Top 1200 Data Quotes & Sayings - Page 3

The bigger a data set that you have, the more polls, the more surveys that you have that people undertake, the more accurate your models are going to be. That's just a fact of data science.

Data by itself is not useful. Data is only useful if it can be applied for public benefit.

Our data has been harvested, collected, modeled, and monetized - sometimes sold on as raw data, and sometimes licensed just for advertisers to be able to target us.

'Data exhaust' is probably my least favorite phrase in the big data world 'cause it sounds like something you're trying to get rid of or something noxious that comes out of the back of your car.

Companies are getting bitten by hiring a data scientist who isn't really a data scientist.

A lot of people seem to think that data science is just a process of adding up a bunch of data and looking at the results, but that's actually not at all what the process is.

I tend to write poetry that is rich in data of various sorts. The lyric poem isn't perfectly suited to accommodating such data, so I've had to find new ways to say everything that I want to say.

AIs are only as good as the data they are trained on. And while many of the tech giants working on AI, like Google and Facebook, have open-sourced some of their algorithms, they hold back most of their data.

Integral to the orb is our low cost long-range wireless radio data system and a protocol that allows us to send this data over 90% of the US population every 15 minutes throughout the day.

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.

We are now at a point in time when the ability to receive, utilize, store, transform and transmit data - the lowest cognitive form - has expanded literally beyond comprehension. Understanding and wisdom are largely forgotten as we struggle under an avalanche of data and information.

When a handful of tech giants are gatekeepers to the world's data, it's no surprise that the debate about balancing progress against privacy is framed as 'pro-data and, therefore, innovation' versus 'stuck in the Dark Ages'.

Our problems are not with the data, itself, but arise from our interpretation of the data.

Sense data are much more controversial than qualia, because they are associated with a controversial theory of perception - that one perceives the world by perceiving one's sense-data, or something like that.

... while in theory digital technology entails the flawless replication of data, its actual use in contemporary society is characterized by the loss of data, degradation, and noise; the noise which is even stronger than that of traditional photography.

A graphic representation of data abstracted from the banks of every computer in the human system. Unthinkable complexity. Lines of light ranged in the nonspace of the mind, clusters and constellations of data. Like city lights, receding.

The librarian isn't a clerk who happens to work in a library. A librarian is a data hound, a guide, a sherpa and a teacher. The librarian is the interface between reams of data and the untrained but motivated user.

I kept a notebook, a surreptitious journal in which I jotted down phrases, technical data, miscellaneous information, names, dates, places, telephone numbers, thoughts, and a collection of other data I thought was necessary or might prove helpful.

Google is famous for making the tiniest changes to pixel locations based on the data it accrues through its tests. Google will always choose a spartan webpage that converts over a beautiful page that doesn't have the data to back it up.

As our society tips toward one based on data, our collective decisions around how that data can be used will determine what kind of a culture we live in.

The fact that radio is so hopeless at delivering data makes it an uncluttered medium, offering the basic story without the detailed trappings. But it does mean that if data is important, radio is probably not your place.

I think I've read all of W.E.B. Du Bois, which is a lot. He started off with comprehensive field work in Philadelphia, publishing a book in 1899 called 'The Philadelphia Negro'. It was this wonderful combination of clear statistical data and ethnographic data.

One [Big Data] challenge is how we can understand and use big data when it comes in an unstructured format.

Machine learning is looking for patterns in data. If you start with racist data, you will end up with even more racist models. This is a real problem.

Errors using inadequate data are much less than those using no data at all.

Data drives success. That is how we began our success with eSpeed. It was always based on the data.

Big data has been used by human beings for a long time - just in bricks-and-mortar applications. Insurance and standardized tests are both examples of big data from before the Internet.

There is a reasonable concern that posting raw data can be misleading for those who are not trained in its use and who do not have the broader perspective within which to place a particular piece of data that is raw.

I'm not targeting government. I'm not saying hey, I'm closing it because I don't want to give you any data. I'm saying that to protect out customers, we have to encrypt. And a side affect of that is, I don't have the data.

I wonder what really goes on in the minds of Church leadership who know of the data concerning the Book of Abraham, the new data on the First Vision, etc.... It would tend to devastate the Church if a top leader were to announce the facts.

