Based on the “Machine Learning ” crash course from Google for Developers: https://developers.google.com/machine-learning/crash-courseIn this episode, we’ll learn what overfitting is, how to detect it using loss curves, why model complexity matters, and how techniques like L2 regularization help improve generalization to unseen data.Disclaimer: This podcast is generated using an AI avatar voice. At times, you may notice overlapping sentences or background noise. That said, all content is directly based on the official course material to ensure accuracy and alignment with the original learning experience.
Based on the “Machine Learning ” crash course from Google for Developers: https://developers.google.com/machine-learning/crash-courseIn this episode, we break down how machine learning models learn effectively by splitting data into training, validation, and test sets. Understand the purpose of each set, why this separation matters, and how it helps reduce overfitting while improving generalization.Disclaimer: This podcast is generated using an AI avatar voice. At times, you may notice overlapping sentences or background noise. That said, all content is directly based on the official course material to ensure accuracy and alignment with the original learning experience.
Based on the “Machine Learning ” crash course from Google for Developers: https://developers.google.com/machine-learning/crash-courseThis episode explores how categorical data is transformed into usable features for machine learning models. From understanding one-hot encoding to tackling real-world labeling challenges and applying feature crosses, we break down key techniques to handle non-numeric data effectively. Perfect for anyone aiming to build better models using human-defined categories.Disclaimer: This podcast is generated using an AI avatar voice. At times, you may notice overlapping sentences or background noise. That said, all content is directly based on the official course material to ensure accuracy and alignment with the original learning experience.
Based on the “Machine Learning ” crash course from Google for Developers: https://developers.google.com/machine-learning/crash-courseIn this episode, we dive into the world of classification in machine learning—exploring how models make decisions and how we evaluate their performance. You'll learn what a confusion matrix is, how thresholds affect predictions, and what metrics like accuracy, precision, recall, and F1 score really mean in practice. Whether you're new to ML or brushing up on the fundamentals, this episode will give you the clarity you need to confidently interpret model results.Disclaimer: This podcast is generated using an AI avatar voice. At times, you may notice overlapping sentences or background noise. That said, all content is directly based on the official course material to ensure accuracy and alignment with the original learning experience.
Based on the “Machine Learning ” crash course from Google for Developers: https://developers.google.com/machine-learning/crash-courseBefore feeding data into a machine learning model, it’s crucial to understand it. This episode walks you through the essential first steps: visualizing data, calculating basic statistics like mean and percentiles, and spotting outliers that could skew your model. Whether you're using pandas or plotting histograms, these techniques lay the foundation for effective ML pipelines.Disclaimer: This podcast is generated using an AI avatar voice. At times, you may notice overlapping sentences or background noise. That said, all content is directly based on the official course material to ensure accuracy and alignment with the original learning experience.
Based on the “Machine Learning ” crash course from Google for Developers: https://developers.google.com/machine-learning/crash-courseIn this episode, we go beyond accuracy and dive into how to evaluate model performance across all thresholds using ROC curves and AUC. We also break down prediction bias—why your model might look accurate but still be off-target—and how to detect early signs of bias caused by data, features, or training bugs. Tune in to learn how to make more reliable and fair classification models!Disclaimer: This podcast is generated using an AI avatar voice. At times, you may notice overlapping sentences or background noise. That said, all content is directly based on the official course material to ensure accuracy and alignment with the original learning experience.
Based on the “Machine Learning ” crash course from Google for Developers: https://developers.google.com/machine-learning/crash-courseIn this episode, we break down logistic regression—a core algorithm used for classification. You'll learn how it calculates probabilities instead of direct values, what loss functions are used to measure mistakes, and how regularization helps prevent overfitting. It’s all about teaching machines to say “yes” or “no” with confidence.Disclaimer: This podcast is generated using an AI avatar voice. At times, you may notice overlapping sentences or background noise. That said, all content is directly based on the official course material to ensure accuracy and alignment with the original learning experience.
