Quotes About Data

We think we have the best matching algorithm, we think we have the best members. So why wouldn't we want to just shine the light onto just how our processes work, what the real data are, and let people come to their own conclusions.

The entire brain of the organization is here. The construction drawings and data processing all takes place in Rotterdam.

I can neither confirm nor deny that if you entered data into an algorithm for 'building the perfect actor' it would produce Brett Goldstein.

Education has always produced an incredible amount of data; that's always been obvious to me. But technology had to catch up.

We know now data is so powerful, and you can learn so much about yourself and creating product with data.

Every time a bit of information is erased, we know it doesn't disappear. It goes out into the environment. It may be horribly scrambled and confused, but it never really gets lost. It's just converted into a different form.

Too often in business, only financial data is gathered - and then it is distributed only to management. Other key indicators that relate to performance areas also need to be tracked. Information on performance has to be made available to those people who can best use it - those doing the work.

A scientist... must accept the results of experiment, and nothing but the results of experiment.

Data science requires having that cultural space to experiment and work on things that might fail.

Individuals should be able to monetise their own data - that's their own human value - not to be exploited.

I'm very lucky to work at bitly, with a data set that allows us to explore human social behavior at the scale of human social behavior.

Sometimes one must make extended conclusions from limited data.

Facebook creeps me out.

I wonder which is ultimately more creepy: shopping at Amazon or using Facebook?

I deal in facts.

I do put my questions in terms of the sharpest polarities of the issue. But I don't want a preponderance of opinion over factual data.

The era during which only governments could put hardware on the Moon is coming to an end. There are 26 private teams competing for the $30 million Google Lunar X-Prize - to be awarded for sending a robotic spacecraft to this nearby world that can roam at least 500 meters, and send back data such as a photo.

It always makes me super nervous how many tech companies don't have data ethicists.

We've seen historically how marginalized communities or historically marginalized communities particularly suffer from tech companies unchecked data collection and use.

Facebook's position with rival tech companies boils down to this: if you want access to all the information we've collected, strike a deal with us.

I'm a very big proponent of cloud. We've used it a lot in private sector, and as far as we can tell, it is not only more efficient, it's probably also more secure for lots of very complicated technical reasons. I think it's a very important thing for government to do, and also to have systems that talk to each other.

If you only have 10 examples of something, it's going to be hard to make deep learning work. If you have 100,000 things you care about, records or whatever, that's the kind of scale where you should really start thinking about these kinds of techniques.

Very simple techniques, when you have a lot of data, work incredibly well.

Ageing is very exciting. But if I didn't work on ageing, I'd want to work on the brain. There are really cool techniques you can use now. And bioinformatics. The methods you can use for comparing large data sets - that's so powerful.