Emergence of Intelligence via Physical Structures

The hypothesis suggests that the emergence of intelligence is inherently possible within our physical structure and can be designed by leveraging the structural methods of Transformers, particularly their predictive capabilities. The framework posits that intelligence arises from the ability to predict and interact with the environment, using a combination of feature compression and action interference. This involves creating a continuous feature space where agents can tool-ize features, leading to the development of self-boundaries and personalized desires. The ultimate goal is to enable agents to interact with spacetime effectively, forming an internal model that aligns with the universe’s essence. This matters because it provides a theoretical foundation for developing artificial general intelligence (AGI) that can adapt to infinite tasks and environments, potentially revolutionizing how machines learn and interact with the world.

The hypothesis presented suggests that the emergence of intelligence is rooted in the physical structures and mechanisms that can be designed to mimic natural processes. This perspective challenges the current reliance on limited reinforcement learning (RL) strategies, which are deemed inadequate for handling “infinite tasks” required for artificial general intelligence (AGI). The argument is that while nature does not inhibit us from designing intelligent systems, the artificial simulation of physical spacetime in current models like Transformers may prevent true intelligence from emerging. By rethinking these systems from first principles, the hypothesis proposes a framework that aligns with the laws of thermodynamics, suggesting that intelligence can emerge from a structure that includes self-boundary, endogenous drive, and the capability to match infinite task requirements with infinite physical spacetime.

At its core, the hypothesis relies on the concept of an “Associator,” a mechanism that compresses environmental information into high-dimensional representations, making the environment predictable. This process is crucial for intelligence, as it allows an agent to make predictions about future states based on current information. The Associator’s ability to work within the constraints of time and space is highlighted, emphasizing that intelligence optimization is achieved through spatial path minimalism. This means that the agent is naturally inclined to choose the simplest path with the highest information gain, which is posited to result in a form of “pleasure” when intelligence time is optimized, and the spatial path is simplest.

The framework also introduces the concept of a “Minimum Actuator,” an executive device that allows the agent to interact with and influence its environment. This interaction is essential for the agent to dynamically expose the “resolution” of features and achieve tool-ization, which is the process of converting features into tools for further exploration and understanding. The hypothesis suggests that the actuator’s role is to provide the agent with the ability to reach all accessible states within the feature space, thereby enhancing its predictive capabilities. This dynamic interaction is posited to lead to the emergence of complex behaviors and a deeper understanding of the environment.

Ultimately, the hypothesis presents a vision of AGI as a supreme tool for human exploration and understanding, capable of reaching feature spaces beyond our current imagination. By aligning the structure of intelligent systems with the intrinsic attributes of time and space, the framework suggests that AGI can adapt to infinite tasks and drive progress in ways that current RL strategies cannot. This matters because it offers a new perspective on how intelligence can be designed and understood, potentially leading to breakthroughs in creating systems that are not only intelligent but also capable of evolving and adapting in complex environments. The implications of such advancements could be profound, impacting fields ranging from education to ethics, and reshaping our understanding of intelligence itself.

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