We’ve all heard of ChatGPT but not Notebook LM… It honestly took a while for me to learn how NotebookLM works before I could experiment with it in today’s lab. But after playing around with it for a while, I could see its usefulness and how it can be implemented into my research procedures going forward.

My research topic was Racial Bias in Face Recognition Algorithms. I first went into ChatGPT and asked it to provide multiple sources discussing this topic. I clicked on the first three and they were all pretty good. I had free access to all of them, I could download them easily, and they were all relevant to the topic. Then, I asked both ChatGPT and NotebookLM “what issues do all three sources raise regarding this topic?” Both platforms provided 6-7 bullet points with most points overlapping. This was a surprise to me. I initially expected NotebookLM to do better on it as it allows users to specifically upload documents and discuss about it whilst ChatGPT allows you to do pretty much anything. However, I would still not trust ChatGPT to be 100% accurate since it may simply be stating whatever is most common out there. With this in mind, I would likely use ChatGPT to find sources given that I can click on their links and verify their accuracy and legitimacy myself. I would also use it for simple, quick searches that do not require analyzing sources or research papers and can instead be answered using general information available on the internet. However, if I am trying to dive deeper into each source or combine and compare multiple sources I have found, I would use NotebookLM. I think it is important to keep in mind the purpose or goal of what I am trying to achieve as well as to understand the strengths and weaknesses of each AI tools when deciding which one to use.

https://pubmed.ncbi.nlm.nih.gov/33585821/?utm_source=chatgpt.com

https://www.mdpi.com/2079-9292/13/12/2317?utm_source=chatgpt.com

https://link.springer.com/article/10.1007/s43681-021-00108-6?utm_source=chatgpt.com