Approaches to Artificial General Intelligence: An Analysis (2202.03153v1)

Abstract: This paper is an analysis of the different methods proposed to achieve AGI, including Human Brain Emulation, AIXI and Integrated Cognitive Architecture. First, the definition of AGI as used in this paper has been defined, and its requirements have been stated. For each proposed method mentioned, the method in question was summarized and its key processes were detailed, showcasing how it functioned. Then, each method listed was analyzed, taking various factors into consideration, such as technological requirements, computational ability, and adequacy to the requirements. It was concluded that while there are various methods to achieve AGI that could work, such as Human Brain Emulation and Integrated Cognitive Architectures, the most promising method to achieve AGI is Integrated Cognitive Architectures. This is because Human Brain Emulation was found to require scanning technologies that will most likely not be available until the 2030s, making it unlikely to be created before then. Moreover, Integrated Cognitive Architectures has reduced computational requirements and a suitable functionality for General Intelligence, making it the most likely way to achieve AGI.

An Analysis of Approaches to Artificial General Intelligence

The paper "Approaches to Artificial General Intelligence: An Analysis" by Soumil Rathi provides a comprehensive evaluation of various methodologies aimed at achieving AGI. AGI is here defined as an artificial system capable of performing any cognitive task that a human can do, encompassing processes like perception, motor control, memory, and decision-making. Three primary methodologies are examined in the paper: Human Brain Emulation, Algorithmic Probability from which AIXI is derived, and Integrated Cognitive Architecture.

The author begins by outlining the crucial requirements of AGI, emphasizing goal-oriented behavior and the necessity for human-like cognitive functions, while potentially eschewing undesirable human traits such as emotional biases. Through this conceptual lens, the paper explores the intricate details of each approach.

Human Brain Emulation

Human Brain Emulation involves simulating the human brain's functional characteristics in a computational system. The paper heavily references Ray Kurzweil's predictions regarding achievable computational power and the necessary advancements in brain scanning technologies, such as the development of nanobots for real-time brain imaging. Despite the sufficiency of current supercomputing capabilities, practical applications are hindered by the unavailability of adequate in vivo scanning technology. Therefore, while theoretically sound, this approach faces significant practical challenges due to technological bottlenecks anticipated to persist until at least the 2030s.

Algorithmic Probability and AIXI

The paper examines Algorithmic Probability and the AIXI model proposed by Marcus Hutter as theoretical constructs for AGI. These rely on Solomonoff's induction and Bayesian principles to predict environments and optimize actions to maximize expected rewards. AIXI, although theoretically perfect in decision-making under given scenarios, suffers from uncomputability due to the need for evaluating infinite potential environments and its reliance on non-terminating calculations. The paper discusses approximations like AIXItl and UCAI but highlights their computational infeasibility in practical domains, casting doubt on their utility for achieving AGI in the near term.

Integrated Cognitive Architecture

The third and most promising methodology examined is Integrated Cognitive Architecture, exemplified by Ben Goertzel’s CogPrime. This hybrid architecture combines symbolic and subsymbolic processes, necessitating cognitive synergy to effectively mimic human cognitive tasks. CogPrime is designed to perform complex analytical and synthetic tasks across varying contexts, leveraging probabilistic logic networks and procedural selection algorithms like MOSES for effective learning and decision-making. Unlike AIXI, CogPrime is computationally feasible with current technology, making it a promising direction for AGI.

Conclusion and Implications

In conclusion, the paper argues that while each method has its theoretical merits and drawbacks, Integrated Cognitive Architectures possess the most viable path towards AGI given current technological constraints. The ability to efficiently simulate cognitive processes without exorbitant computational demands presents a pragmatic approach. This analysis reveals the intricacies involved in the quest for AGI, urging ongoing research in cognitive synergy and technology development. Future advancements may yet enable the realization of AGI through these or other novel methodologies, potentially redefining the landscape of artificial intelligence.