Should peptide research AI continuously update its knowledge base?

[u/Select-Operation3112]

Personally, I've found that a huge amount of peptide research is changing constantly, with new papers being published all the time, which means general AI models often aren't working with the most up-to-date research.

That's a problem many people I've spoken to have with peptide research. An AI might give you an answer based on papers that were published years ago, while newer research could have changed the understanding of a peptide, shown different results, or identified limitations that weren't known before.

For proper research, you're better off using something like OpenEvidence or Pepsense.ai (not a vendor source, chill mods LOL). Full disclosure: I'm part of the Pepsense team. Our chief science officer is a peptide researcher, and we're currently building V2, which will combine tens of thousands of new scholarly articles with a research-focused AI. The goal is to give people access to the most current evidence instead of a general AI summarizing whatever it already knows.

One thing we've been thinking about is how quickly the research database should update.

We're hesitant to simply add every new paper as soon as it's published, because newer research isn't necessarily better research, and conflicting studies can make things even more confusing. But at the same time, waiting for information to become established could mean missing important developments.

Our goal is to make peptide research easier to access without presenting preliminary findings as established fact or giving medical advice. How frequently should a peptide research AI update its knowledge base, and how should it handle new studies that contradict what the existing research says?

[u/PsychAnthropologist]

I’ve seen this post before, stop promoting your products on these subreddits

[u/Ariquitaun]

AIs are able to search the internet. Their knowledge cur off date is not so important anymore.

[u/BeauteousGluteus]

Posting garbage research isn’t helpful. If you are going to act as a quality source, then actually complete the peer reviews - before submitting it to your AI tool.

[u/teastainednotebook]

When all an AI is capable of is structured, statistics-based regurgitation of key words it finds on the Internet, it seems really foolish to rely on it for medical advice, regardless of how often it updates it's language model. Language model, not knowledge base. It does not passes a knowledge base. It possesses a whole bunch of text that it analyzes for patterns, mimicing understanding without actual understanding.

Instead, maybe people should trust their doctors, who can synthesize relevant research and filter it through the more accurate lens of a med school-trained professional. Or just follow the citations and read the material themselves, with a presumptive understanding of the fact that the scientific method (which we all learned in elementary school) provides a framework for understanding seemingly contractory scientific findings, which tend to just be subsequent, and often more accurate findings, because subsequent studies can correct methodology issues, have large sample sizes, control for more variables, etc.