Data Accuracy Quotes

Prediction Machines is not a recipe for success in the AI economy. Instead, we emphasize trade-offs. More data means less privacy. More speed means less accuracy. More autonomy means less control.

Artificial intelligence is defined as the branch of science and technology that is concerned with the study of software and hardware to provide machines with the ability to learn insights from data and the environment, and the ability to adapt to changing situations with increasing precision, accuracy, and speed.

I became like the bee: intensely gathering information from as many sources as possible and analyzing the material to construct my own understanding of Muhammad’s mindset. I analyzed every piece of data, scrutinizing it for accuracy. I sought to shorten as much as possible the chains of scholarly transmission that separated me from Muhammad. Approaching Muhammad with an open mind proved transformational: making my own sense of him forged a much more meaningful personal relationship with his legacy.

Quantum Machine Learning is defined as the branch of science and technology that is concerned with the application of quantum mechanical phenomena such as superposition, entanglement and tunneling for designing software and hardware to provide machines the ability to learn insights and patterns from data and the environment, and the ability to adapt automatically to changing situations with high precision, accuracy and speed. 

Unlike earlier thinkers, who had sought to improve their accuracy by getting rid of error, Laplace realized that you should try to get more error: aggregate enough flawed data, and you get a glimpse of the truth. “The genius of statistics, as Laplace defined it, was that it did not ignore errors; it quantified them,” the writer Louis Menand observed. “…The right answer is, in a sense, a function of the mistakes.

Every time a scientific paper presents a bit of data, it's accompanied by an error bar – a quiet but insistent reminder that no knowledge is complete or perfect. It's a calibration of how much we trust what we think we know. If the error bars are small, the accuracy of our empirical knowledge is high; if the error bars are large, then so is the uncertainty in our knowledge.

Were we dealing with a spectrum-based system that described male and female sexuality with equal accuracy, data taken from gay males would look similar to data taken from straight females—and yet this is not what we see in practice. Instead, the data associated with gay male sexuality presents a mirror image of data associated with straight males: Most gay men are as likely to find the female form aversive as straight men are likely to find the male form aversive. In gay females we observe a similar phenomenon, in which they mirror straight females instead of appearing in the same position on the spectrum as straight men—in other words, gay women are just as unlikely to find the male form aversive as straight females are to find the female form aversive. Some of the research highlighting these trends has been conducted with technology like laser doppler imaging (LDI), which measures genital blood flow when individuals are presented with pornographic images. The findings can, therefore, not be written off as a product of men lying to hide middling positions on the Kinsey scale due to a higher social stigma against what is thought of in the vernacular as male bisexuality/pansexuality. We should, however, note that laser Doppler imaging systems are hardly perfect, especially when measuring arousal in females. It is difficult to attribute these patterns to socialization, as they are observed across cultures and even within the earliest of gay communities that emerged in America, which had to overcome a huge amount of systemic oppression to exist. It’s a little crazy to argue that the socially oppressed sexuality of the early American gay community was largely a product of socialization given how much they had overcome just to come out. If, however, one works off the assumptions of our model, this pattern makes perfect sense. There must be a stage in male brain development that determines which set of gendered stimuli is dominant, then applies a negative modifier to stimuli associated with other genders. This stage does not apparently take place during female sexual development. 

Every waking moment, our brains and bodies assimilate a myriad of sensory stimulation from the environment, as well as images, thoughts, emotions, body sensations, and movements from our internal state. In a millisecond, through operations so complex that they elude the full understanding of even the most brilliant minds, our brains compare this wealth of current data to memories of past experience. The most critical purpose of this comparison is to predict the next moment with sufficient accuracy so that we can make an adaptive physical action (Llinas, 2001). What we expect to happen in the very next instant determines the immediate action we make, whether it is reaching out to another person or for an object, such as a cup of tea.

One of the best-kept secrets in all of health care — understood by few doctors — is that the peer reviewers, medical journal editors, and guideline writers, who are assumed to be performing due diligence to ensure the accuracy and completeness of the data reported from company-sponsored studies, do not have access to the real data from these trials. The published reports that doctors accept as fully vetted scientific evidence can be more accurately described as unverified data summaries prepared largely by or for the sponsoring drug companies.

model’s blind spots reflect the judgments and priorities of its creators. While the choices in Google Maps and avionics software appear cut and dried, others are far more problematic. The value-added model in Washington, D.C., schools, to return to that example, evaluates teachers largely on the basis of students’ test scores, while ignoring how much the teachers engage the students, work on specific skills, deal with classroom management, or help students with personal and family problems. It’s overly simple, sacrificing accuracy and insight for efficiency.

Analysis of your social network and its members can also be highly revealing of your life, politics, and even sexual orientation, as demonstrated in a study carried out at MIT. In an analysis known as Gaydar, researchers studied the Facebook profiles of fifteen hundred students at the university, including those whose profile sexual orientation was either blank or listed as heterosexual. Based on prior research that showed gay men have more friends who are also gay (not surprising), the MIT investigators had a valuable data point to review the friend associations of their fifteen hundred students. As a result, researchers were able to predict with 78 percent accuracy whether or not a student was gay. At least ten individuals who had not previously identified as gay were flagged by the researchers’ algorithm and confirmed via in-person interviews with the students. While these findings might not be troubling in liberal Cambridge, Massachusetts, they could prove problematic in the seventy-six countries where homosexuality remains illegal, such as Sudan, Iran, Yemen, Nigeria, and Saudi Arabia, where such an “offense” is punished by death.

In summary, listen to your “gut feeling,” especially in potentially dangerous situations. If you are a woman and have been asked out on a date or approached by a man who causes a sense something is wrong -don’t do it! If you are with your family and are driving or walking through a neighborhood and you sense something is wrong, don’t go there. If you are in a business deal and you sense your contact is deceiving you, listen to your intuition. Practice listening to this lightning fast retrieval of data, learn how to analyze it for accuracy and how to appropriately act on it. It could save your life.

Given the central place that technology holds in our lives, it is astonishing that technology companies have not put more resources into fixing this global problem. Advanced computer systems and artificial intelligence (AI) could play a much bigger role in shaping diagnosis and prescription. While the up-front costs of using such technology may be sizeable, the long-term benefits to the health-care system need to be factored into value assessments. We believe that AI platforms could improve on the empirical prescription approach. Physicians work long hours under stressful conditions and have to keep up to date on the latest medical research. To make this work more manageable, the health-care system encourages doctors to specialize. However, the vast majority of antibiotics are prescribed either by generalists (e.g., general practitioners or emergency physicians) or by specialists in fields other than infectious disease, largely because of the need to treat infections quickly. An AI system can process far more information than a single human, and, even more important, it can remember everything with perfect accuracy. Such a system could theoretically enable a generalist doctor to be as effective as, or even superior to, a specialist at prescribing. The system would guide doctors and patients to different treatment options, assigning each a probability of success based on real-world data. The physician could then consider which treatment was most appropriate.

