The AI Concepts Podcast

Sheetal ’Shay’ Dhar

The AI Concepts Podcast is my attempt to turn the complex world of artificial intelligence into bite-sized, easy-to-digest episodes. Imagine a space where you can pick any AI topic and immediately grasp it, like flipping through an Audio Lexicon - but even better! Using vivid analogies and storytelling, I guide you through intricate ideas, helping you create mental images that stick. Whether you’re a tech enthusiast, business leader, technologist or just curious, my episodes bridge the gap between cutting-edge AI and everyday understanding. Dive in and let your imagination bring these concepts to life!

  1. 1월 26일

    Module 3: Context Windows & Attention Complexity

    This episode addresses the physical and mathematical limits of a model’s "short-term memory." We explore the context window and the engineering trade-offs required to process long documents. You will learn about the quadratic cost of attention where doubling the input length quadruples the computational work and why this creates a massive bottleneck for long-form reasoning. We also introduce the architectural tricks like Flash Attention that allow us to push these limits further. By the end, you will understand why context is the most expensive real estate in the generative stack.

    11분

  2. 1월 25일

    Module 3: The Lifecycle of an LLM : Pre-Training

    This episode explores the foundational stage of creating an LLM known as the pre-training phase. We break down the Trillion Token Diet by explaining how models move from random weights to sophisticated world models through the simple objective of next token prediction. You will learn about the Chinchilla Scaling Laws or the mathematical relationship between model size and data volume. We also discuss why the industry shifted from building bigger brains to better fed ones. By the end, you will understand the transition from raw statistical probability to parametric memory.

    10분

  3. 1월 6일

    Module 2: The MLP Layer - Where Transformers Store Knowledge

    Shay explains where a transformer actually stores knowledge: not in attention, but in the MLP (feed-forward) layer. The episode frames the transformer block as a two-step loop: attention moves information between tokens, then the MLP transforms each token’s representation independently to inject learned knowledge.

    8분

  4. 1월 5일

    Module 2: The Encoder (BERT) vs. The Decoder (GPT)

    Shay breaks down the encoder vs decoder split in transformers: encoders (BERT) read the full text with bidirectional attention to understand meaning, while decoders (GPT) generate text one token at a time using causal attention. She ties the architecture to training (masked-word prediction vs next-token prediction), explains why decoder-only models dominate today (they can both interpret prompts and generate efficiently with KV caching), and previews the next episode on the MLP layer, where most learned knowledge lives.

    8분

  5. 1월 5일

    Module 2: Multi Head Attention & Positional Encodings

    Shay explains multi-head attention and positional encodings: how transformers run multiple parallel attention 'heads' that specialize, why we concatenate their outputs, and how positional encodings reintroduce word order into parallel processing. The episode uses clear analogies (lawyer, engineer, accountant), highlights GPU efficiency, and previews the next episode on encoder vs decoder architectures.

    9분

  6. 1월 3일

    Module 2: Inside the Transformer -The Math That Makes Attention Work

    In this episode, Shay walks through the transformer's attention mechanism in plain terms: how token embeddings are projected into queries, keys, and values; how dot products measure similarity; why scaling and softmax produce stable weights; and how weighted sums create context-enriched token vectors. The episode previews multi-head attention (multiple perspectives in parallel) and ends with a short encouragement to take a small step toward your goals.

    12분

  7. 1월 3일

    Module 2: Attention Is All You Need (The Concept)

    Shay breaks down the 2017 paper "Attention Is All You Need" and introduces the transformer: a non-recurrent architecture that uses self-attention to process entire sequences in parallel. The episode explains positional encoding, how self-attention creates context-aware token representations, the three key advantages over RNNs (parallelization, global receptive field, and precise signal mixing), the quadratic computational trade-off, and teases a follow-up episode that will dive into the math behind attention.

    12분

  8. 1월 3일

    Module 2: The Transformer Architecture: History - The Bottleneck That Broke Language Models

    Shay breaks down why recurrent neural networks (RNNs) struggled with long-range dependencies in language: fixed-size hidden states and the vanishing gradient caused models to forget early context in long texts. He explains how LSTMs added gates (forget, input, output) to manage memory and improve short-term performance but remained serial, creating a training and scaling bottleneck that prevented using massive parallel compute. The episode frames this fundamental bottleneck in NLP and sets up the next episode on attention, ending with a brief reflection on persistence and steady effort.

    7분
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소개

The AI Concepts Podcast is my attempt to turn the complex world of artificial intelligence into bite-sized, easy-to-digest episodes. Imagine a space where you can pick any AI topic and immediately grasp it, like flipping through an Audio Lexicon - but even better! Using vivid analogies and storytelling, I guide you through intricate ideas, helping you create mental images that stick. Whether you’re a tech enthusiast, business leader, technologist or just curious, my episodes bridge the gap between cutting-edge AI and everyday understanding. Dive in and let your imagination bring these concepts to life!

정보

  • 제작진
    Sheetal ’Shay’ Dhar
  • 방송 연도
    2024년 - 2026년
  • 에피소드
    51
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    전체 연령 사용가
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    © Copyright 2024 All rights reserved.
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    The AI Concepts Podcast