(00:00:00) Intro (00:00:22) Introduction to Snowflake and AI (00:00:34) Guest introduction and background (00:00:57) Excitement about the conversation (00:01:13) Impressions of Snowflake's office (00:01:21) Discussion on Snowflake Summit and AI/ML announcements (00:01:52) Overview of Snowflake as a unified platform (00:02:16) Evolution of Snowflake from data warehousing to AI (00:02:37) AI features and tools in Snowflake (00:02:55) Announcements at Snowflake Summit (00:03:05) Getting started with Snowflake (00:03:16) Starting with Snowflake based on specific roles (00:03:45) Specialization within Snowflake's platform (00:03:55) Deciding where to start with Snowflake (00:04:25) Importance of Python and SQL in Snowflake (00:05:35) Snowflake as a platform for various roles (00:05:57) Snowflake's learning platform and resources (00:06:05) Overview of Snowflake's developer resources (00:06:17) Introduction to Snowflake's North Star program (00:06:53) North Star courses for different workloads (00:07:13) North Star's foundation course (00:07:35) Learning paths within North Star (00:07:59) Availability of North Star courses on Coursera (00:08:34) Discussion on teaching generative AI courses (00:09:18) Differences between teaching online and live talks (00:09:37) Experiences in writing scripts for courses (00:10:30) Challenges in creating course content (00:11:29) Tips for writing course scripts (00:11:50) Experiences in teaching and content creation (00:12:12) Changes in NLP and language models over the years (00:12:24) Evolution of interest in NLP (00:13:21) Excitement about the resurgence of NLP (00:14:07) Shift in momentum for AI research (00:14:38) Concerns about the use of generative AI (00:14:59) Misuse of LLMs in outdated tasks (00:15:54) Pessimism about the overuse of LLMs (00:16:15) Exploring correct use cases for LLMs (00:16:29) Potential use cases for LLMs (00:17:04) Example of Snowflake's internal use of LLMs (00:17:25) Chatbots as a first line of defense in support (00:18:19) Content generation with LLMs (00:18:40) Example of a content engine using LLMs (00:19:23) Excitement about enterprise use of LLMs (00:19:59) Benefits of internal knowledge sharing using LLMs (00:20:03) Use cases for LLMs in organizations (00:21:04) Encouragement to stick with niche interests (00:21:50) Shift in focus from computer vision to NLP (00:22:01) Advice for PhD students on choosing specializations (00:22:35) Importance of following research trends (00:23:15) Recommendations for getting started in NLP (00:24:18) Importance of understanding fundamentals in NLP (00:24:28) Starting with core NLP papers (00:24:58) Keeping up with new research in AI (00:25:05) Strategies for staying updated on AI research (00:26:23) Following key figures in AI on social media (00:26:45) Trends in AI research and their impact (00:27:14) Challenges in staying current with AI papers (00:27:43) Use of social media for AI research (00:28:27) Academic communities on different platforms (00:29:36) Visual learning in AI education (00:29:55) Excitement about the future of AI (00:30:06) AI's impact across industries (00:30:15) Exploration of new use cases in AI (00:30:46) Examples of creative AI use cases (00:31:32) Curiosity about AI's future impact on industries (00:31:50) Potential changes in education through AI (00:33:05) Excitement about new AI-driven education tools (00:33:40) Personalized education with AI (00:33:47) The future of women in data and tech