KNOWLEDGE-DRIVEN AUTOMATION PLATFORM TO CONNECT, CONTEXTUALIZE, AND CONTROL ARTIFICIAL INTELLIGENCE TECHNOLOGIES INCLUDING GENERATIVE AI REPRESENTING A PRACTICAL IMPLEMENTATION OF NEURO-SYMBOLIC AI

Smart overview of the Invention

The patent application introduces a knowledge-driven automation platform that integrates various artificial intelligence (AI) technologies, including generative AI, to achieve a practical implementation of Neuro-Symbolic AI. This approach aims to enhance interactions with AI by providing context, thereby improving the quality of AI outputs and the effectiveness of the automation platform's actions. The integration addresses some of the limitations of current AI systems, particularly large language models (LLMs), which lack contextual understanding and reasoning capabilities.

Background

Generative AI has become a significant trend in the software and IT industry, offering new possibilities for user experiences through interactive and contextual natural language interfaces. However, LLMs, which underpin generative AI, operate on statistical probabilities without true understanding or reasoning. This limitation has led to the development of various techniques to improve LLM outputs, although these methods often involve manual intervention and do not change the probabilistic nature of LLMs.

Challenges with Current Technologies

Current generative AI technologies face several challenges, including inaccuracy, inconsistency, bias, and lack of explainability. These issues are compounded by the probabilistic nature of LLMs, which do not guarantee consistent outputs for given inputs. Despite these challenges, there is a global competition to dominate AI-enabled software markets, with both large vendors and new companies seeking to innovate in this space.

Use Cases and Limitations

Generative AI is particularly adept at tasks like summarizing unstructured information and generating draft documents or code. However, its efficacy diminishes with tasks requiring deep domain knowledge or accuracy. Code generation tools are often used as development assistants but require significant human oversight due to potential defects and safety concerns. The complexity of integrating various tools to improve AI outputs further complicates the landscape.

Emerging Techniques and Industry Trends

Techniques like Retrieval Augmented Generation (RAG) aim to improve LLM outputs by providing external context from databases or documents. These techniques involve complex tool-chains and workflows that remain labor-intensive and specialized. Despite these challenges, the industry is focused on leveraging hybrid-AI solutions combining generative AI with other AI methods to enhance business productivity through real-time contextual automation.