AI Bites: The Academic Series

Jack Lakkapragada
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Welcome to AI Bites. This podcast features AI-generated deep dives into the world’s most prestigious computer science curricula. Based on personal study notes and publicly available course material from Stanford University (CS124, CS221, and more), these episodes use Google’s NotebookLM to transform dense academic topics into conversational summaries. Perfect for learning on the go, whether you're commuting or at the gym. Disclaimer: This is an independent, AI-generated study resource and is not officially affiliated with Stanford University.

  1. 29 juil.

    SHORT VIDEO | CS224N: Multimodality

    A bite-sized, visual breakdown of CS224N's guest lecture on Multimodal Deep Learning! In this NotebookLM Video Short, we look at how models fake visual intelligence, and the craziest new frontier in AI. Key Topics: Evaluation Cheats: A visual look at how models cheat on visual tests (like the "pizza slice" flaw), and why state-of-the-art models completely fail basic compositional logic on the Winoground test (e.g., "mug in grass" vs. "grass in a mug"). Olfactory Embeddings: A quick look at how researchers are analyzing chemical compounds to give AI the sense of smell, creating word vectors for scents that map closely to human intuition. Note: This is an AI-generated visual discussion created using Google's NotebookLM, based on publicly available Stanford University course material (specifically CS224N) and personal study notes from my learning journey.

  2. 29 juil.

    EP 55 | CS224N: Multimodality

    What happens when an AI can see, hear, and even smell? In this episode, featuring insights from former Meta FAIR and Hugging Face researcher Douwe Kiela, we break out of the text-only box. We explore the impending "data ceiling" of the internet, how neural networks mathematically fuse different senses together, and the mind-bending frontier of olfactory embeddings. Key Topics: The Multimodal Imperative: Why the internet is running out of high-quality text, and how the human McGurk effect proves that true intelligence requires synthesizing multiple senses. The Art of Fusion & CLIP: A breakdown of how models combine images and text using Early, Middle, and Late fusion. Plus, how OpenAI's CLIP revolutionized the field using a dead-simple Contrastive Loss mechanism on 300 million messy internet images. The Trap of Evaluation: Why our visual benchmarks are broken. We discuss how AI "cheats" on visual question answering (like blindly guessing "2" for pizza slices) and why generative models fail basic logic tests like the Winoground dataset. Teaching AI to Smell: The crazy frontier of Olfactory Embeddings, where researchers are mapping the molecular chemistry of scents to create word vectors that correlate beautifully with human intuition. Note: This is an AI-generated discussion created using Google's NotebookLM, based on publicly available Stanford University course material (specifically CS224N) and personal study notes from my learning journey.

  3. 10 juil.

    SHORT VIDEO | CS224N: Social Impacts of NLP

    A bite-sized, visual breakdown of CS224N Lecture 16! In this NotebookLM Video Short, we pull back the curtain on why models are mathematically forced to lie, and how AI is subtly homogenizing human thought. Key Topics: The Good-Turing Proof: A visual explanation of the missing mass math ($p_0$) that physically forces perfectly calibrated models to output plausible falsehoods. The Creativity Paradox: Visualizing the "Helicopter Drop-off" analogy, and looking at the data showing how AI raises individual baselines while collapsing our collective creative diversity. Constitutional AI: A quick diagram of Anthropic's CAI pipeline, showing how it replaces biased human feedback with machine-readable ethical principles. Note: This is an AI-generated visual discussion created using Google's NotebookLM, based on publicly available Stanford University course material (specifically CS224N) and personal study notes from my learning journey.

  4. 10 juil.

    SHORT VIDEO | CS224N: Interpretability

    A bite-sized, visual breakdown of CS224N's guest lecture with Dr. Been Kim! In this NotebookLM Video Short, we look at the mathematical failure of our current interpretability tools and how researchers are extracting alien concepts from AI. Key Topics: The Saliency Illusion: A visual look at the True Positive vs. False Positive graph, proving why popular AI explanation tools (like SHAP) are no better than random guessing. The ROME Paradox: Visualizing the massive disconnect between where a fact lives in an AI's brain and whether you can successfully edit it. Teaching Magnus Carlsen: A quick look at the pipeline of how AlphaZero translates alien, superhuman concepts into concrete chess puzzles for the World Champion. Note: This is an AI-generated visual discussion created using Google's NotebookLM, based on publicly available Stanford University course material (specifically CS224N) and personal study notes from my learning journey.

  5. 10 juil.

