Summary

In this episode, Mukund Sankar delves into the often painful experience of job rejection, particularly focusing on the role of Applicant Tracking Systems (ATS) in the hiring process. He shares his personal journey of building an AI-powered resume checker to better understand how these systems work and how they can impact job seekers. Through his experiences, he highlights key lessons about resume optimization, the importance of keyword alignment, and the need to present one's qualifications in a way that resonates with both machines and human recruiters.

Takeaways

• Rejection can feel personal, even when it's not.
• Understanding how ATS works is crucial for job seekers.
• Building an AI-powered tool can provide valuable insights.
• Keyword alignment is essential for passing ATS filters.
• Formatting and structure can significantly affect ATS scoring.
• Tailoring your resume to the job description is key.
• Quantifiable achievements should be highlighted in resumes.
• It's important to translate your experience into machine-readable language.
• Feeling invisible in the job search process is common.
• You don't need to rewrite your resume; just tune it for success.

How You can Build Your Own: https://mukundansankar.substack.com/p/will-your-resume-pass-the-ats-i-built

Medium Members : https://medium.com/towards-artificial-intelligence/will-your-resume-pass-the-ats-i-built-an-ai-app-to-find-out-a0ad9f3ce4ad

Summary

In this episode, Mukundan Sankar discusses the challenges of content promotion and how it often overshadows the joy of content creation. He shares his journey of building an AI content engine that automates the promotion process, allowing creators to focus on their passion for writing. The conversation emphasizes the importance of reclaiming creativity through automation and offers practical insights for content creators looking to enhance their reach without the burnout of manual promotion.

takeaways

• Content creation is often overshadowed by the burden of promotion.
• Each platform requires a unique approach to content sharing.
• Promotion can take longer than the actual writing process.
• Systemizing content promotion can alleviate stress for creators.
• Using AI tools can help automate the promotion process.
• Creating a custom GPT can streamline content distribution.
• Engagement can increase by repurposing content across platforms.
• Automation allows creators to focus on their passion for writing.
• The right tools can save time and enhance creativity.
• Investing in automation tools can lead to greater content impact.

Automate your content so that you can focus on creating the full tutorial with prompt for each platform

My Medium Blog

Summary

In this episode of Data and AI with Mukundan, the host discusses the creation and impact of an AI life planner designed to enhance productivity and time management. The conversation covers the technology behind the planner, including the use of GPT-4, Google Calendar API, and the Pomodoro technique, as well as the personal transformation experienced by the host as a result of implementing this tool.

Takeaways

• Most of us struggle with time management.
• AI can help optimize our schedules.
• The AI life planner analyzes daily habits.
• It syncs with Google Calendar for seamless planning.
• Reminders are sent via Slack API integration.
• A Pomodoro timer helps maintain focus.
• The planner allows for real-time adjustments.
• Productivity can skyrocket with the right tools.
• You can build your own AI life planner.
• Engaging with the audience for feedback is important.

If you want to see exactly how I built this AI Life Planner, check out my full guide here: https://mukundansankar.substack.com/p/i-never-thought-i-had-my-life-together

How the App looks like: https://youtu.be/pyyWV7-Ty5w?feature=shared

Episode Description

Ever feel like your phone knows you a little too well? One Google search, and suddenly, ads follow you across the internet like a digital stalker. AI-powered personalization has long relied on collecting massive amounts of personal data—but what if it didn’t have to?

In this episode of Data & AI with Mukundan, we explore a game-changing shift in AI—personalized experiences without intrusive tracking. Two groundbreaking techniques, Sequential Layer Expansion and FedSelect, are reshaping how AI learns from users while keeping their data private.

We’ll break down: ✅ Why AI personalization has been broken until now ✅ How these new models improve AI recommendations without privacy risks ✅ Real-world applications in streaming, e-commerce, and healthcare ✅ How AI can respect human identity while scaling globally

The future of AI is personal, but it doesn’t have to be invasive. Tune in to discover how AI can work for you—without spying on you.

