For this class activity, I explored the Notebook LM and its capabilities in summarizing the implications of A.I. in facial recognition. I asked Notebook LM to scan the web for surface-level sources relevant to this topic. There was an option for a “deep search” as well, but the surface-level search returned a vast number of peer-reviewed sources, all relevant. Some sources examined the implications for law enforcement, while others examined the implications for health care.
One of the most troubling implications that Notebook LM laid out for me was the high likelihood that darker-skinned females were misidentified. Light-skinned males had an error rate of 0.8%, compared to 34.7% for darker-skinned females. Scraping social media to find training data is another concern. Just because an image is publicly viewable does not mean its biometric data should be a free resource for corporations to train their LLMs.
I think Notebook LM can be used to synthesize sources. I would ask Notebook LM to find its own sources sparingly, as I am concerned about the credibility of the sources that LM generated for me. I think, after finding sources for yourself, LM could be a good tool for summarizing the information sources have in common, though it’s definitely not a substitute for actually reading these sources if you need to write a literature review, or something like that.
Group members who explored ChatGPT found it was very confident in all its responses, even though the ideas were more general than those of Notebook LM. One thing I found interesting about Notebook LM is that it often backs up its passages with citations directly from the sources, so you can go back and read the author’s words to make sure LM isn’t hallucinating. I think Notebook LM has uses in writing papers. They could help you see connections between sources that are hidden in plain sight.
Source: https://notebooklm.google.com/notebook/e8482b60-de6d-4b3c-bd07-23e7e34e3b71
