A Quote by Gore Verbinski
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.
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People think 'big data' avoids the problem of discrimination because you are dealing with big data sets, but, in fact, big data is being used for more and more precise forms of discrimination - a form of data redlining.
While many big-data providers do their best to de-identify individuals from human-subject data sets, the risk of re-identification is very real.
When you are young, coming into this business, you're told how the business works, and you feel very lucky to be here and want to stick around, so you believe the data, and you believe the conversations you're having where they say, 'You can't have that kind of lead because they don't travel here,' or, 'People will think it's not for them.'
I think the first wave of deep learning progress was mainly big companies with a ton of data training very large neural networks, right? So if you want to build a speech recognition system, train it on 100,000 hours of data.
I think Unix is a great system - especially for running data centers - because it is very mature, very reliable, very scalable. But when I want to go out and populate small devices, I think Java.
You have to imagine a world in which there's this abundance of data, with all of these connected devices generating tons and tons of data. And you're able to reason over the data with new computer science and make your product and service better. What does your business look like then? That's the question every CEO should be asking.
We use nearly 5 thousand different data points about you to craft and target a message. The data points are not just a representative model of you. The data points are about you, specifically.
I wanted to separate data from programs, because data and instructions are very different.
Any time scientists disagree, it's because we have insufficient data. Then we can agree on what kind of data to get; we get the data; and the data solves the problem. Either I'm right, or you're right, or we're both wrong. And we move on. That kind of conflict resolution does not exist in politics or religion.
Scientific data are not taken for museum purposes; they are taken as a basis for doing something. If nothing is to be done with the data, then there is no use in collecting any. The ultimate purpose of taking data is to provide a basis for action or a recommendation for action. The step intermediate between the collection of data and the action is prediction.
I'm going to say something rather controversial. Big data, as people understand it today, is just a bigger version of small data. Fundamentally, what we're doing with data has not changed; there's just more of it.
I think philosophers can do things akin to theoretical scientists, in that, having read about empirical data, they too can think of what hypotheses and theories might account for that data. So there's a continuity between philosophy and science in that way.
The franchise model is great, because most people who are entrepreneurial want flexibility and time to do what they love. A lot home business entrepreneurs struggle because they have to do everything.
I think audiences crave something new. I don't think audiences want the same old thing, no matter how much conventional Hollywood tells you that.
I love data. I think it's very important to get it right, and I think it's good to question it.
The biggest mistake is an over-reliance on data. Managers will say if there are no data they can take no action. However, data only exist about the past. By the time data become conclusive, it is too late to take actions based on those conclusions.
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