Gabriele Venturi is founder of PandasAI, a Python library that adds Generative AI capabilities to pandas, the popular data analysis and manipulation tool. In this conversation, we dive deep into various topics related to AI, including the impact of AI on data analysis, the democratization of data analysis, the role of open source vs. closed source in AI, and the business model and sustainability of open source AI. We explore the future of AI interaction and interfaces, the impact of AI on jobs, and the defensibility of AI companies. Additionally, we discuss the future of machine learning professionals and the advancements in copilot technology. [The episode was recorded in October 2023] Takeaways - AI is transforming data analysis by democratizing access to data and providing new ways to interact with and analyze data. - Open source AI is becoming increasingly popular and is challenging closed source AI by providing more customization and flexibility. - The future of AI interfaces will likely involve a combination of conversational and structured data interactions. - AI has the potential to disrupt certain jobs, but it also creates new job opportunities and enhances productivity in other areas. - The defensibility of AI companies lies in building meaningful products and focusing on customer needs rather than just relying on the technology itself. - The future of machine learning professionals will involve a deeper understanding of AI technology, including the ability to build models from scratch and ensure explainability and determinism. - AI copilots have the potential to greatly enhance productivity and assist in various tasks, but their effectiveness will depend on integration and context-specific applications. - The future of AI will involve advancements in explainability and determinism, addressing the black box nature of AI models and ensuring consistent and coherent outputs. Language models can have biases and preferences towards certain options, which can be a challenge to address. - Unlearning and removing data from AI models is an emerging area of research, with applications in correcting incorrect training data and removing biases.