Noreena Hertz Quotes

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.

I've become increasingly fascinated with social media to improve on traditional ways of preparing for and predicting the future.

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.

Because Facebook can't exist without AI, it needs all its engineers to build with it.

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.

If we start to use social media data sets to take the pulse of a nation or understand a crisis - or actually use it to deploy resources - we are getting a skewed picture of what is happening.

Google created the intent graph. Facebook created the social graph. We are creating the emotional graph.

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.

Today, we need to listen more carefully. I read what people say on Twitter, my friends on Path, in addition to formal media. I look for patterns, and then I post questions back to my network.

When I look at the next set of technologies that we have to build in Salesforce, it's all data-science-based technology. We don't need more cloud. We don't need more mobile. We don't need more social. We need more data science.

Artificial intelligence is one of 50 things that Watson does. There is also machine learning, text-to-speech, speech-to-text, and different analytical engines - they're like little Lego bricks. You can put intelligence in any product or any process you have.

The problem with Google is you have 360 degrees of omnidirectional information on a linear basis, but the algorithms for irony and ambiguity are not there. And those are the algorithms of wisdom.

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.

Without your data, Google couldn't pursue the dream of trying to figure out what you're really thinking when you're asking a question, of trying to discern, from the imprecision of your language, the exact answer you're looking for.

Deep learning allows you to create predictive models at a level of quality and sophistication that was previously out of reach. And so deep learning also enhances the product function of data science because it can generate new product opportunities.

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.

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 learned analytics. It's a point of reference, of information. It has to be a factor in decision making.

Technologies like Ayasdi's exist now to automatically discover information from data without having someone making guesses up front.

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

Every day we go over data and use science and data to drive policy and decision-making.

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 bigger a data set that you have, the more polls, the more surveys that you have that people undertake, the more accurate your models are going to be. That's just a fact of data science.

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