— web clipping, 382 words — published 2026-09-28
Post by @mahaoo_ASI on X
The way researchers develop research taste is by reading a lot about the past, doing a few full cycle research projects, and seeing a lot of projects by contemporary peers and colleagues of which they form opinions early, and then later see long term impact
from this they distill insights understand the important problems vs imaginable problems, etc
the full cycle research project is mostly about building the understanding of what research is by having the full generative model to judge step by step decisions, and then the wide distribution dataset is actually the research done by peers and what ended mattering vs what was simply flashy unimportant research directions
its about long term feedback closing, it's about making predictions about would end up important and what will not without knowing the answer, it's about having the experience to 'step inside the shoes' of other researchers from the past and understanding their mistakes in hindsight
as a general rule of thumb that proved useful in the last decade, anything a human can do with N datapoints, if the AI sees 10,000 times more such datapoints, it will match the human capability
for these reasons, to me it's clear that AI will develop impeccable research taste in the upcoming several years as its starts to be used by scientists to do research projects and as it's tries to do research on it's own with all the nitty gritty details that are often not covered in final polished papers
the act of doing research yourself leads to better understanding of research done by others, this means the AI will be able to hindsight compound of all of the research done by humans in the last 50 years
there is nothing particularly special about having research taste, it's just a matter of being smart, capable, having some experience of doing a think yourself, and seeing many others doing it in parallel
it's in many ways the exact same process of how to understand an image, if you have a generative image model you will better understand how to interpret hard to understand images, only the pattern of research taste is not a 1000x1000 pixel image, but rather a long sequence of {(perception, action)} pairs that yields good impactful research vs. incremental vs. simply poor research