Data Classification Quotes

Our electrically-configured world has forced us to move from the habit of data classification to the mode of pattern recognition.

The human beings who appear in the data, survivors or not, are grouped under one machine designated classification: Hero. These damn machines knew us and loved us, even while they were tearing our civilization to shreds.

But you haven't tried. You haven't tried once. First you refused to admit that there was a menace at all! Then you reposed an absolutely blind faith in the Emperor! Now you've shifted it to Hari Seldon. Throughout you have invariably relied on authority or on the past—never on yourselves." His fists balled spasmodically. "It amounts to a diseased attitude—a conditioned reflex that shunts aside the independence of your minds whenever it is a question of opposing authority. There seems no doubt ever in your minds that the Emperor is more powerful than you are, or Hari Seldon Wiser. And that's wrong don't you see?" For some reason, no one cared to answer him. Hardin continued: "It isn't just you. It's the whole Galaxy. Pirenne heard Lord Dorwin's idea of scientific research. Lord Dorwin thought the way to be a good archaeologist was to read all the books on the subject—written by men who were dead for centuries. He thought that the way to solve archaeological puzzles was to weight the opposing authorities. And Pirenne listened and made no objections. Don't you see that there's something wrong with that?" Again the note of near-pleading in his voice. Again no answer. He went on: "And you men and half of Terminus as well are just as bad.. We sit here, considering the Encyclopedia the all-in-all. We consider the greatest end of science is the classification of past data. It is important, but is there no further work to be done? We're receding and forgetting, don't you see? Here in the Periphery they've lost nuclear power. In Gamma Andromeda, a power plant has undergone meltdown because of poor repairs, and the Chancellor of the Empire complains that nuclear technicians are scarce. And the solution? To train new ones? Never! Instead they're to restrict nuclear power." And for the third time: "Don't you see? It's galaxy-wide. It's a worship of the past. It's a deterioration—a stagnation!

Our electrically-configured world has forced us to move from the habit of data classification to the mode of pattern recognition. We can no longer build serially, block-by-block, step-by-step, because instant communication insures that all factors of the environment and of experience coexist in a state of active interplay.

We classify things for the purpose of doing something to them. Any classification which does not assist manipulation is worse than useless.

Although acting inconsistently with one’s own implicit interests and developmental trends can sometimes pay off, the data suggest that those who ignore their deeper impulses, curiosities, and values typically experience sub-optimal outcomes. For example, the latter types tend not to be the ones who make a mark on history.

Empirically, things are poignant, tragic, beautiful, humorous, settled, disturbed, comfortable, annoying, barren, harsh, consoling, splendid, fearful; are such immediately and in their own right and behalf.... These traits stand in themselves on precisely the same level as colours, sounds, qualities of contact, taste and smell. Any criterion that finds the latter to be ultimate and "hard" data will, impartially applied, come to the same conclusion about the former. -Any- quality as such is final; it is at once initial and terminal; just what it is as it exists. it may be referred to other things, it may be treated as an effect or a sign. But this involves an extraneous extension and use. It takes us beyond quality in its immediate qualitativeness.... The surrender of immediate qualities, sensory and significant, as objects of science, and as proper forms of classification and understanding, left in reality these immediate qualities just as they were; since they are -had- there is no need to -know- them. But... the traditional view that the object of knowledge is reality par excellence led to the conclusion that the object of science was preeminently metaphysically real. Hence, immediate qualities, being extended from the object of science, were left thereby hanging loose from the "real" object. Since their -existence- could not be denied, they were gathered together into a psychic realm of being, set over against the object of physics. Given this premise, all the problems regarding the relation of mind and matter, the psychic and the bodily, necessarily follow. Change the metaphysical premise; restore, that is to say, immediate qualities to their rightful position as qualities of inclusive situations, and the problems in question cease to be epistemological problems. They become specifiable scientific problems; questions, that is to say, of how such and such an event having such and such qualities actually occurs.

