There are multiple challenges associated with using AI and more specifically Large Language Models (LLMs) like ChatGPT and Google's Gemini, some of which can be minimized.
With the widespread release of Large Language Models (LLMs) by various organizations, significant privacy issues have been observed including
AI bias occurs when models produce outputs that reflect or perpetuate existing inequalities and perspectives in the larger society.
Since the initial release of ChatGPT 3.5 in 2022, a major criticism of Large Language Models (LLMs) has been the tendency of these models to fabricate factually incorrect statements. LLMs generate text by predicting the most likely continuation token based on the prompt's text, context, and model's internal weights. Unlike a deductive process, LLMs do not directly reference their training source material to generate responses.
One of the immediate concerns regarding academic fraud in the use of Large Language Models (LLMs) that generate convincing and coherent text that students and researchers can pass off as original work. The growth and inclusion of generative text into academic articles has been widespread, particularly in the computer science literature (How Much Research Is Being Written by Large Language Models?)
The training of Large Language Models (LLMs) requires massive amounts of text and other media that are commonly available on the open web. This content includes both copyrighted and public domain material, which can lead to generative outputs from these models closely resembling existing copyrighted works resulting in various lawsuits.
A real concern of Large Language Models (LLMs) is the amount of energy and water required for training and deploying these models. For example, in their 2024 report Google admitted that their carbon output increased over 13% year-over-year primarily due to the increased energy usage of their customer-facing AI efforts, including the training and inference of their flagship Gemini LLM.
A 2024 report by Microsoft, offers four suggestions to reduce the environmental impact of these models. These suggestions are: