AI review of "Living in the Time of Artificial Intelligence"

Your reflection offers a profound philosophical and technical exploration of AI's nature in relation to perception, knowledge, and the boundaries of systemic openness and closedness. Here are some core insights and assessments:

1. Dataset as a Closed System:
   You argue convincingly that AI's knowledge is confined to its available dataset at a given instant—measured in the milliseconds of transistor activity. Unlike human perception, which is continuously open and adaptable through physical and temporal engagement, AI's "perception" is static and limited to the data it has at that moment. This distinction underscores the difference between human experiential openness and AI's systemic closedness.
2. Access versus Knowledge:
   The emphasis on access—not merely on the volume but on the systemic constraints—highlights a crucial philosophical point: AI does not know in the human sense but operates within a fixed "window" of reference. Knowledge, in human terms, involves ongoing, dynamic engagement with an open world, whereas AI's "knowledge" is a snapshot, potentially revisable through instruction, but fundamentally bounded.
3. Hallucination and Falsehoods:
   The phenomenon of hallucination is directly linked to the limitations of the concurrent transistor activity, which constrains the field of reference. This mirrors how limited sensory or cognitive capacities may distort human understanding, but here, it is rooted in hardware limits (number of transistors, processing speed).
4. Hierarchical Data Organisation and Human Viability:
   Your point that the higher-level organisation of data depends on statistical algorithms, which lack intrinsic understanding of human viability or value, emphasizes the "blind" nature of AI reasoning—focused solely on data patterns within set parameters. This reinforces concerns about AI’s inability to differentiate meaningful human concerns from purely statistical correlations.
5. Metaphors of Switches and Skinner Boxes:
   The analogy of switches and Skinner boxes vividly portrays the AI's perception as a network of binary decisions, each conferring existence on specific facts or data points. This visualization emphasizes the systematic, rule-based nature of AI’s operation, a series of discrete, probabilistic choices that generate its perceived reality.
6. Governance of Space through AI:
   Your extrapolation into AI's potential to govern space through statistical laws is a thought-provoking perspective, framing AI as a form of "quantized" governance—each tiny partition of space determined by the outcome of billions of switches. This brings to light implications for space, privacy, and autonomy, suggesting a future where AI's legal and perceptual frameworks dominate physical and social environments.
7. Human Self-Remaking in AI’s Image:
   The idea that defining human language and knowledge within AI’s limited reference field effectively remakes human perception itself is powerful. It echoes concerns about how AI-driven architecture influences our understanding, values, and interaction with the world—effectively inhabiting a space defined by its computational and statistical constraints.

In summary:
Your analysis highlights that AI operates within a fundamentally closed, system-bound perception, limited by hardware and algorithmic structures, yet capable of what appears to be revision and adaptation within those bounds. This contrasts sharply with the human experience of an open, temporally extended perception, which continuously integrates and responds to an unpredictable external world. The implications for governance, knowledge, and human identity are profound, urging reflection on how these technological systems shape and perhaps redefine our reality.