holub008
Nested Knowledge is building the future of Systematic Literature Review (SLR) by consolidating every step of the review process (search, screening, extraction, synthesis) in an updatable, collaborative environment. NK primarily solves biomedical SLR, and our users come from a variety of backgrounds, including academia, insurers, med device / pharma, and regulatory. To date, 2 million studies have been reviewed in our software.
We want you to build models taking our Robot Screener and Smart Tagging products to the next level and design automations in new areas, like critical appraisal. Our team is small, and you would be our first AI specialist, setting standards and frameworks in place. We value independence, creativity, and a focus on end-user value and experience over technology.
https://wellfound.com/jobs/2866132-machine-learning-engineer
It might also damage the sense of identity they provide, if the generated images wander too far from reality of the source images.
"Our results suggest that some sexually transmitted parasites, such as T. gondii, may be correlated with appearance and behavior of the human host."
I appreciate your viewpoint. I would counter it by saying that there are two sources of uncertainty here: choice of model & sampling variance. It's my opinion that in scientific writing, one should be precise with which source of uncertainty they are guarding. If I'm allowed to group these together, why can't I make a similar statement of causation of any old spurious correlation - when obviously my model is bad?
Considering this example again - isn't it arbitrary that the authors get to choose which hypothesis (among many, like attractive people being predisposed to own cats) they get to claim "may" be demonstrated?
Similar line of discussion: https://statmodeling.stat.columbia.edu/2015/04/04/thinking-p...
https://academic.oup.com/aje/article/190/3/386/5905708 https://pubmed.ncbi.nlm.nih.gov/24986706/
Unless there's some nuance I missed, I immensely disagree with this statement.
I'm currently in the biomedical literature review space, and I appreciate the detailed insights. I wonder if the author considered that literature review is used in a wide variety of domains outside pharma/drug discovery (where I perceived their efforts were focused). Regulatory monitoring/reporting, hospital guideline generation, etc.
This is a billion dollar industry, and I couldn't agree more that it's technologically underdeveloped. I do not agree that AI-based extraction is the solution, at least in the near-term. The formal methodologies used by reviewers/meta-analysts: search strategy generation, lit search, screening, extraction, critical appraisal, synthesis/statistical analysis, are IMO more nuanced than an AI can capture. They require human input or review. My business is betting on this premise :)
In your research, were you able to find GPS coordinates of the purported island? I'd be interested in taking a trip out to search myself, but doing a grid search of the whole island sounds challenging based on your report.
> DICKEY: We hiked in as far as we could to try to find the center of the island, was unable to find Moose Flats or Moose Boulder. Who knows; it could have been there, but highly unlikely.
[1] https://www.npr.org/2020/03/13/815546895/the-lonely-non-isla...
I frequently canoe and camp around northern Minnesota and Lake Superior. I do not understand how they could have possibly traveled a total of 18 miles from Malone Bay to Ryan Island, as the article states. At most, it would be a 1 mile portage and a 5 mile paddle. Getting lost and adding 12 miles seems very unlikely, as you mention, due to all the navigable landmarks in Siskiwit Lake. And that's ignoring GPS.
My guess is that they are not strong paddlers and/or navigators, got to the island already tired, saw the amount of bushwacking that would be required to explore the island, and bailed.
For the record, I think this is really cool. Something about the story, as presented, doesn't sit right though.
[1] https://www.openstreetmap.org/#map=17/48.00956/-88.76993&lay...
Those 3 data points were used in a graphic, which is not relevant to the actual experiment performed. Their experiment focused on cohorts of "parents who were (just) entitled to the new paternity leave" and "(just) ineligible parents". This design provides quasi-randomization so that any prior "downward trend" is not relevant, and clearly there are more than "3 data points" at play.