Have you ever written something that looked perfect but felt… off? Like the grammar was flawless, the structure airtight, but the soul? Missing. In this episode, Mukundan shares a deeply personal story about emotional disconnection in writing—and how a simple AI app helped him uncover and correct it. It's part confessional, part technical walkthrough, and completely human. Whether you're a writer, blogger, speaker, or someone who wants their words to indeed land, this episode will make you rethink everything you thought you knew about “tone.” Takeaways I built an AI to tell me the emotional truth. My writing felt emotionally disconnected. Tone is invisible, but it is everything. AI reflects and shows us our blind spots. We need more felt truth in the world. When was the last time you asked your words how they feel? Being real helps you connect with people. I trained a model to use my own writing. The world doesn't need more content, it needs more truth. What is the emotional tone of this paragraph?

Blog: https://medium.com/towards-artificial-intelligence/i-built-a-tool-to-expose-the-lie-my-blog-was-telling-b89ce9903384

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 What Is Synthetic Data?Definition and importance.How it solves privacy and data scarcity challenges.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.Applications Across Industries:Healthcare: HIPAA-compliant AI for diagnostics.Finance: Risk modeling with synthetic transaction data.Retail: Personalization using synthetic customer profiles.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 GenerationGANs for Healthcare DataCaPS: Collaborative Synthetic Data PlatformPrivate Predictions for NLPVertical Federated Learning for Tabular DataTools and Frameworks:TensorFlow Privacy LibraryPyTorch GAN ZooFlower Framework for Federated LearningTakeaways 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-eb7f2181df56Non-Medium Members can read for Free here: https://mukundansankar.substack.com/p/9-hidden-plotly-tricks-every-dataDatasets from the UCI Machine Learning Repository for hands-on practice.https://archive.ics.uci.edu/datasetsTwitter: @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: Introduction to Dynamic Topic ModelingWhat it is and why it matters for businesses.Real-world examples like customer reviews and social media trends.How Dynamic Topic Modeling WorksOver time, analyze text data (e.g., reviews, surveys, reports).Groups words into topics such as price, quality, or features.Applications of Dynamic Topic ModelingAdjusting 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.Step-by-Step Guide to Implementing DTMCollecting text data (e.g., reviews, surveys).Using tools like Python or pre-built software for analysis.Generating clear visuals and actionable insights.Benefits for BusinessesUnderstanding customer and employee feedback more effectively.Staying ahead of competitors.Saving time while making informed, data-driven decisions.Call to ActionEncourage 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:Choose a task (e.g., chatbot, writing assistant, research).Combine Lang Chain with AI tools like ChatGPT.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.

In this captivating episode of Data and AI with Mukundan, we unravel the mysteries of Neurosymbolic AI—a powerful combination of neural networks and symbolic reasoning. This hybrid approach is revolutionizing artificial intelligence, tackling the challenges of explainability, adaptability, and reasoning. Learn why Neurosymbolic AI is more than just a technological innovation—it's a potential game-changer for industries ranging from healthcare to autonomous vehicles. Discover how this blend of brain-inspired learning and logic-driven analysis can solve some of AI's toughest problems, such as ethical decision-making and trustworthiness. Dive into real-world applications, breakthrough cases, and the challenges that researchers face as they push this cutting-edge technology toward its full potential. Episode Chapters: [00:00] Introduction Host Mukundan Sankar welcomes listeners and previews the episode's focus on Neurosymbolic AI—what it is and why it matters.[00:41] What Is Neurosymbolic AI? An exploration of the concept, blending neural networks' pattern recognition with symbolic AI's logic and reasoning.[01:11] The Cold War of AI: Neural vs. Symbolic A breakdown of the strengths and weaknesses of neural networks and symbolic AI, and why each is incomplete on its own.[02:07] Enter Neurosymbolic AI: A Peace Treaty How Neurosymbolic AI bridges the gap, creating a hybrid system that's intuitive yet logical.[02:49] Standalone AI's Weaknesses Examples of where standalone neural or symbolic systems fall short, including face recognition and logical reasoning.[03:14] Real-World ApplicationsMedical Imaging: Detecting tumors and explaining the findings.Fraud Detection: Spotting anomalies and reasoning about patterns.Autonomous Driving: Recognizing pedestrians and predicting their actions.Multimodal AI: Combining text, images, and logic to solve practical problems.[05:43] The Future of AI The potential of Neurosymbolic AI to address ethical decision-making, trustworthiness, and even general intelligence.[06:24] Challenges and Opportunities Can Neurosymbolic AI scale to solve the hardest problems, or will it remain a niche solution?[06:46] Closing Thoughts Mukundan reflects on the promise of Neurosymbolic AI and invites listeners to share their thoughts on its potential.