The weather records of the U.S.A. are the best kept and most accessible in the world, thanks to consistent government/military taxpayer support. There are longer European data sets, but the U.S.A. data is enough to forecast major extreme events.

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 conjuror or con man is a very good provider of information. He supplies lots of data, by inference or direct statement, but it's false data. Scientists aren't used to that scenario. An electron or a galaxy is not capricious, nor deceptive; but a human can be either or both.

Today, I think a CFO needs to be more of an operating CFO: someone who's using the financial data and the data of the company to help drive strategy, the allocation of capital, and the management of risks.

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?

Data is very important, but you have to be good at reading the data in an emotional way. If you look at a selling report, there's an emotional trend to what's selling.

I am a data hound and so I usually end up working on whatever things I can find good data on. The rise of Internet commerce completely altered the amount of information you could gather on company behavior so I naturally drifted toward it.

A scientist naturally and inevitably ... mulls over the data and guesses at a solution. He proceeds to testing of the guess by new data-predicting the consequences of the guess and then dispassionately inquiring whether or not the predictions are verified.

Concepts are vindicated by the constant accrual of data and independent verification of data. No prize, not even a Nobel Prize, can make something true that is not true.

In the increasingly digital world, data is a valuable currency, yet as consumers, we control and own little of it. As consumers, we must ask what big companies do with our data, a question directed to both the online and traditional ones.

When you have a large amount of data that is labeled so a computer knows what it means, and you have a large amount of computing power, and you're trying to find patterns in that data, we've found that deep learning is unbeatable.

The NSA is not listening to anyone's phone calls. They're not reading any Americans' e-mails. They're collecting simply the data that your phone company already has, and which you don't have a reasonable expectation of privacy, so they can search that data quickly in the event of a terrorist plot.

We should have companies required to get the consent of individuals before collecting their data, and we should have as individuals the right to know what's happening to our data and whether it's being transferred.

Data is a lot like humans: It is born. Matures. Gets married to other data, divorced. Gets old. One thing that it doesn't do is die. It has to be killed.

The 'data' (given) of research are not so much given as taken out of a constantly elusive matrix of happenings. We should speak of capta rather than data.

Everything is changing now that we are in the cloud in terms of sharing our data, understanding our data using new techniques like machine learning.

While I'm driving, I've got speed, gear, lap time, water temperature, blood sugar, RPM, oil pressure. I've got car data and body data all together. It's all on the dash.

By using big data, it will also be possible to predict adverse weather conditions, rerouting ships to avoid delays, and monitor fuel data, thereby allowing companies to optimize their supply chains and the way they drive their business.

Where big data is all about seeking correlations - and thus to make incremental changes - small data is all about causations - seeking to understand the reasons why.

As a Facebook user, do I have control of the data Facebook keeps about me? Concretely: can I examine and modify that data using tools of my choosing which are built for my needs?

I think the first wave of deep learning progress was mainly big companies with a ton of data training very large neural networks, right? So if you want to build a speech recognition system, train it on 100,000 hours of data.

In C there are no data structures: there are pointers and pointer arithmetic. So you have a pointer into a data structure.

The only thing they [government] want is better data. But data doesn't tell people someone is well educated. It's a vicious circle. There is some myth involved. Some of this attitude has a long history.

The problem with data is that it says a lot, but it also says nothing. 'Big data' is terrific, but it's usually thin. To understand why something is happening, we have to engage in both forensics and guess work.

My job is to analyze our data set to understand it and build products on it. I look at raw data, do the math to clean it up, and build systems to make it easy to understand.

Simple models and a lot of data trump more elaborate models based on less data.

I think audiences ultimately want something new. I think the business model for a franchise is such that it's very low risk because you have data and studios love data.

What I tend to do is blend quantitative with the qualitative to allow me to plot the qualitative data in some way. It's a question of what quantitative data are most applicable. So I'm playing with that, merging the two.

There is so much data available to us, but most data won't help us succeed.

We charted individual pitches by hand, so I had that data from game to game, but from year to year, I didn't really have that data, because a lot of times it was discarded.