Using AI-Scientist-v2 for research automation requires following the system's workflow, which is described below:
Generate research ideas phase:
- Go to the code directory after activating the Python environment
- Run command:
python launch_scientist_bfts.py --load_ideas "ai_scientist/ideas/i_cant_believe_its_not_better.json" --model_writeup "claude-3-5-sonnet-20240620" - The system will output a JSON file containing the title and detailed description of the study
Execute the experimental phase:
- The system automatically creates experimental code (e.g. experiment.py) for the generated research ideas
- run directly
python experiment.pyCommand Start Experiment - The results of the experiment (including data logging and graphs) will be saved in a log file in the experiments folder
Environmental readiness requirements:
- Requires Linux and NVIDIA GPU support.
- Recommended Python 3.11 environment
- Basic components such as PyTorch and CUDA 12.4 must be installed
The whole process realizes a closed loop from idea generation to experimental execution, and researchers only need to focus on the core problem definition and result evaluation, which greatly simplifies the scientific workflow.
This answer comes from the articleAI-Scientist-v2: Autonomous completion of scientific research and paper writingThe
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