A Quote by Peter Norvig
More data beats clever algorithms, but better data beats more data.
Related Quotes
Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming.
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.
We dont have better algorithms, we just have more data
Biases and blind spots exist in big data as much as they do in individual perceptions and experiences. Yet there is a problematic belief that bigger data is always better data and that correlation is as good as causation.
In my view, our approach to global warming exemplifies everything that is wrong with our approach to the environment. We are basing our decisions on speculation, not evidence. Proponents are pressing their views with more PR than scientific data. Indeed, we have allowed the whole issue to be politicized-red vs blue, Republican vs Democrat. This is in my view absurd. Data aren't political. Data are data. Politics leads you in the direction of a belief. Data, if you follow them, lead you to truth.
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.
In the past, Google has used teams of humans to 'read' its street address images - in essence, to render images into actionable data. But using neural network technology, the company has trained computers to extract that data automatically - and with a level of accuracy that meets or beats human operators.
As a digital technology writer, I have had more than one former student and colleague tell me about digital switchers they have serviced through which calls and data are diverted to government servers or the big data algorithms they've written to be used on our e-mails by intelligence agencies.
The key to a solid foundation in data structures and algorithms is not an exhaustive survey of every conceivable data structure and its subforms, with memorization of each's Big-O value and amortized cost.
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.
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.
If we gather more and more data and establish more and more associations, however, we will not finally find that we know something. We will simply end up having more and more data and larger sets of correlations.
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.
A data scientist is that unique blend of skills that can both unlock the insights of data and tell a fantastic story via the data.
AIs are only as good as the data they are trained on. And while many of the tech giants working on AI, like Google and Facebook, have open-sourced some of their algorithms, they hold back most of their data.
Data!data!data!" he cried impatiently. "I can't make bricks without clay.
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