Key Takeaways

🔹 The Problem: Why AI Personalization Has Been Broken

• Streaming services, e-commerce, and healthcare AI often make irrelevant or generic recommendations.
• Most AI models collect massive amounts of user data, stored on centralized servers—risking leaks, breaches, and misuse.
• AI personalization has been a “one-size-fits-all” approach that doesn’t truly adapt to individual needs.

🔹 The Solution: AI That Learns Without Spying on You ✨ Sequential Layer Expansion – AI that grows with you

• Instead of static AI models, this method builds in layers, adapting over time.
• It learns only what’s relevant to you, reducing unnecessary data collection.
• Think of it like training for a marathon—starting small and progressively improving.

✨ FedSelect – AI that fine-tunes only what matters

• Instead of changing an entire AI model, it selectively updates the most relevant parameters.
• Think of it like tuning a car—you upgrade what’s needed instead of replacing the whole engine.
• Everything happens locally on your device, meaning your raw data never leaves.

🔹 Real-World Impact: How This Changes AI for You 🎬 Streaming Services – Netflix finally gets your taste right—without tracking you across the web. 🛍️ E-commerce – Shopping apps suggest what you actually need, not random trending items. 🏥 Healthcare – AI-powered health plans tailored to your genes and habits—without sharing your medical data.

🔹 The Bigger Picture: Why This Matters for the Future of AI

• Personalized AI at scale: AI adapts to billions of users while remaining privacy-first.
• AI that respects human identity: You control your AI, not the other way around.
• The end of surveillance-style tracking: No more creepy ads following you around.

🌟 AI can be personal—without being invasive. That’s the future we should all demand.

Fedselect: https://arxiv.org/abs/2404.02478 |

Sequential Layer Expansion:https://arxiv.org/abs/2404.17799

🔔 Subscribe, rate, and review for more AI insights!

Episode Summary

In this episode, we dive into the transformative power of synthetic data and its ability to bypass privacy barriers while accelerating AI innovation. Learn how industries like healthcare, finance, and retail leverage synthetic data to fuel progress and discover actionable steps to implement this game-changing technology.

Key Topics Covered

1. What Is Synthetic Data?
   • Definition and importance.
   • How it solves privacy and data scarcity challenges.
2. Top 5 Breakthroughs in Synthetic Data:
   • SafeSynthDP: Differential privacy for secure synthetic data generation.
   • GANs for Healthcare: Generating synthetic patient records.
   • CaPS: Collaborative synthetic data sharing across organizations.
   • Private Text Data: Privacy-safe NLP dataset generation.
   • Vertical Federated Learning: Secure synthetic data creation for tabular datasets.
3. Applications Across Industries:
   • Healthcare: HIPAA-compliant AI for diagnostics.
   • Finance: Risk modeling with synthetic transaction data.
   • Retail: Personalization using synthetic customer profiles.
4. Action Plan:
   • Learn and apply differential privacy techniques.
   • Experiment with large language models for synthetic data.
   • Use federated learning for collaborative data sharing.
   • Build synthetic datasets for complex, messy data.
   • Market privacy-first solutions to build customer trust.

Resources Mentioned

• Research Papers:
  • SafeSynthDP: Privacy-Preserving Data Generation
  • GANs for Healthcare Data
  • CaPS: Collaborative Synthetic Data Platform
  • Private Predictions for NLP
  • Vertical Federated Learning for Tabular Data
• Tools and Frameworks:
  • TensorFlow Privacy Library
  • PyTorch GAN Zoo
  • Flower Framework for Federated Learning

Takeaways

• Synthetic data is not just a workaround—it’s a key enabler of privacy-compliant AI innovation.
• Industries across the board are adopting synthetic data to overcome regulatory and privacy challenges.
• You can start leveraging synthetic data today with available tools and frameworks.

Ready to explore the power of synthetic data? Dive into the resources mentioned and start experimenting with synthetic data generation to give your AI strategy a competitive edge. Subscribe to our podcast for more cutting-edge insights into the world of AI and data innovation.

