Tesla's AI is perfectly aware if its surroundings.
It is not conscious. It is no more aware than a car alarm. That's just normal physical causality. There's no subjectivity involved.
Let's not play word games. "Consciousness" here doesn't just mean "able to sense your environment and respond".
In that case in your opinion we can't equate awareness of something other than yourself with the consciousness regarding the same thing, other than yourself.
That is correct. And this is why there's two parts to the mechanism -- QCT provides the physical end of it, and the Quantum Zeno Effect provides the other end, as described by Henry Stapp. We need both.
The Frame Problem is crucial here. There is a major difference between all AI systems ever constructed and conscious humans (and animals). The AIs cannot overcome the frame problem. This is caused by an infinitely expanding combination of possible futures -- it can't model all of them, but it has no way of deciding what is important and what isn't, and when to stop modelling and make a decision. More processing power does not help -- in fact it makes things worse, because it just increases the combinatorial explosion.
Human brains also process this sort of information, but we solve the frame problem effortlessly. We don't always make the right decision, but we don't end up paralysed in the frame problem. Why the difference?
Materialistic science has no answer. 2PC says this:
3.13 Resolving the Frame Problem through QCT and QZE
The
frame problem inartificial intelligence and cognitive science concerns the challenge of efficiently determining what information is relevant to update in response to a change in the environment. When something in the world changes, a thinking system must decide
which facts to revise and which to keep fixed without exhaustively checking all possibilities – a computationally intractable task in classical systems.
The Frame Problem: A Brief Recap
- When a robot or agent observes a change (e.g., a door opening), it must figure out which aspects of its internal model need updating.
- Over-updating leads to computational explosion, while under-updating leads to incorrect or incomplete representations.
- Humans seemingly solve this intuitively, but classical AI struggles with this problem because it lacks an efficient, principled way to focus updates.
QCT: Selecting Relevant Information
QCT posits that conscious collapse of the wave function occurs when a system’s quantum information crosses a threshold of complexity and coherence, triggering a selective, global update of its state.
- QCT provides a natural quantum boundary determining when a superpositional system must “choose” a particular outcome or representation.
- In cognitive terms, this collapse corresponds to selecting a coherent “frame” or context for updating beliefs and actions.
- The collapse effectively filters out irrelevant quantum possibilities, preserving only those consistent with the current environmental change and the agent’s prior state.
QZE: Stabilizing Relevant Frames
The QZE describes how frequent “observations” or interactions can inhibit the evolution of a quantum state, effectively
“freezing” it in place.
- When an agent focuses attention on a particular aspect of its environment, this corresponds to rapid, repeated “measurement” of relevant quantum states.
- Through the QZE, this attention stabilises the chosen frame or hypothesis, preventing unwanted or irrelevant fluctuations in the cognitive state.
- This selective stabilisation allows the cognitive system to maintain focus on relevant information, avoiding over-updating and the computational explosion characteristic of classical AI systems.
Integration: How QCT + QZE Solve the Frame Problem
Together, the QCT and QZE provide a
quantum mechanism for managing informational relevance and stability:
- QCT triggers collapse only when the system reaches a threshold of coherence that justifies an update, limiting updates to meaningful changes in the environment or internal model.
- QZE maintains stability of the chosen frame by suppressing quantum transitions to irrelevant alternatives during focused attention.
- This dynamic interplay allows a system to efficiently “prune” irrelevant information, update beliefs and actions only where necessary, and maintain coherent cognitive frames over time.
Implications for Consciousness and Artificial Intelligence
- The QCT + QZE mechanism offers a natural, physically grounded solution to the frame problem, explaining how conscious agents filter and stabilize relevant information without exhaustive computation.
- It explains why human cognition is both flexible (able to update when necessary) and stable (able to maintain focus), a balance classical AI struggles to reproduce.
- This quantum approach provides a framework for designing more efficient AI systems that mimic the attentional and selective updating capacities of biological cognition.
Summary:
The combination of QCT and QZE provides a physically principled and computationally efficient mechanism for addressing the frame problem. By selectively collapsing only relevant quantum states (QCT) and stabilizing these choices through focused observation (QZE), conscious systems can update their internal models without exhaustive reprocessing, offering a novel quantum foundation for intelligent cognition.