Vanishing Gradients - Episodes Tagged with “Nlp”

Episode 37: Prompt Engineering, Security in Generative AI, and the Future of AI Research Part 2

Hugo Bowne-Anderson — Tue, 08 Oct 2024 17:00:00 +1100

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 2 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 episode, we cover:

The Prompt Report: A comprehensive survey on prompting techniques, agents, and generative AI, including advanced evaluation methods for assessing these techniques.
Security Risks and Prompt Hacking: A detailed exploration of the security concerns surrounding prompt engineering, including Sander’s thoughts on its potential applications in cybersecurity and military contexts.
AI’s Impact Across Fields: A discussion on how generative AI is reshaping various domains, including the social sciences and security.
Multimodal AI: Updates on how large language models (LLMs) are expanding to interact with images, code, and music.
Case Study - Detecting Suicide Risk: A careful examination of how prompting techniques are being used in important areas like detecting suicide risk, showcasing the critical potential of AI in addressing sensitive, real-world challenges.

The episode concludes with a reflection on the evolving landscape of LLMs and multimodal AI, and what might be on the horizon.

If you haven’t yet, make sure to check out Part 1, where we discuss the history of NLP, prompt engineering techniques, and Sander’s development of the Learn Prompting initiative.

LINKS

Episode 36: Prompt Engineering, Security in Generative AI, and the Future of AI Research Part 1

Hugo Bowne-Anderson — Mon, 30 Sep 2024 18:00:00 +1000

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.

LINKS

Episode 34: The AI Revolution Will Not Be Monopolized

Hugo Bowne-Anderson — Thu, 22 Aug 2024 17:00:00 +1000

Hugo speaks with Ines Montani and Matthew Honnibal, the creators of spaCy and founders of Explosion AI. Collectively, they've had a huge impact on the fields of industrial natural language processing (NLP), ML, and AI through their widely-used open-source library spaCy and their innovative annotation tool Prodigy. These tools have become essential for many data scientists and NLP practitioners in industry and academia alike.

In this wide-ranging discussion, we dive into:

• The evolution of applied NLP and its role in industry
• The balance between large language models and smaller, specialized models
• Human-in-the-loop distillation for creating faster, more data-private AI systems
• The challenges and opportunities in NLP, including modularity, transparency, and privacy
• The future of AI and software development
• The potential impact of AI regulation on innovation and competition

We also touch on their recent transition back to a smaller, more independent-minded company structure and the lessons learned from their journey in the AI startup world.

Ines and Matt offer invaluable insights for data scientists, machine learning practitioners, and anyone interested in the practical applications of AI. They share their thoughts on how to approach NLP projects, the importance of data quality, and the role of open-source in advancing the field.

Whether you're a seasoned NLP practitioner or just getting started with AI, this episode offers a wealth of knowledge from two of the field's most respected figures. Join us for a discussion that explores the current landscape of AI development, with insights that bridge the gap between cutting-edge research and real-world applications.

LINKS

Check out and subcribe to our lu.ma calendar for upcoming livestreams!