Episode Description: In this episode, we dive deep into the world of fine-tuning pre-trained AI models. Starting with the basics of pre-trained models like BERT, GPT, and ResNet, we explore how fine-tuning transforms them from generalists into task-specific experts. We discuss real-world examples, from improving sentiment analysis accuracy by 15% to enhancing tumor detection rates by 25%, and even boosting translation precision by 20% for specialized language pairs. Learn how fine-tuning not only sharpens AI's skills but also saves time, resources, and makes AI applications more sustainable. Wrapping up with best practices for fine-tuning, you'll get actionable tips to ensure success in your AI projects. Whether you're an AI enthusiast or just curious about how these systems get smarter, this episode is packed with insights. Don't forget to check the show notes for the blog link and subscribe for more AI mysteries decoded!

Episode Chapters [00:00 - 02:34] Introduction to Pre-Trained Models Understanding the basics of pre-trained AI models and why they often need fine-tuning to meet specific requirements. [02:35 - 02:55] Cracking Sarcasm in Text Exploring how fine-tuning helps AI understand nuances like sarcasm and slang, improving sentiment analysis. [02:56 - 04:25] Enhancing Image Models for Niche Tasks Discover how fine-tuning image models like ResNet improves accuracy in medical imaging and other specialized tasks. [04:26 - 04:59] Efficiency Through Fine-Tuning How fine-tuning reduces training time by 50%, making it an eco-friendly and resourceful approach. [05:00 - 05:17] Best Practices for Fine-Tuning Key tips for successful fine-tuning, including managing learning rates and avoiding overfitting. [05:18 - 05:56] Conclusion: Smarter, Not Harder A summary of how fine-tuning transforms AI into a sharper, more efficient tool for solving real-world problems. Additional Links: Blog Post that talks about this: https://medium.com/ai-in-plain-english/fine-tuning-pre-trained-models-how-to-make-ai-work-smarter-not-harder-7cf10b5d05f1 Stay tuned for the next episode and check the show notes for more resources!

Episode Notes: Overview In this compelling episode, I dive into the cutting-edge world of Neuro Symbolic AI, a transformative approach to business strategy that goes beyond traditional SWOT analysis. Moving past the capabilities of older models like GPT-3.5, this episode introduces a tool that doesn’t just identify strengths, weaknesses, opportunities, and threats but also explains the deeper implications and potential future outcomes of each. Join me as I explore how Neuro Symbolic AI is setting new standards for strategic analysis. Key Topics Covered Rebranding the Show I open by explaining the recent rebranding to focus entirely on data and AI, setting the stage for a deeper, more specialized dive into AI-driven insights.Introduction to SWOT Analysis Discover the basics of SWOT analysis and its applications in evaluating businesses, projects, and even personal goals. I explore how traditional AI tools, like GPT-3.5, offer simple, list-based insights but often lack depth and context.The Limitations of Traditional AI I discuss how traditional AI, though powerful, can be too simplistic. It provides a list of strengths and weaknesses without fully explaining their significance or forecasting potential impacts. This often leaves critical details overlooked.Neuro Symbolic AI: The Next Evolution Here, I introduce Neuro Symbolic AI, a hybrid model that combines pattern recognition with rule-based logic. This allows it to not only identify key traits but also to explain why they matter and how they could influence future outcomes. Neuro Symbolic AI transforms SWOT analysis from a static list into a dynamic, predictive tool for strategic planning.Real-World Applications and Advantages Using the example of a fictional toy company, I demonstrate how Neuro Symbolic AI can reveal deeper insights and future opportunities or challenges that traditional AI might miss. This tool doesn’t just give a list—it explains how each factor contributes to the company’s overall strategy and growth potential.The Future of Strategic AI I wrap up by discussing the potential of Neuro Symbolic AI to revolutionize strategic analysis across industries. This AI model can anticipate market shifts, rank strategic priorities, and offer actionable insights, making it an invaluable asset for forward-thinking businesses.Why Listen? If you’re interested in the latest advancements in AI or seeking smarter, future-oriented approaches to business strategy, this episode is a must-listen. Neuro Symbolic AI represents a breakthrough in predictive analysis, providing the kind of context and foresight that can turn reactive strategies into proactive ones.

