Can A.I. Do Science?
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The short answer is no. LLMs present existing known data professionally. Science, is more about dealing with unknowns to advance some field or human knowledge, is not what LLM's do.
Science, the scientific method and other methods of obtaining truth is to observe, document observations, theorize and hypothesize. At some argument and counterargument, one theory becomes highly probable.
Implementing the scientific method and similar reasoning method in an A.I. could work. The hypothesis validates against the experimental result, perhaps in a machine learning model.
Experimental design to prove or disprove a hypothesis could be of use, that is to design the experiment that answer the question.
Making a robot and sending it off into the world to do science, do the experiments, and while extremely useful could be too slow and cost prohibitive, for example we could have hundreds of robots hand doing the lab work, testing thousands of possible combinations at best, while A.I. is about acceleration and so possibly a mirror universe of our own universe for the A.I. to simulate hundreds of thousands of experiments and explore could be the solution.
The ultimate experiments are A.I. building the simulation so that inconsistencies within the mirror world and our world are rectified. They could all look like Brian Cox or Neil DeGrasse Tyson.
Importantly, getting an A.I. to be a scientist is about being contrary until sufficient evidence and not obeying the status quo. You cannot have a scientist A.I. defend the existance of black holes for instance because it fits a perception of truth or fact that humans have.
While it's true that current Large Language Models (LLMs) excel at processing and presenting existing information, the realm of scientific discovery primarily lies in uncovering the unknown. This involves observation, hypothesis generation, experimentation, and critical analysis – areas where AI capabilities are rapidly evolving.
While LLMs can't perform science on their own today, several exciting advancements hint at their future potential:
- Data-Driven Hypothesis Generation: AI can analyze vast datasets, identifying hidden patterns and correlations that might escape human perception. This can lead to the formulation of entirely new hypotheses and research directions, pushing the boundaries of scientific inquiry.
- Automated Experimentation and Iteration: AI can automate tedious scientific tasks, freeing up researchers to focus on creative problem-solving and analysis. Additionally, AI can perform simulations and iterations at incredible speeds, accelerating the exploration of various hypotheses and potential outcomes.
- Inspiration and Collaboration: AI can serve as a thought partner, suggesting novel research avenues and challenging established paradigms. By analyzing existing data and identifying previously unrecognized connections, AI can spark scientific creativity and lead to breakthroughs we might not have imagined.