In the ever-evolving landscape of scientific discovery, the role of artificial intelligence (AI) stands tall, promising novel insights and breakthroughs. Yet, a crucial question lingers in the corridors of research: to what extent does AI influence the reproducibility of experiments and the validation of findings?
Abu Rayhan ,
I would suggest narrowing this question since it is way to broad to answer in a useful way.Some of the ways to interpret the question are:
- AI as the purpose of the experiment (benchmarking). They have become so big and data hungry that only few entities can do foundational training.
- As as producing possible combinations(e.g. creating new compounds). Since ML methodology in producing these results is obscured, then it is difficult to obtain reproducibility.
- AI as helper in generating hypothesis- good luck with this one since, the ML only produces combinatorial outputs of what is learned, can it produce really novel ideas?
- AI as generators of content within the scientific paper- this is just naive on the part of a researcher since the possibility of hallucinations on sparse technical/scientific documents (vs millions of trivial posts about cats and dogs which would do the same as running multiple epochs on few shot learning) is a danger.
- AI as generators of content with underlying biases- self explanatory
Thus, each interpretation poses a distinct impact on scientific development ( and these are just the ones I could immediately think of. I am sure someone else can come up with others).
AI role extends and influences to any piece of digital footprint of any form and thus to domain of your query.
If you can be more precise, I can answer deeper to the topic.
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