This episode on the transformative impacts of AI on search technologies features Gad Benram and Gabriel Gonçalves , along with our special guest, Edward Zhou —who has recently led the search ranking team at Notion—and share his experiences and expert insights on AI's impact on search technologies.
Key topics include:
• 00:00 - Introductions and Opening Remarks
• 15:47 - Evaluating Search Systems and Techniques
• 20:40 - Scoring Algorithm and Semantic Searching
• 24:18 - Vector Space Model and Similarity Limitations
• 27:05 - Embedding Models and Relevance Challenges
• 32:00 - Addressing Search Bias Mitigation
• 36:25 - Evaluating Search Results and Language Models
• 41:49 - Language Models and Embedding Technologies
Throughout the episode, the team discussed the potential of AI-powered search tools, including the combination of traditional search algorithms with AI-powered language models, and the importance of evaluating search systems based on user actions and business outcomes. They also explored the workings of a scoring algorithm, the relevance of similarity in a vector space, and the challenges and potential solutions in incorporating embedding models for specific business domains. Additionally, they addressed the issues of position and click bias in search results, the difficulties in evaluating search results and language models, and the current and future state of language models and embedding technologies. Finally, they looked into the future of search systems, considering how advancements in AI and embeddings could revolutionize search experiences.
🔗 Visit our website for more resources and updates: https://www.tensorops.ai/ 👥 Connect with us on social media: Linkedin Twitter 💬 Join our community: https://www.meetup.com/ai-loves/