Additional Links: 1] Neurosymbolic AI vs. Traditional AI Blog Post 2] FREEBIES: Sign up for my substack newsletter (https://mukundansankar.substack.com) and get: free RAG cheatsheet, and wait for it... a FREE Neuro-symbolic AI Cheatsheet! 3] AI for SWOT Analysis Blog Post: https://shorturl.at/RJ9fA

Episode Summary: In this episode, I delve into Google’s NotebookLM, an advanced AI-powered tool designed for research, note-taking, and audio content generation. Originally adopted by content creators for podcasting, NotebookLM offers a range of possibilities beyond mere podcast generation, including making complex ideas easily understandable and serving as an educational tool. Key Discussion Points: Introduction to NotebookLM: NotebookLM, powered by Google’s Gemini 1.5, is a unique AI tool designed to convert written content into natural-sounding audio conversations. I highlight its key features, including support for uploading diverse content sources like web links, YouTube videos, and Google Docs.Functionality and User Experience: I share my firsthand experience with NotebookLM, demonstrating how it transformed a complex blog on retrieval-augmented generation into a conversational podcast format. This feature not only simplifies complex topics but makes learning more accessible and engaging.NotebookLM for Education and Supplemental Learning: I advocate for NotebookLM’s potential as a supplemental learning tool. By breaking down intricate topics, it can serve as an aid for understanding research papers, technical blogs, or any complex written material.Vision for the Future: While the tool’s podcasting capabilities are a game-changer, I envision NotebookLM’s greater impact on education and personal development. I discuss the potential of NotebookLM as a go-to resource for learning on the move, from research papers to blog posts.Takeaway Tips: Use NotebookLM to generate personalized audio content from educational materials.Transform complex topics into digestible audio formats for on-the-go learning.Experiment with NotebookLM as a supplement to traditional learning tools like YouTube or Google Search.Closing Thoughts: I emphasize the potential of NotebookLM as an educational revolution in AI, urging listeners to explore its capabilities and unlock a new way of learning complex topics easily. Additional Resources: Blog Post I used to convert to podcast using NotebookLMTune in to experience NotebookLM’s ability to make complex topics accessible and engaging, and hear my take on its potential future applications!

Episode Overview: In this episode, we dive into how AI is transforming Customer Lifetime Value (CLV) prediction, a crucial metric for marketers aiming to understand and enhance customer relationships. We discuss why traditional CLV models fall short, how AI provides more accurate, real-time insights, and why this shift is vital for modern marketing strategies. Key Takeaways: Importance of CLV: CLV helps identify high-value customers, guiding where to focus marketing efforts for long-term success.Limitations of Traditional CLV Models: Outdated methods rely on static data and often miss dynamic changes in customer behavior.AI-Powered CLV Prediction:Real-time data processing enables timely responses to shifts in customer activity.Enhanced segmentation allows marketers to understand not just who their customers are, but how they engage.Predictive capabilities help foresee customer behavior, enabling proactive marketing strategies.Practical Insights:AI tools like Google AutoML and Salesforce Einstein offer accessible ways to integrate AI into marketing without needing extensive technical expertise.Start by organizing and cleaning customer data to ensure accuracy and effectiveness in AI analysis. Chapter-wise Breakdown Introduction & Topic Overview (00:00 - 00:10)Simplifying CLV & Its Traditional Challenges (00:10 - 02:00)The Power of AI for CLV (02:00 - 05:00)AI-Driven Benefits & Customer Insights (05:00 - 09:16)Case Study: Starbucks' Success with AI (09:16 - 12:45)Practical Steps & Final Takeaways (12:45 - End) Real-Life Example: We highlight how Starbucks uses AI to track customer interactions and adapt their marketing efforts based on real-time insights, showcasing the tangible benefits of adopting AI for CLV prediction. Why It Matters: AI-driven CLV prediction isn’t just a trend; it’s a strategic shift that allows marketers to build stronger, data-backed relationships with their customers and stay ahead in an ever-competitive landscape. Final Thought: If you’re not using AI for CLV yet, now is the time to start. Small, data-driven steps can lead to significant improvements in customer retention and business growth.

