The Risks, Challenges, and Potential Opportunities with GenAI
TL;DR
This paper provides the foundations of AI and risks of GenAI, followed by a Use Case example of data management from a sensor edge node through actionable intelligence describing AI, and a data science strategy underpinning AI for enhancing trusted AI-agentic teaming.
Abstract
Artificial intelligence (AI) is a field where the masses offer declarations about novel advancements to machine intelligence and the everyday person feels like an AI “expert” in Generative AI (GenAI), such as ChatGPT and DALL-E. While 80+ years of research has led to the potential for GenAI to create new, “original” content, the public ought to understand that GenAI’s abilities are predicated on processing massive datasets. These datasets have many potential risks, including overtraining or novel datasets, foundational data science and metadata to AI models to cause incorrect decisions, bypass security, or extract sensitive information. Effective, trusted teaming with AI-agentic teams remains a critical research and development objective. Further, AI effectiveness becomes irrelevant if a human does not understand or trust the AI. This paper provides the foundations of AI and risks of GenAI, followed by a Use Case example of data management from a sensor edge node through actionable intelligence describing AI. This Use Case will walk through a data science strategy underpinning AI for enhancing trusted AI-agentic teaming, outlining the scientific research, challenges, and risks that can occur at each step that can directly impact the trusted relationship.