The business of extracting knowledge from academic publications

1) Identification of all the major research groups working on the problem, including their physical locations and resources, lab standards and data standards, history of grants and proposals, etc. This will not be in the academic literature, and is fairly difficult and possibly expensive to acquire, and if you don't know what to look for, well, you need to hire experts with relevant experience. Think of it as due diligence (Theranos investors got burned because they didn't do this).

2) You have to aggregate a lot of papers to get a good picture of what each research group is up to. A single coherent project might generate a dozen papers scattered here and there, and importantly, they won't have published their failures in most cases, even though that information is just as valuable to an outsider attempting to replicate or advance their work as the successes are.

3) A common mistake is to neglect materials & methods and instead focus on results and discussion. A large fraction of the literature is based on poor methodology and so the results can't really be trusted, and fraud is remarkably widespread in academia, for various reasons from PhDs desperate to graduate to PIs who've made the practice their bread and butter for decades. Clearly written materials and methods sections that include all the information needed to replicate the work are an indication that it's fairly trustworthy. Deliberate obfuscation is a bad sign.

If anybody with an AI pharma start up tells you they plan to make money by licensing their platform, then run the other way because it is a red flag.

If you want to see what a real “AI” pharma company looks like check out Vertex pharmaceuticals.

Think of a global aircraft manufacturer turning maintenance documentation into a knowledge graph, a global clothing and shoes retailer building a model of what social media thinks about them, etc. I told other employees that our product could generate enough value for one customer that it would be worth it for one to buy us and... that's what happened.

- Bureaucracy. Writing silly boilerplate, e.g. data management plans or gender perspective statements in grant proposals.

- Cutting or expanding text (we routinely have lots of forms and submissions where you need to write a text in a given word or character range).

- Polite emails in English to people I don't know much (e.g. "Write a polite professional email reminding this person that the deadline for reviewing paper Y expired yesterday...")

- Brainstorming. "Give me 10 ideas about research direction in topic X". It won't give great ideas, but it's good to set the mind rolling.

- Routine scripts/code used in experiments and papers: write a Python script to make a box plot with such and such data, or to take a file in this format and strip this unneeded content, etc. The typical kind of code that appears a lot in research, is trivial to code but consumes time and ChatGPT does it in seconds.

- Suggest titles (paper titles, grant proposal titles, etc.).

- Suggest ideas for exercises or exam questions (e.g. write an assignment that can be solved with the coin change algorithm but involves no coins or currency).

- How to do X in Excel (although the problem here is that my Excel is in Spanish - why, why did they decide to translate function names? - and it's not that good at that - but anyway, it's very useful).

The productivity boost is very noticeable, well worth the cost, even if it hurts to pay out of pocket for a tool used at work.

2. How much would you pay?