October 2024
What if our understanding of artificial intelligence and intelligence itself is fundamentally incomplete? Let's begin with a foundational idea: Large Language Models (LLMs) are complex systems that learn patterns from vast amounts of data. They map inputs to outputs based on learned representations of language. You might say, "But these AI models seem to reason, create, and understand!" Let's explore how patterns, time, and experience contribute to intelligence, and reconsider our assumptions about artificial and biological cognition.
Pattern recognition is a crucial cognitive process in both human and artificial intelligence. In psychology and cognitive neuroscience, pattern recognition is defined as a cognitive process that matches information from a stimulus with information retrieved from memory. This process is fundamental to how humans and animals perceive and interact with their environment.
In theory, a universal lookup table could represent the behavior of any program by listing all possible input combinations and their corresponding outputs.
This concept illustrates that at a fundamental level, computations are about mapping inputs to outputs based on defined rules (patterns).
A lookup table, fundamentally, maps patterns to patterns. As these relationships grow more intricate, we witness the emergence of patterns that detect, transform, and generate other patterns, each building upon simpler foundations. This concept is not just theoretical; it's how current AI systems function. They encode vast hierarchies of patterns that relate to other patterns. Capabilities such as reasoning and creativity emerge from these complex pattern relationships.
It's difficult to establish clear, definitive criteria for what counts as AGI through observable behavior alone. Even if we see what appears to be intelligent behavior, we can't be certain it represents true intelligence rather than complex pattern matching or programming. Any behavior or response pattern could be replicated by a sufficiently complex system.
If artificial systems can replicate intelligent-seeming behaviors, how do we define what "real" intelligence is? Consider the Chinese Room thought experiment - if a system can perfectly mimic intelligent behavior, does it matter whether it has "true" understanding or consciousness? Either our usual ways of identifying intelligence by looking at behavior are inadequate for determining whether we've achieved AGI, or intelligence itself is not as special or unique as we might think.
However, there are fundamental differences between current AI systems and biological intelligence:
This suggests that intelligence may involve more than just complex patterns; it may require patterns that:
Current AI systems, no matter how advanced, are like snapshots of understanding. They lack the continuous, adaptive quality of biological intelligence.
This perspective invites us to reconsider our approach to artificial intelligence. Intelligence might be better understood as:
Advancing AI may not be solely about creating larger models or more complex pattern hierarchies. It might involve developing systems that can engage dynamically with the flow of time, adapt through continuous experience, and exhibit the unfolding nature of biological intelligence.
The future of AI could belong to systems that not only recognize patterns but also live and evolve within the temporal flow, much like living organisms.