Based on the “Machine Learning ” crash course from Google for Developers: https://developers.google.com/machine-learning/crash-courseWhat drives a machine learning model to learn? In this episode, we explore gradient descent, the optimization engine behind linear regression, and the crucial role of hyperparameters like learning rate, batch size, and epochs. Understand how models reduce error step by step, and why tuning hyperparameters can make or break performance. Whether you're a beginner or reviewing the basics, this episode brings clarity with real-world analogies and practical takeaways.Disclaimer: This podcast is generated using an AI avatar voice. At times, you may notice overlapping sentences or background noise. That said, all content is directly based on the official course material to ensure accuracy and alignment with the original learning experience.
Based on the “Machine Learning ” crash course from Google for Developers: https://developers.google.com/machine-learning/crash-courseLinear Regression - Yale UniversityLinear and Logistic Regression - Stanford UniversityIn this episode, we'll demystify Linear Regression, exploring its power in predicting continuous values and understanding its core mechanics, from the "best-fit line" to the critical role of the least squares method. Discover real-world applications where predicting "how much" is key, and learn how to evaluate its performance effectively.Then, we'll pivot to Logistic Regression, a cornerstone for classification tasks. Understand how it tackles "yes/no" questions by predicting probabilities using the elegant sigmoid function. We'll delve into its distinct mathematical underpinnings and uncover its vital role in scenarios ranging from spam detection to medical diagnostics, alongside its unique evaluation metrics.Disclaimer: This podcast is generated using an AI avatar voice. At times, you may notice overlapping sentences or background noise. That said, all content is directly based on the official course material to ensure accuracy and alignment with the original learning experience.
Based on the “Machine Learning ” crash course from Google for Developers: https://developers.google.com/machine-learning/crash-courseIn this episode, we explore what machine learning (ML) is and why it's at the heart of today’s most innovative technologies from language translation and content recommendation to autonomous vehicles and generative AI.You’ll learn how ML shifts the problem-solving paradigm: instead of manually programming every rule, we teach software to learn from data. Through relatable examples like predicting rainfall, we compare traditional methods with data-driven ML models that uncover patterns and make smart predictions.Whether you’re new to AI or brushing up on core concepts, this episode lays a strong foundation for understanding how ML helps answer complex questions and power real-world applications.Disclaimer: This podcast is generated using an AI avatar voice. At times, you may notice overlapping sentences or background noise. That said, all content is directly based on the official course material to ensure accuracy and alignment with the original learning experience.
What started as a simple Slack bot turned into 8,000+ internal AI assistants—without a single data scientist on the team. In this episode, AI Product Manager Julian Sara Joseph shares how her team quietly scaled generative AI inside a large enterprise, built trust, and let the product speak for itself. No hype. No heavy marketing. Just real usage, smart engineering, and a platform that worked.If you’ve ever wondered what it takes to build AI people actually use, this one’s for you.Guest Speaker - Julian JosephHost - Priti Y.