Taking least squares is no longer optimal, and the very idea of ‘accuracy’ has to be rethought. This simple fact is as important as it is neglected. This problem is easily illustrated in the Logistic Map: given the correct mathematical formula and all the details of the noise model – random numbers with a bell-shaped distribution – using least squares to estimate α leads to systematic errors. This is not a question of too few data or insufficient computer power, it is the method that fails. We can compute the optimal least squares solution: its value for α is too small at all noise levels. This principled approach just does not apply to nonlinear models because the theorems behind the principle of least squares repeatedly assume bell-shaped distributions.

A famous British writer is revealed to be the author of an obscure mystery novel. An immigrant is granted asylum when authorities verify he wrote anonymous articles critical of his home country. And a man is convicted of murder when he’s connected to messages painted at the crime scene. The common element in these seemingly disparate cases is “forensic linguistics”—an investigative technique that helps experts determine authorship by identifying quirks in a writer’s style. Advances in computer technology can now parse text with ever-finer accuracy. Consider the recent outing of Harry Potter author J.K. Rowling as the writer of The Cuckoo’s Calling , a crime novel she published under the pen name Robert Galbraith. England’s Sunday Times , responding to an anonymous tip that Rowling was the book’s real author, hired Duquesne University’s Patrick Juola to analyze the text of Cuckoo , using software that he had spent over a decade refining. One of Juola’s tests examined sequences of adjacent words, while another zoomed in on sequences of characters; a third test tallied the most common words, while a fourth examined the author’s preference for long or short words. Juola wound up with a linguistic fingerprint—hard data on the author’s stylistic quirks. He then ran the same tests on four other books: The Casual Vacancy , Rowling’s first post-Harry Potter novel, plus three stylistically similar crime novels by other female writers. Juola concluded that Rowling was the most likely author of The Cuckoo’s Calling , since she was the only one whose writing style showed up as the closest or second-closest match in each of the tests. After consulting an Oxford linguist and receiving a concurring opinion, the newspaper confronted Rowling, who confessed. Juola completed his analysis in about half an hour. By contrast, in the early 1960s, it had taken a team of two statisticians—using what was then a state-of-the-art, high-speed computer at MIT—three years to complete a project to reveal who wrote 12 unsigned Federalist Papers. Robert Leonard, who heads the forensic linguistics program at Hofstra University, has also made a career out of determining authorship. Certified to serve as an expert witness in 13 states, he has presented evidence in cases such as that of Christopher Coleman, who was arrested in 2009 for murdering his family in Waterloo, Illinois. Leonard testified that Coleman’s writing style matched threats spray-painted at his family’s home (photo, left). Coleman was convicted and is serving a life sentence. Since forensic linguists deal in probabilities, not certainties, it is all the more essential to further refine this field of study, experts say. “There have been cases where it was my impression that the evidence on which people were freed or convicted was iffy in one way or another,” says Edward Finegan, president of the International Association of Forensic Linguists. Vanderbilt law professor Edward Cheng, an expert on the reliability of forensic evidence, says that linguistic analysis is best used when only a handful of people could have written a given text. As forensic linguistics continues to make headlines, criminals may realize the importance of choosing their words carefully. And some worry that software also can be used to obscure distinctive written styles. “Anything that you can identify to analyze,” says Juola, “I can identify and try to hide.

The Commissioner asserts 'motivated intruders' evidence from Professor Anderaon was accepted under cross-examination as an 'over-extension' from his personal experiences with completely unrelated animal rights activists - see para.24 of the closing submissions, Professor Anderson's "wild speculations" about the possibility of "young men, borderline sociopathic or psychopathic" attaching themselves to the PACE trial criticism 'do him no credit". Nor do his extrapolations from benign Twitter requests for information to an "organised campaign” from an "adversarial group" show that he has maintained the necessary objectivity and accuracy that he is required to maintain. He does not distinguish between legitimate ethical and political disagreement, and the use of positions of access to confidential data. He stated that where there was legitimate disagreement one should assume that people will act in unlawful ways, This proposition that one should in every case assume the absolute worst about data disclosure is clearly neither sensible nor realistic. Freedom of Information Act tribunal judgment

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Why is this? How can experience be so valuable in some professions but almost worthless in others? To see why, suppose that you are playing golf. You are out on the driving range, hitting balls toward a target. You are concentrating, and every time you fire the ball wide you adjust your technique in order to get it closer to where you want it to go. This is how practice happens in sport. It is a process of trial and error. But now suppose that instead of practicing in daylight, you practice at night—in the pitch-black. In these circumstances, you could practice for ten years or ten thousand years without improving at all. How could you progress if you don’t have a clue where the ball has landed? With each shot, it could have gone long, short, left, or right. Every shot has been swallowed by the night. You wouldn’t have any data to improve your accuracy. This metaphor solves the apparent mystery of expertise. Think about being a chess player. When you make a poor move, you are instantly punished by your opponent. Think of being a clinical nurse. When you make a mistaken diagnosis, you are rapidly alerted by the condition of the patient (and by later testing). The intuitions of nurses and chess players are constantly checked and challenged by their errors. They are forced to adapt, to improve, to restructure their judgments. This is a hallmark of what is called deliberate practice. For psychotherapists things are radically different. Their job is to improve the mental functioning of their patients. But how can they tell when their interventions are going wrong or, for that matter, right? Where is the feedback? Most psychotherapists gauge how their clients are responding to treatment not with objective data, but by observing them in clinic. But these data are highly unreliable. After all, patients might be inclined to exaggerate how well they are to please the therapist, a well-known issue in psychotherapy. But there is a deeper problem. Psychotherapists rarely track their clients after therapy has finished. This means that they do not get any feedback on the lasting impact of their interventions. They have no idea if their methods are working or failing—if the client’s long-term mental functioning is actually improving. And that is why the clinical judgments of many practitioners don’t improve over time. They are effectively playing golf in the dark.11

Rather, you take the data you have and randomly divide it into a training set, which you give to the learner, and a test set, which you hide from it and use to verify its accuracy. Accuracy on held-out data is the gold standard in machine learning. You can write a paper about a great new learning algorithm you’ve invented, but if your algorithm is not significantly more accurate than previous ones on held-out data, the paper is not publishable. Accuracy on previously unseen data is a pretty stringent test; so much so, in fact, that a lot of science fails it. That does not make it useless, because science is not just about prediction; it’s also about explanation and understanding. But ultimately, if your models don’t make accurate predictions on new data, you can’t be sure you’ve truly understood or explained the underlying phenomena. And for machine learning, testing on unseen data is indispensable because it’s the only way to tell whether the learner has overfit or not.

Peter Norvig, director of research at Google, told me at one point that it was the most widely used learner there, and Google uses machine learning in every nook and cranny of what it does. It’s not hard to see why Naïve Bayes would be popular among Googlers. Surprising accuracy aside, it scales great; learning a Naïve Bayes classifier is just a matter of counting how many times each attribute co-occurs with each class and takes barely longer than reading the data from disk. You could even use Naïve Bayes, tongue-in-cheek, on a much larger scale than Google’s: to model the whole universe.

