Week 2 Readings

Crawford, Atlas of AI (Excerpt) – This reading made me think about how people can create technology without thinking about the people who could be affected by it. I was surprised that some AI researchers can work with information about real people without having to go through the same ethics reviews as other types of research. The example about the gang database stood out to me because the data had many mistakes but researchers still used it to train an AI system. This made me question how useful an AI system can be when the information it learns from is already wrong. I also liked the quote, “The lesson, therefore, is that the scientist and technologist must, by acts of will and of the imagination, actively strive to reduce such psychological distances.” This means that people who create technology should think about how their work affects real people. I agree with this because creating a system should not be separated from thinking about its possible consequences.

IBM What are LLMs – This reading helped me understand that large language models are not thinking like humans when they answer questions. They use patterns from huge amounts of text to predict what words should come next. I was surprised by how much work happens before an AI can answer a simple question. The text has to be broken into tokens, turned into numbers and processed through many layers of a transformer. I also found it interesting that people help improve AI by ranking its answers through reinforcement learning from human feedback. One quote that stood out to me was, “The model does not ‘know’ the final answer in advance.” This made me think differently about AI because it can sound very confident even though it technically is making predictions. It also made me wonder how often people trust AI answers without remembering that the system can still make mistakes or give false information.