Weaviate Podcast
Charles Packer on MemGPT - Weaviate Podcast #73!
Nov 6, 2023
Charles Packer, lead author of MemGPT at UC Berkeley, discusses the concept of explicit memory management in GPT models, the use of prompts to handle memory limitations, interrupts in retrieval augmented generation (RAG), achieving ideal running speed in high parameter models, fine-tuning MemGBT for long conversations, search actions pagination, role-playing language models, and the future integration of memory in chatbot platforms.
51:30
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Quick takeaways
- Current language models lack true creativity and generate subpar content when interacting with each other or relying on their own past output.
- Memory integration in chatbots, including the use of separate vector databases and stateful APIs, will enhance immersion and user experience in the future.
Deep dives
Challenges with Creativity in AI
The speaker discusses the limitations of current language models when it comes to generating creative content. They mention that when language models interact with each other or generate content based on their own past output, the quality tends to deteriorate and lacks true creativity. The speaker shares their own experiments with AI-generated games and stories, which fell short of producing truly creative content. They express optimism that future generations of models, such as GPT-4, may show more promise, but acknowledge that the current state of creativity in AI is still limited.
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