One of the most surprising findings to emerge from neuroscience in recent years is that rather than responding in real time to the vast amount of incoming sensory data, the brain tries to keep one step ahead by constantly predicting what will happen next. It simulates a model of the immediate future based on what has just happened. When its predictions turn out to be wrong—for example, we’re feeling just fine then suddenly experience a stab of anxiety about a romantic date—this mismatch creates an unpleasant sense of dissatisfaction that we can either try to resolve by ruminating and then doing something to alleviate the anxiety (canceling the date, perhaps) or by updating the brain’s model of reality (investigating and accepting the new sensation). These alternative strategies employ the “narrative” and “being” modes of thought I described earlier in this chapter. Of course, both strategies have their place according to the situation, but an overreliance on avoidance behavior rather than acceptance stores up problems for the future because there are many things in life that cannot be changed and therefore need to be faced. Mindfulness through interoception is all about accepting the way things are. When we are mindful, the insula continually updates its representation of our internal world to improve its accuracy by reducing discrepancies between expectation and reality. As we’ve seen in previous chapters, this reality check—the focusing of dispassionate attention on unpleasant sensations such as pain or anxiety—loosens the hold that they have over us. So the structural changes in the brains of highly experienced meditators of Siddhārtha’s caliber, in particular in their insula and ACC, may be responsible for the imperturbable calm and acceptance that is the ultimate goal of contemplative practice, sometimes described as enlightenment or nirvana.

With 70 percent accuracy, my source tells me, software can assess how people feel based on the way they type, and the number of typos they make. With 79 percent precision, software can determine a user’s credit rating based on the degree to which they write in ALL CAPS.

Empirical logic achieved a signal triumph in the Old Testament, where survivals from the early proto-logical stage are very few and far between. With it man reached a point where his best judgments about his relation to God, his fellow men and the world, were in most respects not appreciably inferior to ours. In fundamental ethical and spiritual matters we have not progressed at all beyond the empirico-logical world of the Old Testament or the unrivalled fusion of proto-logical intuition, 64 [see Coomaraswamy, Review of Religion, 1942, p. 138, paragraph 3] empirico-logical wisdom and logical deduction which we find in the New Testament. In fact a very large section of modern religion, literature and art actually represents a pronounced retrogression when compared with the Old Testament. For example, astrology, spiritism and kindred divagations, which have become religion to tens of millions of Europeans and Americans, are only the outgrowth of proto-logical interpretation of nature, fed by empirico-logical data and covered with a spurious shell of Aristotelian logic and scientific induction. Plastic and graphic art has swung violently away from logical perspective and perceptual accuracy, and has plunged into primordial depths of conceptual drawing and intuitive imagery. While it cannot be denied that this swing from classical art to conceptual and impressionistic art has yielded some valuable results, it is also true that it represents a very extreme retrogression into the proto-logical past. Much of the poetry, drama and fiction which has been written during the past half-century is also a reversion from classical and logical standards of morality and beauty into primitive savagery or pathological abnormality. Some of it has reached such paralogical levels of sophistication that it has lost all power to furnish any standards at all to a generation which has deliberately tried to abandon its entire heritage from the past. All systematic attempts to discredit inherited sexual morality, to substitute dream-states for reflection, and to replace logical writing by jargon, are retreats into the jungle from which man emerged through long and painful millennia of disillusionment. With the same brains and affective reactions as those which our ancestors possessed two thousand years ago, increasing sophistication has not been able to teach us any sounder fundamental principles of life than were known at that time. . . . Unless we can continue along the pathway of personal morality and spiritual growth which was marked out for civilized man by the founders of the Judaeo-Christian tradition, more than two thousand years ago, our superior skill in modifying and even in transforming the material world about us can lead only to repeated disasters, each more terrible than its predecessor. (Archaeology and the Religion of Israel, 5th Ed. New York: Doubleday Anchor, 31-33.)

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An MIT study examined a data set of 126,000 tweets in all categories of information—from science to terrorism to finance—and sorted them based on factual accuracy. The time taken for falsehoods to reach 1,500 people, they found, was six times shorter than it was for the truth. Meanwhile, 7 in 10 U.S. adult Twitter users say they get news on the site, and 80% of all tweets come from 10% of its users.

One of Clark’s earliest statements on the proper nature of economics argued that economics should be “based on a foundation of terms, conceptions, standards of measurement, and assumptions which is sufficiently realistic, comprehensive, and unbiased” to provide a basis for the analysis and discussion of practical issues (Clark 1919, p. 280). Relevance to practical issues, accuracy of data, and comprehensiveness, in the sense of not excluding any evidence relevant to the problem at hand, were the characteristics of a scientific approach to economics that Clark most frequently stressed (Clark 1924, p. 74).

RESEARCH SUPPORTS THE use of intuition in at least a limited capacity for subtle—and maybe even all—energy work. Norman Shealy, MD, for example, published a study referencing the work of eight psychics to diagnose seventeen patients. These diagnoses were 98 percent accurate in making personality diagnoses and 80 percent correct in determining physical conditions.28 Research by the HeartMath Research Center at the Institute of HeartMath in California is corroborating the existence of intuition and its accuracy. Most of its studies showcase the heart as a key intuitive center, responding even to information about the future. As an example, the heart decelerates when receiving futuristic, calming stimuli versus agitating emotional stimuli.29 A myriad of issues are involved in using intuition for energy work, including questions about boundaries; the importance or applicability of the information; accuracy of interpretation; the unpredictable and changeable nature of the future; the effects of the information on the recipient (i.e., to “prove” or “disprove” the data); and overriding all of these, the intuitive skills of the energy professional. Regardless of the inexact nature of intuition, a professional should not be embarrassed to exercise intuition in his or her trade. Energy work is an art and has traditionally encompassed intuition. Your energy fields interact with your patients’ fields. How you feel about yourself—what you hold near and dear in your heart-space—transfers into a client’s heart-space, and from there, into his or her body. (There is more information about heart-centered healing below.) As mind-body practitioner Dr. Herbert Benson of Harvard puts it, “Our brains are wired for beliefs and expectancies. When activated, our body can respond as it would if the belief were a reality, producing deafness or thirst, health or illness.”30

In 1997, money manager David Leinweber wondered which statistics would have best predicted the performance of the U.S. stock market from 1981 through 1993. He sifted through thousands of publicly available numbers until he found one that had forecast U.S. stock returns with 75% accuracy: the total volume of butter produced each year in Bangladesh. Leinweber was able to improve the accuracy of his forecasting “model” by adding a couple of other variables, including the number of sheep in the United States. Abracadabra! He could now predict past stock returns with 99% accuracy. Leinweber meant his exercise as satire, but his point was serious: Financial marketers have such an immense volume of data to slice and dice that they can “prove” anything.

Perfection—complete accuracy—is the goal of every map, but it’s not actually . . . it’s not actually possible, William. Even for us. Even if we could manage to make the Haberson Map completely accurate for every single data point for one instant, the world is always changing. Something will shift, and we’ll be right back to square one again.

