AI Leaders Podcast #55: From Productivity to Creativity, The Real Value of Generative AI Pt.2
Jan 22, 2024
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Guests Teresa Tung, May Habib, and Marc Appel discuss the impact of Generative AI from a trust perspective. They explore the importance of security, accuracy, legality, and transparency in generative AI platforms. They also delve into the challenges and opportunities in the field, including the use of semantic layer and domain knowledge graph for increased accuracy and contextualization. The chapter highlights the significance of trust in AI and suggests potential future implementation of AI guardrails and certifications.
Enterprise-ready generative AI should prioritize security, accuracy, legality, and transparency.
Implementing generative AI responsibly requires careful consideration of data security and integrity.
Deep dives
Attributes of Enterprise-Ready Generative AI
Enterprise-ready generative AI should prioritize security, accuracy, legality, and transparency. Security involves ensuring that user data remains within the environment and complies with privacy and regulatory frameworks. Accuracy is essential to generate high-quality results that surpass human capabilities. Legal considerations are crucial due to increasing scrutiny on the training data used for generative AI, requiring compliance with international regulatory frameworks. Transparency, particularly in code, training data, and model weights, is vital for collaboration and building powerful, useful solutions.
Implementing Generative AI Responsibly
Implementing generative AI responsibly requires careful consideration of data security and integrity. For low-risk use cases, a focused approach that starts with net-new data can be suitable, gradually expanding to more sensitive or confidential information. The importance of establishing a data governance structure, involving data librarians to ensure data integrity and compliance, becomes critical. While generative AI certifications are not prevalent yet, companies must proactively stay ahead of malicious actors and evolving legal and regulatory frameworks, adhering to compliance requirements specific to individual industry verticals.
Enhancing Retrieval-Augmented Generation (RAG) with a Full Stack Approach
In optimizing retrieval augmented generation (RAG), a graph-based approach is employed to enhance accuracy and contextualization of enterprise data. By fine-tuning models to create relationships between entities and using compression and fusion techniques, RAG results can be improved. Writer provides integrated tools, enabling the reuse and customization of RAG models for building knowledge retrieval use cases. An upcoming API will further facilitate accessibility to the graph-based RAG feature, empowering organizations to augment their applications and leverage their own first-party data more effectively.
Using semantic layer and domain knowledge graph for increased accuracy and contextualization, and the importance of playtime moments in embracing generative AI
In this second episode about Generative AI and its influence in growth acceleration, Teresa Tung, May Habib, and Marc Appel exchange views about the impact of Generative AI from a trust perspective. They explore the implications of technology being accurate, legal, and transparent as part of a trustworthy reinvention of businesses and their processes.
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