Human and Organizational Input: Exploring the Best Approach to Training AI Agents
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| Source: Dev.to | Original article
AI failures often stem from context issues, sparking a deeper look into human and organizational approaches to feeding AI agents.
The distinction between personal context and shared context has emerged as a crucial factor in the development and functioning of AI agents. As we delve into the complexities of AI failures, it becomes apparent that most issues stem from context-related problems. This realization underscores the importance of understanding how humans and organizations interact with their AI agents, and how context influences these interactions.
The concept of context is multifaceted, encompassing physical, relational, individual, and cultural aspects. Research highlights the interplay between shared meaning and context, demonstrating how communication shapes our understanding of the world. Moreover, studies have shown that human perception is context-dependent, integrating sensory input with prior information and social interactions. This context dependency is essential for navigating uncertainty and making predictions based on past experiences.
As the field of AI continues to evolve, it is essential to consider the implications of personal and shared context on AI agent development. By recognizing the significance of context, researchers and organizations can work towards creating more effective and reliable AI systems. The next step will be to explore how to apply this understanding in practical applications, ultimately leading to more sophisticated and human-like AI agents.
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