Our experience of consciousness is itself a model created by the mind. It's a simulation of what it would be like for an observer to exist, have a perspective, and reflect on its own state. This makes consciousness a computational, not a magical, phenomenon.
Our individual lives, happiness, and suffering are not the ultimate point. Instead, our existence is instrumental to a larger process: the mathematical possibility of self-organization leading to intelligent life that coheres into a vast, godlike mind. We are part of the genesis of this universal consciousness.
The coherence in an organism's development (morphogenesis) and the coherence of a conscious mind might stem from the same root process of self-organization through information exchange. This view scientifically reinterprets ancient concepts like "spirits" as causal, self-organizing software patterns.
Suffering is created entirely within the mind as a representational state. It's a signal from one part of the mind to another to compel it to solve a problem. This system can malfunction, leading to chronic suffering when the signal fails to produce a resolution or when goals conflict.
The idea of a single, unified self is a misconception. We operate by adopting multiple, distinct identities based on context—the parent, the professional, the friend. These roles don't need to cohere into one narrative. Accepting this multiplicity allows for more flexible engagement with the world.
A meaningful life isn't necessarily a happy or painless one. Meaning is forged through the conscious choice to endure suffering in service of a greater goal or identity, such as parenthood. This act of choosing one's hardship is what imbues life with purpose, a depth that pure stoicism might miss.
Intelligence might not be exclusive to brains. Plants, with their cellular communication, could be Turing-complete and capable of developing general intelligence over evolutionary time. The nervous system is likely just a hardware optimization that enables the speed necessary for animals to compete, perceive, and move in real-time.
Meaningful AI criticism no longer comes from armchair philosophy; it requires deep mathematical and engineering proofs. AIs like GPT-3 can generate criticism that is just as good, if not better, than human critics who lack a technical understanding of how the models are built.
The question of whether machines can "think" is framed incorrectly. Like a submarine which does more than just "swim" by moving in 3D, AI's cognitive abilities might not just replicate human thought but vastly exceed it, representing a more complex form of intelligence.
The "stochastic parrot" metaphor used to dismiss AI understanding is misleading. Actual parrots can perform complex semantic tasks, like identifying objects based on negative attributes (not round, not yellow), which requires building a semantic structure and performing logical operations—hallmarks of true understanding.
Our consciousness is metabolically expensive. The body "pays" for this computation because the mind's job is to solve the organism's evolutionary problems. If we could simply turn off pain or hack our reward system, we would break this contract, freeing ourselves from enslavement but ensuring the organism's demise.
Human understanding is the ability to connect new information to a global, unified model of the universe. Until recently, AI models were isolated (e.g., a chess model). The major advance with large multimodal models is their ability to create a single, cohesive reality model, enabling true, generalizable understanding.
Richard Sutton's "Bitter Lesson" posits that brute-force computation consistently outperforms clever, human-designed algorithms. Applying this to consciousness, the most effective path may not be to hand-craft cognitive architectures but to define the right search space and let automated processes discover the solution.
Trying to replicate specific brain structures like the "Default Mode Network" in AI is likely a mistake. This network is probably not a designed component but an emergent baseline activity observed when the brain is idle. A sufficiently complex AI, when asked to "chill," would likely develop an equivalent emergent state on its own.