AI on Trial is a special episode of Human in the Loop, where we take a deep dive into Model Autophagy Disorder (MAD)—a growing risk in artificial intelligence systems. From feedback loops to synthetic data overload, we unpack how models trained on their own outputs begin to degrade in performance and reliability. With real-world examples, emerging research, and ethical implications, this episode explores what happens when AI starts learning from itself—and what we can do to prevent it.💡 Whether you're an AI engineer, researcher, or just AI-curious, this episode gives you the tools to recognize, explain, and respond to MAD.Featured Tool:Try out the companion tool featured in the episode:MADGuard – AI ExplorerA lightweight diagnostic app to visualize feedback loops, compare input sources, and score MAD risks.Read the deeper explainer blog:🔗 What Is Model Autophagy Disorder? – Human in Loop BlogA plain-language breakdown of the research, risks, and terminology.Other Detection Tools & Frameworks DVC – Data Version Controlhttps://dvc.org/Label Studio – Open-Source Data Labeling Toolhttps://labelstud.io/DetectGPT – Classify AI-generated Texthttps://arxiv.org/abs/2301.11305Grover – Neural Fake News Detector (Allen AI)https://rowanzellers.com/grover/SynthID – AI Watermarking by DeepMindReferences- Alemohammad et al. (2023). Self-Consuming Generative Models Go MADPaper introducing MAD and simulating performance collapse in generative models.🔗 arXiv:2307.01850Yang et al. (2024). Model Autophagy Analysis to Explicate Self-consumptionBridges human-AI interaction with MAD dynamics.🔗 arXiv:2402.11271UCLA Livescu Initiative – Model Autophagy Disorder (MAD) PortalResearch hub on epistemic risk and feedback loop governance.🔗 https://livescu.ucla.edu/model-autophagy-disorder/Earth.com (2024) – Could Generative AI Go MAD and Wreck Internet Data?Reports on future data degradation and the "hall of mirrors" risk.🔗 https://www.earth.com/news/could-generative-ai-go-mad-and-wreck-internet-data/New York Times (2023) – Avianca Airline Lawsuit Involving ChatGPT BriefsLegal case where synthetic text led to real-world sanctions.🔗 https://www.nytimes.com/2023/05/27/nyregion/avianca-airline-lawsuit-chatgpt.html
In this episode, we explore the measurable expenses of pushing AI boundaries, from CO2 emissions to workforce stress, drawing on recent research. Key Learnings:The energy cost of LLMs, with training and usage emitting thousands of tons of CO2, comparable to powering entire countries by 2026.Career impacts, including stress and adaptation demands on professionals navigating automation and new roles.Operational challenges, such as mitigating hallucination and bias, requiring resource-intensive techniques like retrieval methods and dataset curation.The financial scale of AI investment, with $1 trillion at risk, contrasted by potential environmental gains if experimentation focuses on efficiency.The importance of evaluating these costs independently to understand their implications for AI/ML, data management, and career trajectories.Speaker - Priti— Founder, Human in Loop PodcastsCheck the references below. Dig deeper if you’d like! We all see things our own way, so feel free to explore.References:LLM Reliability (Rush Shahani).Carbon emissions of the ChatGPT usage: environmental impacts of the ChatGPT in different regions (Scientific Reports, 2024).Explained: Generative AI’s environmental impact (MIT News, January 2025).9 top AI and machine learning trends to watch in 2025 (TechTarget, January 2025).Energy and Policy Considerations for Deep Learning in NLP (University of Massachusetts, 2019) — Relevant for historical energy context.ChatGPT says our GPUs are melting as it puts limit on image generation requests (The Verge, March 2025) — Relevant for GPU strain from trends.
Discover how Agentic AI moves beyond reactive systems to proactive, goal-driven intelligence.Host Priti and guest Vino explore real-world uses, ethical challenges, and human-AI collaboration.Learn how Agentic AI supports productivity, especially for neurodivergent individuals, and what's next in multi-agent systems.Key Learnings:What defines Agentic AI and how it differs from traditional AIReal-world applications and productivity benefitsEthical concerns: bias, safety, and explainabilityThe role of intelligent routing and agent collaborationTools and frameworks to start building your own AI agentsGuest Speaker - Vinodhini Ravikumar — Engineering Leader at Microsoft & Founder of Mind Mosaic AICheck the references below. Dig deeper if you’d like! We all see things our own way, so feel free to explore.References -ReAct: Synergizing reasoning and acting in language models (ICLR 2023) — Yao et al.Talk to Right Specialists: Routing and Planning in Multi-agent System for Question Answering (2025) — Wu et al.Language Models as Zero-Shot Planners (ICML 2022) — Huang et al.Do As I Can, Not As I Say: Grounding Language in Robotic Affordances (2022) — Ahn et al.Code as Policies: Language Model Programs for Embodied Control (ICRA 2023) — Liang et al.BOLAA: Benchmarking and Orchestrating LLM-Augmented Autonomous Agents (ICLR 2024 Workshop) — LiuAgent AI Towards a Holistic Intelligence (2023) — Huang et al.
