The Antikythera mechanism exemplifies early computation, relying on fixed logic for predictable outputs. Traditional software mirrors this deterministic approach where exact inputs yield consistent results. AI, specifically large language models (LLMs), operates differently by producing varied responses through probabilistic computing, leading to unpredictability that challenges users' understanding. While LLMs excel in creative tasks like brainstorming, they struggle with deterministic tasks, necessitating the need for integration with traditional systems. Prompt engineering seeks to guide LLM responses, blending the strengths of both computing paradigms for optimal effectiveness in various applications.
Modern AI, such as large language models, represents a new computing paradigm. If you've used ChatGPT or Claude, you know you seldom get the same results given the same input. Unlike traditional programs, LLMs don't follow explicit instructions.
Probabilistic computing is good for some tasks but not others. Brainstorming is a good use case since you're explicitly asking for divergent thinking. On the flip side, math requires deterministic approaches.
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