Open questions for AI engineering

The article is a transcript of a keynote speech given by the author at the AI Engineer Summit in San Francisco on October 17, 2023. The author discusses the evolution of AI engineering over the past year, highlighting the emergence of AI engineers and the rapid advancements in the field. He shares his experiences with using AI models, particularly large language models (LLMs), to automate tasks and build applications. He also raises several open questions about the future of AI engineering, including the potential for prompt injection attacks, the possibility of training AI models on public domain data, and the impact of AI on new programmers.

The author emphasizes the transformative potential of AI, particularly in leveling the learning curve for new programmers and enabling people to automate tedious tasks. He also discusses his own projects, such as the LLM command-line tool and the ChatGPT Code Interpreter. Despite the challenges and ethical considerations, the author expresses optimism about the future of AI engineering and encourages the audience to consider how they can contribute to making automation accessible to as many people as possible.

Key takeaways:

Simon Willison, the speaker at the AI Engineer Summit, discussed the evolution and potential of AI engineering, focusing on the use of large language models (LLMs).
He highlighted the importance of understanding prompt injection, a security issue in AI applications, and the need for robust solutions to prevent vulnerable systems.
Willison shared his experience with ChatGPT Code Interpreter, a tool that allows for programming in various languages, and its potential for flattening the learning curve for new programmers.
He concluded by emphasizing the potential of AI in automating tedious tasks and the need to make this capability accessible to as many people as possible.

Open questions for AI engineering

Key takeaways:

Comments (0)

Newsletter