Excellence in Statistics: Rigor Statisticians are specialists in coming to conclusions beyond your data safely—they are your best protection against fooling yourself in an uncertain world. To them, inferring something sloppily is a greater sin than leaving your mind a blank slate, so expect a good statistician to put the brakes on your exuberance. They care deeply about whether the methods applied are right for the problem and they agonize over which inferences are valid from the information at hand. The result? A perspective that helps leaders make important decisions in a risk-controlled manner. In other words, they use data to minimize the chance that you’ll come to an unwise conclusion. Excellence in Machine Learning: Performance You might be an applied machine-learning/AI engineer if your response to “I bet you couldn’t build a model that passes testing at 99.99999% accuracy” is “Watch me.” With the coding chops to build both prototypes and production systems that work and the stubborn resilience to fail every hour for several years if that’s what it takes, machine-learning specialists know that they won’t find the perfect solution in a textbook. Instead, they’ll be engaged in a marathon of trial and error. Having great intuition for how long it’ll take them to try each new option is a huge plus and is more valuable than an intimate knowledge of how the algorithms work (though it’s nice to have both). Performance means more than clearing a metric—it also means reliable, scalable, and easy-to-maintain models that perform well in production. Engineering excellence is a must. The result? A system that automates a tricky task well enough to pass your statistician’s strict testing bar and deliver the audacious performance a business leader demands. Wide Versus Deep What the previous two roles have in common is that they both provide high-effort solutions to specific problems. If the problems they tackle aren’t worth solving, you end up wasting their time and your money. A frequent lament among business leaders is, “Our data science group is useless.” And the problem usually lies in an absence of analytics expertise. Statisticians and machine-learning engineers are narrow-and-deep workers—the shape of a rabbit hole, incidentally—so it’s really important to point them at problems that deserve the effort. If your experts are carefully solving the wrong problems, your investment in data science will suffer low returns. To ensure that you can make good use of narrow-and-deep experts, you either need to be sure you already have the right problem or you need a wide-and-shallow approach to finding one.

However, the answer can be inferred from the WMAP data, by measuring the sizes of the temperature fluctuations—the hot and cold (light and dark) splotches in Figure 3 ([>]). Before WMAP was launched, theorists had already worked out how big the physical sizes of the strongest fluctuations should be. Converting that into apparent angular size in the sky depends on the geometry of space: if the universe is positively curved, it would make the angles appear larger, while negative curvature would make them smaller. If the universe is geometrically flat (that is, has Euclidean geometry), the angular size of the strongest hot and cold fluctuations should be about 1° across. The results that flowed back from the satellite were definitive.14 The fluctuations were very close to 1° in size, a result confirmed by ground-based and balloon-based experiments. Cosmologists then declared that to within observational accuracy of about 2 percent, space is flat.15

The history of such predictions should have taught us that we cannot predict, with any accuracy, the social, economic, and technological changes and consequences likely to accompany unprecedented shifts in ways of working and living. We should not expect that today's pundits and technology advocates will be more prescient than their predecessors.

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One of the reasons for its success is that science has built-in, error-correcting machinery at its very heart. Some may consider this an overbroad characterization, but to me every time we exercise self-criticism, every time we test our ideas against the outside world, we are doing science. When we are self-indulgent and uncritical, when we confuse hopes and facts, we slide into pseudoscience and superstition. Every time a scientific paper presents a bit of data, it's accompanied by an error bar - a quiet but insistent reminder that no knowledge is complete or perfect. It's a calibration of how much we trust what we think we know. If the error bars are small, the accuracy of our empirical knowledge is high; if the error bars are large, then so is the uncertainty in our knowledge. Except in pure mathematics nothing is known for certain (although much is certainly false). Moreover, scientists are usually careful to characterize the veridical status of.their attempts to understand the world - ranging from conjectures and hypotheses, which are highly tentative, all the way up to laws of Nature which are repeatedly and systemati­cally confirmed through many interrogations of how the world works. But even laws of Nature are not absolutely certain. There may be new circumstances never before examined - inside black holes, say, or within the electron, or close to the speed of light -where even our vaunted laws of Nature break down and, however valid they may be in ordinary circumstances, need correction. Humans may crave absolute certainty; they may aspire to it; they may pretend, as partisans of certain religions do, to have attained it. But the history of science - by far the most successful claim to knowledge accessible to humans - teaches that the most we can hope for is successive improvement in our understanding, learning from our mistakes, an asymptotic approach to the Universe, but with the proviso that absolute certainty will always elude us. We will always be mired in error. The most each generation can hope for is to reduce the error bars a little, and to add to the body of data to which error bars apply. The error bar is a pervasive, visible self-assessment of the reliability of our knowledge.

Consumer Privacy Bill of Rights applies comprehensive, globally recognized Fair Information Practice Principles (FIPPs) to the interactive and highly interconnected environment in which we live and work today.Specifically, it provides for: − Individual Control: Consumers have a right to exercise control over what personal data companies collect from them and how they use it. − Transparency: Consumers have a right to easily understandable and accessible information about privacy and security practices. − Respect for Context: Consumers have a right to expect that companies will collect, use, and disclose personal data in ways that are consistent with the context in which consumers provide the data. − Security: Consumers have a right to secure and responsible handling of personal data. − Access and Accuracy: Consumers have a right to access and correct personal data in usable formats, in a manner that is appropriate to the sensitivity of the data and the risk of adverse consequences to consumers if the data is inaccurate. − Focused Collection: Consumers have a right to reasonable limits on the personal data that companies collect and retain. − Accountability: Consumers have a right to have personal data handled by companies with appropriate measures in place to assure they adhere to the Consumer Privacy Bill of Rights★ 1 ★

miscoding of such crimes is masking the high incidence of rape in the United States. We don’t have an overestimation of rape; we have a gross underestimation. A thorough analysis of federal data published earlier this year by Corey Rayburn Yung, associate professor at the University of Kansas School of Law, concludes that between 1995 and 2012, police departments across the country systematically undercounted and underreported sexual assaults. Yung used murder rates—the statistic with the most reliable measure of accuracy and one that is historically highly correlated with the incidence of rape—as a baseline for his analysis. After nearly two years of work, he estimates conservatively that between 796,213 and 1,145,309 sexual assault cases never made it into national FBI counts during the studied period.

The truth is that any figure of Africans imported into the Americas which is narrowly based on the surviving records is bound to be low, because there were so many people at the time who had a vested interest in smuggling slaves (and withholding data. Nevertheless, if the low figure of ten million was accepted as basis for evaluating the impact of slaving on Africa as a whole, the conclusions that could legitimately be drawn would confound those who attempt to make light of the experience of the rape of Africans from 1445 to 1870.

PhOne Number:352-587-2948 ADDRESS:407 Lincoln Rd. Suit 10g Miami Beach FL 33139 Miami Realtor, South Beach Realtor, Miami Beach Realto, Miami Real Estate Agent, Miami Beach Real Estate Agent, Miami Luxury Realtor, South Beach Real Estate Agent, Beach Real Estate Agent, MIAMI Association of REALTORS® is not responsible for the accuracy of the information listed above. The data relating to real estate for sale on this website comes in part from the Internet Data Exchange Program and the South East Florida Regional MLS and is provided here for consumers' personal, non-commercial use. It may not be used for any purpose other than to identify prospective properties consumers may be interested in purchasing. Real estate listings held by brokerage firms other than the office owning this website are marked with the IDX logo and detailed information about them includes the name of the listing brokers. Data provided is deemed reliable but not guaranteed. Copyright MIAMI Association of REALTORS®, MLS All rights reserved.

