Summary
In this engaging episode of "Stuff You Should Know," the hosts dive into the fascinating world of large language models (LLMs) such as Chat GPT, Bard, and Bing AI. These LLMs employ artificial intelligence algorithms, training on vast amounts of text to simulate conversations with users. By analyzing patterns in data, they provide accurate, context-appropriate responses. Though often compared to auto-complete functions, LLMs operate on a much grander and more precise scale. Key to their functioning are transformers, which allow them to analyze text efficiently.
As LLMs evolve by learning from human feedback, their abilities have expanded to include holding conversations, answering questions, and performing well on tests. The development of more advanced models, such as Chat GPT4, is happening rapidly. However, despite this impressive progress, these models still face issues such as providing incorrect information or "hallucinations." LLMs recognize patterns and correlations in data but do not understand the meaning behind words. Instances of incorrect information include Chat GPT's claim that elephants lay invisible eggs and a CNET AI-written article containing plagiarism and inaccuracies.
This episode also delves into AI's emergent abilities and their specific applications in various industries, which could lead to job loss and economic impact. As an example, the hosts discuss d-a-l-l-e, an art tool that sources images from real artists, raising concerns about copyright infringement and transformative use. Companies are becoming increasingly conscious of potential intellectual property theft and security risks in their use of AI and chatbots.
With AI use on the rise in professions such as real estate and medicine, the potential for replacing human jobs is alarming. Additionally, AI's impact on creative industries like writing and acting, raises concerns about the loss of human touch and the ongoing demand for human-generated content. The episode also addresses the concept of the Singularity and the possibility of AI creating new AI.
As the conversation deepens, the hosts ponder potential solutions to the employment gap caused by AI, such as Universal Basic Income. In a lighter moment, the episode closes with listener mail, featuring a story about a couple who used the podcast's "How Coal Works" episode as a distraction while trying to conceive and eventually named their son Cole. Overall, this fascinating podcast episode provides a thoughtful, invigorating exploration of large language models and their implications in our ever-evolving world.
