A Quote by Heather Brooke
There are corporate private investigators, companies doing very forensic background checks on people. They buy data, they get their own data... They don't want their industry publicised.
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We get more data about people than any other data company gets about people, about anything - and it's not even close. We're looking at what you know, what you don't know, how you learn best. The big difference between us and other big data companies is that we're not ever marketing your data to a third party for any reason.
Go out and collect data and, instead of having the answer, just look at the data and see if the data tells you anything. When we're allowed to do this with companies, it's almost magical.
One of the myths about the Internet of Things is that companies have all the data they need, but their real challenge is making sense of it. In reality, the cost of collecting some kinds of data remains too high, the quality of the data isn't always good enough, and it remains difficult to integrate multiple data sources.
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.
The NSA buys data from private companies, so the private companies are the source of all this stuff.
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.
You can harvest any data that you want, on anybody. You can infer any data that you like, and you can use it to manipulate them in any way that you choose. And you can roll out an algorithm that genuinely makes massive differences to people's lives, both good and bad, without any checks and balances.
Every company has messy data, and even the best of AI companies are not fully satisfied with their data. If you have data, it is probably a good idea to get an AI team to have a look at it and give feedback. This can develop into a positive feedback loop for both the IT and AI teams in any company.
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.
In the increasingly digital world, data is a valuable currency, yet as consumers, we control and own little of it. As consumers, we must ask what big companies do with our data, a question directed to both the online and traditional ones.
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.
We tend to assume that data is either private or public, either owned by one person or shared by many. In fact there's more to it than that, above and beyond the upsetting reality that private data is now anything but.
One way of building private foresight out of public data is looking where others aren't ... if you want to see the future, go to an industry confab and get the list of what was talked about. Then ask, "What did people never talk about?" That's where you're going to find opportunity.
I believe that it's fine if the university wants to regulate, for example, bandwidth access, but they should treat the students data as private data.
We should have companies required to get the consent of individuals before collecting their data, and we should have as individuals the right to know what's happening to our data and whether it's being transferred.
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.
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