I wouldn't trust any powdered turmeric. Especially in the quantities people are consuming it these days.
Buy it fresh and grind it yourself. It tastes better that way anyway.
I've seen a scientific researcher do literature review manually.
Search, refine, print, read, highlight, collate.
Lots of time can be saved by automating those steps (and many researchers don't enjoy it so their job satisfaction could be increased). Also the resulting output could be improved if the researcher had a well structured summary to use as the foundation of their outline.
Improve search with semantic search (search by concept not keyword)
Improve refinement by preprocessing and summarizing
Don't print, display clean and concise data.
Summarize, cite and display.
This stops short of literature based discovery, you have to bring your own research question.
We've also had some luck finding a gap in existing research.
We did a POC where we scraped pubmed and graphed study results by concept. We then used the graphed concepts to explore the conceptual space.
It seems that vitamin D protects against cancer and heart disease. It seems that vitamin D supplementation protects against cancer but not heart disease. Is this because of some previously unknown effect of sun exposure (the primary natural source of vitamin D) or is it just that people with adequate vitamin D go outside a lot more and therefore also get more exercise? Don't know, would love to read the paper if someone studies it ;- )
It's actually possible to use LLMs to assist literature reviews. We built a product that works smashingly. The key is to keyword extract, use a vector database and do search based generation.
Our key insight is that the process of citation needs to be handled outside the LLM. They're good for text processing and summarization but as you said, the LLM itself is poor at citation.
I founded a company working to help companies use AI to organize their private knowledge.
We have focused on semantic search and knowledge graphs, but we started integrating a chatbot last week and it seems a short leap from where we are.
We'd be happy to help implement something.
You'll certainly want an embedding database.
The open models are getting pretty good, but you'll want to stand up a testing framework. I have a reasonably good model running on a desktop machine in my office with a reasonably priced consumer grade nvidia GPU.
We also have some tactics and practices around hallucination prevention that we'd be happy to share.
Feel free to reach out:
human at summitlabs.ai
Wasn't that the previous wave of remote work? And didn't it end badly for IT and software engineering?
I'm currently cleaning up a mess left by some well-meaning but unskilled and improperly managed overseas teams. They spent months and didn't deliver. My team cleaned it up in a few weeks but it wasn't cheap. And consider months of opportunity cost for lost execution time. In a startup with a fixed runway that can mean death.
The IT story is even worse. Poor outcomes, unhappy employees, data breaches. I heard from a neteng friend that when Google purchased Motorola the offshore neteng contractor was so bad Google that classified the Motorola network as actively hostile. The contractor was so deeply entrenched it was impossible to get them out and they were actively serving malware. Any machine that visited the motorola network had to be wiped before being allowed back on the corp network.
So yeah, if that's the next phase we already know how it ends.
This example stands out: Eat a balanced diet focused on fresh fruits and veggies, whole grains and healthy (non-saturated) fats. https://www.cdc.gov/healthyweight/healthy_eating/index.html