Some things to know about achieving artificial general intelligence

TL;DR

Current AI models lack true general intelligence due to reliance on human-designed data and problems, and they cannot autonomously solve diverse problem types, highlighting the need for new approaches beyond existing benchmarks.

Contribution

The paper critically analyzes limitations of current AI models and evaluation methods, emphasizing the necessity for novel strategies to achieve genuine artificial general intelligence.

Findings

Current models depend heavily on human input and structured problems.

Existing benchmarks cannot reliably measure generality of solutions.

Multiple problem types require different computational approaches.

Abstract

Current and foreseeable GenAI models are not capable of achieving artificial general intelligence because they are burdened with anthropogenic debt. They depend heavily on human input to provide well-structured problems, architecture, and training data. They cast every problem as a language pattern learning problem and are thus not capable of the kind of autonomy needed to achieve artificial general intelligence. Current models succeed at their tasks because people solve most of the problems to which these models are directed, leaving only simple computations for the model to perform, such as gradient descent. Another barrier is the need to recognize that there are multiple kinds of problems, some of which cannot be solved by available computational methods (for example, "insight problems"). Current methods for evaluating models (benchmarks and tests) are not adequate to identify the generality of the solutions, because it is impossible to infer the means by which a problem was solved from the fact of its solution. A test could be passed, for example, by a test-specific or a test-general method. It is a logical fallacy (affirming the consequent) to infer a method of solution from the observation of success.