Steve Hsu on building AI assistants that focus on your private data, stored securely in a separate memory module outside of the LLM.
Steve Hsu wears many hats, but most recently he is co-founder of SuperFocus, a startup building LLM-backed knowledge co-pilots for enterprises. A significant challenge in the realm of Large Language Models (LLMs) is the issue of “hallucination”, where the models generate non-factual or inaccurate information. This problem persists despite advancements in machine learning and even in larger, more complex models like GPT-4. Solving this issue is critical, especially in our current era of LLMs, to ensure reliable AI interactions across various applications, including education and enterprise operations, as it affects the credibility, trustworthiness, and ultimate effectiveness of these powerful tools.
Interview highlights – key sections from the video version:
- SuperFocus – the origin story
- Hallucination: measurement, evaluation and mitigation.
- User tolerance for hallucination
- Lessons from early use cases
- High-level architecture of SuperFocus
- Latency, robustness and other challenges
- LLMs and the rise of intelligent and more capable assistants
- What impact will LLMs have in Computational Genomics?
- How is deep learning impacting research in physics?
- SuperFocus is hiring!
- A video version of this conversation is available on our YouTube channel.
- Building LLM-powered Apps: What You Need to Know
- Navigating the Future of Search
- Vector Database Primer
- Andrew Feldman: The Rise of Custom Foundation Models
- Jerry Liu: An Open Source Data Framework for LLMs
- Brian Raymond: ETL for LLMs
- Jonas Andrulis: Building and Deploying Foundation Models for Enterprises
- David Talby: Delivering Safe and Effective LLM and NLP Applications
- Amin Ahmad: LLMs Are the Key to Unlocking the Next Generation of Search
- Dylan Patel: The Open Source Stack Unleashing a Game-Changing AI Hardware Shift
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[Image: Knowledge Co-pilots by Ben Lorica, using images and icons from Infogram.]