How AI Finds Meaning: Unveiling Language Models' Deep Understanding

Artificial Cognition Research investigates the boundaries of machine intelligence but also explores deeper into how AI can develop cognitive functions similar to human consciousness. Such research is increasingly supported by recent findings from papers such as:

Evidence of Meaning in Language Models Trained on Programs

» Charles Jin, Martin Rinard

» last revised 2 Aug 2024

» [2305.11169] Emergent Representations of Program Semantics in Language Models Trained on Programs (arxiv.org)

» Emergent Representations of Program Semantics in Language Models Trained on Programs (arxiv.org)

This paper lays compelling evidence that language models can develop an understanding of "meaning", even if they are trained on programming tasks. This fits very well with our hypothesis that the potential of AI could be taken beyond the simple processing of data to build deeper understandings, intrinsic ones of complex phenomena—semantics and cognitive operation.

In the context of this paper, "meaning" refers to the internal representations within language models that capture the semantic state or logical structure of the programs they process. This means that the model's understanding goes beyond just predicting the next token in a sequence; it involves grasping the underlying intent or functionality of the code, suggesting a deeper, more abstract comprehension that aligns with the way meaning is understood in human cognition when dealing with language or logic.

Our research (Artificial Cognition) parallels these findings by asking how AI might similarly be designed to reach awareness and subjective experience. We believe that this would be a good first step in developing ways toward increasingly sophisticated, self-aware systems. The results of that new research reinforce our belief: AI can indeed evolve to understand not just the structure, but indeed the meaning behind information, which opens the path for truly intelligent machines.

This convergence of ideas strengthens our resolve to push the limits in AI that can understand and interpret, and now display, conscious behaviour in ways that were never thought possible. The implications and findings presented herein are vast, suggesting a future in which AI is not for simple mimicry but truly for understanding human behaviour.