Sergey Brin Quotes

To learn something from your data, the forming of a hypothesis lies with the human being, which turns into a query, which becomes a result. The problem is that there are too many queries to make, too many questions to ask.

One of the things we did at PayPal was collaborative filtering and machine learning: looking at patterns of human behavior. We used it there to predict when people would try to cheat the system to get money. But you can predict pretty much any behavior with a certain amount of accuracy.

The possibilities for creation and insight are endless. We're constantly collecting more data, and it's starting to be very relevant to our lives.

In genomics, there's a massive amount of information in which you can look for patterns and develop insights.

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 job of the data scientist is to ask the right questions.

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.

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.

The power of Ayasdi is its unique ability to automatically discover insights - regardless of complexity - without asking questions.

Data science is the combination of analytics and the development of new algorithms.

Ayasdi's customers can finally learn the answers to questions that they didn't know to ask in the first place.

Networks love data. They love to be able to look at numbers and try to predict what they think will work.

I say I write extrapolations. I look at data points and ask what the world could look like.

Big data is mostly about taking numbers and using those numbers to make predictions about the future. The bigger the data set you have, the more accurate the predictions about the future will be.

While data can only tell you what has happened in the past, it can in some ways give you a sense of what might be of interest to an audience in the future.

There's definitely a huge opportunity for businesses to transform their operations and decision making by using data.

The people who are thinking most about big data right now are corporations and governments.

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.

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.

Most of 'big data' is a fraud because it is really 'dumb data.'

We're rapidly entering a world where everything can be monitored and measured. But the big problem is going to be the ability of humans to use, analyze and make sense of the data.

The world is now awash in data and we can see consumers in a lot clearer ways.

Social media has given companies access to unprecedented amounts of information on client behavior and preferences - so-called Big Data. But making sense of it all and turning it into actionable policy has been elusive.

I think you can have a ridiculously enormous and complex data set, but if you have the right tools and methodology then it's not a problem.

Forget artificial intelligence - in the brave new world of big data, it's artificial idiocy we should be looking out for.