The Game-Changer in Diabetes Management: Continuous Glucose Monitors Continuous Glucose Monitors (CGMs) have revolutionized diabetes management, offering real-time insights into blood sugar levels like never before. In this article, we'll delve into the significance of CGMs, their benefits, and why they are a game-changer for individuals living with diabetes. Understanding Continuous Glucose Monitors Continuous Glucose Monitors are wearable devices that constantly monitor glucose levels in the interstitial fluid, providing users with real-time data on their blood sugar levels. Unlike traditional finger-prick tests, CGMs offer a continuous stream of information, allowing for proactive management of diabetes. Benefits of Continuous Glucose Monitors Real-Time Monitoring: CGMs offer instant feedback on blood sugar levels, enabling users to make informed decisions about their diet, medication, and lifestyle choices. Early Detection of Trends: CGMs track glucose trends over time, allowing users to identify patterns and adjust their management strategies accordingly. Improved Diabetes Management: With continuous monitoring, individuals can better manage their blood sugar levels, reducing the risk of hyperglycemia and hypoglycemia episodes. Enhanced Quality of Life: CGMs provide greater freedom and flexibility, reducing the need for frequent finger pricks and offering peace of mind to individuals and their caregivers. Why CGMs Are a Game-Changer Precision Medicine: Continuous Glucose Monitors enable personalized diabetes management by providing individualized insights into glucose fluctuations and responses to various factors. Empowerment Through Data: CGMs empower users with valuable data, enabling them to take control of their health and make informed decisions in collaboration with healthcare providers. Continuous Innovation: Advancements in CGM technology, such as improved accuracy and connectivity features, continue to enhance the user experience and expand the capabilities of these devices. Integration with Digital Health Ecosystem: CGMs seamlessly integrate with mobile apps and other digital health platforms, facilitating data sharing, remote monitoring, and telehealth consultations. Conclusion Continuous Glucose Monitors represent a significant advancement in diabetes management, offering real-time insights, personalized care, and improved quality of life for individuals living with diabetes. As technology continues to evolve, CGMs will play an increasingly vital role in empowering individuals to live healthier, more active lives while effectively managing their condition.

The Importance of Accounting Services for Businesses In today’s competitive business environment, maintaining accurate financial records and ensuring compliance with tax regulations is essential for long-term success. Accounting services provide businesses with the necessary tools and expertise to manage their finances efficiently. Whether for small businesses or large corporations, professional accounting services help streamline financial processes, ensure regulatory compliance, and offer strategic insights for growth. What Are Accounting Services? Accounting services encompass a wide range of tasks, including bookkeeping, financial reporting, tax preparation, payroll management, and auditing. These services are designed to help businesses track their income, expenses, and overall financial health. By outsourcing accounting tasks to professionals, businesses can focus on their core activities while ensuring that their financial operations run smoothly. Additionally, accurate and timely accounting services help businesses avoid costly errors and penalties. Benefits of Professional Accounting Services One of the main advantages of hiring professional accounting services is the accuracy they bring to financial management. Skilled accountants have a deep understanding of financial regulations and tax laws, ensuring that businesses remain compliant. Moreover, accountants can identify tax-saving opportunities, helping businesses reduce their tax liabilities. This level of expertise allows businesses to save time and money, as they no longer need to navigate complex financial tasks on their own. Strategic Financial Planning In addition to managing day-to-day financial tasks, accounting services play a crucial role in strategic financial planning. Accountants analyze a company’s financial data to provide valuable insights into cash flow, profitability, and potential areas for improvement. This data-driven approach enables business owners to make informed decisions, allocate resources efficiently, and plan for future growth. Compliance and Risk Management Compliance with financial regulations is vital for businesses to avoid legal and financial risks. Accounting services ensure that all financial documents are in order, tax filings are accurate, and deadlines are met. By maintaining accurate records and staying up to date with tax laws, businesses can reduce the risk of audits and penalties. In conclusion, accounting services are an essential component of successful financial management for businesses of all sizes. By providing accurate financial reporting, strategic insights, and ensuring compliance, professional accountants enable businesses to focus on growth and sustainability.

The Importance of Bookkeeping Services for Businesses Effective bookkeeping is the foundation of any successful business. It involves the systematic recording, organizing, and managing of a company’s financial transactions. Whether you're a small business owner or running a large corporation, bookkeeping services help ensure that your financial records are accurate, up-to-date, and compliant with regulations. By outsourcing bookkeeping tasks to professionals, businesses can focus on growth and core operations without worrying about financial details. What Is Bookkeeping? Bookkeeping is the process of maintaining accurate records of all financial transactions, including sales, purchases, receipts, and payments. It involves organizing these records into categories like income, expenses, assets, and liabilities. The information generated through bookkeeping is essential for creating financial statements, tax filings, and understanding the overall financial health of the business. However, managing these tasks manually can be time-consuming and prone to errors, which is why many businesses opt for professional bookkeeping services. Benefits of Professional Bookkeeping Services One of the key benefits of hiring professional bookkeeping services is the accuracy they bring to financial management. Experienced bookkeepers are well-versed in the latest accounting software and financial regulations, ensuring that all records are kept accurately and consistently. Additionally, outsourcing this task allows business owners to save time and focus on other aspects of their business. As a result, they can make better financial decisions based on reliable data. Improved Financial Reporting Accurate bookkeeping leads to better financial reporting, which is critical for making informed business decisions. By keeping detailed and organized records, bookkeepers provide valuable insights into cash flow, profitability, and expenses. This allows businesses to plan their budgets more effectively, track financial performance, and identify areas for cost-saving or investment. Tax Compliance and Preparation Another important advantage of bookkeeping services is the ability to stay compliant with tax regulations. Bookkeepers ensure that all financial records are properly maintained and ready for tax season. With accurate and up-to-date records, businesses can avoid penalties and reduce the risk of audits, making tax preparation much smoother. In conclusion, professional bookkeeping services offer businesses the support they need to manage their financial records accurately and efficiently. By ensuring proper financial reporting and tax compliance, these services contribute to long-term financial stability and growth.

However, at the present time this is merely speculation. There is little hard data about the accuracy of material transformed from traumatic to semantic memory. More research is needed in this area.

The three legs of the agreement-tripod are desire, data and doubt. Accuracy and honesty have little to do with it.

Bafflement refers to the specific ways that proxies avoid our detection. Eluding and proxying aren’t illegal, but if someone at Harvest is working to help the eluders baffle us, thereby tainting our data with a statistically significant number of vacant identities and thus compromising the quality and accuracy of our work, that would fall under the rubric of industrial crime. Which explains why Phil and Patrice, our ombudsmen, are flanking Avery, looking more like cops than I’ve ever seen them look.

Second, CA provided clients, political and commercial, with a benefit that set the company apart: the accuracy of its predictive algorithms. Dr. Alex Tayler, Dr. Jack Gillett, and CA’s other data scientists constantly ran new algorithms, producing much more than mere psychographic scores. They produced scores for every person in America, predicting on a scale of 0 to 100 percent how likely, for example, each was to vote; how likely each was to belong to a particular political party; or what toothpaste each was likely to prefer.

Be a data sharer. That’s what experts do. In fact, that’s one of the reasons experts become experts. They understand that sharing data is the best way to move toward accuracy because it extracts insight from your listeners of the highest fidelity.

