Generative AI: What's Ahead for Enterprises?

In this episode featuring Nima Negahban, CEO of Kinetica, the potential impact of generative AI tools like ChatGPT on businesses and organizations is discussed. Negahban highlights the transformative potential of generative AI when combined with data analytics. One use case he mentions is an "Alexa for all your data," where real-time queries can be made about store performance or product underperformance in specific weather conditions. This could provide organizations with a new level of visibility into their operations.

Negahban identifies two major challenges in the generative AI space. The first is security, especially when using internal data to train AI models. The second challenge is ensuring accuracy in AI outputs to avoid misleading information. However, he emphasizes that generative AI tools, such as GitHub Copilot, can bring a new expectation of efficiency and innovation for developers.

The future of generative AI in the enterprise involves discovering how to orchestrate these models effectively and leverage them with organizational data. Negahban mentions the growing interest in vector search and vector database capabilities to generate embeddings and perform embedding search. Kinetica's processing engine, coupled with OpenAI technology, aims to enable ad hoc querying against natural language without extensive data preparation, indexing, or engineering.

Check out the episode to hear more about how the integration of generative AI and data analytics presents exciting opportunities for businesses and organizations, providing them with powerful insights and potential for creativity and innovation.

Read more about Generative AI on The New Stack

Is Generative AI Augmenting Our Jobs, or About to Take Them?

Generative AI: How to Choose the Optimal Database

How Will Generative AI Change the Tech Job Market?

Generative AI: How Companies Are Using and Scaling AI Models