733: OpenAssistant: The Open-Source ChatGPT Alternative, with Dr. Yannic Kilcher

Yannic Kilcher, a leading ML YouTuber and DeepJudge CTO, teams up with Jon Krohn this week to delve into the open-source ML community, the technology powering Yannic’s Swiss-based startup, and the significant implications of adversarial examples in ML. Tune in as they also unpack Yannic's approach to tracking ML research, future AI prospects and his startup challenges. This episode is brought to you by Gurobi (https://gurobi.com/sds), the Decision Intelligence Leader, and by CloudWolf (https://www.cloudwolf.com/sds), the Cloud Skills platform. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information. In this episode you will learn: • About OpenAssistant project [03:39] • Alignment issues in open-source vs closed-source [08:36] • Alternative formulas vital for crafting superior LLMs [20:29] • Strategies to foster open-source LLM ecosystems [27:07] • Yannic's pioneering work in legal document processing at DeepJudge [31:31] • Comprehensive overview of adversarial examples [1:04:02] • The future AI's landscape [1:18:08] • Startup challenges [1:25:35] Additional materials: www.superdatascience.com/733

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The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast. As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact. Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy. We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.