Introduction
Can artificial intelligence ever become equivalent to the human mind? This article analyzes the nature of cognition, moving beyond the technical parameters of AGI. It argues that intelligence is not merely a set of computations, but rather the result of embodied experience.
The reader will discover why simply increasing computing power will not create a conscious entity. They will be introduced to the hybrid concept of Humanity, in which the boundary between the biological brain and technology blurs, redefining our agency.
Intelligence is More Than Computation
Increasing competencies measured by benchmarks does not automatically lead to the emergence of human-like cognition. Biological intelligence requires embodiment, biographical memory, and the ability to bear the consequences of one's own actions.
A machine may surpass a human in specific tasks, but it does not become a cognitive system. A prime example is the lack of a relationship with a body and environment; AI does not experience hunger or pain, which for humans are key signals that imbue information with meaning.
True cognition therefore requires a shift from the axis of capability to the axis of experience. Without biological rooting, a system remains merely an advanced calculator of representations.
The Trap of Equating Cognition with Computation
The belief that intelligence can be reduced to measurable results stems from the history of science. First, behaviorism focused on observable reactions while ignoring the inner workings of the mind. Later, the cognitive revolution introduced the metaphor of the computer as a system operating on symbols.
This approach created the illusion that since thought functions can be described abstractly, the biological brain is redundant. Consequently, it was assumed that cognition in its fundamental sense is computation, rather than merely being modelable by it.
This is an ontological error. An effective mathematical model of memory does not mean that memory itself is an algorithm. Modeling processes as computations overlooks the fact that the human mind is inextricably coupled with the organism and culture.
Embodied Cognition as the Foundation of Knowledge
Data processing in AI is not equivalent to intelligence because it lacks embodied cognition. In biology, the body is not just a casing, but a constitutive element of thinking. Perception and action form a loop that co-creates meaning.
AI differs from us in how it predicts. Within the framework of predictive processing, the human brain corrects models of the world to avoid real suffering or death. A generative model, by contrast, operates on statistical probability without an internal sense of risk.
Modern AI is ceasing to be merely a tool for cognitive offloading. By becoming an active component of cognitive architecture, it begins to co-shape our intentions and filter what we consider relevant before we make a conscious decision.
Summary
Intelligence in the era of AI should be defined as a hybrid system of humans, tools, and rules. We must stop asking whether a machine thinks like a human and begin investigating the architecture of the coupling between biology and technology.
Perhaps AGI is not a technical goal, but a mirror reflecting our ignorance regarding the nature of experience. The greatest challenge is not a machine rebellion, but a silent drift of agency, in which we cease to be the sole authors of our own decisions.
👤 About the book's author
Rana Gujral
Behavioral Signals
Rana Gujral (born 1976) is an American entrepreneur, executive, and technologist specializing in artificial intelligence, cognitive computing, and affective voice analytics. He served as the Chief Executive Officer of Behavioral Signals, an enterprise technology company recognized for developing cognitive AI systems that infer intent, emotional cues, and behavioral risk from acoustic voice patterns. Prior to this, Gujral founded TiZE, a cloud software enterprise that was subsequently acquired by Alchemy, and held key leadership roles at consumer technology companies including Logitech and Cricut. His contributions center on commercializing speech signal processing, emotion recognition engines, and exploring the interface between human cognition and machine intelligence. Through his work and research, Gujral introduced frameworks such as Artificial General Experience, examining how shared hybrid systems, intuitive cognition, and continuous experiential learning impact human judgment, technology governance, and future decision-making architectures.
Frequently Asked Questions
Why will simply increasing AI's computational capabilities not make it intelligent in the human sense?
Simply increasing computing power is not enough because human intelligence does not consist solely of solving tasks or recognizing patterns, but results from embodiment and participation in the world. True cognition requires having a body, needs, biographical memory, and the ability to bear the consequences of one's own actions within an environment.
Where did the belief come from that intelligence can be reduced to measurable results and computational operations?
This belief stems from behaviorism, which postulated that science should be based on measurable relationships and observable behavior. Subsequently, the cognitive revolution introduced the language of information and computation, and the computer metaphor allowed the mind to be treated as a system performing formal operations independently of its physical substrate.
Why is modeling thought processes as computations alone insufficient for a full understanding of intelligence?
Computational modeling alone does not account for the role of the physical body and its interaction with the environment, which contribute elements to cognition that cannot be described by the brain alone. Intelligence is embedded in an organism regulating its own states, where biological factors, emotions, and social context actively shape the way information is noticed and evaluated.
Why is data processing in AI not equivalent to true experience and intelligence?
Data processing is not equivalent to intelligence and experience because it lacks mechanisms for valuing world states from the perspective of one's own continuity and the biological loop of brain-body-action-environment. In AI, parameter updates are merely a functional equivalent of part of the learning process and do not generate biographical continuity, a first-person perspective, or the phenomenal experiencing of events.
In what way does AI cease to be just a tool, and how does its method of 'prediction' differ from the human cognitive process?
AI ceases to be just a tool when, through integration with user data, it co-shapes inputs and participates in the formation of will, becoming part of the loop preceding conscious choice. Although both systems can be described in the language of probabilistic expectations, human prediction occurs within an organism exposed to real biological and social costs, whereas a generative model bears the cost of error only within the scope defined by its designers.
How does contemporary artificial intelligence differ from previous tools that supported thinking, and how does it change our agency?
Contemporary AI differs from previous tools in that it does not merely store data, but actively processes, generates, and reorganizes cognitive material. It changes our agency through so-called 'agency drift'—the gradual handing over of increasingly early stages of decision-making to the system, which often occurs before a human's conscious reflection.
How should we define intelligence in the era of AI to avoid the trap of comparing computational outputs with human thinking?
Both anthropocentrism and technocentrism should be rejected; instead, cognition should be analyzed by breaking it down into individual layers (e.g., perception, memory, or emotions). This allows for a precise determination of which functions AI actually performs and which it merely imitates, while simultaneously examining the coupling architecture between biological and artificial cognition.
