The Role of Open Source in AI Acceleration

In this episode of the Futurum Tech Webcast Interview Series, I speak with Ken Exner, the VP of Product at Elastic. We discussed Elastic's role in the open-source community and its connection to the company's wider strategy. Exner explains that Elasticsearch, an open-source search and indexing technology, gained popularity and enabled Elastic to expand into different industries. We then discussed the relationship between open source and commercial interests, highlighting the increasing use of Elasticsearch in the context of AI. Our conversation covered: The market trends and opportunities brought about by AI. The advancements in generative AI, that allow for the generation of data and content rather than just analyzing historical data. The impact of large language models, such as GPT-3 and GPT-4, in the AI space and how they have accelerated the capabilities of AI by at least a decade. How Elastic is focused on helping customers leverage generative AI in various enterprise settings, such as security, observability, and search. Elastic's new offering, aims to bring generative AI to a company's proprietary enterprise data. Elastic sits between public language models and enterprise data, bridging the gap by providing context to the models using proprietary information. The importance of relevance and context shaping the answers from the models. The role of Elastic as a ‘picks and shovels’ type company in the AI Gold Rush. How Elastic provides the foundational capabilities and building blocks for developers to create unique solutions on top of their platform. Elastic's investments in foundational capabilities, transformer model integration, relevance capabilities, and vector database technology. How Elastic has been working on search capabilities for years and is now well-positioned to facilitate the connection between public language models and enterprise data, allowing businesses to harness the power of AI in a relevant and context-aware manner.