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Testμ 2024
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Toni Ramchandani

Toni Ramchandani

VP, MSCI Inc.

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session

Automated Testing of AI-ML Models

About the Talk

In an era where artificial intelligence (AI) and machine learning (ML) are revolutionizing industries, ensuring the accuracy and reliability of these models is paramount.

This talk delves into the diverse array of testing techniques crucial for validating AI/ML models.

We begin with an overview of the automated testing landscape, highlighting its critical role in AI/ML development. Emphasis is placed on the necessity of tools like DeepXplore, a deep learning testing tool that employs differential testing strategies to identify inconsistencies in model behavior. We discuss its application in uncovering neural network vulnerabilities and improving model robustness. Moving forward, we delve into SHAP (SHapley Additive exPlanations), a tool for interpreting model predictions and understanding feature importances. SHAP's contribution to model transparency and its implications for testing model reliability are explored. Also we will talk about tools like CleverHans, FOOLBOX etc

In conclusion, we reflect on the future of AI/ML model testing, pondering the evolving nature of AI technologies and the continuous adaptation of testing methodologies to keep pace. The audience will leave with a comprehensive understanding of the automated testing landscape in AI/ML and equipped with the knowledge to implement effective testing strategies in their AI projects.

Key Takeaways:

  • Understanding AI Hallucinations: Gaining a clear understanding of what AI hallucinations are, why they occur, and their potential impact on real-world AI applications.
  • Importance of Robust Testing: Emphasizing the critical role of thorough and sophisticated testing in ensuring the reliability and accuracy of AI and ML models.
  • Tool Proficiency:Learning about key tools such as DeepXplore, CleverHans, FOOLBOX, and Adversarial Robustness Toolbox (ART), and how they can be utilized to identify and mitigate AI hallucinations.
  • Implementing Adversarial Testing:Understanding the principles and techniques of adversarial testing, and how it helps in uncovering vulnerabilities like hallucinations in AI models.
  • Model Security and Ethical Implications:Recognizing the importance of securing AI models against hallucinations and other errors, and the ethical implications of deploying untested or vulnerable AI systems.
  • Practical Strategies for AI Reliability:Providing actionable strategies and best practices for integrating robust testing methodologies into the AI model development lifecycle.
  • Future of AI Testing:Discussing emerging trends and future directions in AI testing, particularly in addressing complex challenges like hallucinations and ensuring model robustness.
  • These takeaways are designed to provide attendees with a comprehensive understanding of the challenges, tools, and best practices in testing AI and ML models, particularly focusing on the detection and mitigation of AI hallucinations.

About the Speaker

With 14 years of experience in the tech industry, Toni has established themselves as a seasoned technocrat specializing in quality assurance and software testing. Their expertise spans various domains, including web, API, mobile, desktop, data, and RPA testing. Throughout their career, Toni has demonstrated a keen ability to lead teams towards software excellence, leveraging proficiency in cloud technologies, security, performance testing, and DevOps practices.

Driven by a passion for continuous learning, Toni has explored cutting-edge fields such as AI, ML, IoT, and blockchain, along with cloud solutions like Databricks and Azure. They believe in the transformative power of these technologies and strive to integrate them into their leadership approach to elevate the software testing paradigm.

Outside of work, Toni is an adventure enthusiast who enjoys pushing the limits of mind and body. They believe in maintaining a dynamic balance between physical and mental well-being as a foundation for success.

Toni is committed to collaborating with like-minded visionaries to redefine the future of technology. They are eager to connect with organizations that prioritize excellence and embrace future-forward technologies to create a shared legacy of success.

About Testµ Conference

Testµ Conference is a virtual or online-only conference to define the future of testing. Join over 30,000+ software testers, developers, quality assurance experts, industry experts, and thought leaders for 3 days of learning, testing, and networking at Testμ Conference 2024 by LambdaTest.

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