His fists balled spasmodically. “It amounts to a diseased attitude—a conditioned reflex that shunts aside the independence of your minds whenever it is a question of opposing authority. There seems no doubt ever in your minds that the Emperor is more powerful than you are, or Hari Seldon wiser. And that’s wrong, don’t you see?” For some reason, no one cared to answer him. Hardin continued: “It isn’t just you. It’s the whole Galaxy. Pirenne heard Lord Dorwin’s idea of scientific research. Lord Dorwin thought the way to be a good archaeologist was to read all the books on the subject—written by men who were dead for centuries. He thought that the way to solve archaeological puzzles was to weigh the opposing authorities. And Pirenne listened and made no objections. Don’t you see that there’s something wrong with that?” Again the note of near-pleading in his voice. Again no answer. He went on: “And you men and half of Terminus as well are just as bad. We sit here, considering the Encyclopedia the all-in-all. We consider the greatest end of science is the classification of past data. It is important, but is there no further work to be done? We’re receding and forgetting, don’t you see? Here in the Periphery they’ve lost nuclear power. In Gamma Andromeda, a power plant has undergone meltdown because of poor repairs, and the Chancellor of the Empire complains that nuclear technicians are scarce. And the solution? To train new ones? Never! Instead they’re to restrict nuclear power.” And for the third time: “Don’t you see? It’s Galaxy-wide. It’s a worship of the past. It’s a deterioration—a stagnation!

We may conceive of a machine constructed for the purpose of performing simple processes of classification of this kind. We can, for instance, imagine a machine which “sorts out” balls of various size which are placed into it by distributing them between different receptacles. . . . Another kind of machine performing this simplest kind of classification might be conceived as in a similar fashion sorting out individual signals arriving through any one of a large number of wires or tubes.We shall regard here any signal arriving through one particular wire or tube as the same recurring event which will always lead to the same action of the machine. The machine would respond similarly also to signals arriving through some different tubes or wires, and any such group to which the machine responded in the same manner would be regarded as events of the same class. Such a machine would act like a simplified telephone exchange in which each of a number of incoming wires was permanently connected with, say a particular bell, so that any signal coming in on any one of these wires would ring that bell. All the wires connected with any one bell would then carry signals belonging to the same class. An actual instance of a machine of this kind is provided by certain statistical machines for sorting cards on which punched holes represent statistical data.

Ultimately, the World Top Incomes Database (WTID), which is based on the joint work of some thirty researchers around the world, is the largest historical database available concerning the evolution of income inequality; it is the primary source of data for this book.24 The book’s second most important source of data, on which I will actually draw first, concerns wealth, including both the distribution of wealth and its relation to income. Wealth also generates income and is therefore important on the income study side of things as well. Indeed, income consists of two components: income from labor (wages, salaries, bonuses, earnings from nonwage labor, and other remuneration statutorily classified as labor related) and income from capital (rent, dividends, interest, profits, capital gains, royalties, and other income derived from the mere fact of owning capital in the form of land, real estate, financial instruments, industrial equipment, etc., again regardless of its precise legal classification). The WTID contains a great deal of information about the evolution of income from capital over the course of the twentieth century. It is nevertheless essential to complete this information by looking at sources directly concerned with wealth. Here I rely on three distinct types of historical data and methodology, each of which is complementary to the others.25 In the first place, just as income tax returns allow us to study changes in income inequality, estate tax returns enable us to study changes in the inequality of wealth.26 This

The largest and most rigorous study that is currently available in this area is the third one commissioned by the British Home Office (Kelly, Lovett, & Regan, 2005). The analysis was based on the 2,643 sexual assault cases (where the outcome was known) that were reported to British police over a 15-year period of time. Of these, 8% were classified by the police department as false reports. Yet the researchers noted that some of these classifications were based simply on the personal judgments of the police investigators, based on the victim’s mental illness, inconsistent statements, drinking or drug use. These classifications were thus made in violation of the explicit policies of their own police agencies. There searchers therefore supplemented the information contained in the police files by collecting many different types of additional data, including: reports from forensic examiners, questionnaires completed by police investigators, interviews with victims and victim service providers, and content analyses of the statements made by victims and witnesses. They then proceeded to evaluate each case using the official criteria for establishing a false allegation, which was that there must be either “a clear and credible admission by the complainant” or “strong evidential grounds” (Kelly, Lovett, & Regan,2005). On the basis of this analysis, the percentage of false reports dropped to 2.5%." Lonsway, Kimberly A., Joanne Archambault, and David Lisak. "False reports: Moving beyond the issue to successfully investigate and prosecute non-stranger sexual assault." The Voice 3.1 (2009): 1-11.

