it allows the user to trustlessly encode rules for any type of transaction and even create scarce assets with specialized functionality. Many of the clauses of traditional business agreements could be shifted to a smart contract, which not only would enumerate but also algorithmically enforce those clauses.
Campbell R. Harvey (DeFi and the Future of Finance)
Nevertheless, many of these models encoded human prejudice, misunderstanding, and bias into the software systems that increasingly managed our lives.
Cathy O'Neil (Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy)
The machines enter the competition by simultaneously and rapidly coming up with new potential block hashes to encode and capture all the data in the new, fully packaged block and link it to the block hash of the previous block. The winning block hash must match one that bitcoin’s core algorithm has decided will be the current block’s winning number. The match is extremely difficult to make, so the computers keep coming up with new hashes until they get it right, tweaking the process each time to change the readout—over and over and over. Each of the countless new hashes produced by the computer is created by adding a unique, randomly generated number called a nonce to the other data contained in the block hash, which, as mentioned, includes the hashed underlying transaction information and the block hash of the previous block. Adding a new nonce each time completely alters the output hash.
Paul Vigna (The Age of Cryptocurrency: How Bitcoin and Digital Money Are Challenging the Global Economic Order)
Use manual sanity checks in data pipelines. When optimizing data processing systems, it’s easy to stay in the “binary mindset” mode, using tight pipelines, efficient binary data formats, and compressed I/O. As the data passes through the system unseen, unchecked (except for perhaps its type), it remains invisible until something outright blows up. Then debugging commences. I advocate sprinkling a few simple log messages throughout the code, showing what the data looks like at various internal points of processing, as good practice — nothing fancy, just an analogy to the Unix head command, picking and visualizing a few data points. Not only does this help during the aforementioned debugging, but seeing the data in a human-readable format leads to “aha!” moments surprisingly often, even when all seems to be going well. Strange tokenization! They promised input would always be encoded in latin1! How did a document in this language get in there? Image files leaked into a pipeline that expects and parses text files! These are often insights that go way beyond those offered by automatic type checking or a fixed unit test, hinting at issues beyond component boundaries. Real-world data is messy. Catch early even things that wouldn’t necessarily lead to exceptions or glaring errors. Err on the side of too much verbosity.
Micha Gorelick (High Performance Python: Practical Performant Programming for Humans)
Contrary to popular belief, you come equipped with not just one but four brains: the head brain, the heart brain, the gut brain, and the nervous system brain.  All of those brains should be understood as the body’s hard drives, which are highly connected and serve as the main information data centers and highways used for data processing, encoding, storage, retrieval, and deletion.  The
Karo Reiss (FREELISM - Hum with Sweet Lightness of Being)
Nevertheless, many of these models encoded human prejudice, misunderstanding, and bias into the software systems that increasingly managed our lives. Like gods, these mathematical models were opaque, their workings invisible to all but the highest priests in their domain: mathematicians and computer scientists. Their verdicts, even when wrong or harmful, were beyond dispute or appeal. And they tended to punish the poor and the oppressed in our society, while making the rich richer.
Cathy O'Neil (Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy)
The idea that the dainas represent an authentic source of ancient Baltic mythology – indeed, almost a kind of Latvian pagan scriptures – is of immense cultural significance in Latvia. It is possible that the dainas do indeed encode genuinely ancient mythological information. But there is no way to be certain that the dainas actually preserve data on pre-Christian Baltic beliefs.
Francis Young (Silence of the Gods: The Untold History of Europe's Last Pagan Peoples)
All memories are a lie. You are recalling data that was filtered through your senses and encoded to create a memory. The memory is only an interpretation of the events that took place.
Derek Borthwick (How to Talk to Anybody: Learn the Secrets to Small Talk, Business, Management, Sales & Social Conversations & How to Make Real Friends (Communication Skills))
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