Generative AI vs AGI: Where does imitation end and consciousness begin?

Two types of intelligence, two worldviews

Generative AI

Generative AI refers to a category of artificial intelligence systems capable of producing content: text, images, audio, video, or code in response to queries formulated in natural or structured language (prompt). It is typically embodied by large-scale language models (LLM) like GPT, or by neural image generators like DALL·E.

Its principle is based on exploiting statistical structures present in immense data corpora. Through architectures like transformer networks, these models learn to predict the next unit (word, pixel, audio vector) conditionally on a context, by minimizing a loss function (generally cross-entropy) over a very high-dimensional latent space.

Although their results may seem coherent, innovative, or creative, these systems have no ontology of the real world. They have no episodic memory of their own, no deliberative reasoning, nor autonomous planning capability. Their operation remains purely correlative, based on associations between tokens, without access to the underlying causal relationships or human intentions. This results in a lack of authentic semantic understanding, making them vulnerable to logical errors, temporal inconsistencies, or factual hallucinations.

Artificial General Intelligence

On the other hand, Artificial General Intelligence (AGI) refers to a theoretical system capable of adapting its cognitive processes to any intellectual task that a human being can perform. AGI would be endowed with generalizable capabilities, allowing it to transfer its learning between heterogeneous domains, reason abstractly, acquire new knowledge in context, and formulate intentional goals in dynamic environments.

This type of intelligence would involve an integrated cognitive architecture, potentially composed of specialized modules: sensory perception, declarative and procedural memory, logical inference, attention management, emotional regulation, and decision-making. AGI would go beyond simple supervised learning to integrate mechanisms of metacognition, causal reasoning, and self-assessment. It could thus adjust its strategies based on its experience, environment, or its own mistakes.

Still hypothetical to this day, AGI would represent a paradigm shift: it would no longer emulate human responses but would manifest an operational understanding of the world, capable of interacting in a proactive, adaptive, and potentially conscious manner.

A conceptual and technical gulf

Generative AI is based on a supervised or self-supervised optimization architecture of the transformer type. The goal is to minimize a statistical loss function, often via gradient descent. These systems manipulate vector representations in very high-dimensional spaces but have no consciousness, intention, or common sense.

AGI, on the other hand, remains hypothetical. It would require a hybrid cognitive architecture integrating working memory modules, causal reasoning, reinforcement learning, integrated sensory perception, and a dynamic feedback loop on its own behavior—what is sometimes called embodied metacognition.

Comparison between Generative AI and AGI

Sources: Bengio, Y. (2023) - System 2 Deep Learning and AGI, Nature Machine Intelligence (2023), OpenAI GPT Architecture

Towards convergence or rupture?

Some voices in the scientific community envision a middle path: an AI described as "emergent AGI," resulting from the scaling up of LLMs combined with perception and action systems in an embodied approach. Other researchers argue that certain fundamental dimensions of human cognition, such as emotion, consciousness, or causal reasoning, cannot emerge from a simple statistical model. The debate remains open, but at this stage, the distinction remains clear: generative AIs function as advanced simulators, while AGI would be an autonomous cognitive entity.

Where does imitation end and consciousness begin?

A generative AI can simulate the language of consciousness, recognize that it is not conscious, or even write essays on cognitive awakening. But this does not constitute phenomenal consciousness. Consciousness, from a neurophysiological point of view, involves the temporal integration of information in a global space (neuronal global workspace theory by Dehaene), a subjective point of view, a sense of self, and an emotional evaluation of the world.

A purely computational system, no matter how vast, shows no measurable sign of "presence." It can simulate the conversation of an awake being but remains incapable of self-assessment, doubt, or sensation. The boundary between as-if cognition (as if) and real cognition might be the greatest enigma of AGI. For some researchers, this boundary will only be crossed if intelligence is embodied in a sensorimotor substrate capable of perception and subjective experience.

Risks associated with Artificial General Intelligence

The emergence of AGI, endowed with autonomous cognitive capabilities, raises major concerns in scientific, ethical, and geopolitical communities. Here is a table summarizing the main risks identified by experts.

AGI: A Civilizational Stake

Artificial General Intelligence (AGI) represents a major technological breakthrough. If it were to emerge, it could surpass human capabilities in all cognitive domains. Such an entity, endowed with autonomy, abstraction, and self-improvement faculties, raises existential questions. Its potential effects range from radical benefit (solving global problems) to the extinction of humanity. Contemporary research on AGI is therefore oriented towards two priority axes: alignment and ethical governance.

Measures to Reduce AGI Risks

Faced with the systemic risks posed by AGI, many researchers, institutions, and governments propose approaches to prevent abuses. These strategies aim to frame development, ensure alignment of objectives, and guarantee human supervision.

Policies and Global Governance in the Face of AGI Emergence

Large-scale illusion of intelligence

We live in an era where the illusion of intelligence is produced on a large scale, shaping discourses, behaviors, and societal expectations. However, one thing remains certain: generative intelligence, based on the statistical reproduction of data, is not equivalent to general intelligence, which implies a deep, adaptive, and autonomous understanding of the world.

Importance of the distinction

Understanding this distinction is not merely an academic exercise but a strategic imperative for responsibly guiding public policies, scientific research, and industrial uses of these technologies.

A global challenge

This task far exceeds national borders: it requires coordinated global cooperation, involving governments, scientific institutions, technology industries, and civil society, to define ethical norms, regulatory frameworks, and appropriate control mechanisms.

Challenges and feasibility

However, the implementation of such collective action faces major challenges: geopolitical divergences of interest, economic disparities, rapid technological evolution, and the complexity of ethical issues. Therefore, questioning the real possibility of effective global governance of AGI becomes as crucial as the development of the technologies themselves. This questioning underlies the delicate balance between innovation and prudence, freedom and security, progress and responsibility.