One question I have in these orchestration based multi agent systems is the out of domain generalization. Biotech and Pharma is one domain where not all the latest research is out there in public domain (hence big labs havent trained models on it). Then, there are many failed approaches (internal to each lab + tribal knowledge) which would not be known to the world outside. In both these cases, any model or system would struggle to get accuracy (because the model is guessing on things it has no knowledge of). In context learning can work but it's a hit and miss with larger contexts. And it's a workflow + output where errors are not immediately obvious like coding agents. I am curious as to what extent do you see this helping a scientist? Put another way, do you see this as a co-researcher where a person can brainstorm with (which they currently do with chatgpt) or do you expect a higher involvement in their day to day workflow? Sorry if this question is too direct.
One question I have in these orchestration based multi agent systems is the out of domain generalization. Biotech and Pharma is one domain where not all the latest research is out there in public domain (hence big labs havent trained models on it). Then, there are many failed approaches (internal to each lab + tribal knowledge) which would not be known to the world outside. In both these cases, any model or system would struggle to get accuracy (because the model is guessing on things it has no knowledge of). In context learning can work but it's a hit and miss with larger contexts. And it's a workflow + output where errors are not immediately obvious like coding agents. I am curious as to what extent do you see this helping a scientist? Put another way, do you see this as a co-researcher where a person can brainstorm with (which they currently do with chatgpt) or do you expect a higher involvement in their day to day workflow? Sorry if this question is too direct.