Top 1200 Gathering Data Quotes & Sayings - Page 3

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.

The data are what matter in economics, and the more ruthlessness that an economist can summon to make sense of the data, the more useful his findings will be.

Emotion AI uses massive amounts of data. In fact, Affectiva has built the world's largest emotion data repository.

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

Data, I think, is one of the most powerful mechanisms for telling stories. I take a huge pile of data and I try to get it to tell stories.

We face two overlapping challenges. The first concerns real-time court-ordered interception of what we call 'data in motion,' such as phone calls, e-mail, and live chat sessions. The second challenge concerns court-ordered access to data stored on our devices, such as e-mail, text messages, photos, and videos - or what we call 'data at rest.'

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.

They made it very clear that I shouldn't try to be Data because there is only one Data, you can't ever recreate the magic that Brent Spiner made.

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?

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.

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

Rule 1. Original data should be presented in a way that will preserve the evidence in the original data for all the predictions assumed to be useful.

As a leader it is your job to protect the missional integrity of the Jesus gathering to which you have been called. It is your responsibility to see to it that the church under your care continues as a gathering of people in process; a place where the curious, the unconvinced, the skeptical, the used-to-believe and the broken, as well as the committed, informed and sold-out come together around Peter's declaration that Jesus is the Christ, the Son of the living God.

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?

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.

If you consider any set of data without a preconceived viewpoint, then a viewpoint will emerge from the 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.

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.

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.

I like to say I've been working on big data for so long, it used to be small data when I started working on it.

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.

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.

It is not a medicine. You don't know what's in it. If there were compelling scientific and medical data supporting marijuana's medical benefits that would be one thing. But the data is not there.

Data scientists are statisticians because being a statistician is awesome and anyone who does cool things with data is a statistician.

Data are just summaries of thousands of stories - tell a few of those stories to help make the data meaningful.

The key to a solid foundation in data structures and algorithms is not an exhaustive survey of every conceivable data structure and its subforms, with memorization of each's Big-O value and amortized cost.

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.

Data will always bear the marks of its history. That is human history held in those data sets.

People should have to opt in for any kind of data sharing, and they should know what the data is being used for.

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.

Disruptive technology is a theory. It says this will happen and this is why; it's a statement of cause and effect. In our teaching we have so exalted the virtues of data-driven decision making that in many ways we condemn managers only to be able to take action after the data is clear and the game is over. In many ways a good theory is more accurate than data. It allows you to see into the future more clearly.

Scientists do not collect data randomly and utterly comprehensively. The data they collect are only those that they consider *relevant* to some hypothesis or theory.

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.

Connectivity offers a great opportunity for General Motors. When you look at the investment we have made in OnStar and putting 4GLT in and the access you have to not only put data in, and we haven't really tapped into the data you can use from the vehicle.

There's a project that I started at HHS called the Health Data Initiative. The whole idea was to take a page from what the government had done to make weather data and GPS available back in the day.

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 '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.

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

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

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.

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.

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.

'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.

I think there's data, and then there's information that comes from data, and then there's knowledge that comes from information. And then, after knowledge, there is wisdom. I am interested in how to get from data to wisdom.

Let's look at lending, where they're using big data for the credit side. And it's just credit data enhanced, by the way, which we do, too. It's nothing mystical. But they're very good at reducing the pain points. They can underwrite it quicker using - I'm just going to call it big data, for lack of a better term: "Why does it take two weeks? Why can't you do it in 15 minutes?"

Search engines generally treat personal names as search terms like any others: Data is data.

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

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.

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

People treat citizens like they're some kind of unreliable source, but citizens are data. They are a data set.

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

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'.

I was always a data guy, not a theorist. Theorists can maintain total purity. The data are always messy.

Evolving technologies that allow economists to gather new types of data and to manipulate millions of data points are just one factor among several that are likely to transform the field in coming years.

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.

To be honest, more than what I prepare, it's the directors who do the bulk of the work, researching, collecting data and all that. I like to see myself as a processor: they feed me with the data, I give the output.

The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.

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.

The weaker the data available upon which to base one's conclusion, the greater the precision which should be quoted in order to give the data authenticity.

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.