เกี่ยวกับตอนนี้
In this episode, Shashank Rajput, Research Scientist at Mosaic and Databricks, explores innovative approaches in large language models (LLMs), with a focus on Retrieval Augmented Generation (RAG) and its impact on improving efficiency and reducing operational costs.
Highlights include:
- How RAG enhances LLM accuracy by incorporating relevant external documents.
- The evolution of attention mechanisms, including mixed attention strategies.
- Practical applications of Mamba architectures and their trade-offs with traditional transformers.
ภาษาอังกฤษ
สหรัฐอเมริกา
สำเนาบทสนทนา 🔗
Are you the producer of this podcast?
Add a podcast transcript
Need Audio-to-Text?
Transcribe with Listen411 in Just 60 Seconds
ตอนอื่นๆ ใน PODCAST นี้
In this episode, Sharon Zhou, Co-Founder and CEO of Lamini AI, shares her expertise in the world of AI, focusing on fine-tuning models for improved performance and reliability.Highlights include:- The integration of determinism and probabilism for handling unstructured data and user queries effecti…
In this episode, Yev Meyer, Chief Scientist at Gretel AI, explores how synthetic data transforms AI and ML by improving data access, quality, privacy, and model training.Highlights include:- Leveraging synthetic data to overcome AI data limitations.- Enhancing model training while mitigating ethica…
In this episode, Julia Neagu, CEO & co-founder of Quotient AI, explores the challenges of deploying Generative AI and LLMs, focusing on model evaluation, human-in-the-loop systems, and iterative development.Highlights include:- Merging reinforcement learning and unsupervised learning for real-t…
In this episode, Jure Leskovec, Co-founder of Kumo AI and Professor of Computer Science at Stanford University, discusses Relational Deep Learning (RDL) and its role in automating feature engineering. Highlights include:- How RDL enhances predictive modeling.- Applications in fraud detection and re…
ข้อสงวนสิทธิ์: พอดแคสต์และอาร์ตเวิร์คที่ฝังอยู่ในหน้านี้มาจาก Databricks ซึ่งเป็นทรัพย์สินของเจ้าของและไม่มีส่วนเกี่ยวข้องหรือรับรองโดย Listen Notes, Inc.