AI Bites: The Academic Series

What happens when you take the Transformer architecture and scale it up with massive amounts of compute and data? You get Large Language Models (LLMs). We wrap up Week 1 by connecting the base architecture to the modern AI tools you use every day. Key Topics: Scaling Up: How adding more parameters and training data fundamentally changes the capability of a Transformer. Pre-training vs. Fine-tuning...

Now that we know why Transformers work, it’s time to look under the hood. In this episode, we strip the architecture down to its studs, exploring the specific structural components that allow these models to understand complex language. Key Topics: Encoders and Decoders: The difference between "reading" the input data and "writing" the output prediction. Self-Attention & Mu...

We are kicking off our brand new CME295 series by going back to the breakthrough that started the current AI era. If you want a clear, foundational understanding of what a Transformer actually is and why it replaced older AI models, this is your starting point. Key Topics: The Pre-Transformer Era: Why older sequence models (like RNNs and LSTMs) bottlenecked when trying to process long paragraphs o...

Short on time? We’ve distilled the entire Stanford CS25: Transformers United course into a single 10-minute video. This "Cram Session" covers the journey of the Transformer architecture from a text-processing breakthrough to the engine driving the future of biology, visual media, and reasoning. Watch or listen for the "Best Of" our course deep dives: The Architecture: How Atten...

Generating a single image is hard; generating a coherent video means mastering the dimension of time. In our CS25 finale, we break down how Transformers are being engineered to understand motion, physics, and object permanence to generate high-fidelity video content frame-by-frame. Key Topics: The Time Dimension: How "Temporal Attention" allows a model to track where an object is moving...

Can an AI learn the "physics" of the human body? In this episode, we dive into the concept of World Models in medicine. We explore how Transformers are moving beyond simply analyzing static medical records to actually simulating and predicting disease progression and treatment outcomes over time. Key Topics: Defining World Models: What it means for an AI to build an internal representati...

Transformers aren't just for text anymore. This episode unpacks the massive shift in visual AI: merging the power of Transformers with Diffusion models. We break down how the architecture behind text generation is now the engine driving state-of-the-art image and video creation. Key Topics: The Evolution of Visual AI: Moving away from traditional U-Nets and fully embracing Diffusion Transforme...

What happens when you treat the building blocks of life as a language? In this episode, we explore how the exact same Transformer architecture used for ChatGPT is being applied to biology. DNA, RNA, and proteins are essentially biological sequences, and AI is learning to "read" them to revolutionize medicine and research. Key Topics: Nature's Language: How amino acids and DNA sequenc...

We wrap up Week 1 by zooming out to the endgame of AI research: Artificial General Intelligence (AGI). What separates our current generative tools from true, human-level reasoning? We discuss the theoretical hurdles, scaling laws, and what the leap from "narrow AI" to AGI might actually look like. Key Topics: Defining AGI: What it means for a system to match or exceed human cognitive cap...

Description: Having covered the base architecture, we now look at how these models learn to behave. This episode explores Reinforcement Learning (RL) within the context of modern foundation models, focusing on how AI transitions from simply predicting text to making optimized decisions. Key Topics: RL Fundamentals: Agents, environments, and reward functions. Beyond Next-Word Prediction: How models...

We are kicking off a brand new course with the architecture that changed everything: CS25. In this episode, we break down the fundamental mechanics of Transformers. If you've ever wondered how modern large language models actually process information, this is where it starts. Key Topics: The Attention Mechanism: How models learn to weigh the importance of different words in a sequence. Moving...

Short on time? We’ve distilled the entire Duke University "Machine Learning Foundations for Product Managers" course into a single 10-minute recap. This is the ultimate PM "Cram Session" for bridging the gap between business strategy and data science. Watch or listen for the "Best Of" our course deep dives: The Core Vocabulary: Features, labels, and the 3 types of ML....

What happens when your data doesn't fit neatly into a straight line? We move to Tree Models. This episode explores how algorithms can mimic human decision-making through a series of "If/Then" splits, and how combining them creates incredibly powerful predictive engines. Key Topics: Decision Trees: Understanding roots, nodes, and leaves to visualize exactly how a model reaches its con...

Sometimes the best solution is the simplest one. In this episode, we unpack Linear Models—the most interpretable and transparent tools in a Product Manager's AI toolkit. We break down how the math works in plain English so you can explain your model's decisions to any stakeholder. Key Topics: Linear vs. Logistic Regression: The difference between predicting a continuous number (like price)...

A model can have 99% accuracy and still fail your users. In this episode, we tackle Model Evaluation from the Product Manager's perspective. We bridge the gap between technical model metrics (what engineers care about) and product/business metrics (what stakeholders care about). Key Topics: The Accuracy Trap: Why "Accuracy" is often a misleading metric, especially with imbalanced dat...

Building a model is about more than just data—it’s about a repeatable process. This episode walks through the lifecycle of a machine learning project, focusing on the strategic decisions a Product Manager must navigate to ensure a model is production-ready. Key Topics: The 5-Step Process: From problem definition and data collection to model evaluation. Feature & Algorithm Selection: How PMs in...

We kick off a new course from Duke University designed specifically for those leading AI products. In this episode, we strip away the code and focus on the core vocabulary and intuition every Product Manager needs to collaborate effectively with data scientists. Key Topics: Defining ML for Business: What machine learning actually is and—more importantly—what it is not . The PM’s Vocabulary: Breaki...

Short on time? We’ve distilled the entire Stanford CS21SI: AI for Social Good course into a single 10-minute video. This "Cram Session" covers the journey from ethical frameworks to technical execution across four major domains of social impact. Watch to see the "Best Of" our course deep dives: Education: The Minerva High School case study and the pitfalls of "Magic Box&qu...

Description: We’ve reached the finish line for Stanford’s CS21SI! In our final episode, we move beyond passive observation and into the world of sequential decision-making. We explore how Reinforcement Learning (RL) allows AI to learn through trial and error—and why that’s a game-changer for protecting our planet's most endangered species. Key Topics: From Perception to Action: Why "Socia...

We’re exploring how machines "see" and how that vision can be used to restore human agency. In this installment of Stanford’s CS21SI, we move from the technical mechanics of Convolutional Neural Networks (CNNs) to life-changing applications in healthcare, accessibility, and conservation. Key Topics: The Social Model of Disability: Reframing disability not as a medical failure, but as a l...

Description: How does AI decide what is "true"? This week, we dive into Natural Language Processing (NLP) . We trace the evolution from RNNs to Transformers and discuss the massive threat Large Language Models (LLMs) pose to our information ecosystems and democracy. Key Topics: The Disinformation Threat: How GPT-class models can generate coherent, grammatical, and completely fictional &q...

We’re moving from basic ML into the technical engine of modern AI: Deep Learning . In this week of Stanford’s CS21SI, we explore how neural networks can model complex, non-linear realities—specifically applying them to the escalating global wildfire crisis. Key Topics: The Neural Analogy: Understanding neurons, forward/backward passes, and backpropagation through the lens of social impact. Wildfir...

We’re kicking off a brand new series! This week we dive into CS21SI , a course that bridges the gap between technical AI development and its real-world impact. We start at the beginning: how machines learn and the ethical guardrails we need to build alongside them. Key Topics: Intro to Machine Learning: A high-level look at how models learn from data patterns. AI Ethics: Moving beyond the code to...

A quick and easy way to understand CS124 in a quick video byte. Generated by Google NotebookLM using my personal class notes.

A quick video bite on CS221 and what the whole course is about - generated by Google NotebookLM from my class notes.