An AI glossary: the words and terms to know about the booming industry
This article provides a foundational glossary for navigating the rapidly evolving landscape of artificial intelligence, clarifying essential terminology amidst pervasive jargon. It distinguishes between traditional AI, which classifies information, and generative AI, which creates content such as text or images. This shift highlights how modern tools like chatbots and image generators operate, emphasizing the transition from passive identification to active synthesis. A critical focus is placed on the reliability and ethical implications of AI systems, particularly regarding training data and algorithmic bias. The text explains that poor data quality can lead to prejudiced decision-making, while phenomena like hallucinations and emergent behavior underscore the unpredictability of large language models. Furthermore, it introduces concepts like alignment and prompt engineering, which are crucial for ensuring AI systems remain safe, value-aligned, and usable for human operators. This resource is highly relevant to open data because it underscores the direct relationship between data quality and algorithmic integrity. Open data advocates must understand these technical mechanisms to promote transparency in how AI models are trained and biased. By demystifying terms like neural networks and training epochs, the article provides a vocabulary necessary for rigorous scrutiny of AI development, encouraging a more informed public discourse on data sourcing, ethical governance, and the societal impact of automated systems.
Source: nbcnews.comPublished on 2023-05-17