language parsing, photometric redshift templates

Brendan O'Connor (CMU) gave a talk late morning about natural language models for data science in the foreign policy and politics domains. He showed nice results based on subject–verb–object parsing of news stories. He also looked at some twitter data, showing an analysis of the emergence and propagation of the new twitter "words" (or acronyms) "idk" and "af". After the talk, at lunch, I complainined that natural language processing does not do a good job of understanding sentences and doesn't even give probabilistic results when uncertain. Yann LeCun (NYU) opined that understanding sentences is "AI complete", which is a phrase I need to use more often.

In the afternoon, Gabe Brammer (STScI) appeared and we talked about photometric redshifts. He is tweaking the model spectra using the data, and I suggested we go further in that direction. We gave each other homework on the subject.

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