why pattern recognition is not considered true intelligence?
- True intelligence is defined by the ability to handle novelty – to deal with situations never encountered before and to spontaneously create new, relevant models on the fly.
- Current Large Language Models (LLMs) are fundamentally interpolative databases or “approximate retrieval systems,” primarily engaging in memorization and interpolation of complex “vector functions” or “program templates.”
- Their generalization power is “very weak,” only adapting to things “very, very close to things it has actually seen before.” This means LLMs fail significantly at problems different from their training data.
- Benchmarks like the Abstraction and Reasoning Corpus (ARC) are designed to test adaptation to novelty and resist memorization, and state-of-the-art LLMs perform poorly on them.
- The idea that “scale is all you need” for AI progress is flawed because most benchmarks measure memorization and preparation, not intelligence. Scaling compute primarily allows LLMs to memorize more, improving performance in a “memory game” which is “orthogonal to intelligence.”
- A major bottleneck for LLMs is their lack of continual learning and high-level feedback loops. Unlike humans who constantly build context and learn from failures, LLMs are largely “out-of-the-box” and cannot truly learn “on the job.”
- Any subtle understanding of preferences or style developed during an LLM session is completely lost by the end of the session, preventing them from operating as reliable, persistent “employees.”
- While LLMs excel at “System 1” (fast, intuitive pattern recognition), they struggle with “System 2” (deliberate, slow, introspectible reasoning), which involves step-by-step, verifiable logical operations.
- Deep learning models are inherently “recognition engines” and are fundamentally limited in learning generalizable discrete programs. They embed “programs” as “vector functions on a continuous curve,” which is a poor substrate for discrete computation.
- This is why LLMs, even after millions of examples, struggle immensely with algorithmic tasks like sorting a list or adding digits, often achieving low accuracy on new inputs.
- LLM outputs are frequently “directionally accurate but not actually accurate,” requiring “post-hoc verification” by a human or an external symbolic system, confirming their role as suggestion engines rather than self-correcting agents.
- The “Kaleidoscope hypothesis” suggests that intelligence actively mines experience to identify reusable “atoms of meaning” (abstractions) and then synthesizes these to make sense of novel situations, a dual process critical for adapting to true novelty that LLMs currently lack.