121–122 of 122 posts
Knowledge extraction is weird. Just because I extracted some knowledge doesn't mean that I now 'have' that knowledge. The better use case for this is teaching, not creating knowledge bases that nobody will use.
For anyone interested, my whole PhD was in biomedical hypothesis generation! I think the most "serious" attempts at building these systems have been focused around providing assistance to scientists, and not just coming up with new ideas on their own. here's an actual medical paper that my first system, Moliere, was able to help discover: https://link.springer.com/article/10.1007/s11481-019-09885-8
It seems that one major bias that the author of the post's blog has is their heavy conflation of 'worth' with money.
Many of us probably realize that this is not true, as academia clearly shows. Furthermore, It's not that crazy that they had trouble making money off of software, as I wouldn't expect many startups at all to be able to get customers from software solutions alone. They also seem to try to compare their success with that of essentially 'other biotech ml companies'; for which I would expect there to be quite a bit of tangible resources that these 'other companies' provide. For example, a startup looking to provide a service of detecting diseases or conditions from DNA methylation data would likely perform the sequencing required before doing an analysis (in order to have good control over experimental conditions). The materials alone in that case could cost quite a bit - so charging a bit more for the analysis isn't that problematic, since the transaction of some currency is already required for covering material costs.
Anyway, as you mentioned - it seems it's important to recognize that these systems aren't necessarily meant to generate revenue for a startup, but rather are much more useful as a tool in academia.