Speaker "Serg Masis" Details Back
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Name
Serg Masis
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Company
Syngenta
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Designation
Data Scientist
Topic
Harvesting Trust: Methods for Validating Chatbots in Agriculture
Abstract
In an era where AI is transforming every aspect of agriculture, the creation of trustworthy systems is not just beneficial—it's essential. This talk delves into the rigorous evaluation necessary to build reliable AI tools, specifically focusing on large language models used in agricultural chatbots. Drawing parallels with established QA practices in other industries, we will explore the unique challenges AI presents and outline a robust framework for its assessment. - The Necessity of Scrutiny: Highlighting the importance of Quality Assurance in the iterative improvement of AI, this section will explain why rigorous controls commonly seen in other product categories are crucial for the nascent field of AI, setting the stage for enhanced reliability and user trust. - Unpacking AI Complexities: LLMs introduce specific challenges in evaluation due to their complex, non-deterministic nature. This segment will unpack these intricacies, explaining why traditional QA methodologies fall short and what must be adapted for AI systems. - AI on the Farm: Using Syngenta’s Cropwise AI chatbot for farmers as a case study, this part will detail the critical requirements of delivering relevant, timely, and actionable information to farmers. Examples will demonstrate how AI can assist in decision-making processes from planting to harvest. - Metrics of Improvement: Emphasizing the adage, "you cannot improve what you do not measure," this final section will propose a comprehensive evaluation solution with metrics designed to assess every aspect of the AI workflow repeatedly and under varied conditions to ensure consistency and robustness. Through this session, attendees will gain insights into the specialized needs of QA in AI development, particularly for agricultural applications, empowering them to deploy these technologies with greater confidence and reliability.
Who is this presentation for?
Machine learning and data science practitioners as well as business stakeholders involved in the process of planning and executing on AI projects
Prerequisite knowledge:
None
What you'll learn?
About QA for AI and LLM systems in particular