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The AI Concepts Podcast

The AI Concepts Podcast

Written by: Sheetal ’Shay’ Dhar
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About this listen

 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!Copyright 2024 All rights reserved. Education Science
Episodes
  • Module 2: The MLP Layer - Where Transformers Store Knowledge

    Module 2: The MLP Layer - Where Transformers Store Knowledge
    Jan 6 2026

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

    Module 2: The Encoder (BERT) vs. The Decoder (GPT)
    Jan 5 2026

    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.
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    8 mins
  • Module 2: Multi Head Attention & Positional Encodings

    Module 2: Multi Head Attention & Positional Encodings
    Jan 5 2026

    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.
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    9 mins
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