Data Quotes

“The brain is machine which collects data, use it to collect data. Then use that data for a purpose!”

“Wesley Crusher: Say goodbye, Data. Lt. Cmdr. Data: Goodbye, Data. [crew laughs] Lt. Cmdr. Data: Was that funny? Wesley Crusher: [laughs] Lt. Cmdr. Data: Accessing. Ah! Burns and Allen, Roxy Theater, New York City, 1932. It still works. [pauses] Lt. Cmdr. Data: Then there was the one about the girl in the nudist colony, that nothing looked good on? Lieutenant Worf: We're ready to get under way, sir. Lt. Cmdr. Data: Take my Worf, please. Commander William T. Riker: [to Captain Picard] Warp speed, sir? Captain Jean-Luc Picard: Please.”

“Perception requires imagination because the data people encounter in their lives are never complete and always equivocal. For example, most people consider that the greatest evidence of an event one can obtain is to see it with their own eyes, and in a court of law little is held in more esteem than eyewitness testimony. Yet if you asked to display for a court a video of the same quality as the unprocessed data catptured on the retina of a human eye, the judge might wonder what you were tryig to put over. For one thing, the view will have a blind spot where the optic nerve attaches to the retina. Moreover, the only part of our field of vision with good resolution is a narrow area of about 1 degree of visual angle around the retina’s center, an area the width of our thumb as it looks when held at arm’s length. Outside that region, resolution drops off sharply. To compensate, we constantly move our eyes to bring the sharper region to bear on different portions of the scene we wish to observe. And so the pattern of raw data sent to the brain is a shaky, badly pixilated picture with a hole in it. Fortunately the brain processes the data, combining input from both eyes, filling in gaps on the assumption that the visual properties of neighboring locations are similar and interpolating. The result - at least until age, injury, disease, or an excess of mai tais takes its toll - is a happy human being suffering from the compelling illusion that his or her vision is sharp and clear.We also use our imagination and take shortcuts to fill gaps in patterns of nonvisual data. As with visual input, we draw conclusions and make judgments based on uncertain and incomplete information, and we conclude, when we are done analyzing the patterns, that out “picture” is clear and accurate. But is it?”

“Database Management System [Origin: Data + Latin basus "low, mean, vile, menial, degrading, ounterfeit."] A complex set of interrelational data structures allowing data to be lost in many convenient sequences while retaining a complete record of the logical relations between the missing items. -- From The Devil's DP Dictionary”

“If we have data, let’s look at data. If all we have are opinions, let’s go with mine.”

“Random search for data on ... off-chance is hardly scientific. A questionnaire on 'Intellectual Immoralities' was circulated by a well-known institution. 'Intellectual Immorality No. 4' read: 'Generalizing beyond one's data'. [Wilder Dwight] Bancroft asked whether it would not be more correct to word question no. 4 'Not generalizing beyond one's data.”

“And yet Rebecca felt that it was hard to tell whether the secret algorithms of Big Data did not so much reveal you to yourself as they tried to dictate to you what you were to be. To accept that the machines knew you better than you knew yourself involved a kind of silent assent: you liked the things Big Data told you you were likely to like, and you loved the people it said you were likely to love. To believe entirely in the data entailed a slight diminishment of the self, small but crucial and, perhaps, irreversible.”

“We need a data network that can easily carry voice, instead of what we have today, a voice network struggling to carry data.”

“Due to the various pragmatic obstacles, it is rare for a mission-critical analysis to be done in the “fully Bayesian” manner, i.e., without the use of tried-and-true frequentist tools at the various stages. Philosophy and beauty aside, the reliability and efficiency of the underlying computations required by the Bayesian framework are the main practical issues. A central technical issue at the heart of this is that it is much easier to do optimization (reliably and efficiently) in high dimensions than it is to do integration in high dimensions. Thus the workhorse machine learning methods, while there are ongoing efforts to adapt them to Bayesian framework, are almost all rooted in frequentist methods. A work-around is to perform MAP inference, which is optimization based.Most users of Bayesian estimation methods, in practice, are likely to use a mix of Bayesian and frequentist tools. The reverse is also true—frequentist data analysts, even if they stay formally within the frequentist framework, are often influenced by “Bayesian thinking,” referring to “priors” and “posteriors.” The most advisable position is probably to know both paradigms well, in order to make informed judgments about which tools to apply in which situations.”