Website: https://mukundansankar.substack.com/

Key Takeaways:

1. Why Plotly is a Game-Changer

• Unlike Matplotlib or Seaborn, Plotly offers interactive and dynamic visualizations that are perfect for storytelling.
• Unlock powerful features that go beyond basic bar charts or scatter plots.

2. 9 Hidden Plotly Tricks:

• Custom Pairwise Correlation Matrix: Add annotations and custom color scales for deeper insights.
• Dynamic Data Highlighting: Like Excel, conditional formatting but on steroids.
• Density Contours: Visualize class distribution and clustering with ease.
• Faceted Histograms: Compare subgroups in a single view.
• Threshold Lines: Emphasize decision boundaries effectively.
• Custom Annotations: Turn visuals into storytelling tools.
• 3D Scatter Plots: Explore invisible relationships in 3D.
• Animated Visualizations: Reveal dynamic patterns over time.
• Interactive Tooltips: Make charts engaging and informative.

3. Real-world Applications

• Business intelligence, scientific research, and education examples.
• Techniques aren’t just about aesthetics—they’re about actionable insights.

4. Bonus Resources

Complete code examples are in the links below:

• Medium Members: https://medium.com/towards-artificial-intelligence/9-hidden-plotly-tricks-every-data-scientist-needs-to-know-eb7f2181df56
• Non-Medium Members can read for Free here: https://mukundansankar.substack.com/p/9-hidden-plotly-tricks-every-data
• Datasets from the UCI Machine Learning Repository for hands-on practice.https://archive.ics.uci.edu/datasets
• Twitter: @sankarmukund475

Episode Summary:

In this episode, Mukundan simplifies the concept of Dynamic Topic Modeling (DTM) for listeners and discusses its transformative impact on businesses. DTM is a machine learning method used to track the evolution of themes in text data over time. It helps companies to make smarter decisions by staying in tune with customer needs and market trends.

Key Topics Covered:

1. Introduction to Dynamic Topic Modeling
   • What it is and why it matters for businesses.
   • Real-world examples like customer reviews and social media trends.
2. How Dynamic Topic Modeling Works
   • Over time, analyze text data (e.g., reviews, surveys, reports).
   • Groups words into topics such as price, quality, or features.
3. Applications of Dynamic Topic Modeling
   • Adjusting marketing strategies to customer priorities.
   • Enhancing product features based on evolving feedback.
   • Predicting and responding to trends like sustainability in physical products.
   • Tracking employee feedback to refine HR strategies and reduce churn.
4. Step-by-Step Guide to Implementing DTM
   • Collecting text data (e.g., reviews, surveys).
   • Using tools like Python or pre-built software for analysis.
   • Generating clear visuals and actionable insights.
5. Benefits for Businesses
   • Understanding customer and employee feedback more effectively.
   • Staying ahead of competitors.
   • Saving time while making informed, data-driven decisions.
6. Call to Action
   • Encourage listeners to explore DTM to gain a competitive edge.
   • Mukundan invites questions and collaboration via email: mukundansankar.substack.com.

Memorable Quotes:

• "Dynamic Topic Modeling helps businesses turn text data into actionable business strategies."
• "With DTM, you can stay ahead of competitors by understanding what customers truly care about over time."
• "It's not just about making decisions but smarter decisions driven by data."

Real-Life Examples:

• Amazon Reviews: How DTM categorizes feedback into price, durability, and other topics.
• Marketing Adjustments: Shifting focus to features customers prioritize.
• Trend Analysis: Tracking the rise of sustainability in customer demands.
• Employee Insights: Using DTM to predict trends in employee satisfaction and churn.

Resources Mentioned:

• Dynamic Topic Modeling Tools: Python and other software solutions for beginners and professionals.
• Email for Guidance: mukundansankar.substack.com

Description

In this episode of Data & AI with Mukundan, we dive into Lang Chain—a powerful tool that connects AI systems for smarter, more efficient applications. Mukundan breaks down how Lang Chain simplifies complex processes by acting as a bridge between AI tools, enabling automation and improved decision-making.

