Quotes on Data

With this emergence of big data and social mobility, you will, in fact, see the death of 'average,' Instead, you will see the era of you.

If you have all the research, all the ground rules, all the directives, all the data - it doesn't mean the ad is written. Then you've got to close the door and write something - that is the moment of truth which we all try to postpone as long as possible.

As the amount of inputs go up, as the number of people and ideas that clamor for attention continue to increase, we do what people always do: we rely on the familiar, the trusted and the personal. The incredible surplus of digital data means that human actions, generosity and sacrifice are more important than they ever were before.

Belief Systems contradict both science and ordinary "common sense." B.S. contradicts science, because it claims certitude and science can never achieve certitude: it can only say, "This model"- or theory, or interpretation of the data- "fits more of the facts known at this date than any rival model." We can never know if the model will fit the facts that might come to light in the next millennium or even in the next week.

We live in the digital age and, unfortunately, it’s degrading our music, not improving it It’s not that digital is bad or inferior, it’s that the way it’s being used isn’t doing justice to the art. The MP3 only has 5 percent of the data present in the original recording. … The convenience of the digital age has forced people to choose between quality and convenience, but they shouldn’t have to make that choice.

Statistics is the grammar of science.

Rushing to optimize before the bottlenecks are known may be the only error to have ruined more designs than feature creep. From tortured code to incomprehensible data layouts, the results of obsessing about speed or memory or disk usage at the expense of transparency and simplicity are everywhere. They spawn innumerable bugs and cost millions of man-hours - often, just to get marginal gains in the use of some resource much less expensive than debugging time

The hope is that, in not too many years, human brains and computing machines will be coupled together very tightly, and that the resulting partnership will think as no human brain has ever thought and process data in a way not approached by the information-handling machines we know today.

I would only have been too pleased if someone had asked me for my data. If you really believed in your data, you wouldn't mind someone looking at it. You should be able to respond that if you don't believe me go out and do the measurements yourself.

Many of us now expect our online activities to be recorded and analyzed, but we assume the physical spaces we inhabit are different. The data broker industry doesn't see it that way. To them, even the act of walking down the street is a legitimate data set to be captured, catalogued, and exploited.

I think philosophers can do things akin to theoretical scientists, in that, having read about empirical data, they too can think of what hypotheses and theories might account for that data. So there's a continuity between philosophy and science in that way.

Soon I knew the craft of experimental physics was beyond me - it was the sublime quality of patience - patience in accumulating data, patience with recalcitrant equipment - which I sadly lacked.

Marketers use big data profiling to predict who is about to get pregnant, who is likely to buy a new car, and who is about to change sexual orientations. That's how they know what ads to send to whom. The NSA, meanwhile, wants to know who is likely to commit an act of terrorism - and for this, they need us.

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.

When you're an engineer, you want to analyze things a lot. But if you believe that the most important data points are people, then you have to make conclusions in relatively short order. Because you want to push the people who are doing great. And you want to either develop the people who are not or, in a worst case, they need to be somewhere else.

It's difficult to imagine the power that you're going to have when so many different sorts of data are available.

We need new, dynamic models for growth through the sharing economy, using big data to unlock new insights and adopting closed-loop cycles.

I don't drive by the seat of my pants and happen to win races. I work very hard to interpret the data and drive a certain way. My engineers have confidence in me, and more often than not, when I tell them what I need or what I am feeling with the car, it's right.

People think 'big data' avoids the problem of discrimination because you are dealing with big data sets, but, in fact, big data is being used for more and more precise forms of discrimination - a form of data redlining.

In Quakerism, your understanding of God is revised in light of your own experience, while in research science, you revise your model in light of data from experiments.

Supervised learning works so well when you have the right data set, but ultimately unsupervised learning is going to be a really important component in building really intelligent systems - if you look at how humans learn, it's almost entirely unsupervised.