A hot trend is to train generative AI and LLMs by using logical reasoning exhibited by other AI. This is clever and a nifty AI hack. Here's the insider scoop.
In today’s column, I identify how the latest generative AI and large language models are being cleverly data-trained on how to best make use of logical reasoning. This is a hot trend. Here’s how it goes. First, obtain logic-based reasoning traces from a more advanced AI and feed those into a developing AI. Second, the newbie AI uses pattern-matching to quickly catch onto the various logical reasoning facets and then incorporates that aspect into its go-forward processing.
The person tells you that they really like pickles. They eat pickles with just about anything and everything. Blueberries are like pickles. Therefore, they eat blueberries with their burgers.You are bound to be puzzled by this claimed-to-be stepwise explanation or logical reasoning.
This is considered an inductive form of learning, namely that by looking at lots of examples, the hope is to learn generalized precepts based on the examples. I’ve previously explained how AI is, at times, trained and leveraged via both inductive and deductive reasoning approaches Identify the facts, Use a comparison rule that compares the facts, Determine the answer based on the rule outcome. Fact 1: Sarah is 22 years old. Fact 2: The minimum required age for drinking is 21.
The AI then uses computational pattern-matching to try and gauge how those three elements are related to each other.
Generative AI Large Language Model LLM Anthropic Claude Google Gemini Meta Llama Microsoft Copilot Deepseek R1 Distillation Knowledge Sharing Logical Reasoning Inductive Deductive Learning By Examples
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