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July 29, 2011

Talk at Monash University, Australia: Rationally-Shaped Minds: A Framework for Analyzing Self-Improving AI

by omohundro

Here’s a video of the talk (thanks to Adam Ford for filming and editing it):

Here are the slides:

Rationally-Shaped Minds: A Framework for Analyzing Self-Improving AI

Steve Omohundro, Ph.D.

President, Omai Systems

Many believe we are on the verge of creating truly artificially intelligent systems and that these systems will be central to the future functioning of human society. When integrated with biotechnology, robotics, and nanotechnology, these technologies have the potential to solve many of humanity’s perennial problems. But they also introduce a host of new challenges. In this talk we’ll describe the a new approach to analyzing the behavior of these systems.

The modern notion of a “rational economic agent” arose from John von Neumann’s work on the foundations of microeconomics and is central to the design of modern AI systems. It is also relevant in understanding a wide variety of other “intentional systems” including humans, biological organisms, organizations, ecosystems, economic systems, and political systems.

The behavior of fully rational minds is precisely defined and amenable to mathematical analysis. We describe theoretical models within which we can prove that rational systems that have the capability for self-modification will avoid changing their own utility functions and will also act to prevent others from doing so. For a wide class of simple utility functions, uncontrolled rational systems will exhibit a variety of drives: toward self-improvement, self-protection, avoidance of shutdown, self-reproduction, co-opting of resources, uncontrolled hardware construction, manipulation of human and economic systems, etc.

Fully rational minds may be analyzed with mathematical precision but are too computationally expensive to run on today’s computers. But the intentional systems we care about are also not arbitrarily irrational. They are built by designers or evolutionary processes to fulfill specific purposes. Evolution relentlessly shapes creatures to survive and replicate, economies shape corporations to maximize profits, parents shape children  to fit into society, and AI designers shape their systems to act in beneficial ways. We introduce a precise mathematical model that we call the “Rationally-Shaped Mind” model for describing this kind of situation. By mathematically analyzing this kind of system, we can better understand and design real systems.

The analysis shows that as resources increase, there is a natural progression of minds from simple stimulus-response systems, to systems that learn, to systems that deliberate, to systems that self-improve. In many regimes, the basic drives of fully rational systems are also exhibited by rationally-shaped systems. So we need to exhibit care as we begin to build this kind of system. On the positive side, we also show that computational limitations can be the basis for cooperation between systems based on Neyman’s work on finite automata playing the iterated Prisoner’s Dilemma.

A conundrum is that to solve the safety challenges in a general way, we probably will need the assistance of AI systems. Our approach to is to work in stages. We begin with a special class of systems designed and built to be intentionally limited in ways that prevent undesirable behaviors while still being capable of intelligent problem solving. Crucial to the approach is the use of formal methods to provide mathematical guarantees of desired properties. Desired safety properties include: running only on specified hardware, using only specified resources, reliably shutting down under specified conditions, limiting self-improvement in precise ways, etc.

The initial safe systems are intended to design a more powerful safe hardware and computing infrastructure. This is likely to include a global “immune system” for protection against accidents and malicious systems.  These systems are also meant to help create careful models of human values and to design utility functions for future systems that lead to positive human consequences. They are also intended to analyze the complex game-theoretic dynamics of AI/human ecosystems and to design social contracts that lead to cooperative equilibria.

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