This episode dives into one of the most debated questions in AI today: Are large language models actually reasoning, or are they just incredibly sophisticated parrots?
Our discussion traces two schools of thought. On one side, new ideas like grokking and adaptive reward systems suggest that AI may soon cross the threshold into true problem-solving—discovering novel solutions it was never explicitly trained on. On the other side, researchers argue that LLMs mainly excel at pattern recognition and context-driven extrapolation, not genuine abstraction. When patterns shift or problems fall outside their training distribution, even the best models tend to break.
The episode explores the fragility of current AI reasoning and introduces a promising direction: Neuro-Symbolic AI—a hybrid approach, which combines the statistical power of LLMs with structured logical systems.
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