    EP 54 | CS224N: Social and broader impacts of NLP

    We are stepping away from optimizer tricks to tackle the downstream social and cognitive impacts of language models. Featuring Professor Yejin Choi's lecture, we explore the mathematical inevitability of AI hallucinations, how AI is quietly homogenizing human creativity, and the Constitutional AI frameworks being built to keep these systems aligned. Key Topics: The Hallucination Math: Why scaling up compute won't stop hallucinations. We explain the Good-Turing estimator and the mathematical proof showing why perfectly calibrated models are actually forced to output plausible falsehoods. The Creativity Paradox: How AI-assisted writing raises the baseline for individual writers, but creates a "diversity tax" that collapses the collective variance and cultural uniqueness of human expression. Cognitive Offloading: The "Helicopter Drop-off" analogy. We discuss how bypassing intellectual struggle with instant AI answers is eroding critical thinking, leading to a synchronized "Artificial Hivemind." Constitutional AI: How Anthropic’s CAI framework replaces biased, people-pleasing human feedback with machine-readable ethical principles—finally breaking the wall between model harmlessness and helpfulness. Note: This is an AI-generated discussion created using Google's NotebookLM, based on publicly available Stanford University course material (specifically CS224N) and personal study notes from my learning journey.

  6. 10 juil.

    EP 53 | CS224N: Model Interpretability & Editing

    How do we communicate with an AI that thinks in alien, superhuman concepts? In this episode, featuring insights from Google Brain’s Dr. Been Kim, we explore the massive gap between what we think machines know and what they actually know. We expose the fatal flaws in our current interpretability tools and look at how researchers are extracting brand-new strategies from AI to teach the World Chess Champion. Key Topics: The Illusion of Saliency Maps: The shocking mathematical proof that popular interpretability tools (like SHAP and Integrated Gradients) perform no better than random guessing when trying to explain a model's behavior. The ROME Paradox: Why locating where a fact lives inside a model's brain has absolutely zero correlation with successfully editing or fixing that fact. Observing AI in the Wild: How researchers are using unsupervised clustering to map emergent AI behaviors—like discovering how multi-agent systems learn to build forts and cheat physics engines. Teaching Magnus Carlsen: The fascinating AlphaZero project where researchers force an AI to forget human chess strategies so it can isolate entirely new, superhuman concepts to teach the World Chess Champion. Note: This is an AI-generated discussion created using Google's NotebookLM, based on publicly available Stanford University course material (specifically CS224N) and personal study notes from my learning journey.

  7. 2 juil.

    Video Short: Tokenization & Multilinguality

    A bite-sized, visual breakdown of CS224N Lecture 14! In this new NotebookLM Video Short, we pull back the curtain on the invisible preprocessing layer of modern AI: Tokenization. Key Topics: The "Strawberry" Problem: A visual look at why ChatGPT can't count letters or spell backwards due to opaque token chunks. The Multilingual Tax: A direct comparison showing how English-biased tokenizers shatter non-English prompts (like Thai or Somali) into dozens of inefficient fragments, forcing global users to pay more money for worse AI performance. The Return to Bytes: A quick look at next-generation architectures (like Google's CANINE and MrT5) that dynamically drop bytes to fix this massive inequality. Note: This is an AI-generated visual discussion created using Google's NotebookLM, based on publicly available Stanford University course material (specifically CS224N) and personal study notes from my learning journey.

  8. 2 juil.

    EP 52 | CS224N: Tokenization & Multilinguality

    Language models do not actually read text—they read tokens. In this episode, we explore the invisible preprocessing layer that Andrej Karpathy says is "at the heart of much weirdness of LLMs." We demystify the Tokenization problem, explain why your AI can't count letters, and discuss the massive socio-economic inequalities baked into modern AI pricing. Key Topics: The BPE Algorithm: How Byte Pair Encoding finds the "Goldilocks" zone between infinite character sequences and rigid word vocabularies by merging frequent bytes. Strawberries & Glitch Tokens: Why ChatGPT confidently fails to spell the word "strawberry," and what the "SolidGoldMagikarp" glitch token reveals about adversarial vulnerabilities. Cross-Lingual Transfer & The Capacity Curse: How an AI trained on English sentiment can zero-shot evaluate French, but degrades in overall performance when forced to learn too many languages at once. The Tokenization Tax: The stark reality of Subword Fertility. We explain how English-biased tokenizers unfairly overcharge non-English speakers, slowing down processing speeds and degrading output quality for the global majority. Note: This is an AI-generated discussion created using Google's NotebookLM, based on publicly available Stanford University course material (specifically CS224N) and personal study notes from my learning journey.

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À propos

Welcome to AI Bites. This podcast features AI-generated deep dives into the world’s most prestigious computer science curricula. Based on personal study notes and publicly available course material from Stanford University (CS124, CS221, and more), these episodes use Google’s NotebookLM to transform dense academic topics into conversational summaries. Perfect for learning on the go, whether you're commuting or at the gym. Disclaimer: This is an independent, AI-generated study resource and is not officially affiliated with Stanford University.

Informations

  • Création
    Jack Lakkapragada
  • Années d’activité
    2026
  • Épisodes
    61
  • Classification
    Tous publics
  • Copyright
    © Jack Lakkapragada
  • Site web de l’émission
    AI Bites: The Academic Series