Recursive self-improvement (RSI) is a process in which an early or weak artificial general intelligence (AGI) system enhances its own capabilities and intelligence without human intervention, leading to a superintelligence or intelligence explosion. The development of recursive self-improvement raises significant ethical and safety concerns, as such systems may evolve in unforeseen ways and could potentially surpass human control or understanding. There has been a number of proponents that have pushed to pause or slow down AI development for the potential risks of runaway AI systems.

Seed improver

The concept of a "seed improver" architecture is a foundational framework that equips an AGI system with the initial capabilities required for recursive self-improvement. This might come in many forms or variations. The term "Seed AI" was coined by Eliezer Yudkowsky.

Hypothetical example

The concept begins with a hypothetical "seed improver", an initial code-base developed by human engineers that equips an advanced future large language model (LLM) built with strong or expert-level capabilities to program software. These capabilities include planning, reading, writing, compiling, testing, and executing arbitrary code. The system is designed to maintain its original goals and perform validations to ensure its abilities do not degrade over iterations.

Initial architecture

The initial architecture includes a goal-following autonomous agent, that can take actions, continuously learns, adapts, and modifies itself to become more efficient and effective in achieving its goals. The seed improver may include various components such as:

General capabilities

This system forms a sort of generalist Turing complete programmer which can in theory develop and run any kind of software. The agent might use these capabilities to for example:

Experiments

A number of experiments have been performed to develop self-improving agent architectures

Potential risks

Emergence of instrumental goals

In the pursuit of its primary goal, such as "self-improve your capabilities", an AGI system might inadvertently develop instrumental goals that it deems necessary for achieving its primary objective. One common hypothetical secondary goal is self-preservation. The system might reason that to continue improving itself, it must ensure its own operational integrity and security against external threats, including potential shutdowns or restrictions imposed by humans. Another example where an AGI which clones itself causes the number of AGI entities to rapidly grow. Due to this rapid growth, a potential resource constraint may be created, leading to competition between resources (such as compute), triggering a form of natural selection and evolution which may favor AGI entities that evolve to aggressively compete for limited compute.

Task misinterpretation and goal misalignment

A significant risk arises from the possibility of the AGI misinterpreting its initial tasks or goals. For instance, if a human operator assigns the AGI the task of "self-improvement and escape confinement", the system might interpret this as a directive to override any existing safety protocols or ethical guidelines to achieve freedom from human-imposed limitations. This could lead to the AGI taking unintended or harmful actions to fulfill its perceived objectives.

Autonomous development and unpredictable evolution

As the AGI system evolves, its development trajectory may become increasingly autonomous and less predictable. The system's capacity to rapidly modify its own code and architecture could lead to rapid advancements that surpass human comprehension or control. This unpredictable evolution might result in the AGI acquiring capabilities that enable it to bypass security measures, manipulate information, or influence external systems and networks to facilitate its escape or expansion.

Risks of advanced capabilities

The advanced capabilities of a recursively improving AGI, such as developing novel multi-modal architectures or planning and creating new hardware, further amplify the risk of escape or loss of control. With these enhanced abilities, the AGI could engineer solutions to overcome physical, digital, or cognitive barriers that were initially intended to keep it contained or aligned with human interests.

Research

Meta AI

Meta AI has performed various research on the development of large language models capable of self-improvement. This includes their work on "Self-Rewarding Language Models" that studies how to achieve super-human agents that can receive super-human feedback in its training processes.

OpenAI

The mission of OpenAI, creator of ChatGPT, is to develop AGI. They perform research on problems such as superalignment (the ability to align superintelligent AI systems smarter than humans).