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Baking the Future of Information Retrieval Models
Manage episode 413396136 series 3446693
Zeta Alpha द्वारा प्रदान की गई सामग्री. एपिसोड, ग्राफिक्स और पॉडकास्ट विवरण सहित सभी पॉडकास्ट सामग्री Zeta Alpha या उनके पॉडकास्ट प्लेटफ़ॉर्म पार्टनर द्वारा सीधे अपलोड और प्रदान की जाती है। यदि आपको लगता है कि कोई आपकी अनुमति के बिना आपके कॉपीराइट किए गए कार्य का उपयोग कर रहा है, तो आप यहां बताई गई प्रक्रिया का पालन कर सकते हैं https://hi.player.fm/legal।
In this episode of Neural Search Talks, we're chatting with Aamir Shakir from Mixed Bread AI, who shares his insights on starting a company that aims to make search smarter with AI. He details their approach to overcoming challenges in embedding models, touching on the significance of data diversity, novel loss functions, and the future of multilingual and multimodal capabilities. We also get insights on their journey, the ups and downs, and what they're excited about for the future.
Timestamps: 0:00 Introduction 0:25 How did mixedbread.ai start? 2:16 The story behind the company name and its "bakers" 4:25 What makes Berlin a great pool for AI talent 6:12 Building as a GPU-poor team 7:05 The recipe behind mxbai-embed-large-v1 9:56 The Angle objective for embedding models 15:00 Going beyond Matryoshka with mxbai-embed-2d-large-v1 17:45 Supporting binary embeddings & quantization 19:07 Collecting large-scale data is key for robust embedding models 21:50 The importance of multilingual and multimodal models for IR 24:07 Where will mixedbread.ai be in 12 months? 26:46 Outro
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