THE RIGHT TOOL FOR THE JOB?
Brian Perron is our human in the loop BY DAVID PRATT
Prof. Robert Taylor curates a series of periodic trainings at the School for early social work career professionals. Prof. Brian Perron has for some time contributed talks on technology. Perron’s topic this year? The pros and cons of using artificial intelligence in quantitative and qualitative research. Perron spoke about social work research in the era of large language models, that is, generative AI, the type that creates new content (as opposed to discriminative AI, which primarily classifies existing data.). Most of us know generative AI by the name ChatGPT, though there are also Bard (Google), Claude (Anthropic), and many others. When asked what these models can actually do, “I recommend that people test-drive the models for themselves,” Perron says. “Build your own understanding of what a model can and cannot do. Learn the art and science of giving prompts to the platform.” Soon, one will see limitless, almost dizzying possibilities. “AI provides some amazing tools for writing code,” Perron says. “I have been more efficient and effective in my data analysis, because AI helps me generate, debug, and optimize code much faster.” Perron’s own tools of choice
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are GPT-4 for writing code, and, for text analysis, Claude, because of the amount of input it can accept. AI can also have a high accuracy rate for translating code from one programming language to another, and it can also clean up code, comment on it, and even help the user learn code. Perron cites in particular Code Interpreter, a Python-based ChatGPT plug-in that lets you upload and analyze data using natural language (that is, any human
“We should not approach AI with fear. The sooner people learn about it, the better. We don’t want scholars to have discrepancies in their AI knowledge.”
language that has developed organically, through use, repetition, and change, rather than been built). “You tell the model what you want it to do,” Perron explains, “and it will translate your natural language into actual code and analyze the data.” In Perron’s data