AI Agents and Long Context Windows with Mark Huang
Jun 18, 2024
auto_awesome
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.
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.
Mark Wong's Role and Insights on Autonomous AI Agents
Mark Wong's expertise from companies like Box Splunk informs his work at Gradient on autonomous AI agents. By extending models like Llama 3 with data scaling, they enhanced context windows up to 1 million tokens. Their project explores the impacts of longer context windows on serving varied use cases.
Distinctions in Enterprise AI and Challenges Faced
In enterprise AI, considerations shift towards data governance, security, and reliability tailored to the corporate environment. Addressing privacy and security requirements is vital, alongside understanding model limitations. The complexity of selling AI solutions to management requires bridging gaps between AI strategy, value proposition, and stakeholder needs.
Future Implications and Challenges of AI Autonomy
Enhancing AI planning and reasoning capabilities, alongside ensuring interpretability and security, is critical for wider autonomous agent adoption. Progress in meta-learning and model mastery aims to facilitate agent use across industries, transforming tasks like JIRA ticket closure and decision-making processes. Overcoming security and interpretability challenges will pave the way for more AI autonomy applications.
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
Get the Snipd podcast app
Unlock the knowledge in podcasts with the podcast player of the future.
AI-powered podcast player
Listen to all your favourite podcasts with AI-powered features
Discover highlights
Listen to the best highlights from the podcasts you love and dive into the full episode
Save any moment
Hear something you like? Tap your headphones to save it with AI-generated key takeaways
Share & Export
Send highlights to Twitter, WhatsApp or export them to Notion, Readwise & more
AI-powered podcast player
Listen to all your favourite podcasts with AI-powered features
Discover highlights
Listen to the best highlights from the podcasts you love and dive into the full episode