Learn AI by shipping small, useful things—no fluff. In 30 seconds: who this is for, what you’ll get, and how to use each episode today. New episodes every Tuesday. Welcome to Data & AI with Mukundan—a weekly show where real-world problems meet practical AI. You’ll get honest stories, clear steps, and tiny builds you can apply the same day you listen. We cover what to automate, how to check quality, and how to keep outputs reliable—so you don’t ship junk. Starter Pack Get Interviews: Rejected in minutes? Use AI to rewrite your portfolio (so recruiters care). https://rss.com/podcasts/data-ai-podcast/2142985/Present Faster: Turn your blog into a presentation—without PowerPoint stress. https://rss.com/podcasts/data-ai-podcast/2147688/Save Family Stories: The app I built to talk to my mom & dad after they’re gone. https://rss.com/podcasts/data-ai-podcast/2153606/Podcast home: https://rss.com/podcasts/data-ai-podcast/ Email: [email protected]

In this episode of The Deep Dive, we explore Retrieval-Augmented Generation, or RAG, and its revolutionary impact on AI. We break down five game-changing applications of RAG, each transforming how AI interacts with real-time data and complex information. Discover how RAG is enhancing everything from customer service to academic research, by tackling challenges like outdated information and static AI models. Key Highlights: Real-time Q&A Systems: How RAG ensures that AI provides the most up-to-date answers, making customer support smarter and more reliable.Dynamic Content Creation: No more stale reports—learn how RAG allows for content that updates in real-time.Multi-Source Summarization: Summarizing complex, often conflicting information from multiple sources for balanced insights.Intelligent Chatbots: Discover how RAG-driven chatbots bring up-to-the-minute responses, improving user experience in real-time.Specialized Knowledge Integration: From medical diagnoses to legal precedents, see how RAG is revolutionizing fields requiring precise, specialized knowledge.Tune in to see how RAG is shaping the future of AI, making it more adaptable, intelligent, and responsive to our world’s ever-changing landscape! Resources: Article: "5 Game-Changing Techniques to Boost Your NLP Projects with Retrieval Augmented Generation"Explore hands-on with RAG at Hugging FaceResearch and community forums for deeper learning and discussions on RAG

Episode SummaryIn this episode, we dive into the power of AI to tackle the often-overwhelming world of PDFs and technical documents. We explore an innovative tool that makes PDFs more accessible and actionable, from summarizing key insights to generating audio and even preparing Q&A. If you work in data science, AI, or any field that requires you to stay up-to-date with extensive documentation and research, this tool could be your new best friend. Topics Covered: * The PDF Dilemma * How data professionals face information overload from research papers, reports, and white papers. * Why keeping up with technical documents can feel like a “black hole” for your time. * AI-Powered PDF Assistance * Overview of an AI tool that leverages PyPDF2 and HuggingFace for seamless PDF extraction and summarization. * Using Google Text-to-Speech to convert summaries into audio for learning on the go. * Interactive Content Generation * How the tool creates a more interactive PDF experience by generating questions and answers. * Scenarios where this could be useful: preparing for presentations, understanding dense research, and managing technical documentation. * Real-World Scenarios and Use Cases * Examples of how a data scientist, data analyst, or any professional could save time and improve understanding. * AI as a “study buddy” for deeper learning and faster, more efficient information processing. * Balancing AI with Critical Thinking * The importance of using AI as a tool rather than a replacement for human expertise. * How AI challenges us to become more thoughtful consumers of information and better thinkers overall. Key Takeaways: * Save Time and Boost UnderstandingEmbrace AI to extract core insights from complex documents, potentially freeing up hours each week to focus on high-impact tasks. * Learn on the GoTurn PDF content into audio to make commuting, exercising, or downtime more productive. * Engage with Information InteractivelyUse the tool’s Q&A generation feature to explore documents in a more interactive way, perfect for preparing presentations or deep-diving into research. Final Thought:Imagine applying this technology not only to PDFs but also to other information sources like websites, articles, and even books. As AI continues to evolve, how might it transform the way we learn, work, and think? Call to ActionIf this resonates with you, let us know! Share what types of PDFs or documents you’d tackle with this AI-powered tool and how you think it could change your workflow. Don’t forget to subscribe for more insights on the latest AI tools and how they’re shaping the future of work and learning. Link: Blog Post Link This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit mukundansankar.substack.com

