Explainable AI: Opening the Black Box

Explainable AI: Opening the Black Box

In this episode, we look at how researchers are making AI models more transparent and interpretable. We discuss techniques like SHAP values and LIME that explain model predictions by attributing importance to features! So an AI system isn’t just a black box, you can understand why it made a decision. You’ll hear about example use cases (like explaining a medical AI’s diagnosis to a doctor or a loan model’s decision to a loan officer) and recent research into interpreting the internals of neural networks (from visualizing what vision models detect to “probing” language models’ knowledge). By the end, you’ll appreciate the growing toolkit for Explainable AI (XAI) and why it’s crucial for building trust in AI systems.

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Episoder(25)

The Evaluation Crisis: We Do Not Know How Good Our Models Actually Are

The Evaluation Crisis: We Do Not Know How Good Our Models Actually Are

MMLU is saturated. Chatbot Arena is gameable. Public benchmarks leak into training data. The only eval that matters is the one you build yourself, on your data, for your task.

30 Jul 20min

Mixture of Experts at the Edge: Running 30B Parameter Models on Your Laptop

Mixture of Experts at the Edge: Running 30B Parameter Models on Your Laptop

A 30B parameter model runs on a MacBook because only 3B parameters fire per token. Mixture of Experts splits memory cost from compute cost, and that changes everything about where AI can run.

16 Jul 20min

The Agent Interoperability Problem: Why Your AI Agents Can Not Talk to Each Other

The Agent Interoperability Problem: Why Your AI Agents Can Not Talk to Each Other

90% of enterprises deploy AI agents. Only 23% scale them. The gap is interoperability. Three protocols, MCP, A2A, and ACP, are racing to build the connective tissue before the ecosystem fragments.

2 Jul 21min

KV Cache Compression: The Memory Wall Nobody Talks About

KV Cache Compression: The Memory Wall Nobody Talks About

Your GPU is not compute-bound. It is memory-bound. The KV cache is eating half your inference budget, and two ICLR 2026 breakthroughs KVTC and TurboQuant are about to change the math entirely.

18 Jun 21min

Context Rot: Why Million-Token Windows Quietly Fail

Context Rot: Why Million-Token Windows Quietly Fail

Models advertise million-token windows but accuracy degrades well before the limit. Three recent studies, the mechanisms behind the rot, and a practitioner playbook for what to do Monday.

4 Jun 21min

LLMOps: Operating Large Language Models in Production

LLMOps: Operating Large Language Models in Production

Building an AI model is one thing: keeping a large language model running reliably in the real world is another. In this episode, we discuss LLMOps, the emerging set of practices and tools for deployi...

26 Mai 28min

TinyML & Edge AI: Machine Learning on Devices

TinyML & Edge AI: Machine Learning on Devices

In this episode, we explore how AI is moving from the cloud to tiny devices. TinyML is the field of optimizing models and algorithms to run on microcontrollers, smartphones, and other edge devices wit...

12 Mai 25min

AI Hardware: GPUs, TPUs and Beyond

AI Hardware: GPUs, TPUs and Beyond

This episode is all about the specialized hardware that makes modern AI possible. We explain how GPUs became the workhorses of deep learning by offering massive parallelism for matrix math, and how co...

28 Apr 25min

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