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Hosted by Jack Lakkapragada · 🇺🇸 US · EN-US · 59 episodes
Established thought leaders with verified media credentials.
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.
Jack Lakkapragada hosts AI Bites: The Academic Series, a education show with 59 episodes published.
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: T
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 ali
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 hallucination
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 th
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 v
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 Tokenizati
Today, we are pushing the absolute limits of how Language Models generate text. We move beyond basic architecture to explore how engineers are making AI insanely fast, teaching models to recover from their own mistakes,
How does a language model actually "think"? In this episode, we dive into the fascinating mechanics of AI reasoning. We move past basic text prediction to explore how modern models generate complex, multi-step logic, sel
We spend so much time building massive AI models, but how do we actually know if they are any good? In this episode, we tackle the multi-billion-dollar scientific bottleneck: evaluation. We explore why the science of mea
Up until now, we’ve looked at Language Models as isolated brains trapped in a box. In this episode, we cross the threshold into the absolute bleeding edge of AI: giving models a search engine to browse the web, memory to
We know how to build and align massive foundational models, but what if you don't have a $100 million supercomputer? In this episode, we tackle the practical wall of modern AI: compute costs. We explore how researchers a
How do we turn a raw, chaotic text-predictor into a helpful, conversational AI assistant? In this episode, we dive into the massive pipeline of Post-training. We explore the transition from Instruction Fine-Tuning to com
If the Transformer architecture gave us the engine for modern AI, this episode is all about the fuel. We are diving into the single most consequential paradigm shift in modern NLP: Pre-training. We explore how we train t
Last week, we saw how RNNs struggled with the "Bottleneck Problem" and sequential processing. This week, we explore the architecture that solved it and changed natural language processing forever: the Transformer. We bre
We are continuing our journey through Stanford's CS224N by exploring the absolute foundation of modern natural language processing. In this episode, we break down Language Models and Recurrent Neural Networks (RNNs), unp
We are looking under the hood of deep learning to understand the mathematical engine driving modern artificial intelligence: Backpropagation. In this episode, we break down how neural networks transition away from rigid
How do you teach a computer the actual meaning of a word? In this episode, we dive into the fundamental building block of modern NLP: Word Vectors. We break down how algorithms map words into a dimensional space, allowin
Welcome to a brand new series! We are diving into Stanford's CS224N. To understand where AI is today, we first need to understand how we got here. In this episode, we trace the evolution of Natural Language Processing fr
Short on time? We’ve distilled the entire Stanford CME295 course into a single, high-energy video recap. This "Cram Session" takes you on a complete journey from the absolute basics of natural language processing to the
We have reached the end of Stanford's CME295! In this course finale, we zoom out to summarize the entire journey—from the underlying Transformer architecture to the massive engineering feat of training and tuning LLMs. T
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AI Bites: The Academic Series is hosted by Jack Lakkapragada. The show is categorised under education (courses) and has published 59 episodes.
AI Bites: The Academic Series has published 59 episodes.
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