The Deep Understanding Principle

For decades, the quest to develop human-level artificial general intelligence (AGI) has been framed primarily as an information processing challenge. Could we create systems capable of taking in data, reasoning over representations, and producing intelligent outputs in ways that match or exceed human cognitive capabilities? This has driven approaches ranging from symbolic logic systems to machine learning statistical models over vast datasets. However, a groundbreaking new philosophical perspective argues that such approaches, even those using extremely complex brain-inspired architectures, fundamentally miss what is essential for genuine understanding and meaningful cognition. The ability to make qualitative judgments about meaning, rooted in qualitative first-person subjective experiences or "qualia", may be an indispensable requirement that information processing models alone cannot satisfy. The Deep Understanding Principle captures this insight.

Qualitative Experience as the Cornerstone of Understanding

The principle states that for any system or entity to achieve authentic understanding beyond mere data manipulation, it must possess the capacity for qualitative judgments grounded in qualitative phenomenal experiences. Specifically: 1) Grasping meaning and true comprehension requires the ability to make qualitative assessments about semantic content, conceptual relationships, and contextual significance - not just formalistic rule-following. 2) Such qualitative evaluations of meaning depend on having qualitative subjective experiences - a felt, first-person perspective that imbues representations with meaning rather than just processing abstract symbols. 3) Purely syntactic operations following formal rules are insufficient for genuine understanding, as they fundamentally lack the qualitative grounding to differentiate intentional content and parse meaning like humans.

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Implications for Artificial Intelligence

Any computational model claiming to achieve general intelligence and natural language understanding must solve the "hard problem" of explaining how qualitative, phenomenal experiences can emerge from its particular physical/computational processes. Conversely, a solution to the hard problem of consciousness could provide a direct path to realizing artificial general intelligence with real world understanding, not just narrow capabilities or mimicking intelligent behaviors. Without an account of phenomenal qualitative experiences, there is an unbridgeable gap in how any system can genuinely comprehend semantic contents, contexts and conceptual relationships in the way humans do naturally. Information processing alone may be fundamentally insufficient. This places solving the "hard problem" of qualitative subjective experience as potentially the key unsolved requirement for achieving true artificial general intelligence.

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Conclusion

In putting qualia and qualitative capacities at the center of theories of comprehension, The Deep Understanding Principle represents a philosophical reconceptualization with major implications: It refutes views that have treated qualia as epiphenomenal and ultimately unimportant or eliminable from scientific explanations of cognition and behavior. It suggests that insofar as humans do have a qualitative experiential dimension, any AGI system must have analogous qualitative capacities to reach human-level understanding. It opens up new avenues of investigation into qualitative representational frameworks, semantic groundings in first-person experience, and computational models that can instantiate qualia or qualitative properties.

The Deep Understanding Principle presents a grand reframing and unifying explanatory framework - suggesting that the ability to make qualitative judgments of meaning grounded in qualitative experience is the still missing linchpin for reproducing general human-level intelligence in machines. It takes subjective qualitative properties out of the philosophical sidelines and makes them the central frontier for the next pivotal breakthrough in artificial intelligence.