This episode provides a concise overview of Large Language Models (LLMs), AI systems that generate human-like text using transformer-based neural networks. It explores how LLMs learn from large datasets and highlights real-world applications such as conversational AI, document summarization, healthcare diagnostics, personalized education, and creative tasks like writing and coding. The episode also covers customization through prompt tuning and emphasizes responsible development to address biases and ensure transparency. Reference - Introduction to Large Language Models: https://developers.google.com/machine-learning/resources/intro-llms Getting Started with LangChain + Vertex AI PaLM API: https://github.com/GoogleCloudPlatform/generative-ai/blob/main/language/orchestration/langchain/intro_langchain_palm_api.ipynb Learn about LLMs, PaLM models, and Vertex AI: https://cloud.google.com/vertex-ai/docs/generative-ai/learn-resources Training Large Language Models on Google Cloud: https://github.com/GoogleCloudPlatform/llm-pipeline-examples Prompt Engineering for Generative AI: https://developers.google.com/machine-learning/resources/prompt-eng PaLM-E: An embodied multimodal language model: https://ai.googleblog.com/2023/03/palm-e-embodied-multimodal-language.html Parameter-efficient fine-tuning of large-scale pre-trained language models: https://www.nature.com/articles/s42256-023-00626-4 Parameter-Efficient Fine-Tuning of Large Language Models with LoRA and QLORA: https://www.analyticsvidhya.com/blog/2023/08/lora-and-glora/ Solving a machine-learning mystery: https://news.mit.edu/2023/large-language-models-in-context-learning-0207 Background: What is a Generative Model?: https://developers.google.com/machine-learning/gan/generative Gen AI for Developers: https://cloud.google.com/ai/generative-ai#section-3 Ask a Techspert: What is generative AI?: https://blog.google/inside-google/googlers/ask-a-techspert/what-is-generative-ai/ What is generative AI?: https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-generative-ai Building the most open and innovative AI ecosystem: https://cloud.google.com/blog/products/ai-machine-learning/building-an-open-generative-ai-partner-ecosystem Stanford U & Google's Generative Agents Produce Believable Proxies of Human Behaviors: https://syncedreview.com/2023/04/12/stanford-u-googles-generative-agents-produce-believable-proxies-of-human-behaviours/ Generative AI: Perspectives from Stanford HAI: https://hai.stanford.edu/sites/default/files/2023-03/Generative_AI_HAI_Perspectives.pdf Generative AI at Work: https://www.nber.org/system/files/working-papers/w31161/w31161.pdf The implications of Generative AI for businesses: https://www2.deloitte.com/us/en/pages/consulting/articles/generative-artificial-intelligence.html How Generative AI Is Changing Creative Work: https://hbr.org/2022/11/how-generative-ai-is-changing-creative-work Attention is All You Need: https://research.google/pubs/pub46201/ Transformer: A Novel Neural Network Architecture for Language Understanding: https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html What is Temperature in NLP?: https://lukesalamone.github.io/posts/what-is-temperature/ Model Garden: https://cloud.google.com/model-garden Auto-generated Summaries in Google Docs: https://ai.googleblog.com/2022/03/auto-generated-summaries-in-google-docs.html Few-shot learning: https://www.digitalocean.com/community/tutorials/few-shot-learning Few-shot learning: https://www.ibm.com/think/topics/few-shot-learning Few-shot prompting: https://www.promptingguide.ai/techniques/fewshot Zero-shot prompting: https://www.promptingguide.ai/techniques/zeroshot What are zero-shot prompting and few-shot prompting?: https://machinelearningmastery.com/what-are-zero-shot-prompting-and-few-shot-prompting/ NotebookLM - https://notebooklm.google.com/
