Software Huddle

AI Agents and Long Context Windows with Mark Huang

Jun 18, 2024

Mark Huang, Co-founder of Gradient, discusses the exciting world of autonomous AI agents and extending context windows for AI models. They explore the impact of longer context windows on use cases and share insights on advancements in AI technology.

50:24

Episode guests

Mark Huang

AI Summary

AI Chapters

Episode notes

Podcast summary created with Snipd AI

Quick takeaways

Autonomous AI agents require mechanisms to interpret user intent for decision-making, while extending context windows enhances serving varied use cases.

Implementing AI systems in enterprises involves tackling privacy and security concerns, especially with large-scale models, to ensure data governance and reliability.

Deep dives

Enterprise AI Automation and Privacy Considerations

Implementing AI systems in enterprises necessitates considering privacy and security aspects, especially with large-scale models. Autonomous agents require mechanisms interpreting user intent to make decisions, based on available information, to achieve high-level goals. The agent's ability to interact with the environment and make decisions aligns with the overall objective.

Intro

2min

Enterprise AI Automation and Building Relationships in the Market

9min

Advancements in AI Agents and Long Context Models

17min

Extending Context Window in AI Models

4min

Extending Model to One Million Contexts and Open Source Contribution

2min

Challenges and Success of Training AI Agents

10min

Technological Tools and Career Drive

6min

Today we have Mark Huang on the show. Mark has previously held roles in Data Science and ML at companies like Box and Splunk and is now the co-founder and chief architect of Gradient, an enterprise AI platform to build and deploy autonomous assistants.

In our chat, we get into some of the stuff he’s seeing around autonomous AI agents and why people are so excited about that space. Mark and his team has also recently been working on a project to extend the Llama-3 context window. They were able to extend the model from 8K tokens all the way to 1 million through a technique called theta-scaling. He walks us through the details of this project and how longer context windows will impact the types of use cases we can serve with LLMs.

Follow Mark: https://x.com/markatgradient

Follow Sean: https://x.com/seanfalconer

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Software Huddle

AI Agents and Long Context Windows with Mark Huang

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

Enterprise AI Automation and Privacy Considerations

Mark Wong's Role and Insights on Autonomous AI Agents

Distinctions in Enterprise AI and Challenges Faced

Future Implications and Challenges of AI Autonomy

Remember Everything You Learn from Podcasts