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Ugh, that's a fairly egregious patent. It's literally of a mathematical method. :(
Just say yours does lower bound of Wilson score confidence interval for a Bernoulli parameter [0]. It has been used for sorting shopping items by ratings for years. [0] http://www.evanmiller.org/how-not-to-sort-by-average-rating....
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Could you explain what problem you're trying to solve here? Are there really that many researchers who have access to modern (and expensive) GPU hardware that don't have bandwidth or disk space available? Or are there many researchers who are putting in lots of time assembling a dataset but don't have the bandwidth to distribute it?
It's more a case of providing a quick and easy way to share large datasets, backed by HDFS. So, researchers don't have a good way to share datasets (apart from AWS/GCE). We work with climate science researchers who have multi-TB datasets, and they have no efficient way to share them. Same goes for genomics researchers who routinely pay lots of money for Aspera licenses just to download datasets faster than TCP allows…
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It appears TrueSkill is patented - https://en.wikipedia.org/wiki/TrueSkill
Ugh, that's a fairly egregious patent. It's literally of a mathematical method. :(
Earlier quoted context omitted.
Ugh, that's a fairly egregious patent. It's literally of a mathematical method. :(
Doesn't the language tie it necessarily to the application? i.e., player skill determination?
If a person figured out a way to apply it usefully to some other area - which doesn't seem hard at all - (job skill ranking? :D), MS is the kind of company that would attempt to collect $$$ from it regardless. :(
There are a few services popping up with aim to provide data repositories for analysis/ML (Kaggle, data.world, /r/datasets) As someone who likes making analyses from random datasets, I have a few issues with these types of services: 1) There is often no indication of the distribution rights of the data, or whether the data was obtained ethically from the source (i.e. following the ToS). I made this mistake when I use…
When it comes to data quality, the world is a messy place and the data that comes from it is messy too. Most professional data scientists spend an inordinate amount of time cleaning datasets, doing feature engineering etc… it’s part of their job description. We’re trying to eliminate some of that repetitive work by making sure that people can comment on, contribute to and give some signal back on the quality of the dataset. We also think a dataset is more than just the data... on data.world you can upload code, Notebooks, images, etc... anything that helps add context to the data.
Finally, when it comes to size... ML definitely needs it. However, there's a lot of interesting data out there thats still very complicated, very useful but not that big. Most datasets in the world are well under the terabyte size (or even 100s of GBs in size). We're rapidly expanding the size of datasets we support because we want that stuff too but we really want to help people understand all the data in the world!