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arXiv · 2109.12075

Towards A Measure Of General Machine Intelligence

Gautham Venkatasubramanian·Sibesh Kar·Abhimanyu Singh·Shubham Mishra·Dushyant Yadav·Shreyansh Chandak
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Abstract

To build general-purpose artificial intelligence systems that can deal with unknown variables across unknown domains, we need benchmarks that measure how well these systems perform on tasks they have never seen before. A prerequisite for this is a measure of a task's generalization difficulty, or how dissimilar it is from the system's prior knowledge and experience. If the skill of an intelligence system in a particular domain is defined as it's ability to consistently generate a set of instructions (or programs) to solve tasks in that domain, current benchmarks do not quantitatively measure the efficiency of acquiring new skills, making it possible to brute-force skill acquisition by training with unlimited amounts of data and compute power. With this in mind, we first propose a common language of instruction, a programming language that allows the expression of programs in the form of directed acyclic graphs across a wide variety of real-world domains and computing platforms. Using programs generated in this language, we demonstrate a match-based method to both score performance and calculate the generalization difficulty of any given set of tasks. We use these to define a numeric benchmark called the generalization index, or the g-index, to measure and compare the skill-acquisition efficiency of any intelligence system on a set of real-world tasks. Finally, we evaluate the suitability of some well-known models as general intelligence systems by calculating their g-index scores.

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Gautham Venkatasubramanian, Sibesh Kar, Abhimanyu Singh, Shubham Mishra, Dushyant Yadav, Shreyansh Chandak. 2021-09-24. Towards A Measure Of General Machine Intelligence. https://arxiv.org/abs/2109.12075

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