This educational podcast episode is designed to explain the complex topic of generative AI to beginners. The episode covers the fundamentals of generative AI, including how it differs from traditional machine learning, explores specific types of generative AI like diffusion models (for images) and large language models (LLMs, for text), and discusses the potential benefits and challenges associated with this technology. It also delves into a real-world study of generative AI's impact on customer service agents, highlighting how AI can augment human capabilities and potentially lead to new job creation. Finally, the episode touches on future trends in generative AI, such as multimodal AI and increasing accessibility, emphasizing the importance of ethical development and responsible use. It's suitable for anyone interested in learning about AI, particularly those without a strong technical background. Reference - Ask a Techspert: What is generative AI? https://blog.google/inside-google/googlers/ask-a-techspert/what-is-generative-ai/ What is generative AI? https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-generative-ai Google Research, 2022 & beyond: Generative models: https://research.google/blog/google-research-2022-beyond-language-vision-and-generative-models/ Building the most open and innovative AI ecosystem: https://cloud.google.com/blog/products/ai-machine-learning/building-an-open-generative-ai-partner-ecosystem Stanford U & Google's Generative Agents Produce Believable Proxies of Human Behaviors: https://syncedreview.com/2023/04/12/stanford-u-googles-generative-agents-produce-believable-proxies-of-human-behaviours/ Generative AI: Perspectives from Stanford HAI: https://hai.stanford.edu/sites/default/files/2023-03/Generative_AI_HAI_Perspectives.pdf Generative AI at Work: https://www.nber.org/system/files/working_papers/w31161/w31161.pdf How Generative AI Is Changing Creative Work: https://hbr.org/2022/11/how-generative-ai-is-changing-creative-work NLP's ImageNet moment has arrived: https://thegradient.pub/nlp-imagenet/ LaMDA: our breakthrough conversation technology: https://blog.google/technology/ai/lamda/ PaLM-E: An embodied multimodal language model: https://ai.googleblog.com/2023/03/palm-e-embodied-multimodal-language.html PaLM API & MakerSuite: an approachable way to start prototyping and building generative AI applications: https://developers.googleblog.com/2023/03/announcing-palm-api-and-makersuite.html The Power of Scale for Parameter-Efficient Prompt Tuning: https://arxiv.org/pdf/2104.08691.pdf Solving a machine-learning mystery: https://news.mit.edu/2023/large-language-models-in-context-learning-0207 Attention is All You Need: https://research.google/pubs/pub46201/ Transformer: A Novel Neural Network Architecture for Language Understanding: https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html What is Temperature in NLP? https://lukesalamone.github.io/posts/what-is-temperature/ Model Garden: https://cloud.google.com/model-garden Auto-generated Summaries in Google Docs: https://ai.googleblog.com/2022/03/auto-generated-summaries-in-google-docs.html NotebookLM
This podcast is based on learnings from following resources-Cuncic, A. (2023, December 6). How to manage public speaking anxiety. Verywell Mind. https://www.verywellmind.com/tips-for-managing-public-speaking-anxiety-3024336Gershman, S. (2019, September 17). To overcome your fear of public speaking, stop thinking about yourself. Harvard Business Review. https://hbr.org/2019/09/to-overcome-your-fear-of-public-speaking-stop-thinking-about-yourselfMayo Clinic Staff. (2024, December 20). Fear of public speaking: How can I overcome it? Mayo Clinic. https://www.mayoclinic.org/diseases-conditions/specific-phobias/expert-answers/fear-of-public-speaking/faq-20058416Montijo, S. (2022, March 8). Public speaking anxiety: What is it and tips to overcome it. Psych Central. https://psychcentral.com/anxiety/public-speaking-anxietyMoseley, J. (n.d.). How I overcame my fear of public speaking [Video]. TEDx Talks. YouTube. https://www.youtube.com/watch?v=aImrjNPrh30National Social Anxiety Center. (n.d.). Public speaking anxiety. https://nationalsocialanxietycenter.com/social-anxiety/public-speaking-anxiety/Reddit user dannyjerome0. (n.d.). Speaking anxiety is killing my career [Online forum