Dr. Lance B. Eliot is a world-renowned expert on Artificial Intelligence (AI) with over 7.8+ million amassed views of his AI columns and been featured on CBS 60 Minutes. As a CIO/CTO seasoned executive and high-tech entrepreneur, he combines practical industry experience with deep academic research.
I am going to walk you through some advanced AI magic and bring you up-to-speed on state-of-the-art boundary-pushing when it comes to generative AI. It is a bit of a mystery story too.
Knowing how to develop generative AI to produce multiple chain-of-thought instances when solving a stated problem or answering a question is relatively straightforward and can be done without too much rocking of the boat. It’s not a piece of cake, but it is a known thing. That’s the good news. The gist is that there are lots of ways to devise meta-reasoning. Some are good, some are bad, and some are in-between. Active research is underway on finding the best ways to perform meta-reasoning in conjunction with a multiple chain-of-thought approach.
“Furthermore, research has shown that sampling multiple chains of thought and returning their majority output further improves accuracy, a method which they term self-consistency .” The more promising approach is process-based meta-reasoning, entailing examining each of the steps of each of the chains. This is much more complicated. The belief and hope are that the results will be much more stellar and worth the added computational and algorithmic effort accordingly.
Generative AI Genai Large Language Models LLM Openai Chatgpt GPT-4O O1 Meta-Reasoning Chain-Of-Thought Cot Speculation Outcome-Based Process-Based Thinking
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