Maria Konnikova Quotes

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.

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

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.

I was interested in data mining, which means analyzing large amounts of data, discovering patterns and trends. At the same time, Larry started downloading the Web, which turns out to be the most interesting data you can possibly mine.

Machine learning is the science of getting computers to learn without being explicitly programmed.

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.

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

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

People are building apps that are doing super-crazy things, and there's a lot of talk about modeling and microtargeting. Facebook can predict when people are going to break up, and Target is able to predict if a woman is pregnant before she knows just based on the type of lotion she bought.

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.

Across our great nation, we've begun to see an acceleration of the power of data to deliver value.

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

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

People are saying, 'Big Data is the new oil.'

We have entered an era where the kinds of data and analyses that people run have gotten incredibly complicated. So you have researchers using sophisticated software programs that they probably don't understand but are generally accepted and everyone [in the field] uses.

There will always be plenty of things to compute in the detailed affairs of millions of people doing complicated things.

You can't keep fraud out of the system. The key to good regulation is how quickly you catch it.

Collect It All Process It All Exoloit It All Partner It All Snff It All Know It All

If you have a lot of data and you want to create value from that data, one of the things you might consider is building up an AI team.

Managers used to say, 'I have a gut feeling.' Do you know what a gut feeling is for a professional manager? It's a pattern that they recognize. But if your system can recognize that pattern, if it's not just a couple of managers who know that pattern, then the system's gut feeling can tell you which way to go. That's really liberating.

I just thought making machines intelligent was the coolest thing you could do. I had a summer internship in AI in high school, writing neural networks at National University of Singapore - early versions of deep learning algorithms. I thought it was amazing you could write software that would learn by itself and make predictions.

Every corporation worth its salt is throwing money at Deep Web research, not least Google. The company that unlocks the mysteries of the Deep Web will obtain power of an enormous magnitude.

We are going to completely change what it means to do advanced analytics with our data solutions. We have machine-learning stuff that is about really bringing advanced analytics and statistical machine learning into data-science departments everywhere.

Watson augments human decision-making because it isn't governed by human boundaries. It draws together all this information and forms hypotheses, millions of them, and then tests them with all the data it can find. It learns over time what data is reliable, and that's part of its learning process.

I've always believed that human learning is the result of relatively simple rules combined with massive amounts of hardware and massive amounts of data.