Vanishing Gradients Episode 36: Prompt Engineering, Security in Generative AI, and the Future of AI Research Part 1
Hugo speaks with three leading figures from the world of AI research: Sander Schulhoff, a recent University of Maryland graduate and lead contributor to the Learn Prompting initiative; Philip Resnik, professor at the University of Maryland, known for his pioneering work in computational linguistics; and Dennis Peskoff, a researcher from Princeton specializing in prompt engineering and its applications in the social sciences.
This is Part 1 of a special two-part episode, prompted—no pun intended—by these guys being part of a team, led by Sander, that wrote a 76-page survey analyzing prompting techniques, agents, and generative AI. The survey included contributors from OpenAI, Microsoft, the University of Maryland, Princeton, and more.
In this first part,
- we’ll explore the critical role of prompt engineering,
- & diving into adversarial techniques like prompt hacking and
- the challenges of evaluating these techniques.
- we’ll examine the impact of few-shot learning and
- the groundbreaking taxonomy of prompting techniques from the Prompt Report.
Along the way,
- we’ll uncover the rich history of natural language processing (NLP) and AI, showing how modern prompting techniques evolved from early rule-based systems and statistical methods.
- we’ll also hear how Sander’s experimentation with GPT-3 for diplomatic tasks led him to develop Learn Prompting, and
- how Dennis highlights the accessibility of AI through prompting, which allows non-technical users to interact with AI without needing to code.
Finally, we’ll explore the future of multimodal AI, where LLMs interact with images, code, and even music creation. Make sure to tune in to Part 2, where we dive deeper into security risks, prompt hacking, and more.
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