Information Has Value

Artificial intelligence (AI) is a way to train computers to complete complex tasks and then learn from these tasks.

Under the "AI umbrella," machine learning is a method used to identify patterns in text, images, data, etc. The process of machine learning can involve human input or be fully automated.

Generative artificial intelligence, like ChatGPT or Google's Gemini, is a specific kind of AI that is designed to generate new text, images, video, or audio based on the materials used to "train" it.

It's important to understand how generative AI models work, even at a very basic level, because that can help us understand how we can use them most effectively. What are they good at? When should we be suspicious of their output?

You may have heard of the issue of AI "hallucinations," which is when a generative AI large language model (like ChatGPT) gives you incorrect or nonexistent information. These hallucinations are part of how large language models work.

Hallucinations occur because LLMs are trained to predict the next word in a sequence based on patterns in their training data, not to verify facts or reason logically like a human. During training, the model processes vast amounts of text from the internet, books, and other sources, learning statistical relationships between words, phrases, and ideas. However, it does not have a built-in understanding of what is true or false; its primary "goal" is to generate text that is statistically likely.

Generative AI may "fill in the blanks" by generating text that aligns with the patterns it has seen, even if the content is entirely made up. This behavior reflects its design: to maximize the likelihood of producing natural-sounding language, not to guarantee factual accuracy.

For example, if asked for a citation or detailed explanation, the model might create a realistic-sounding response, including plausible but fabricated sources, because it has learned that these elements often appear in similar contexts. This limitation underscores the importance of understanding that LLMs are powerful tools for language generation but require careful oversight and fact-checking when used in academic or professional contexts.

"AI slop" is low-quality content generated by AI that is shared online. Over time, as AI-generated content becomes widespread, it risks degrading the overall quality of information online, especially if this "slop" is used as training data for future AI models. This creates a feedback loop of declining quality, making it harder to find accurate, well-reasoned information and emphasizing the need for thoughtful human oversight in using AI tools.