From content creation and chatbots to research and customer support, discover real-world examples of Lang Chain in action. Learn how it can fetch, summarize, and synthesize information from multiple sources, and how businesses and individuals alike can use it to save time and effort. Whether you're a content creator, academic researcher, or entrepreneur, this episode is packed with insights on how Lang Chain can work for you.

Key Takeaways

• What is Lang Chain? A tool that connects different AI systems, enabling smarter, automated workflows.
• Why Lang Chain Matters:
  • Automates repetitive tasks.
  • Saves time by integrating multiple AI tools.
  • Facilitates smarter decision-making through contextual understanding.
• Real-Life Applications:
  • Chatbots for intelligent customer interaction.
  • Content creation (e.g., blogs, emails, social media posts).
  • Research tools for summarizing academic papers and lengthy documents.
• How Lang Chain Works: Combines AI tools like ChatGPT, Google, and specialized engines (e.g., Perplexity.ai) to fetch and synthesize data into actionable outputs.
• Getting Started with Lang Chain:
  1. Choose a task (e.g., chatbot, writing assistant, research).
  2. Combine Lang Chain with AI tools like ChatGPT.
  3. Let it automate tasks for smarter results.

Highlights & Examples

• Use Lang Chain to brainstorm blog ideas using ChatGPT and Perplexity.ai.
• Simplify customer support with automated chatbots.
• Accelerate academic research by summarizing complex documents.
• Enhance workflows with tools like Zapier, which integrates Lang Chain.

Actionable Steps

• Start with simple tasks to explore Lang Chain’s potential.
• Experiment with connecting AI tools for tailored solutions.
• Gradually build smarter systems that save you time and effort.

Closing Notes

Mukundan encourages listeners to experiment with Lang Chain and see its magic in action. Don’t forget to subscribe for more episodes on harnessing AI for smarter living!

Additional Links

Mukundan's website: https://mukundansankar.substack.com/

Summary

In this episode, Mukundan Sankar discusses the importance of analytics for Newsletter creators using Substack, emphasizing how understanding traffic sources can significantly enhance newsletter growth. He breaks down various traffic types, including direct, email, referral, and social media, and provides actionable strategies for optimizing each source. The conversation also highlights common mistakes creators make with their analytics and offers the next steps for leveraging data effectively to engage audiences and grow subscriptions.

Chapters

00:00 Introduction to Content Creation and Analytics

00:00 Understanding Substack and Its Analytics

01:11 The Importance of Traffic Analytics

03:01 Exploring Traffic Sources

09:58 Leveraging Traffic Analytics for Growth

12:54 Common Traffic Mistakes and Next Steps

14:57 Conclusion and Future Updates

Takeaways

• Analytics provide insights into audience engagement.
• Understanding traffic sources is crucial for growth.
• Direct traffic indicates loyal audience members.
• Email traffic reflects the effectiveness of subject lines.
• Referral traffic can introduce new readers to your content.
• Social media can convert casual readers into subscribers.
• Data must be acted upon to be valuable.
• Avoid focusing solely on top-line metrics.
• Experimentation is key to finding effective strategies.
• Set measurable goals to track progress.

Links:

Website: https://mukundansankar.substack.com/

Summary

In this conversation, Mukundan Sankar discusses the transformative role of AI agents in content creation. He emphasizes how these tools can help streamline workflows, automate repetitive tasks, and ultimately allow creators to focus on high-quality content. The discussion covers various AI tools like ChatGPT, Zapier, and Canva, and highlights the benefits of integrating these technologies into the content creation process.

Takeaways

• AI agents can assist with content creation and brainstorming.
• Automation tools like Zapier can save time for creators.
• Content creators often spend too much time on repetitive tasks.
• Using AI tools allows for more strategic focus in content creation.
• AI can help generate visuals for social media posts.
• Streamlined workflows lead to higher quality content production.
• AI agents can alleviate the overwhelming nature of content creation.
• Experimenting with different AI tools can enhance creativity.
• AI tools can help manage and schedule social media posts.
• The ultimate goal is to express creativity more freely with AI assistance.