A Quote by Samuel Karlin
The purpose of models is not to fit the data but to sharpen the question.
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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.
I am arguing that climate models are not fit for the purpose of detection and attribution of climate change on decadal to multidecadal timescales.
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.
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 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.
MapReduce has become the assembly language for big data processing, and SnapReduce employs sophisticated techniques to compile SnapLogic data integration pipelines into this new big data target language. Applying everything we know about the two worlds of integration and Hadoop, we built our technology to directly fit MapReduce, making the process of connectivity and large scale data integration seamless and simple.
Tape with LTFS has several advantages over the other external storage devices it would typically be compared to. First, tape has been designed from Day 1 to be an offline device and to sit on a shelf. An LTFS-formatted LTO-6 tape can store 2.5 TB of uncompressed data and almost 6 TB with compression. That means many data centers could fit their entire data set into a small FedEx box. With LTFS the sending and receiving data centers no longer need to be running the same application to access the data on the tape.
Daymark asks the right question. So we get it right the first time. We didn't want to overbuy or underbuy. They understood our business and our data. Daymark knew exactly which models we should order - not too much, not too little.
You have to imagine a world in which there's this abundance of data, with all of these connected devices generating tons and tons of data. And you're able to reason over the data with new computer science and make your product and service better. What does your business look like then? That's the question every CEO should be asking.
Some models are naturally very thin, but if they aren't naturally like that, then what these girls do to their health to fit in ... To be a size zero or a two when you're tall is incredible to me. It would be nice if models were allowed to be a more healthy weight - for the models, and for the young women who look up to them. We were athletic and healthy, and we looked like women.
Random search for data on ... off-chance is hardly scientific. A questionnaire on 'Intellectual Immoralities' was circulated by a well-known institution. 'Intellectual Immorality No. 4' read: 'Generalizing beyond one's data'. [Wilder Dwight] Bancroft asked whether it would not be more correct to word question no. 4 'Not generalizing beyond one's data.
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.
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