Though Hoover conceded that some might deem him a “fanatic,” he reacted with fury to any violations of the rules. In the spring of 1925, when White was still based in Houston, Hoover expressed outrage to him that several agents in the San Francisco field office were drinking liquor. He immediately fired these agents and ordered White—who, unlike his brother Doc and many of the other Cowboys, wasn’t much of a drinker—to inform all of his personnel that they would meet a similar fate if caught using intoxicants. He told White, “I believe that when a man becomes a part of the forces of this Bureau he must so conduct himself as to remove the slightest possibility of causing criticism or attack upon the Bureau.” The new policies, which were collected into a thick manual, the bible of Hoover’s bureau, went beyond codes of conduct. They dictated how agents gathered and processed information. In the past, agents had filed reports by phone or telegram, or by briefing a superior in person. As a result, critical information, including entire case files, was often lost. Before joining the Justice Department, Hoover had been a clerk at the Library of Congress—“ I’m sure he would be the Chief Librarian if he’d stayed with us,” a co-worker said—and Hoover had mastered how to classify reams of data using its Dewey decimal–like system. Hoover adopted a similar model, with its classifications and numbered subdivisions, to organize the bureau’s Central Files and General Indices. (Hoover’s “Personal File,” which included information that could be used to blackmail politicians, would be stored separately, in his secretary’s office.) Agents were now expected to standardize the way they filed their case reports, on single sheets of paper. This cut down not only on paperwork—another statistical measurement of efficiency—but also on the time it took for a prosecutor to assess whether a case should be pursued.

In other words, it is about finding homogeneous subgroups within the data. Some uses of clustering algorithms include network traffic classification, document categorization, gene clustering, or product categorization.

Deep learning techniques are used when data features are numerous and there is a lack of domain understanding to identify and understand them. This data is typically complex, such as images, videos, or voice recordings. For example, an image contains an indefinite amount of data features (e.g., points, edges, shapes, or objects), and some are relevant to the problem being solved, but others are not. Deep learning is used to solve problems such as image classification, natural language processing, and speech recognition.

Generativity is considered normative in the middle and late adult years—so much so that people are considered “off time” or at odds with the “social clock” if, by their 40s, they are not assuming such responsibility through family or work. Generativity is not limited to family or kin. Nor does generativity seek necessarily to maintain the status quo ... Engaging in community volunteer work, especially for religious causes, confers particular benefits on the elderly. Panel data reveal that such community involvement lowers levels of depression in the elderly.

An important aim for computational psychiatry is the development of computational assays that can be used to separate patients into subgroups, generate treatment recommendations, and make predictions for the outcome of those treatments. Reinforcement learning has been described as especially promising in this regard and has indeed shown potential for classification of depression from purely behavioral data without the need for (subjective) questionnaires.

We consider the greatest end of science is the classification of past data. It is important but is there no further work to be done? We’re receding and forgetting, don’t you see? Here in the Periphery they’ve lost atomic power. In Gamma Andromeda, a power plant has blown up because of poor repairs, and the Chancellor of the Empire complains that atomic technicians are scarce. And the solution? To train new ones? Never! Instead they’re to restrict atomic power.’ And for the third time: ‘Don’t you see? It’s Galaxy-wide. It’s a worship of the past. It’s a deterioration – a stagnation!’ He stared from

Thirdly, pragmatism is impractical for the reason that standard intellectual problems in the history of knowledge are among those which we encounter in our environment and trouble us, and yet pragmatism arbitrarily relegates them to the classification of impertinence. But why should social reform be worthy of inquiry, but overcoming skepticism's nagging difficulties ignored? Intellectual problems are just as real problems as other kinds. Therefore, we can ask just how well Dewey's viewpoint 'works' if it fails to give us a coherent and unified conceptual mastery over the data of experience. On this score, pragmatism must be rated quite low, for the coherence of Dewey's philosophy can be seriously questioned.

Knowledge Graphs (KGs) provide ways to efficiently organize, manage and retrieve this type of information, and are increasingly used as an external source of knowledge for problems like recommender systems, language modeling [2], question answering or image classification.

I came up with three classifications of potential gain and added that information to all the previously entered data. The classifications were: Meaningless Synchronicity, Synchronicity of Need, and Synchronicity of Want. I had some three thousand predictions already in the database when I re-ran the analysis. "That analysis showed me I was predicting Meaningless Synchronicities with a really high degree of statistical certainty.

Clustering analysis developed originally from anthropology in 1932, before it was introduced to psychology in 1938 and was later adopted by personality psychology in 1943 for trait theory classification. Today, clustering analysis is used in data mining, information retrieval, machine learning, text mining, web analysis, marketing, medical diagnosis, and numerous other fields.

We consider the greatest end of science is the classification of past data. It is important, but is there no further work to be done?

I came up with three classifications of potential gain and added that information to all the previously entered data. The classifications were: Meaningless Synchronicity, Synchronicity of Need, and Synchronicity of Want. I had some three thousand predictions already in the database when I re-ran the analysis.

Research on workplace engagement funded by the Bavarian State Ministry for Education and Culture found that with EEG, it is now possible to classify the type of activity an individual is engaged in—central tasks (e.g., programming, database, web development), peripheral tasks (e.g., setting up a development environment, writing documentation), and meta tasks (e.g., social media browsing, reading news sites).47 As pattern classification of brain wave data becomes ever more sophisticated, employers will be able to tell not just whether you are alert or your mind is wandering but also whether you are surfing social media or developing code.

