Google’s new Notebook LM hosts a 16 minute podcast on “Key Statements about the Turin Shroud as a Textile (Revised 2024)” by Joe Marino. Entertaining and informative. Definitely worth 16 minutes of your time.
Google’s new Notebook LM hosts a 16 minute podcast on “Key Statements about the Turin Shroud as a Textile (Revised 2024)” by Joe Marino. Entertaining and informative. Definitely worth 16 minutes of your time.
Hi, Dan,
I just did a quick listen to just a minute or two (or so) of the show, and already A.I. got info wrong —saying the 3:1 weaving pattern is very common. Well, even today, a herringbone pattern is rarely seen —and when seen, it’s most likely in wool suiting fabric. But, I hardly ever notice such a weave even nowadays. But, of course, we know that the British Museum (with their access to who knows how many linen textiles) was unable to find a herringbone linen cloth to use as a sample in an (failed) attempt for the researchers to not know which test sample was from the Holy Shroud (in their [failed] attempt for the testing to be “blind.”). This, of course, is indicative, instead, of the RARITY of this weave —particularly in a cloth with obviously hand-spun threads (since they could vary quite wildly in thickness from 100-200 fibers per thread.)
So, this is an A.I. generated conversation in a “podcast?” That’s just quite wild. I’m swamped right now and don’t have time to listen to the entire show, but I’m going to guess it’s riddled with more errors. A.I. has to be approached so cautiously since one never knows what percentage of the information is in error. But, humans are not immune to the problem if producing error-free information, either. But, I still trust a human mind and human judgment and, presumably, a human critically thinking and working on a piece before publication, as being more likely to get things right over a computer merging information from countless sources without a (hopefully knowledgeable human reviewing the information.)
People claim that A.I. will produce unbiased responses to questions. While there can be some truth to this, it still suffers from (and is limited by) the age-old problem of “garbage in-garbage out) if most (or all) of the information that it is drawing from is incorrect.
It can be a useful starting point. But, the problem is that many will use the information as an endpoint.
So, “user beware.”
Wishing you and all a merry Christmas and a happy and healthy new year,
Teddi