No AI is 100% Accurate - Ft. Muthumari S, Global Head of Data & AI Studio, Brillio | SCALER POD 25
Jun 9, 2024
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Global Head of Data & AI at Brillio, Muthumari S, discusses generative AI, prompt engineering, and RAGs in NLP. Exploring problem-solving with AI, human intelligence, and risks of generative models. Insights on technology adaptation, AI usage, and problem solving. Valuable advice for future data science learners.
Embracing AI technology across roles is essential for professional growth and opportunities in innovative projects.
Humans possess unique emotional and social intelligence crucial in decision-making; AI enhances productivity but can't replace human intelligence.
Developing problem-solving skills, adapting to new technologies, and enhancing communication abilities are vital for success in technology and AI integration.
Data scientists must understand data engineering for effective model training and decision-making processes for scalability and problem-solving strategies.
Optimizing recommendation engines based on customer behavior can improve efficiency and personalization in recommendations.
Deep dives
The Shift in AI Market Dynamics
AI has transitioned from building AI to building with AI, thanks to OpenAI, marking a shift where humans are central to technology. This shift emphasizes the need for human-centric AI governance and the development of problem-solving skills coupled with utilizing technologies for informed decision-making.
Importance of Adapting to AI Technology
Embracing AI technology across roles like software engineering and data engineering is vital for professional growth. Understanding AI is crucial as it plays a significant role in enhancing productivity and opportunities to work on innovative projects.
The Role of Humans in Technology's Future
Humans play a vital role in technology as they possess unique capabilities like emotional and social intelligence that AI cannot replicate effectively. The integration of AI enhances productivity but cannot replace human intelligence in decision-making processes.
Future Focus on Skill Development
Focusing on developing problem-solving skills, adapting to new technologies, and enhancing communication and articulation abilities will be crucial for professional success in the evolving landscape of technology and AI integration.
Balancing Data Science with Data Engineering
Data scientists must understand data engineering to effectively structure data for model training and decision-making processes. The integration of full-stack expertise allows for scalability and problem-solving strategies in data science projects.
Enhancing Decision-Making at Scale
Considering data engineering strategies for scaling models and implementing fallback options for algorithmic failures are essential for making informed decisions and enhancing recommendations in data science projects.
Ensuring Cost Efficiency in Recommendations
Optimizing recommendation engines by implementing varying pipelines based on customer data complexity to balance cost efficiency and sophistication in recommendations. Personalizing recommendations based on customer behavior can improve efficiency in recommendations.
Ownership and Decision-Making in AI Projects
Encouraging a culture of ownership and decision-making across roles in AI projects, where individuals take responsibility for their decisions and collaborate to optimize architectures and solutions for enhanced project outcomes.
Adoption of Technology by the Next Generation
The podcast delves into the increasing excitement among the younger generation towards embracing advanced technology and architecture patterns. The discussion highlights how the pace of technological advancements influences the ability of individuals to adapt quickly to emerging solutions. It emphasizes the importance of staying tech-savvy and adaptable in a fast-evolving market, where traditional methods may not suffice in forecasting future trends and requirements.
Practical Considerations in Implementing AI Solutions
The episode provides insights on the practical aspects of deploying AI solutions, stressing the significance of realistic AI implementation strategies. It underlines the initial stages of categorizing user preferences into broad segments before delving into intricate AI models. The podcast highlights the cost implications and challenges of deploying AI at scale, emphasizing the need for a gradual approach towards implementing advanced AI technologies. Moreover, it discusses the critical factors of data quality, feasibility analysis, governance, and infrastructure requirements essential for successful AI integration.
In this Scaler Pod episode, we have Muthumari S, Global Head of Data & AI at Brillio. Muthumari S and Arnav talk about how data science and artificial intelligence have changed the current tech world.
They discuss the uses of generative AI, prompt engineering, and RAGs in Natural Language Processing, how to approach a problem, and much more. They also discuss whether AI can replace human intelligence.
Muthumari talks about her role at Brillio and her journey so far, including her experiences working at different organizations like Mu Sigma and Accenture. This podcast will be very insightful for anyone enthusiastic about data science, generative AI, statistics, machine learning models, and much more.
In this video we talk about:
0:00 - Trailer
1:28 - Introduction
2:00- A day in her life
4:02- Landscape of her career in the field of technology
10:35 - Advice for freshers in the tech field
15:32 - Changes in the world of Data Science
25:45 - Humans' in the world of AI
30:30 - Increased risks with generative models
33:35- Human's productivity and usage of AI
38:27 - Importance of technology adaptation and skills
47:33 - Usage of AI and problem solving
55:04 - Solving problems w/o Gen AI
1:07:00 - Advice for future Data Science learners and Conclusion
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