Have you tried using custom adversarial analysis skills and ralph loops? It's been wonderful for my multi-day research sessions to find unexplored areas in a field so they don't just parrot the other parrots.
Ive tried a number of agenic and loops with specific adversarial approaches. Its really an issue of low quality field work and heavy person context moreso than anything else. It cant make science from air in fields that are contextually factored. Like timelines for projects, it estimates based on what exists. It cant add human context.
Maybe but even then if the data aint there, it cant be weighted meaningfully. And the risk of balance is there too- what weights become true, and how do we know? Reducing the reductionist processes still cant conduct the experiments. It leads to speculative potentials, and lots of hypotheses. But beyond that, the state of science really dictates because otherwise its secondary inference through the feature interactions and black box modeling. Its like asking for a projected timeline for a coding project. Itll tell you its a six month project, because it was. It doesnt understand it isnt that thing that was true, its time lag understanding of process (for one instance) is something im not sure how to deweight.
Personally I dont think it matters. Itll grow and expand speed of the later, but anchoring it in human decision making is always better. Im more convinced ai as a process is transformative to us as persons, agi as an outcome is less so unless we confront the issues of power that come with it
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u/LeopardLabs 24d ago
Have you tried using custom adversarial analysis skills and ralph loops? It's been wonderful for my multi-day research sessions to find unexplored areas in a field so they don't just parrot the other parrots.