One might suppose that analog computers would be more powerful, since they can represent a continuum of values, whereas digital computers can represent data only as discrete numbers. However, this apparent advantage disappears if we take a closer look. A true continuum is unrealizable in the physical world. The problem with analog computers is that their signals can achieve only a limited degree of accuracy.

After a seminal paper in 2010 (see Bollen et al., 2011), the topic of alternative data started getting traction both in academia and in the hedge fund industry. The paper showed an accuracy of 87.6% in predicting the daily up and down changes in the closing values of the Dow Jones index when using Twitter mood data. This

The Future of Diabetes Management: Continuous Glucose Monitors by Med Supply US In the realm of diabetes management, continuous glucose monitors (CGMs) have emerged as a revolutionary technology, transforming the way individuals monitor their blood sugar levels. Med Supply US, a leading name in healthcare solutions, is at the forefront of this innovation, offering cutting-edge CGM devices that enhance the quality of life for those with diabetes. What sets continuous glucose monitors apart is their ability to provide real-time glucose readings, allowing users to track their levels throughout the day and night, without the need for constant finger pricks. This continuous monitoring not only offers convenience but also helps individuals make informed decisions about their diet, exercise, and insulin dosages. Med Supply US has established itself as a trusted provider of CGMs, offering a range of devices that cater to different needs and preferences. Whether it's the ease of use of their user-friendly interfaces or the accuracy of their readings, Med Supply US CGMs are designed to empower users in managing their diabetes effectively. One of the key advantages of Med Supply US CGMs is their compatibility with smartphone apps, allowing users to conveniently view their glucose data on their devices. This seamless integration with technology makes monitoring glucose levels more accessible and less intrusive, leading to better diabetes management outcomes. In conclusion, continuous glucose monitors by Med Supply US are revolutionizing diabetes management, offering a level of convenience, accuracy, and integration with technology that was previously unimaginable. With Med Supply US CGMs, individuals can take control of their diabetes with confidence, knowing that they have a reliable partner in their journey towards better health.

Unveiling London E-commerce Triumph: Decoding Data with WooCommerce Analytics In the bustling realm of London e-commerce, navigating the digital landscape requires not just intuition but informed decision-making backed by data. This is where the marriage of WooCommerce and analytics becomes a game-changer. In this exploration, we delve into the nuances of leveraging WooCommerce Analytics for e-commerce success in London. As we embark on this journey, the expertise of a dedicated woocommerce development in london adds a unique perspective, unraveling the potential of data decoding in the heart of the e-commerce landscape. Understanding the London E-commerce Scene This section emphasizes the importance of understanding the unique characteristics of the London e-commerce landscape. It underscores the need for businesses to be attuned to local market trends, consumer preferences, and the digital sophistication of the London audience to effectively leverage WooCommerce Analytics. The Role of WooCommerce Agency in London E-commerce Analytics 1. Proactive Data Strategy: Setting the Foundation This point explains the proactive role of a WooCommerce agency in London in establishing a robust data strategy. It involves setting up analytics tools, defining KPIs, and aligning data collection with the specific goals of London e-commerce businesses. 2. Tailoring Analytics to London Market Trends Here, the focus is on tailoring analytics solutions to capture and interpret data that is directly relevant to the ever-evolving market trends of London. A WooCommerce agency in London customizes analytics approaches to provide actionable insights for businesses in the local market. Key Metrics and KPIs for London E-commerce Success 3. Conversion Rate Optimization (CRO): Turning Clicks into Transactions This point explores the pivotal role of Conversion Rate Optimization (CRO) in London e-commerce. It delves into how a WooCommerce agency in London optimizes the conversion rate by refining the checkout process, analyzing user journeys, and enhancing the overall user experience to maximize sales. 4. Customer Lifetime Value (CLV): Fostering Long-Term Relationships The focus here is on the importance of Customer Lifetime Value (CLV) analytics. It explains how a WooCommerce agency in London helps businesses identify high-value customers, tailor marketing strategies, and foster long-term relationships for sustained success. WooCommerce Analytics Tools and Implementations 5. Google Analytics Integration for Comprehensive Insights This point delves into the integration of Google Analytics with WooCommerce. It explains how a WooCommerce agency in London guides businesses through the integration process, utilizing Google Analytics to gain comprehensive insights into user behavior, traffic sources, and website performance. 6. Custom Reports and Dashboards: Tailoring Insights for London Businesses Here, the emphasis is on the creation of custom reports and dashboards by a WooCommerce agency in London. These tailored insights provide businesses with specific information relevant to their products, target audience, and market trends, enhancing decision-making accuracy. Analyzing User Behavior for Enhanced User Experience 7. Heatmaps and User Flow Analysis: Optimizing the Customer Journey This point explores the use of heatmaps and user flow analysis to optimize the customer journey in London e-commerce. A WooCommerce agency in London employs these tools to uncover patterns, identify bottlenecks, and make strategic adjustments for a seamless user experience. 8. Abandoned Cart Analysis: Recovering Lost Opportunities This section discusses the significance of abandoned cart analysis. It explains how a WooCommerce agency in London utilizes analytics to understand the reasons behind cart abandonment and implements targeted strategies to recover potentially lost sales through personalized retargeting campaigns.

The Dexcom Continuous Glucose Monitoring System Living with diabetes requires constant vigilance over blood sugar levels. For decades, individuals with diabetes relied on periodic finger pricks to monitor glucose levels, but this method offered only snapshots of a dynamic condition. However, with the advent of continuous glucose monitoring (CGM) systems like Dexcom, managing diabetes has entered a new era of convenience and precision. The Dexcom Continuous Glucose Monitoring system is a game-changer for people with diabetes, offering real-time insights into glucose levels without the need for multiple finger pricks throughout the day. The system consists of a small sensor that is inserted just beneath the skin, typically on the abdomen, and continuously measures glucose levels in the interstitial fluid. This sensor communicates wirelessly with a receiver or compatible smart device, providing users with real-time glucose readings every few minutes. One of the key advantages of the Dexcom CGM system is its ability to track glucose trends over time. By providing continuous data, users can see how their glucose levels respond to food, exercise, medication, and other factors, empowering them to make informed decisions about their diabetes management. Additionally, the system includes customizable alerts for high and low glucose levels, helping users proactively manage their condition and avoid dangerous fluctuations. The Dexcom Continuous Glucose Monitoring system is not only beneficial for individuals with diabetes but also for their caregivers and healthcare providers. Caregivers can remotely monitor the glucose levels of loved ones, offering peace of mind and the ability to intervene quickly in case of emergencies. Healthcare providers can access detailed reports of a patient's glucose data, enabling more personalized treatment plans and adjustments to medication regimens. Furthermore, Dexcom has been at the forefront of innovation in CGM technology, continuously improving the accuracy, reliability, and usability of its systems. Recent advancements include longer sensor wear time, smaller and more comfortable sensors, and integration with insulin pumps and artificial pancreas systems for automated insulin delivery. In conclusion, the Dexcom Continuous Glucose Monitoring system has revolutionized diabetes management by providing real-time insights, customizable alerts, and greater convenience for users. With continuous advancements in technology, Dexcom continues to empower individuals with diabetes to live healthier, more active lives while effectively managing their condition.

