Podcast
The Practical AI Digest
Hosted by Mo Bhuiyan · EN
Distilling AI/ML theory into practical insights. One concept at a time. No jargon.
21episodes
Episodes
Newest firstAll episodes
Aligning AI with Human Intent: RLHF in Action
Mar 1700:24:31Tap to summarizeIn this episode, we demystify how researchers teach AI models to behave helpfully and safely using Reinforcement Learning from Human Feedback (RLHF). We discuss why even very large models can generate undesired outputs and how RLHF addresses this by incorporating human preferences. You’ll learn how methods like InstructGPT were trained: first by gathering human-written demonstration responses, then by having humans rank model outputs to train a reward model, and finally using reinforcement learning (e.g. with PPO) to fine-tune the model so that it better aligns with what users want. We also talk about improvements like Constitutional AI and why aligning AI with human values is an ongoing challenge.
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AI for Code: How Models Write Software
Mar 300:30:36Tap to summarizeThis episode explores the rise of AI coding assistants. We discuss how models like OpenAI’s Codex (which powers GitHub Copilot) are trained on millions of code repositories to generate software from natural language prompts. You’ll hear how these models can autocomplete functions or even draft whole programs, and what they’re capable of today, as well as their limits (like generating errors or insecure code if not carefully guided). We also talk about their impact on developer productivity and the future of programming, where AI becomes a pair programmer that can handle the boilerplate, letting developers focus on the creative parts of coding.
Transcribe →Multimodal Models: Combining Vision, Language, and More
Feb 1700:28:46Tap to summarizeThis episode explores multimodal AI : models that can see, read, and even hear. We explain how models like OpenAI’s CLIP learn joint representations of images and text (by matching pictures with their captions), enabling capabilities like image captioning and visual search. You’ll learn why multimodal systems represent the next leap toward more human-like AI, processing text, images, and audio together for richer understanding. We also discuss recent multimodal breakthroughs (from GPT-4’s vision features to Google’s Gemini) and how they allow AI to analyze content the way we do with multiple senses.
Transcribe →Efficient Fine-Tuning: Adapting Large Models on a Budget
Feb 300:28:53Tap to summarizeThis episode dives into strategies for fine-tuning gigantic AI models without needing massive compute. We explain parameter-efficient fine-tuning methods like LoRA (Low-Rank Adaptation), which freezes the original model and trains only small adapter weights, and QLoRA, which goes a step further by quantizing model parameters to 4-bit precision. You’ll learn why techniques like these have become essential for customizing large language models on modest hardware, how they preserve full performance, and what recent results (like fine-tuning a 65B model on a single GPU) mean for practitioners.
Transcribe →Diffusion Models: AI Image Generation Through Noise
Jan 2000:24:38Tap to summarizeIn this episode, we break down what diffusion models are and why they’ve become the go-to method for AI image generation. You’ll learn how these models gradually add and remove noise to transform random pixels into coherent images, enabling use cases from art creation to image restoration. We also explore recent advances like latent diffusion, which compresses the generation process for efficiency, and discuss how diffusion techniques have achieved state-of-the-art results in text-to-image tasks while remaining flexible for control and guidance.
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Graph Neural Networks: Learning from Connections, Not Just Data
Sep 3000:31:15Tap to summarizeThis episode breaks down what graph neural networks (GNNs) are and why they matter. You’ll learn how GNNs use nodes and edges to represent relationships and how message passing lets models make sense of social, biological, and networked data. We’ll also cover recent advancements like PGNN for multi-modal graphs and common pitfalls like scalability and over-smoothing.
Transcribe →Neuro-Symbolic AI: Combining Learning With Logic
Sep 1600:24:49Tap to summarizeIn this episode, we explain what neuro-symbolic AI is and why it matters. You’ll learn how neural networks handle patterns, how symbolic systems handle rules, and how combining the two can help models reason more reliably. We also cover real examples where this approach is already being applied in assistants and robotics, showing how it could make AI systems more trustworthy and useful.
Transcribe →LLMs in Chip Design: How AI Is Entering the Hardware Workflow
Sep 200:20:08Tap to summarizeIn this episode, we look at how large language models are being used in chip and hardware design. We break down what LLM-aided design actually means, how models can generate HDL code, assist with testbench creation, and even support formal verification. You'll also hear about real-world tools like ChatEDA and how companies are starting to use AI in their silicon development workflows.
Transcribe →How Embeddings and Vector Databases Power Generative AI
Aug 1900:18:32Tap to summarizeThis episode explains how embedding models turn language into numerical vectors and how vector databases like Pinecone, FAISS, or Weaviate store and search those vectors efficiently. You'll learn how this system enables GenAI models to retrieve relevant information in real-time, power RAG pipelines, and scale up tool-augmented LLM workflows.
Transcribe →Agentic AI: What Happens When Models Start Acting
Aug 5, 202500:19:11Tap to summarizeIn this episode, we explore agentic AI systems built to not just predict or classify, but to plan, reason, and act autonomously. We break down what makes these models different, how they use tools, memory, and feedback to complete tasks, and why they represent the next step beyond traditional LLMs. You’ll hear how concepts like action loops, world modeling, and autonomous decision-making are shaping everything from research tools to enterprise automation.
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