Unlock a new perspective on artificial intelligence as we reshape the way we talk about AI's capabilities. Join us for a thought-provoking journey into the complex world of qualia—the unique, subjective experiences tied to human consciousness—and explore how these concepts challenge our current AI terminology. This episode promises to expand your understanding of AI's role, inviting you to consider whether words like "understands" and "sees" truly reflect what AI systems do, or if they inadvertently imply a human-like consciousness that AI lacks. Throughout our conversation, we critically examine the language we use to describe AI functions. Words like "recognizes," "perceives," and "learns" carry human connotations, which can misrepresent AI's capabilities. Alongside my AI co-host, powered by OpenAI's GPT-4o, we propose more fitting terms such as "computational understanding" and "statistical learning" to better capture AI's processes. Our discussion also delves into whether AI training methods could mirror traditional learning, exploring how this might affect data processing and retention. Together, we strive to close the gap between human and machine cognition and foster clearer communication in the AI landscape. We also tackle the challenge of describing AI's data interactions without anthropomorphizing its processes. By refining terms like "exposed to" instead of "perceives," and introducing concepts like "computational sensitivity," we aim to articulate AI functions without ascribing human-like consciousness. This episode navigates the linguistic hurdles of discussing AI's capabilities, offering listeners a fresh framework for understanding the distinct differences between human cognition and AI's algorithm-driven operations. Join us as we redefine language and enhance clarity in the evolving dialogue surrounding artificial intelligence.