Summary
Nir Diamant is an AI researcher and community builder, focusing on making cutting-edge AI accessible.
Diamant authors one of the leading AI substack newsletterson cutting-edge AI techniques called DiamantAI.
Much of its content is available for free. Paying subscribers get more content including:
- Exclusive tutorials and code walkthroughs
- Our extensive publication archives.
- Updates on our open-source projects
Jonathan Yarkoni – 20/07/2024 (24:11)
OnAir Post: Nir Diamant
News
What Are Guardrails and Why Do We Need Them?
Guardrails are the safety measures we build around AI systems – the rules, filters, and guiding hands that ensure our clever text-generating models behave ethically, stay factual and respect boundaries. Just as we wouldn’t let a child wander alone on a busy street, we shouldn’t deploy powerful AI models without protective barriers.
The need for guardrails stems from several inherent challenges with large language models:
The Hallucination Problem
The Bias Echo Chamber
The Helpful Genie Problem
The Accidental Leaker
How Guardrails Work in Practice
This blog post is a tutorial based on, and a simplified version of, the course “Long-Term Agentic Memory With LangGraph” by Harrison Chase and Andrew Ng on DeepLearning.AI.
Conclusion
We’ve now built an email agent that’s far more than a simple script. Like a skilled human assistant who grows more valuable over time, our agent builds a multi-faceted memory system:
- Semantic Memory: A knowledge base of facts about your work context, contacts, and preferences
- Episodic Memory: A collection of specific examples that guide decision-making through pattern recognition
- Procedural Memory: The ability to improve its own processes based on feedback and experience
This agent demonstrates how combining different types of memory creates an assistant that actually learns from interactions and gets better over time.
Imagine coming back from a two-week vacation to find that your AI assistant has not only kept your inbox under control but has done so in a way that reflects your priorities and communication style. The spam is gone, the urgent matters were flagged appropriately, and routine responses were handled so well that recipients didn’t even realize they were talking to an AI. That’s the power of memory-enhanced agents.
This is just a starting point! You can extend this agent with more sophisticated tools, persistent storage for long-term memory, fine-grained feedback mechanisms, and even collaborative capabilities that let multiple agents share knowledge while maintaining privacy boundaries.
About
More on Newsletter
As an AI researcher, even years ago when the advancements weren’t as rapid, I wondered how I could keep up-to-date with new technological advancements – both at a high level and hands-on.
I created the DiamantAI community for this purpose: to provide people with simple access to everything new that comes up, well explained, and with code tutorials. 💎
There is a gap between academia, industry, and the ways people can digest this information. We are here to bridge that gap for you.
We currently run three very successful OPEN SOURCE GitHub repositories:
Both adhere to the same format: a collection of Python notebooks implementing a vast number of tutorials, with detailed explanations, motivation, and well-documented code that can make complex new concepts easy to understand. 📚
We keep working on these constantly, providing the most up-to-date techniques and code.
Source: Website
Web Links
Videos
Controlling LLM agents with Nir Diamant
July 20, 2024 (24:11)
By: Jonathan Yarkoni
Books
Prompt Engineering from Zero to Hero – Master the Art of AI Interaction
Source: Website
Transform the way you work with AI – Develop the intuition to get exactly what you need from AI models
This comprehensive digital guide will take you from the absolute basics to advanced prompt engineering techniques that dramatically improve your AI interactions. More than just techniques, this book builds your intuitive understanding of how AI models think and respond, allowing you to craft effective prompts for any situation.
What’s Inside:
- 22+ detailed chapters covering the complete prompt engineering journey
- Practical code examples using LangChain that you can implement immediately
- Deep intuition development – I don’t just show you how, but explain why techniques work with mental models and frameworks that make concepts click
- Intuitive explanations of complex concepts using analogies and real-world comparisons
- Step-by-step breakdowns of every function with the underlying reasoning clearly explained
- Hands-on exercises in each chapter to build your skills and reinforce your intuitive understanding
- Real-world applications showing how to apply these techniques with the reasoning behind each decision
DiamantAI Newsletter
Building a Comprehensive GenAI Knowledge Hub
Source: Website
This newsletter features organic content that our community creates. It will contain:
– Updates on new code tutorials of the new techniques that were added to our GitHub Repositories – You are very welcome to contribute your implementations!
– Blog posts explaining these methods
– New interesting papers that should and may be implemented
We also help related academic papers to get exposed by publishing tutorials regarding the paper method in our repo, and let you enjoy the cutting-edge new technologies – both conceptually and hands-on.
– Check out our GitHub Repositories for all our code tutorials and implementations:
– Join our flourishing Discord Community where you can discuss these techniques and ask for academic paper implementations:
AI Policy Posts
Guardrails for AI
Source: DiamantAI
What Are Guardrails and Why Do We Need Them?
Guardrails are the safety measures we build around AI systems – the rules, filters, and guiding hands that ensure our clever text-generating models behave ethically, stay factual and respect boundaries. Just as we wouldn’t let a child wander alone on a busy street, we shouldn’t deploy powerful AI models without protective barriers.
The need for guardrails stems from several inherent challenges with large language models:
