{"podcast":{"title":"The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)","slug":"twiml-ai-podcast","podcast_index_feed_id":1045879,"rss_url":"https://feeds.megaphone.fm/MLN2155636147","website_url":"https://twimlai.com","image_url":"https://megaphone.imgix.net/podcasts/35230150-ee98-11eb-ad1a-b38cbabcd053/image/TWIML_AI_Podcast_Official_Cover_Art_1400px.png?ixlib=rails-4.3.1&max-w=3000&max-h=3000&fit=crop&auto=format,compress","author":"TWIML","episode_count":785,"summary":"Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. The TWIML AI Podcast brings the top minds and ideas from the world of ML and AI to a broad and influential community of ML/AI researchers, data scientists, engineers and tech-savvy business and IT leaders. Hosted by Sam Charrington, a sought after industry analyst, speaker, commentator and thought leader. Technologies covered include machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, computer science, data science and more.","last_synced_at":null,"page_url":"https://stenobird.com/podcast/twiml-ai-podcast"},"episode":{"title":"AI Agents for Data Analysis with Shreya Shankar - #703","slug":"ai-agents-for-data-analysis-with-shreya-shankar-703","published_at":"2024-09-30T13:09:00+00:00","page_url":"https://stenobird.com/podcast/twiml-ai-podcast/ai-agents-for-data-analysis-with-shreya-shankar-703","show_page_url":"https://stenobird.com/podcast/twiml-ai-podcast","url":"https://twimlai.com/podcast/twimlai/ai-agents-for-data-analysis/","audio_url":"https://pscrb.fm/rss/p/traffic.megaphone.fm/MLN9363051879.mp3?updated=1727745514","summary":"Today, we're joined by Shreya Shankar, a PhD student at UC Berkeley to discuss DocETL, a declarative system for building and optimizing LLM-powered data processing pipelines for large-scale and complex document analysis tasks. We explore how DocETL's optimizer architecture works, the intricacies of building agentic systems for data processing, the current landscape of benchmarks for data processing tasks, how these differ from reasoning-based benchmarks, and the need for robust evaluation methods for human-in-the-loop LLM workflows. Additionally, Shreya shares real-world applications of DocETL, the importance of effective validation prompts, and building robust and fault-tolerant agentic systems. Lastly, we cover the need for benchmarks tailored to LLM-powered data processing tasks and the future directions for DocETL. The complete show notes for this episode can be found at https://twimlai.com/go/703.","meta_description":"Today, we're joined by Shreya Shankar, a PhD student at UC Berkeley to discuss DocETL, a declarative system for building and optimizing LLM-powered data p…","key_points":[],"chapters":[],"topics":[],"duration_seconds":2904,"processing_state":"failed","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/twiml-ai-podcast/episodes/ai-agents-for-data-analysis-with-shreya-shankar-703/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/twiml-ai-podcast/ai-agents-for-data-analysis-with-shreya-shankar-703.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}