One challenge is having enough training data. Another is that the training data needs to be free of contamination. For a model trained up till 1900, there needs to be no information from after 1900 that leaks into the data. Some metadata might have that kind of leakage. While it’s not possible to have zero leakage - there’s a shadow of the future on past data because what we store is a function of what we care about - it’s possible to have a very low level of leakage, sufficient for this to be interesting.
The earliest incarnation of this project was built as a way of running Haskell snippets in knitr (a report generation software for R). Jonathan Carroll, a DataHaskell contributor, was working on an article showcasing Haskell’s viability for data science workloads. We built a small shell script that took Haskell code snippets, transformed them to work with GHCi (particularly putting multi-line functions in blocks), evaluated them in the command line, and then captured the output.
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Dan Simmons, the author of more than 30 novels and short story collections spanning horror, political thrillers and science fiction such as Hyperion and The Terror, has died at age 77.