Applications of GenAI at Google

Summary

In this episode of True ML, Nikunj, co-founder and CEO at True Foundry, engaged in a comprehensive dialogue with Priya Mathur, the head of AI Devices at Google. This discussion not only shed light on Priya's journey and experiences but also touched upon various facets of machine learning, AI, and the implications of generative AI in the tech landscape. Here are the key takeaways from their conversation:

-Introduction and Background
-Women in ML and Learning
-Challenges in Data Science
-Innovations at Google
-AI in Different Industries
-Building Trust with Generative AI

Introduction and Background

Priya brings over a decade of experience in AI, notably her time at Groupon dealing with marketing data science challenges and her current role at Google, focusing on product data science for Devices Services. Her insights come from solving complex problems and spearheading AI/ML initiatives. 

Priya has accumulated a rich background in artificial intelligence, spanning more than ten years. During her tenure at Groupon, Priya led the marketing data science team, focusing on a variety of challenges including the measurement of ROI for TV ads and interactive campaigns, showcasing her ability to leverage AI to solve intricate business problems.

Women in ML and Learning

As a woman leading in a traditionally male-dominated field, Priya’s achievements and leadership role underscore her individual capabilities and represent an important stride toward diversifying the AI industry. Her participation in forums like Women in ML further exemplifies her commitment to community learning and mentoring future leaders in AI. She gives an emphasis on importance of such forums on contributing to AI/ML domain.

Challenges in Data Science 

Priya is at the forefront of AI and machine learning for Devices Services, indicating a significant responsibility in driving innovation and applying AI solutions to improve product experiences and functionalities. 

• Quantifying Marketing ROI: Priya developed models to measure the effectiveness of marketing channels, including TV ads, at Groupon, facilitating strategic decision-making and budget optimization.
• Calculating Customer Acquisition Costs: She tackled the challenge of estimating customer acquisition costs from TV ads, employing data science to guide marketing strategies and resource allocation.
• Predictive Baseline Estimation: Priya devised methods to predict customer acquisition in the absence of certain ads, using innovative ML models for accurate baseline estimations.
• Solving Product Data Science Problems at Google: At Google, she focused on enhancing product funnels through the integration of AI and ML, addressing the unique challenges presented by Google’s diverse product ecosystem.
• Implementing Large Language Models: Priya led a project to simplify SQL query generation using LLMs, aiming to improve productivity and reduce reliance on data teams for querying tasks.
  

Innovations at Google

Transitioning to her role at Google, Priya explained her focus on improving product funnels for devices and services. Despite the change in industry and data availability, the core data science tools remained consistent.

• SQL Queries via Large Language Model: Priya developed an LLM to generate SQL queries, streamlining data analysis for non-technical users and boosting productivity.
• Focus on Data Privacy: Her projects at Google prioritized user data privacy and control, setting a benchmark for responsible AI and ML development.
• Interdisciplinary Collaboration: Priya led cross-collaborative efforts among different teams at Google understanding and catering Chatbot solutions for the team with specific needs.
• Generative AI Project: Priya's venture into leveraging large language models (LLMs) for simplifying SQL query generation exemplifies the innovative approaches being adopted to enhance productivity and reduce dependency on specialized data teams.

AI in Different Industries

Priya Mathur emphasized the transformative potential of generative AI across various industries, particularly those yet to fully benefit from technological advancements, such as healthcare, finance, and education. 

She highlighted the exponential gains these sectors could achieve with generative AI, from accelerating drug discovery through simulations to personalizing learning experiences. By increasing productivity and enabling personalized experiences at scale, generative AI promises to revolutionize industry practices, making operations more efficient and customer-focused. Priya's insights suggest that as AI technologies evolve, they will drive significant shifts, not just in how businesses operate, but also in enhancing user experiences and outcomes in critical sectors.

Building Trust with Generative AI 

Priya and Nikunj discussed the crucial aspects of fostering trust in AI technologies, emphasizing user education, control over personal data, and the need for comprehensive regulations.

• User Education: Emphasizing the need for user education about how generative AI works, Priya views increased knowledge as a pathway to trust, mirroring the internet's adoption curve.
• Control Over Data: Advocating for users to have control over their personal data, she suggests that the ability to manage and erase one’s data fosters trust in AI technologies.
• Regulatory Protections: Highlighting the role of regulation, Priya calls for policies that prevent data misuse and protect users against potential harms of AI, reinforcing trust through legal safeguards.
• Transparency and Understanding: Trust is built on transparency about AI's capabilities and limitations, encouraging a realistic understanding of what AI can and cannot do.
• Ethical AI Development: By prioritizing ethical considerations in AI development, such as fairness and non-discrimination, trust can be established in AI applications across industries, ensuring they serve the common good effectively.

This insightful dialogue between Nikunj and Priya Mathur sheds light on the transformative potential of AI across industries. It underscores the importance of continuous learning, collaboration, and ethical considerations in advancing the field. As AI continues to evolve, its impact on solving complex problems, enhancing productivity, and improving lives remains undeniably significant.

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