A Quote by Peter Norvig

Simple models and a lot of data trump more elaborate models based on less data. — © Peter Norvig
Simple models and a lot of data trump more elaborate models based on less data.
The paradigm shift of the ImageNet thinking is that while a lot of people are paying attention to models, let's pay attention to data. Data will redefine how we think about models.
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.
Machine learning is looking for patterns in data. If you start with racist data, you will end up with even more racist models. This is a real problem.
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.
Thus even supposedly unadulterated facts of observation already are interfused with all sorts of conceptual pictures, model concepts, theories or whatever expression you choose. The choice is not whether to remain in the field of data or to theorize; the choice is only between models that are more or less abstract, generalized, near or more remote from direct observation, more or less suitable to represent observed phenomena.
Apple knows a lot of data. Facebook knows a lot of data. Amazon knows a lot of data. Microsoft used to, and still does with some people, but in the newer world, Microsoft knows less and less about me. Xbox still knows a lot about people who play games. But those are the big five, I guess.
There are great slender models, great tall models, Amazonian models, great busty models - my point is models of all shapes and sizes, age, ethnic background should be embraced and celebrated.
The only basis for even talking about global warming is the predictions spewed out by computer models. The only quote/unquote "evidence" of global warming is what models are predicting the climate and the weather will be in the next 50 to 100 years. Now, what those models spit out is only as good as the data that's put in, and it's an absolute joke. In terms of science, it's a total joke. There is no warming, global or otherwise!
For the theory-practice iteration to work, the scientist must be, as it were, mentally ambidextrous; fascinated equally on the one hand by possible meanings, theories, and tentative models to be induced from data and the practical reality of the real world, and on the other with the factual implications deducible from tentative theories, models and hypotheses.
The climate-studies people who work with models always tend to overestimate their models. They come to believe models are real and forget they are only models.
The purpose of models is not to fit the data but to sharpen the question.
There's a lot of amazing women out there. There's a lot of hot models. But models are the worst, because they're models, you have to always step up and always look good.
It amazes me how people are often more willing to act based on little or no data than to use data that is a challenge to assemble.
How reliable are the computer [climate] models on which possible future climates are based? Not very. All will agree that the task of modeling climate is vast, because of the estimates that have to be made and the rubbery quality of much of the data.
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.
It turns out it's important to build a product and not just a bunch of data models.
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