Top 1200 Data Structures Quotes & Sayings - Page 3

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.

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

Uncontrolled access to data, with no audit trail of activity and no oversight would be going too far. This applies to both commercial and government use of data about people.

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

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

We should always be suspicious when machine-learning systems are described as free from bias if it's been trained on human-generated data. Our biases are built into that training data.

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

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.

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 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 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.

... negative feelings are not true feelings at all; rather, they are your thoughts about something, based always on the previous experience of yourself and others. You will not find Truth in your past data, only past data that is based on other past data that is based on other past data, and so forth. Forget your "past experience" and look directly at the experience you are having. Right Here, Right Now. There is your Truth.

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.

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.

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.

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

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

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.

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.

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.'

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.

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.

The first wave of the Internet was really about data transport. And we didn't worry much about how much power we were consuming, how much cooling requirements were needed in the data centers, how big the data center is in terms of real estate. Those were almost afterthoughts.

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.

In all of America, there is no more promising an urban area for revitalization than your own Over-the-Rhine. When I look at that remarkably untouched, expansive section of architecturally uniform structures, unmarred by clashing modern structures, I see in my mind the possibility for a revived district that literally could rival similar prosperous and heavily visited areas.

My study is NOT as a climatologist, but from a completely different perspective in which I am an expert … For decades, as a professional experimental test engineer, I have analyzed experimental data and watched others massage and present data. I became a cynic; My conclusion - 'if someone is aggressively selling a technical product who's merits are dependent on complex experimental data, he is likely lying'. That is true whether the product is an airplane or a Carbon Credit.

The farmers markets were another step to giving people an opportunity to take more power over their own lives-and also to provide another outlet for organic produce. That is important because the production and distribution of food is increasingly being monopolized and controlled by large corporate structures, large financial structures.

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?"

Human artifacts not only include material structures and objects, such as buildings, machines, and automobiles, but they also include organizations, organizational structures like extended families . . . tribes, nations, corporations, churches, political parties, governments, and so on. Some of these may grow unconsciously, but they all originate and are sustained by the images in the human mind.

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?

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

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.

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

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

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.

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.

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.

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.

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

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.

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.

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.

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.

There are corporate private investigators, companies doing very forensic background checks on people. They buy data, they get their own data... They don't want their industry publicised.

Here is a guiding principle: If a business collects data on consumers electronically, it should provide them with a version of that data that is easy to download and export to another Web site.

The computer is here to stay, therefore it must be kept in its proper place as a tool and a slave, or we will become sorcerer's apprentices, with data data everywhere and not a thought to think.

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

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

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.

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'.

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.

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

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.

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.

To make a vehicle autonomous, you need to gather massive streams of data from loads of sensors and cameras and process that data on the fly so that the car can 'see' what's around it.

We were very deliberately not playing 12-bar structures, blues structures, which rock musicians turned into such a cliche. We tried to... listen to the rhythms within ourselves and take the normal words we used every day in our normal thoughts, which girls hadn't written about before.

Traditional society was more like a set of concentric circles of meaningful structures, while modern man must learn how to find meaning in many structures to which he is only marginally related. In the village, language and architecture and religion and work and family customs were consistent with one another, mutually explanatory and reinforcing. To grow into one implied a growth into others.

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.

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.