post]. Reddit, r/PublicSpeaking. https://www.reddit.com/r/PublicSpeaking/comments/15el5w3/speaking_anxiety_is_killing_my_career/U.S. National Library of Medicine. (2013). Cognitive behavioral therapy vs. exposure therapy in public speaking anxiety: A comparative clinical trial [PDF]. Neuropsychiatric Disease and Treatment, 9, 609–619. https://pmc.ncbi.nlm.nih.gov/articles/PMC3647380/pdf/ndt-9-609.pdfThe provided sources a comprehensive look at public speaking anxiety, also known as glossophobia, a common social fear. Dr. Justin Moseley's TEDx talk provides a personal narrative of overcoming this fear, emphasizing the importance of sharing one's message, shifting focus from self to audience, and transforming fear into excitement. This personal account is complemented by two medical/psychological articles from Psych Central and Verywell Mind, which define public speaking anxiety as a social anxiety disorder, list its psychological and physical symptoms, discuss potential causes and risk factors, and outline various treatment options, including therapy (CBT, VR exposure) and medication (beta-blockers like Propranolol). Finally, a Reddit thread from r/PublicSpeaking showcases real-world experiences, with individuals sharing their struggles, seeking advice, and discussing the effectiveness of various strategies, including medication, in managing their public speaking anxiety.
Ever Wonder Why Stuff Slips Your Mind So Quick? Seriously, why do we blank on things we just learned? It’s not you—it’s your brain’s quirky way. Microlearning’s here to save the day, and it’s crazy easy. Let’s dig into it on this podcast!Note: This is a demo episode with voices from an AI avatar, packed with solid research—check the references below. Dig deeper if you’d like! We all see things our own way, so feel free to explore.References - The Effectiveness of Microlearning to Improve Students’ Learning Ability Exploring learner satisfaction and the effectiveness of microlearning in higher educationMicrolearning in Diverse Contexts: A Bibliometric Analysis Using Micro-learning on Mobile Applications to Increase Knowledge Retention and Work Performance: A Reviewof LiteratureWhy Microlearning Is A Game-changer for Corporate Training Microlearning: Responding to New Learning StylesSeven Statistics that Prove the Value of Microlearning for Corporate Training Knowledge Retention: 8 Main Strategies To Improve It 8 studies that prove microlearning can't be ignoredMicrolearning in Health Professions Education: Scoping ReviewWays Microlearning Increases Attention And Retention Comparing the Effectiveness of Microlearning and eLearning Courses in the Education of Future Teachers
Join us on a deep dive into the evolving world of search technology. We explore how vector embedding search, which maps words and concepts in multidimensional space, is revolutionizing search engines by uncovering hidden relationships that traditional keyword-based search might miss. Discover how this innovation is shaping e-commerce with personalized recommendations, transforming customer service chatbots, and redefining education through adaptive learning platforms. We also discuss critical issues like algorithmic bias and its impact on healthcare and other sensitive fields, highlighting the importance of ethical AI development for a fair and inclusive future.Reference -Speculative RAG: Enhancing Retrieval Augmented Generation through DraftingA Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future DirectionsA Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language ModelsArithmetic Sampling: Parallel Diverse Decoding for Large Language ModelsUnderstanding the Dataset Practitioners Behind Large Language Model DevelopmentTowards Accurate Differential Diagnosis with Large Language ModelsThe Devil is in the Errors: Leveraging Large Language Models for Fine-grained Machine Translation EvaluationFID-LIGHT: EFFICIENT AND EFFECTIVE RETRIEVAL-AUGMENTED TEXT GENERATIONMulti-task retrieval-augmented Text Generation with Relevance SamplingPaRaDe: Passage Ranking using Demonstrations with Large Language ModelsNotebookLM