Kate Crawford Quotes

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

Artificial intelligence is just a new tool, one that can be used for good and for bad purposes and one that comes with new dangers and downsides as well. We know already that although machine learning has huge potential, data sets with ingrained biases will produce biased results - garbage in, garbage out.

Error-prone or biased artificial-intelligence systems have the potential to taint our social ecosystem in ways that are initially hard to detect, harmful in the long term, and expensive - or even impossible - to reverse.

Emotion AI uses massive amounts of data. In fact, Affectiva has built the world's largest emotion data repository.

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.

There is huge demand for artificial intelligence technologies.

It's difficult to imagine the power that you're going to have when so many different sorts of data are available.

The world that our children living in is going to be completely different because of big data.

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.

When human judgment and big data intersect there are some funny things that happen.

The ability to collect, analyze, triangulate and visualize vast amounts of data in real time is something the human race has never had before. This new set of tools, often referred by the lofty term 'Big Data,' has begun to emerge as a new approach to addressing some of the biggest challenges facing our planet.

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

Algorithms are opinions embedded in code. It’s really different from what you think most people think of algorithms. They think algorithms are objective and true and scientific. That’s a marketing trick. It’s also a marketing trick to intimidate you with algorithms, to make you trust and fear algorithms because you trust and fear mathematics. A lot can go wrong when we put blind faith in big data.

Data is going to be dynamic... and, combined with the performance of computing that allows for artificial intelligence to be applied, is going to bring all kinds of insights and additives to enhance everyday life.

Big data knows and can deduce more about you than Big Brother ever could.

I am worried that algorithms are getting too prominent in the world. It started out that computer scientists were worried nobody was listening to us. Now I'm worried that too many people are listening.

The whole concept of data science is that the software becomes the expert, and you, as the average user, are able to understand what's going on.

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.

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.

The answers to today's most important scientific, business, and social problems lie in data.

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.

Things like chatbots, machine learning tools, natural language processing, or sentiment analysis are applications of artificial intelligence that may one day profoundly change how we think about and transact in travel and local experiences.

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

We should always be suspicious when machine-learning systems are described as free from bias if it's been trained on human-generated data. Our biases are built into that training data.

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.