Summary: In this episode, we dive into the world of pet health and AI with a focus on an innovative AI-powered pet health chatbot. Inspired by the real-life experience of pet owners, this project tackles the late-night panic every pet parent knows all too well—when your pet gets into something they shouldn’t and you're left searching for answers online. Using the cutting-edge capabilities of GPT-3.5, this chatbot cross-references trusted sources like the ASPCA and AVMA to provide clear, reliable advice on common pet health concerns. While it’s not a replacement for a vet, it’s an invaluable tool for empowering pet owners to take charge of their pet’s health, reducing stress, and improving early detection of potential issues. Join us as we explore how this technology can make a real difference, not just for individual pet owners, but for the entire field of veterinary medicine. Key Takeaways: * The inspiration behind developing an AI-powered pet health chatbot for everyday pet owners. * How GPT-3.5 compares to a human "genius" (think Mike Ross from Suits). * Why trustworthy sources like the ASPCA and AVMA are crucial for providing reliable pet health advice. * A breakdown of how the chatbot works and what it can do for you (or your pet-loving friends). * The future potential of AI in pet health and how it could change the landscape of veterinary care. How do you think having an AI-powered tool like this at your fingertips would change the way we approach pet health? Share your thoughts in the comments or reach out on mukundansankar.substack.com! Links: How I Built an AI-based Chatbot for Diagnosing Pet Health - Blog ASPCA AVMA This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit mukundansankar.substack.com

Episode Notes Ever wondered how AI can transform business strategy? In this episode, we dive into the fascinating world of AI-powered SWOT analysis. We are tackling an old problem using a new approach. Using the latest technology, like GPT-3.5, companies can now analyze their strengths, weaknesses, opportunities, and threats with lightning speed. Join us as we explore how AI is reshaping the way we understand market dynamics, financial data, and competitive landscapes, with real-world examples from Google and Meta. Whether you're an investor, entrepreneur, or just curious about the future of business, this episode is packed with insights you won't want to miss! Thanks for reading Data, AI, Productivity & Business with a Little Personality! Subscribe for free to receive new posts and support my work. Key Topics Covered: * What is SWOT Analysis?: A quick refresher on this cornerstone of business strategy (Strengths, Weaknesses, Opportunities, and Threats). * AI Meets Business Strategy: How GPT-3.5 and AI technology are revolutionizing traditional SWOT analysis by speeding up data processing and uncovering deeper insights. * Real-World Examples: AI-driven SWOT analysis of Google and Meta, revealing potential vulnerabilities and opportunities for these tech giants. * Google: Over-reliance on ad revenue and the challenges posed by ad blockers. * Meta: Data privacy issues, regulatory hurdles, and user trust challenges. * Competitive Edge: How AI can give businesses a leg up by performing real-time competitive analysis and market trend predictions. * Beyond Business: Could AI also be used to analyze career paths, personal strengths, and even suggest side hustle ideas? We explore the exciting future possibilities of AI-powered insights. Why Listen? This episode is perfect for anyone interested in how cutting-edge AI tools are transforming not just the business world, but potentially the way we approach decision-making in our own lives. Tune in to find out how AI is making sophisticated analysis more accessible, and what that means for the future. Links & Resources: Blog Post on how AI is changing the SWOT game What is SWOT Analysis? What is Python Programming? More about GPT-3.5 Build and share Python Based Data Apps with Streamlit Thanks for reading Data, AI, Productivity & Business with a Little Personality! Subscribe for free to receive new posts and support my work! This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit mukundansankar.substack.com

