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How does Elemental Cognition’s Hybrid AI stack up?

Posted February 14, 2023

The power of adding precision and logic to Large Language Models.

The popularity of ChatGPT has brought large language models (LLMs) and generative AI into the popular vernacular. LLMs generate fluent language based on mimicking statistical word patterns found in large volumes of text.  Given a sequence of words, they determine the next most-likely word expected from a similar context. LLMs have demonstrated an uncanny ability to generate fluent summaries and are a helpful writing aid when part of a human-in-the-loop process. However, they do not search or analyze the content for specific and correct answers, nor do they logically analyze their responses as sensible or consistent. The prompt you give an LLM will strongly influence the text it generates as it is designed to generate content that structurally follows the prompt. The responses sound authoritative and persuasive, even when they are wrong in obvious or, even worse, subtle ways. Businesses can’t make decisions using an application with those limitations.  To apply this technology to business problems, it must be part of a more comprehensive approach.

Elemental Cognition’s AI has been integrating LLMs as part of its hybrid AI architecture since its inception in 2015. EC leverages LLMs for what they are good at – recognizing and generating variable linguistic forms commonly used by people – but doesn’t try to solve critical business problems with them.

Instead, Elemental combines LLMs with specific knowledge and logical reasoning. The result can empower business applications that require precision, traceability, and expert problem-solving dialogs that demand trust and transparency.

Specifically, Elemental Cognition’s AI is used in research and discovery applications and intelligence applications where accuracy, evidence, and logic are essential to produce precise, verifiable solutions. This includes creating fluent but trusted applications in financial services, insurance, investment management, healthcare, life-sciences, problem or product diagnosis, configuration, and planning. It excels in advanced search, customer experience, and complex configuration problems. Real-life examples of how EC’s Hybrid AI can tackle business problems are improving customer experience, optimizing cost and efficiency, and increasing sales conversion in complex purchasing decisions.

With great power comes great responsibility: How to (not) use LLMs for research

Recently, a NY law firm was in the news for the wrong reasons – the firm’s lawyer used ChatGPT to do legal research to find similar historical cases to support his argument and entered the AI’s responses directly in the final brief without manually cross-checking the referenced citations.

Why a reasoning engine can solve your problems

In an earlier post, we discussed the recent progress of large language models (LLMs). We discussed how they are impressively fluent and creative, but struggle with precise and correct reasoning. One might argue they are too creative: prone to coloring outside the lines.