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Outliers can skew the results and affect the accuracy of statistical analysis, while anomalies may indicate errors in data collection or data entry.

Intuitive information—unuttered, mind-locked data—does pass from person to person. Energy medicine is largely dependent upon a practitioner getting an image, gut sense, or inner messages that provide diagnostic and treatment insight. Edgar Cayce, a well-known American psychic, was shown to be 43 percent accurate in his intuitive diagnoses in a posthumous analysis made from 150 randomly selected cases.43 Medical doctor C. Norman Shealy tested now well-known intuitive Caroline Myss, who achieved 93 percent diagnostic accuracy when given only a patient’s name and birth date.44 Compare these statistics to those of modern Western medicine. A recent study published by Health Services Research found significant errors in diagnostics in reviewed cases in the 1970s to 1990s, ranging from 80 percent error rates to below 50 percent. Acknowledging that “diagnosis is an expression of probability,” the paper’s authors emphasized the importance of doctor-patient interaction in gathering data as a way to improve these rates.45 A field transfers information through a medium—even to the point that thought can produce a physical effect, thus suggesting that T-fields might even predate, or can at least be causative to, L-fields. One study, for example, showed that accomplished meditators were able to imprint their intentions on electrical devices. After they concentrated on the devices, which were then placed in a room for three months, these devices could create changes in the room, including affecting pH and temperature.46 Thought fields are most often compared to magnetic fields, for there must be an interconnection to generate a thought, such as two people who wish to connect. Following classical physics, the transfer of energy occurs between atoms or molecules in a higher (more excited) energy state and those in a lower energy state; and if both are equal, there can be an even exchange of information. If there really is thought transmission, however, it must be able to occur without any physical touch for it to be “thought” or magnetic in nature versus an aspect of electricity. Besides anecdotal evidence, there is scientific evidence of this possibility. In studying semiconductors, solid materials that have electrical conduction between a conductor and an insulator, noteworthy scientist Albert Szent-Györgyi, who won the Nobel Prize in 1937, discovered that all molecules forming the living matrix are semiconductors. Even more important, he observed that energies can flow through the electromagnetic field without touching each other.47 These ideas would support the theory that while L-fields provide the blueprints for the body, T-fields carry aspects of thought and potentially modify the L-fields, influencing or even overriding the L-field of the body.48

AI enables marketers to: •​Accelerate revenue growth •​Create personalized consumer experiences at scale •​Drive costs down •​Generate greater return on investment (ROI) •​Get more actionable insights from marketing data •​Predict consumer needs and behaviors with greater accuracy •​Reduce time spent on repetitive, data-driven tasks •​Shorten the sales cycle •​Unlock greater value from marketing technologies

Before we explore the account setup, let's take a closer look at how Immediate Momentum functions. Understanding the mechanics of this trading software is crucial to comprehend its potential benefits. According to Immediate Momentum's official website, the software harnesses sophisticated algorithms to analyze cryptocurrency price movements with pinpoint accuracy. It relies on technical indicators and historical data to identify lucrative trading opportunities by monitoring market trends. Immediate Momentum review operates fully automatically, executing every action on behalf of traders. Users have the flexibility to fine-tune trade parameters to align with their risk tolerance, investment objectives, and experience level. This customization empowers the software to analyze market trends and generate precise trade signals. Immediate Momentum continually assesses price fluctuations, notifying users of any significant value changes in the cryptocurrencies they're trading. All it takes is twenty minutes to set up the software's parameters, after which it takes over the trading process with efficiency.

One of Clark’s earliest statements on the proper nature of economics argued that economics should be “based on a foundation of terms, conceptions, standards of measurement, and assumptions which is sufficiently realistic, comprehensive, and unbiased” to provide a basis for the analysis and discussion of practical issues (Clark 1919, p. 280). Relevance to practical issues, accuracy of data, and comprehensiveness, in the sense of not excluding any evidence relevant to the problem at hand, were the characteristics of a scientific approach to economics that Clark most frequently stressed (Clark 1924, p. 74). Clark certainly thought of theory as playing a key role, but he saw the aim of theorizing as that of forming hypotheses “grounded in experience” for further study and inductive verification, rather than the production of a highly abstract system of laws.

Unleashing Reliable Insights from Generative AI by Disentangling Language Fluency and Knowledge Acquisition Generative AI carries immense potential but also comes with significant risks. One of these risks of Generative AI lies in its limited ability to identify misinformation and inaccuracies within the contextual framework. This deficiency can lead to mistakenly associating correlation with causation, reliance on incomplete or inaccurate data, and a lack of awareness regarding sensitive dependencies between information sets. With society’s increasing fascination with and dependence on Generative AI, there is a concern that the unintended consequence that it will have an unhealthy influence on shaping societal views on politics, culture, and science. Humans acquire language and communication skills from a diverse range of sources, including raw, unfiltered, and unstructured content. However, when it comes to knowledge acquisition, humans typically rely on transparent, trusted, and structured sources. In contrast, large language models (LLMs) such as ChatGPT draw from an array of opaque, unattested sources of raw, unfiltered, and unstructured content for language and communication training. LLMs treat this information as the absolute source of truth used in their responses. While this approach has demonstrated effectiveness in generating natural language, it also introduces inconsistencies and deficiencies in response integrity. While Generative AI can provide information it does not inherently yield knowledge. To unlock the true value of generative AI, it is crucial to disaggregate the process of language fluency training from the acquisition of knowledge used in responses. This disaggregation enables LLMs to not only generate coherent and fluent language but also deliver accurate and reliable information. However, in a culture that obsesses over information from self-proclaimed influencers and prioritizes virality over transparency and accuracy, distinguishing reliable information from misinformation and knowledge from ignorance has become increasingly challenging. This presents a significant obstacle for AI algorithms striving to provide accurate and trustworthy responses. Generative AI shows great promise, but addressing the issue of ensuring information integrity is crucial for ensuring accurate and reliable responses. By disaggregating language fluency training from knowledge acquisition, large language models can offer valuable insights. However, overcoming the prevailing challenges of identifying reliable information and distinguishing knowledge from ignorance remains a critical endeavour for advancing AI algorithms. It is essential to acknowledge that resolving this is an immediate challenge that needs open dialogue that includes a broad set of disciplines, not just technologists Technology alone cannot provide a complete solution.

The gap between a conventional forecast and one that uses RCF varies by project type, but for over half the projects for which we have data, RCF is better by 30 percentage points or more. That’s on average. A 50 percent increase in accuracy is common. Improvements of more than 100 percent are not uncommon. Most gratifyingly, given the method’s intellectual roots, Daniel Kahneman wrote in Thinking, Fast and Slow that using reference-class forecasting is “the single most important piece of advice regarding how to increase accuracy in forecasting through improved methods.

