AI Coffee Break with Letitia
40 Episodes
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Lighthearted, bite-sized explainers on the latest technical advances in AI, from large language models and text-to-image models to natural language processing and computer vision.Links:Patreon: https://www.patreon.com/AICoffeeBreakKo-Fi: https://ko-fi.com/aicoffeebreakTwitter / X: https://twitter.com/AICoffeeBreakThreads: https://www.threads.net/@ai.coffee.breakLinkedIn: https://www.linkedin.com/in/letitia-parcalabescu/Reddit: https://www.reddit.com/r/AICoffeeBreak/Website: https://aicoffeebreak.com/Substack: https://aicoffeebreakwl.substack.com/
PCA explained with intuition, a little math and code
Today at 3:37 PM
Graph Language Models EXPLAINED in 5 Minutes! [Author explanation đź”´ at ACL 2024]
![Graph Language Models EXPLAINED in 5 Minutes! [Author explanation đź”´ at ACL 2024] episode artwork](https://megaphone.imgix.net/podcasts/8a19733a-cc94-11f1-9326-3b660b2cf165/image/eb181587f52d4aa449224330586ce999.jpg?ixlib=rails-4.3.1&max-w=3000&max-h=3000&fit=crop&auto=format,compress)
Today at 3:08 PM
GANs explained | Generative Adversarial Networks video with showcase!
Today at 2:40 PM
Data-efficient Image Transformers EXPLAINED! Facebook AI's DeiT paper
Today at 2:11 PM
GPT2 wrote this 1000 subscribers special!
Today at 1:42 PM
Math Anxiety? and what it has to do with AI – 🔴at #HLF24 with Prof. Yael Tauman Kalai
Today at 1:13 PM
Can language models understand? Bender and Koller argument.
Today at 12:45 PM
REPA Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You ...
Today at 12:16 PM
The ultimate intro to Graph Neural Networks. Maybe.
Today at 11:47 AM
LLM Lecture: A Deep Dive into Transformers, Prompts, and Human Feedback
Today at 11:18 AM
Training large language models to reason in a continuous latent space – COCONUT Paper explained
Today at 10:50 AM
BERTology meets Biology | Solving biological problems with Transformers
Today at 10:21 AM
s1: Simple test-time scaling: Just “wait…” + 1,000 training examples? | PAPER EXPLAINED
Today at 9:52 AM
Adversarial Machine Learning explained! | With examples.
Today at 9:23 AM
Sparse LLMs at inference: 6x faster transformers! | DEJAVU paper explained
Today at 8:55 AM
4-Bit Training for Billion-Parameter LLMs? Yes, Really.
Today at 8:26 AM
GPT-3 explained with examples. Possibilities, and implications.
Today at 7:57 AM
Stealing Part of a Production LLM | API protects LLMs no more
Today at 7:28 AM
Token-Efficient Long Video Understanding for Multimodal LLMs | Paper explained
Today at 7:00 AM
Pre-training of BERT-based Transformer architectures explained – language and vision!
Today at 6:31 AM
AlphaEvolve: Using LLMs to solve Scientific and Engineering Challenges | AlphaEvolve explained
Today at 6:02 AM
Transformer combining Vision and Language? ViLBERT - NLP meets Computer Vision
Today at 5:33 AM
GaLore EXPLAINED: Memory-Efficient LLM Training by Gradient Low-Rank Projection
Today at 5:05 AM
Supercharging RAG with Generative Feedback Loops from Weaviate
Today at 4:36 AM
Shapley Values Explained | Interpretability for AI models, even LLMs!
Today at 4:07 AM
OpenAI's DALL-E explained. How GPT-3 creates images from descriptions.
Today at 3:38 AM
[Own work] On Measuring Faithfulness or Self-consistency of Natural Language Explanations
![[Own work] On Measuring Faithfulness or Self-consistency of Natural Language Explanations episode artwork](https://megaphone.imgix.net/podcasts/3f9ae280-cc9e-11f1-8ce0-a76051e8803b/image/22ebc5014d9640e84df319aced987a4f.jpg?ixlib=rails-4.3.1&max-w=3000&max-h=3000&fit=crop&auto=format,compress)
Today at 3:10 AM
Greedy? Min-p? Beam Search? How LLMs Actually Pick Words – Decoding Strategies Explained
Today at 2:41 AM
Preparing for Virtual Conferences – 7 Tips for recording a good conference talk
Today at 2:12 AM
The Transformer neural network architecture EXPLAINED. “Attention is all you need”
Today at 1:43 AM
Energy-Based Transformers explained | How EBTs and EBMs work
Today at 1:15 AM
Our paper at CVPR 2020 - MUL Workshop and ACL 2020 - ALVR Workshop
Today at 12:46 AM
Flow-Matching vs Diffusion Models explained side by side
Today at 12:17 AM
Transformers explained | The architecture behind LLMs
Today at 12:01 AM
A brief history of the Transformer architecture in NLP
Yesterday at 11:48 PM
Reducing Hallucinations: Merlin-Arthur Training and Evaluation EXPLAINED
Yesterday at 11:20 PM
My PhD Journey in AI / ML (while doing YouTube on the side)
Yesterday at 10:51 PM
AI understanding language!? A roadmap to natural language understanding.
Yesterday at 10:22 PM
Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution – Paper Explained
Yesterday at 9:53 PM
An image is worth 16x16 words: ViT | Vision Transformer explained
Yesterday at 9:25 PM