Welcome back to the podcast! The host, Mukundan Sankar, is an experienced data professional and AI researcher. This episode will discuss Retrieval Augmented Generation (RAG) and how it's transforming our relationship with information.23 The Problem of Information Overload: We are constantly bombarded with information, making it challenging to find what truly matters. Traditional AI models and search engines can provide inaccurate, outdated information, or even fabricate information (AI hallucination). What is RAG? RAG is an AI model combining retrieval and generation, offering the best of both worlds. Retrieval: Like a super-powered search engine, it searches vast data sources (documents, articles, reports) for the most relevant information based on the user's query. Generation: Takes the retrieved data and summarizes it clearly, concisely, and engagingly. How RAG Differs from Traditional Methods: RAG goes beyond simple keyword matching; it seeks deeper connections, patterns, and contextual data. It's grounded in real-time data from reliable sources, ensuring accuracy and trustworthiness. Real-World Applications of RAG: Personalized News Podcasts: RAG can scan news articles, extract key points, and convert them into an easily digestible audio format. Here is a look at my blog which looks at the application of RAG to convert Text News to Audio. Research Summarization: It can condense complex research papers and scientific reports into key takeaways, saving users time and effort. Efficient Workflows: RAG can summarize lengthy reports, highlighting the most crucial points for faster decision-making. The Benefits of RAG: Personalized Learning and Information Processing: RAG filters out irrelevant data and presents only what's useful to the individual. Increased Efficiency: It automates information gathering and summarization, freeing up time for other tasks. The Importance of Responsible AI Use: While RAG is a powerful tool, its impact depends on our choices. It's crucial to use RAG ethically and thoughtfully to shape a positive future. What’s Next? Don't miss out on future episodes exploring exciting tech trends, data projects, and innovations! If you found this useful, please subscribe to stay updated! Embrace curiosity, keep learning, and stay tuned – the AI revolution is just beginning! You can also find me on Medium and Substack. A blog that talks about application of RAG in News Articles here This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit mukundansankar.substack.com

Episode Summary: In this episode, we dive into the exciting world of AI and Large Language Models (LLMs) and how they're revolutionizing marketing. Gone are the days of generic campaigns and guesswork. With AI, marketing is becoming highly personalized, insight-driven, and responsive to individual customer needs—all in real-time. Key Points Covered: * The Shift from Data-Driven to Insight-Driven MarketingDiscover how marketing is evolving from simply collecting data to understanding the "why" behind customer behavior. AI allows marketers to predict customer preferences, making campaigns more targeted and effective. * AI-Powered Personalization at ScaleLearn how AI can dig into customer data to deliver hyper-personalized experiences, like suggesting a product based on your previous purchases, time of day, or even the weather in your location. * Customer Journey Mapping with AIAI is now capable of mapping every step of a customer’s interaction with a brand, from the first website visit to the final purchase, helping marketers identify friction points and optimize the entire journey. * The Power of Real-Time AI DashboardsForget the overwhelming spreadsheets! AI-powered dashboards are the new standard, delivering clear, actionable insights in real-time across all marketing channels. * Ethical Considerations in AI-Driven MarketingWith great power comes great responsibility. We explore how marketers can walk the fine line between personalization and privacy, and why transparency and trust are critical in this AI-powered era. * The Future of AI in Customer ExperienceFrom chatbots that truly understand your needs to online shopping experiences that adapt to you, AI is poised to make our everyday interactions with brands smoother and more enjoyable. Memorable Quote:"It’s like having a dedicated marketing team for every single customer." Ethical Discussion:We discuss the responsibility marketers have in ensuring AI respects data privacy and builds trust with consumers. Regulations like GDPR are setting important standards, but it’s up to each brand to find the balance between personalization and privacy. Final Thought:As AI continues to reshape the marketing landscape, it's crucial for brands and customers alike to stay informed, ask questions, and participate in the conversation about how these technologies are used. Have thoughts on how AI is transforming marketing? Share your insights with us, and stay curious for the next episode as we dive deeper into the world of AI, marketing, and beyond. Send me an email at [email protected] This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit mukundansankar.substack.com