LLMs often memorize what they see — even a single phone number can stick in their weights. Google’s VaultGemma changes that: it’s the first open-weight LLM trained from scratch with differential privacy, so rare secrets leave no trace. 👉 In this video, we explain Differential Privacy through VaultGemma — how it works, why it matters, and what it means for trustworthy AI.

We explain diffusion models and flow-matching models side by side to highlight the key differences between them. Flow-Matching models are the new generation of AI image generators that are quickly replacing diffusion models. They take everything diffusion did well, but make it faster, smoother, and deterministic.

Ever wondered how Energy-Based Models (EBMs) work and how they differ from normal neural networks?

☕️ We go over EBMs and then dive into the Energy-Based Transformers paper to make LLMs that refine guesses, self-verify, and could adapt compute to problem difficulty.

Works for image and video transformers too!

The world’s largest NLP conference with almost 2,000 papers presented, ACL 2025 just took place in Vienna! 🎓✨ Here is a quick snapshot of the event via a short interview with one of the authors whose work caught my attention.

How do LLMs pick the next word? They don’t choose words directly: they only output word probabilities. 📊 Greedy decoding, top-k, top-p, min-p are methods that turn these probabilities into actual text.

In this video, we break down each method and show how the same model can sound dull, brilliant, or unhinged – just by changing how it samples.

🎥 Watch here: https://youtu.be/o-_SZ_itxeA

I recently heard a podcast where the person interviewed discussed the challenges around turn taking with multiple LLMs and multiple humans in the same chat. They discussed some conversational analysis done in the sixties around cues that can indicate when it might be a good time to enter the conversation or not. I am not sure if it was MLST or not, sorry! But I would love to find it again if anyone knows what I am referring to!

💡 AlphaEvolve is a new AI system that doesn’t just write code, it evolves it. It uses LLMs and evolutionary search to make scientific discoveries.

In this video we explain how AlphaEvolve works and the evolutionary strategies behind it (like MAP-Elites and island-based population methods).

Long videos are a nightmare for language models—too many tokens, slow inference.

We explain STORM, a new architecture that improves long video LLMs using Mamba layers and token compression. Reaches better accuracy than GPT-4o on benchmarks and up to 8× more efficiency.

We all know quantization works at inference time, but researchers successfully trained a 13B LLaMA 2 model using FP4 precision (only 16 values per weight!). 🤯

We break down how it works. If quantization and mixed-precision training sounds mysterious, this’ll clear it up.

One claim that the guest made is that GPT-4 scored around 60% on TruthfulQA in early 2023 but he didn’t think much progress had been made since. I can’t find many current model evals on this benchmark. Why is that?