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!

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

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