A Seed AI (a term coined by Eliezer Yudkowsky) is an Artificial General Intelligence (AGI) which improves itself by recursively rewriting its own source code without human intervention. Initially this program would likely have a minimal intelligence, but over the course of many iterations it would evolve to human-equivalent or even trans-human reasoning. The key for successful AI takeoff would lie in creating adequate starting conditions.

History

The notion of machine learning without human intervention has been around nearly as long as the computers themselves. In 1959, Arthur Samuel stated that "Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed."¹ Since that time, computers have been able to learn by a variety of methods, including neural networks and Bayesian inference.

While these approaches have enabled machines to become better at various tasks² ³, it has not enabled them to overcome the limitations of these techniques, nor has it given them the ability to understand their own programming and make improvements. Hence, they are not able to adapt to new situations without human assistance.

Properties

A Seed AI has abilities that previous approaches lack:

Understanding its own source code. It must understand the purpose, syntax and architecture of its own programming. This type of self-reflection enables the AGI to comprehend its utility and thus preserve it.
Rewriting its own source code. The AGI must be able to overhaul the very code it uses to fulfill its utility. A critical consideration is that it must remain stable under modifications, preserving its original goals.

This combination of abilities would, in theory, allow an AGI to recursively improve itself by becoming smarter within its original purpose. A Gödel machine rigorously defines a specification for such an AGI.

Development

Currently, there are no known Seed AIs in existence, but it is an active field of research. Several organizations continue to pursue this goal, such as the Singularity Institute, OpenCog, and Adaptive AI.

See Also

References

Han, Zhimeng. Smoothing in Probability Estimation Trees. The University of Waikato.
Jump up↑ Lenat, Douglas. Eurisko. A program that learns news heuristics and domain concepts.. Artificial Intelligence.
Jump up↑ Chellapilla, Ku

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