[T]here exists a long history of political and social struggles over the design of classification systems that present themselves as ‘purely technical’ but promote a biased account of the social world. … Critical race theorists, such as Richard Delgado and Jean Stanfancic, have similarly argued that races operate as ‘categories that society invents, manipulates, or retires when convenient’. Although invented as a category, the effects of race on social relations and people's life opportunities are material and multiple.

Hacking described his research interest ‘in classifications of people, in how they affect the people classified, and how the affects on the people in turn change the classifications.’ Hacking labeled the subjects of these studies ‘moving targets’ because researchers’ investigatory efforts change them in ways so ‘they are not quite the same kind of people as before.

Decision Trees: They help classify populations into classes. It is said that 70% of all data mining work is about classification solutions; and that 70% of all classification work uses decision trees.

To test that prediction, we needed empirical examples. They weren’t easy to find. Any candidate had to be fully characterized, its wiring diagram known down to the last detail, every node and link documented, or we couldn’t calculate the clustering and average path length. Then I remembered that Koeunyi Bae, a student in my chaos course the year before, had done a project about the Western States power grid, a collection of about 5,000 electric power plants tied together by high-voltage transmission lines across the states west of the Rocky Mountains and into the western provinces of Canada. Koeunyi and her adviser Jim Thorp provided the data to Duncan. It contained a great deal of detailed information that an engineer would find crucial—the voltage capacity of the transmission lines, the classification of the nodes as transformers, substations, or generators—but we ignored everything except the connectivity. The grid became an abstract pattern of dots connected by lines. To check whether it was a small-world network, we compared its clustering and average path length to the corresponding values for a random network with the same number of nodes and links. As predicted, the real network was almost as small as a random one, but much more highly clustered. Specifically, the path length was only 1.5 times larger than random, whereas the clustering was 16 times larger.

You see, Richard,” said Valentine, “we’ve been digging through the Zone for two decades, but we don’t even know a thousandth part of what it contains. And if you count the Zone’s effect on man … By the way, we’re going to have to add another, fourth group to our classification. Not of objects, but of effects. This group has been outrageously badly studied, even though, in my opinion, we’ve gathered more than enough data. And you know, Richard, I’m a physicist and therefore a skeptic. But sometimes even I get goose bumps when I think about this data.

Narrative essay writing affords sufficient opportunity for the writer to collect data, organize information, rationally process a matrix of collected material, reduce the essence of experience to assigned territories, and by doing so logically quantify their personal existence. Essay writing is an apt form to catalogue discordant incidences and as such writing prose oftentimes calls for the essayist to draw hard and fast classifications and conclusions.

Correspondingly, despite many decades of expensive and exhaustive research, no specific biological markers or mechanisms have ever been reliably linked with diagnostic labels like schizophrenia or depression, a fact recently acknowledged in the United States by the National Institute of Mental Health.26 Indeed, NIMH announced its intention to “reorient” away from existing psychiatric diagnoses because of their invalidity, preferring to focus on classifications based on genetics and neurobiology (despite the acknowledgement that there is still insufficient data to develop such a system).

First, it is important to note that in the London data, a mother’s attachment classification before the birth of the child was a powerful predictor of the child’s theory-of-mind competence at 5 years; 75% of children of secure, autonomous mothers passed the cognitive-emotion task, whereas only 16% of children of preoccupied mothers and 25% of those of unresolved mothers did so (Fonagy 1997).

Aristotle made much of observation and strict classification of data in his studies. For this reason he is often considered as the father of empirical science and scientific method.

Handling High-Dimensional Data Quantum systems can naturally represent high-dimensional vectors and patterns. In machine learning, where data can often be multidimensional (think of images, videos, or intricate datasets), quantum systems provide a natural playground, making processes like data classification or clustering more efficient.

Quantum Support Vector Machines (QSVMs) At the heart of machine learning, the primary goal is often to find patterns or make classifications. In classical systems, Support Vector Machines (SVMs) have been the go-to for this. They operate by finding a hyperplane, a boundary, that best separates the data into distinct classes. Now, enter the quantum realm, where Quantum Support Vector Machines (QSVMs) take flight.

Machine Learning is the field of study which focuses on developing algorithms that allow computers to learn from data. It involves the development of algorithms that can recognize patterns in data and use them to make predictions or decisions. Machine Learning algorithms use statistical methods to find patterns in data, such as clustering and classification.

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