A Quote by George Saunders
I'm repledging myself to human-scale values. As a fiction writer, the best data comes through the senses and is then processed through many revisions. We have to learn to be intelligent assessors of the data coming in to us and what it's doing to our mental process.
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The beginning of human knowledge is through the senses, and the fiction writer begins where the human perception begins. He appeals through the senses, and you cannot appeal through the senses with abstractions.
Fifty years ago, the way that we consumed food was revolutionized. We began eating processed foods, and it seemed amazing. And then we woke up many decades later, and we realized that food was engineered to make us fat. And I think that such companies as Google, Facebook, Amazon, Apple are doing the same thing with the stuff that we ingest through our brains. They're attempting to addict us, and they're addicting us on the basis of data.
People believe the best way to learn from the data is to have a hypothesis and then go check it, but the data is so complex that someone who is working with a data set will not know the most significant things to ask. That's a huge problem.
Data and data sets are not objective; they are creations of human design. We give numbers their voice, draw inferences from them, and define their meaning through our interpretations.
MapReduce has become the assembly language for big data processing, and SnapReduce employs sophisticated techniques to compile SnapLogic data integration pipelines into this new big data target language. Applying everything we know about the two worlds of integration and Hadoop, we built our technology to directly fit MapReduce, making the process of connectivity and large scale data integration seamless and simple.
Listening to the data is important... but so is experience and intuition. After all, what is intuition at its best but large amounts of data of all kinds filtered through a human brain rather than a math model?
Every day, I absorb countless data bits through emails, phone calls, and articles; process the data; and transmit back new bits through more emails, phone calls, and articles. I don't really know where I fit into the great scheme of things and how my bits of data connect with the bits produced by billions of other humans and computers.
The premise and promise of Big Data is that there are no stories, only patterns; that the human preference for story is aligned with the human tendency for error; and that only through dislocations in scale - the scale of sample size and of time - will truth emerge.
While many big-data providers do their best to de-identify individuals from human-subject data sets, the risk of re-identification is very real.
As a digital technology writer, I have had more than one former student and colleague tell me about digital switchers they have serviced through which calls and data are diverted to government servers or the big data algorithms they've written to be used on our e-mails by intelligence agencies.
We get more data about people than any other data company gets about people, about anything - and it's not even close. We're looking at what you know, what you don't know, how you learn best. The big difference between us and other big data companies is that we're not ever marketing your data to a third party for any reason.
Watson augments human decision-making because it isn't governed by human boundaries. It draws together all this information and forms hypotheses, millions of them, and then tests them with all the data it can find. It learns over time what data is reliable, and that's part of its learning process.
Scientific data are not taken for museum purposes; they are taken as a basis for doing something. If nothing is to be done with the data, then there is no use in collecting any. The ultimate purpose of taking data is to provide a basis for action or a recommendation for action. The step intermediate between the collection of data and the action is prediction.
When dealing with data, scientists have often struggled to account for the risks and harms using it might inflict. One primary concern has been privacy - the disclosure of sensitive data about individuals, either directly to the public or indirectly from anonymised data sets through computational processes of re-identification.
We need a new generation of executives who understand how to manage and lead through data. And we also need a new generation of employees who are able to help us organize and structure our businesses around that data.
Radio astronomy reflects our fascination with how audio can be used to understand information or ideas. Just as scientists visualize data through charts and pictures, we can use 'data sonification' to translate radio signals into sound that help us better understand some of our most enigmatic planetary systems.
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