Neuro-Symbolic AI: Combining Learning With Logic

Neuro-Symbolic AI: Combining Learning With Logic

In this episode, we explain what neuro-symbolic AI is and why it matters. You’ll learn how neural networks handle patterns, how symbolic systems handle rules, and how combining the two can help models reason more reliably. We also cover real examples where this approach is already being applied in assistants and robotics, showing how it could make AI systems more trustworthy and useful.

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Avsnitt(26)

World Models: When AI Learns Physics Instead of Memorizing Data

World Models: When AI Learns Physics Instead of Memorizing Data

A language model can describe a falling glass. It cannot predict where the water goes. World models close the gap between AI that talks about reality and AI that can act in it.

13 Aug 19min

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 Juli 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 Juli 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 Juli 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 Juni 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 Juni 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 Maj 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 Maj 25min

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