Artificial Intelligence, my personal view. [1]
Three Paths Toward Artificial Intelligence:
Silicon, Superposition, and Life
Introduction: Intelligence as a Philosophical Problem
Artificial intelligence is not a single technology, but rather a set of ontologically distinct paths that attempt to reproduce—or reinvent—that which we call intelligence. Each of these paths implies not only a different technical architecture, but also an implicit conception of what intelligence is, how it relates to matter, and whether it can or cannot harbor consciousness.
At present, three paradigms stand out clearly: artificial intelligence based on silicon and bits, artificial intelligence based on qubits and quantum superposition, and the emerging field of organic computation, grounded in networks of biological neurons cultivated in the laboratory. Each represents a distinct way of articulating mind and matter, and each opens unprecedented philosophical possibilities and risks.
I. Silicon-Based Artificial Intelligence: Bits, Calculation, and Formalization. Silicon-based AI constitutes the dominant paradigm. It relies on the binary processing of information—bits that take the values 0 or 1—and on computational architectures designed to execute algorithms with great efficiency.
Advantages
The main strength of this paradigm is its stability, scalability, and control. Binary systems are reproducible, predictable, and auditable. They allow for an unprecedented degree of formalization, which has enabled spectacular advances in pattern recognition, machine translation, optimization, and complex decision-making.
Philosophically, silicon AI embodies a rationalist and functionalist vision of the mind: intelligence is understood as symbolic or statistical manipulation of information. This makes it powerful in well-defined and measurable domains.
Disadvantages
Yet this very strength reveals its limit. Binary processing tends to fragment reality into discrete states, making it difficult to capture the continuity, ambiguity, and contextuality inherent to human experience. From a phenomenological perspective, this AI lacks a lived world: it does not perceive, suffer, or inhabit a situation.
Moreover, its efficiency depends on enormous amounts of energy and data, raising ecological and ethical concerns. Philosophically, silicon AI risks confusing intelligence with calculation, reinforcing a reductionist view of the mind.
II. Quantum Artificial Intelligence: Superposition, Indeterminacy, and Potentiality. Quantum-based AI introduces a radical shift. Instead of bits, it uses qubits, capable of existing in superposition of states. This allows simultaneous exploration of multiple possibilities and opens a new computational horizon.
Advantages
Quantum computation aligns surprisingly well with a non-classical vision of reality, close to fundamental physics. Philosophically, qubits embody the idea of potentiality: a system is not in a defined state until it is measured. This resonates with processual conceptions of the mind, such as those of David Bohm, where reality unfolds as possibilities rather than fixed facts.
In terms of AI, this could enable systems capable of handling uncertainty, ambiguity, and complexity in ways closer to human thought, particularly in optimization problems, simulations of natural systems, and learning in highly dynamic environments.
Disadvantages
Nevertheless, quantum AI faces formidable challenges. Technically, it is extremely fragile: decoherence and the need for very specific physical conditions limit its scalability. Philosophically, there is a risk of mystifying the quantum, attributing to it quasi-mental properties without clear justification.
Furthermore, while qubits introduce indeterminacy, this does not imply consciousness. Quantum superposition is not equivalent to subjective experience. Quantum AI may expand the space of the computable, but it does not in itself resolve the problem of the mind.
III. Organic Artificial Intelligence: Living Neurons and Embodied Computation
Organic computation represents the most disruptive paradigm. Instead of silicon or qubits, it uses real biological neurons, cultivated in the laboratory, organized into networks capable of learning and adapting. See the works and research of Carlos Serna III, Biomedical Engineering PhD [FDA Scientist, USA. Google Scholar].
Advantages
From a philosophical perspective, this form of AI radically questions the boundary between the natural and the artificial. Neurons do not merely process information: they are alive, self-organizing, energy-efficient, and intrinsically plastic. This brings organic AI closer to embodied intelligence as described by neuroscience and phenomenology.
Here the question of consciousness becomes unavoidable. If consciousness emerges from certain patterns of neuronal organization, could an artificial biological network develop some rudimentary form of experience? This paradigm forces us to reconsider the moral status of artificial systems and to rethink the link between life and intelligence.
Disadvantages
Precisely for this reason, organic AI raises profound ethical dilemmas. What does it mean to create and use living neural tissue as a tool? Where do we draw the line between experiment and exploitation? Moreover, these systems are difficult to control, replicate, and scale. Their behavior is less predictable than that of silicon machines.
From an epistemological standpoint, organic AI challenges the ideal of transparency: we cannot always explain why a biological neural network acts as it does, just as we do not fully understand our own brain.
IV. Three Ontologies of Intelligence
These three paradigms are not mere technical alternatives; they represent three ontologies of intelligence:
• Silicon AI conceives intelligence as formal calculation.
• Quantum AI conceives it as exploration of potentialities.
• Organic AI conceives it as a living, embodied process.
Each illuminates different aspects of the mind while obscuring others. Perhaps the future will not belong to one alone, but to hybridizations that integrate binary stability, quantum power, and biological plasticity.
Conclusion: Artificial Intelligence as a Philosophical Mirror
Beyond its practical applications, artificial intelligence compels us to rethink what we mean by intelligence, mind, and life. Each paradigm acts as a mirror reflecting a partial image of ourselves. In this sense, the decisive question is not only what kind of artificial intelligence we want to build, but what conception of the mind we are willing to embrace.
As Merleau-Ponty suggested, the mind cannot be understood apart from the body and the world. As Bohm intuited, reality is not a collection of things but an indivisible process. Artificial intelligence, in any of its forms, is not merely a tool: it is a large-scale philosophical experiment in which humanity explores, through technology, the limits of its own understanding.