Can you unpack what is meant by "to be parsed semantically by artificial intelligence algorithms"?
I understand the words, but in this context it has me a bit confused as to what problem you see AI solving.
I understand the words, but in this context it has me a bit confused as to what problem you see AI solving.
The semantic parsing part is an annotation process. AI algorithms are required to make accurate annotations - a huge amount of context is required.
So if authors instead marked up their papers such that each mentioned entity's semantic meaning was obvious, it would make it much easier to AI that scans all papers and generates hypotheses.
Thank you very much for unpacking it and all the best in your career in Biology! I am in tech now, but studied Biology at Texas A&M for undergrad so hearing the words in your response reminded me of the good ol' days!
I want AIs to automatically find conflicting papers/hypotheses, and propose experiments that resolve the ambiguities.
I mean, I don't know much about LaTeX, but I doubt there are Elements for "Hypthese", "Definition", "exact reference" etc. If you would have those, described in a structured, simple language - then I guess, it will be much easier to process those Information for a KI, when the context is clear.
Or something pythonlike (also supported by a IDE):
hypothesis:
(indent) blablabla link:"link_to_Element_in_paper"
When I spoke to them they said if they had an AI that could do as good a job as people, they wouldn't need contractors. I think Google's approach would be to contract a bunch of scientists, have them read and interpret the papers, then use that data to train a deep net that could do it more accurately (you need some baseline humans to act as golden standards). This worked well for Google in several publicized examples, such as discriminating house numbers from numbers on cards in Street View imagery.