Dennis Crowley Quotes

If I'm reading my Facebook feed, it's using algorithms, procedures, and methods to give me what I want, or what it thinks that I want, or what suits its business plan.

Asking Siri where the nearest sushi bar is - that's not interesting. What's interesting is asking your phone where one of your friends have last had dinner in the neighborhood, or having it recommend a cool paella place in Barcelona because it knows you eat paella all the time at home.

When Spotify launched in the U.S. in 2011, it relied on simple usage-based algorithms to connect users and music, a process known as 'collaborative filtering.' These algorithms were more often annoying than useful.

It is probable that Facebook boasts the broadest, deepest, and most comprehensive dataset of human information, interests, and activity ever collected.

We want Google to be the third half of your brain.

Facebook is studying emotional reaction to things and bringing you fewer of things you don't engage with and more of what you do.

Facebook is uniquely positioned to answer questions that people have, like, what sushi restaurants have my friends gone to in New York lately and liked? These are queries you could potentially do with Facebook that you couldn't do with anything else, we just have to do it.

Facebook is teachable. If you hide items, you'll see fewer of those kinds of items in the future. Like more items, and you'll see more of those in the future.

It turns out a human being in two, three or four hours can build a search result that's much better than Google, Yahoo or Ask.

There is a calculus, it turns out, for mastering our subconscious urges. For companies like Target, the exhaustive rendering of our conscious and unconscious patterns into data sets and algorithms has revolutionized what they know about us and, therefore, how precisely they can sell.

We are moving into a world where companies will be able to offer us products and services based on our last two hours of activity. This is both exciting and frightening at the same time.

Before Google, and long before Facebook, Bezos had realized that the greatest value of an online company lay in the consumer data it collected.

Before Google, I spent the summer building a program that would look at what websites you would go to and what websites other people would go to - and built a collaborative filtering program that helped you find related sites to look at.

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.

My laptop seems to know where I am, even if I don't. My cellphone asks me if I want directions to anywhere from the spot I am standing in. I buy a record online and Amazon.com sends me letters, telling me that people who bought what I bought also bought these other records.

Ideally, Facebook would take all our clicks and information and would magically give us everything we want, without us even knowing we want it.

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.

For me, the most exciting aspect of my success is how the insights that Ayasdi's technology has found in data can truly impact people's lives.

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

Scientists need the infrastructure for scientific search to aid their research, and they need it to offer relevancy and ways to separate the wheat from the chaff - the useful from the noise - via AI-enabled algorithms. With AI, such an infrastructure would be able to identify the exact study a scientist needs from the tens of thousands on a topic.

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

The world is looking for big data scientists, and there just aren't enough to go around.

Figuring out how to scale the very human art of personalization is difficult, but I believe that it is also the key to building a lasting connection with customers for the long term.

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.

Everybody has a different Internet.