No problem,” muttered Mr. Raymo, waiting for the door to the secret passageway to glide shut. When Kyle was absolutely certain that the Krinkle brothers wouldn’t follow Mr. Raymo into the Rotunda Reading Room, he popped up and waved. “Mr. Keeley!” whispered Mr. Raymo. “Did you create the error code in Abraham Lincoln’s software?” “It was a group effort,” Kyle answered modestly. “But, yeah, that was us. We need to ask you a question.” “Please hurry,” said Mr. Raymo, looking over his shoulder. “If the brothers catch you kids…” “Mr. Raymo,” said Kyle, “can you use your Nonfictionator to replicate anybody saying anything?” “Yes. But I prefer to have the characters generated by the device speak with historical accuracy. That is why those of us on the Nonfictionator team have put such a high premium on proper research.” “But,” said Kyle, “if we did the research and gave you the audio and visual data you needed to create a truthful, honest representation of someone, or two someones…” “Then I can easily re-create that person or persons in holographic form,” said Mr. Raymo. “It’s also extremely helpful if an audio recording exists of the subject. For instance, I am quite confident that we have correctly captured Michael Jordan’s authentic voice, since we had primary source material to work with. Abraham Lincoln, on the other hand, sounds like Daniel Day-Lewis from the movie.

Fundamental to the feasibility of multidimensional collaborations is the ability to ensure accuracy of large-scale data and integrate it across multiple health record technologies and platforms. Efforts to ensure data quality and accessibility must be promoted

Under the direction of General Westmoreland, significantly himself a graduate of the Harvard Business School in which McNamara had at one time taught, the computers zestfully went to work. Fed on forms that had to be filled in by the troops, they digested data on everything from the amount of rice brought to local markets to the number of incidents that had taken place in a given region in a given period of time. They then spewed forth a mighty stream of tables and graphs which purported to measure “progress” week by week and day by day. So long as the tables looked neat, few people bothered to question the accuracy, let alone the relevance, of the data on which they were based. So long as they looked neat, too, the illusion of having a grip on the war helped prevent people from attempting to gain a real understanding of its nature. This is not to say that the Vietnam War was lost simply because the American defense establishment’s management of the conflict depended heavily on computers. Rather, it proves that there is, in war and presumably in peace as well, no field so esoteric or so intangible as to be completely beyond the reach of technology. The technology in use helps condition tactics, strategy, organization, logistics, intelligence, command, control, and communication. Now, however, we are faced with an additional reality. Not only the conduct of war, but the very framework our brains employ in order to think about it, are partly conditioned by the technical instruments at our disposal.

During an interview with Diversity Inc.’s director of research and product development, she walked me through a typical presentation used to pitch the value of the company’s software to prospective clients. I learned that their products are especially valuable to those industries not allowed to collect ethno-racial data directly from individuals because of civil rights legislation that attempts to curb how these data are used to discriminate. But now those who work in finance, housing, and healthcare can use predictive software programs to ascertain information that they cannot request directly. The US Health Insurance Portability and Accountability Act (HIPAA) privacy rule, for example, strictly monitors the collection, storage, and communication of individuals’ “protected health information,” among other features of the law. This means that pharmaceutical companies, which market to different groups, need indirect methods to create customer profiles, because they cannot collect racial-ethnic data directly. This is where Diversity Inc. comes in. Its software programs target customers not only on the basis of race and ethnicity, but also on the basis of socioeconomic status, gender, and a growing list of other attributes. However, the company does not refer to “race” anywhere in their product descriptions. Everything is based on individuals’ names, we are told. “A person’s name is data,” according to the director of research and product development. She explains that her clients typically supply Diversity Inc. with a database of client names and her team builds knowledge around it. The process, she says, has a 96 percent accuracy rate, because so many last names are not shared across racial–ethnic groups – a phenomenon sociologists call “cultural segregation.”18

Well, since we started keeping data on the bottlenecks, I’ve been noticing I’m able to predict several weeks in advance what each bottleneck will be working on at a particular time. See, as long as I know exactly what’s in queue, I just take the average setup and process times for each type of part, and I’m able to calculate when each batch should clear the bottleneck. Because we’re only dealing with one work center, with much less dependency, we can average the statistical fluctuations and get a better degree of accuracy.

As we saw, machine learning algorithms optimizing solely for predictive accuracy may discriminate against racial or gender groups, while algorithms computing aggregate statistics or models from behavioral or medical data may leak compromising information about specific individuals.

We are already seeing car insurance premiums linked to tracking devices in cars, and health insurance coverage that depends on people wearing a fitness tracking device. When surveillance is used to determine things that hold sway over important aspects of life, such as insurance coverage or employment, it starts to appear less benign. Moreover, data analysis can reveal surprisingly intrusive things: for example, the movement sensor in a smartwatch or fitness tracker can be used to work out what you are typing (for example, passwords) with fairly good accuracy [98]. And algorithms for analysis are only going to get better.

Unfortunately, the selection mechanism is often some combination of beauty and shock value, rather than pertinence and accuracy.

The first 20 percent often begins with having the right data, the right technology, and the right incentives. You need to have some information—more of it rather than less, ideally—and you need to make sure that it is quality-controlled. You need to have some familiarity with the tools of your trade—having top-shelf technology is nice, but it’s more important that you know how to use what you have. You need to care about accuracy—about getting at the objective truth—rather than about making the most pleasing or convenient prediction, or the one that might get you on television. Then you might progress to a few intermediate steps, developing some rules of thumb (heuristics) that are grounded in experience and common sense and some systematic process to make a forecast rather than doing so on an ad hoc basis.

Imagine an inverted U-curve: as data initially becomes more available, decision accuracy improves; but beyond an inflection point of increasing information, the amount of data diminishes management’s capacity to process the information and thus its ability to reach optimal decisions.20

Today, successful AI algorithms need three things: big data, computing power, and the work of strong—but not necessarily elite—AI algorithm engineers. Bringing the power of deep learning to bear on new problems requires all three, but in this age of implementation, data is the core. That’s because once computing power and engineering talent reach a certain threshold, the quantity of data becomes decisive in determining the overall power and accuracy of an algorithm.

More than just vehicles on a map, CompassCom empowers GIS centric fleet tracking and management that supports data-driven decisions, bringing efficiencies and accuracy across all departments of your operation.Enhanced command and control with real-time asset tracking and after-action analytics leveraging the power of ArcGIS. The knowledge base of records supports continuous improvement using location intelligence to empower results you can trust. An effective fleet tracking solution can help improve fleet operations in a number of ways. For example, it can reduce engine idling time and harsh cornering, make smart routing decisions for drivers, improve customer satisfaction with accurate ETAs, and track vehicle maintenance costs.

What we lose in accuracy at the micro level we gain in insight at the macro level.

Pratt & Whitney, the aerospace manufacturer, now can predict with 97% accuracy when an aircraft engine will need to have maintenance, conceivably helping it run its operations much more efficiently, says Anjul Bhambhri, VP of Big Data at IBM.

In the olden days (half a century ago), programs were big, undifferentiated masses of code and data. Control could flow from anywhere to anywhere. Data could be accessed from anywhere. Calculations, the original purpose of computers, occurred with (relatively speaking) lightning speed and perfect accuracy. Then people discovered an awkward fact: programs are written as much to be changed as to be run. All this control jumping around and self-modifying code and data accessed from everywhere was great for execution, but it was terrible if you wanted to change the program later. And so began the long and halting road to find models of computation so a change here doesn’t cause an unanticipated problem there.