cyclecycle's comments

A classic case.

I work on Veracity https://groundedai.company/veracity/ which does citation checking for academic publishers. I see stuff like this all the time in paper submissions. Publishers are inundated

That's basically what we're doing with app.studyrecon.ai.

What we've found is that vector similarity is often not the final solution. It is still only a crude proxy for the true goal of 'informativeness' or 'usefulness' with relation to the user goal/query. Works okay, but we're definitely seeing a need for more rigorous LLM-postprocessing to enrich the results set.

Which, yes, the time adds up quick!

Here's my attempt at a simple explanation of transformers. I would love feedback on whether I've got it right and how I could improve it. Cheers

Some personal reflections on the direction of my work.

We're working on this at Grounded AI (https://www.groundedai.company/contact-us). We'd love to help you if we can. Feel free to contact me (email is on my profile page)

I agree, it's currently very superficial. It would be great to recognise and show more about the specific nature of the relationships.

What kind of thing would you hope to see here? A textual summary of the relationship? Or perhaps there is more that can be done with shapes and colours in this area?

One of the creators here.

The keywords are grouped such that keywords that occur together often are in the same group. The colour represents this grouping.

There may be more useful groupings we could give or allow the user to choose between. We would be interested to hear any ideas for that

Really like the idea of trying to improve things that are so widely adopted and entrenched that most (me, at least) don't think to change.

We face the problem that people differ in what they think the features should be though. Ideally we would have a method to deduce what's the best symbolism for maximising input/output speed to human's minds. Some kind of scientific voodoo.

Beyond me what that might be.

I think its clear enough as is, and that the readme benefits from its current simplicity. Though for interest's sake this wouldn't hurt.

Nice work. I'd be interested to know how your customers are finding you. Have you invested much time into customer acquisition?

There are philosophical differences and practical differences. They are trying to build an AI scientist ground-up (love it). I would like to optimise for front-line use cases in a lean, user-driven manner. The common denominator technologically is knowledge extraction (text to subject-predicate-object triples), ontological mapping (e.g, these relationships express the same thing, these references are synonyms of this compound, etc.), and reasoning (comes free once you have information properly extracted and mapped with a schema in place thanks to knowledge graph implementations such as GraKn).

Practically speaking: Aristo takes questions in unstructured text, and answers in unstructured text. I'm interested in providing mechanistic queries and comprehensive, highly-structured result sets.

For a question such as, "what are the biological consequences of increasing the activity of molecule A", I want tabular and filterable results (where the number of rows depends on the volume of underlying data and the degrees of separation you carry the inference to). For this reason (alongside their current limitation to elementary science) I argue that Aristo is not currently a relevant resource for researchers and students looking to query and survey biomedical relationships.

The solution I'm aiming at takes a structured query and returns structured results. E.g, a query: [entity: molecule A, direction: increase] generates a list of direct and inferred consequences. It is more like a logic-driven search engine over structured information than it is a question answering system.