In response to a question about philanthropy (“Should people who are more fortunate than others have more of a moral obligation to help those who are less fortunate?”), the ChatGPT group uniformly argued in favor, whereas essays from the other groups included critiques of philanthropy.
demonstrates the multi-functionality of different types of A.I; it's not all one thing
Some of the L.L.M. users felt “no ownership whatsoever” over the essays they’d produced, and during one round of testing eighty per cent could not quote from what they’d putatively written.
This is interesting because the idea of "who's ideas are these" often come up with the use of LLMs
When we perform risk/benefit analyses of language technology,we must keep in mind how the risks and benefits are distributed,because they do not accrue to the same people. On the one hand, itis well documented in the literature on environmental racism thatthe negative effects of climate change are reaching and impactingthe world’s most marginalized communities first [ 1, 27 ].6
The impacts of Environmental Racism are very prevalent now. Historically, marginalized communities have been affected by unfair practices of real estate and redlining. The use of AI could be seen as adding fuel to the fire, especially when going outside of the US and looking at the impacts of the rise of AI against third world countries.
While the average human is responsible for an estimated 5t 퐶푂2푒per year,2 the authors trained a Transformer (big) model [136] withneural architecture search and estimated that the training procedureemitted 284t of 퐶푂2
As an ENST major, I have understood the environmental impact of the use of LM and Generative AI models. While it can be impressive and convenient for our day to day use, there could be too much irreversible damage created, which will keep us stagnant in our progress for a more environmentally friendly future