In this engaging and thought-provoking debut episode, Kimia and Amber sit down with Dr. Max Krasnow to unpack the science behind what it means to “understand”—and whether AI models like ChatGPT actually do.

Dr. Krasnow begins by distinguishing true understanding from mere mimicry. He explains that while large language models (LLMs) can produce fluent, convincing language, they lack internal representations and the ability to make genuine inferences, which are crucial for human-like understanding. Using the classic Chinese Room thought experiment, he illustrates how AI may appear intelligent but doesn’t comprehend meaning the way humans do.

The conversation explores why humans so readily assign minds to machines. Dr. Krasnow introduces Error Management Theory, which suggests that our brains evolved to over-detect agency as a survival mechanism—it’s safer to assume a presence than to miss a threat. This leads to our tendency to interpret chatbots as having thoughts or feelings simply because they “talk” like us.

Kimia draws out key distinctions between how AI and humans recognize and apply patterns, prompting Dr. Krasnow to explain why human understanding goes beyond surface-level associations. Her questions help clarify what current AI models are missing—like context, intent, and real-world experience. Amber brings in the education angle, asking how we can help the public better understand AI without confusion or hype. In response, Dr. Krasnow emphasizes that fluency doesn’t equal intelligence and encourages hands-on engagement with AI to reveal its limitations. He cautions against mistaking confident output for genuine understanding or consciousness.

Throughout the episode, the hosts and guest stress the importance of keeping humans in the loop. Dr. Krasnow compares AI to a calculator: useful, efficient, but ultimately a tool—not a mind. He warns that trusting AI without scrutiny could lead to poor decision-making if users mistake surface-level fluency for deep understanding.

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