Natural language and machine translation

Ryan Cotterell - Two views of a language model - IPAM at UCLA

Agents & Arbiters - An Adventurer’s Guide to Multi-Agent Collaboration with LangGraph.js - Guy Royse

Vésteinn Snæbjarnarson: Transducing Language Models

JSALT Summer School Natural Language Processing

Error 404: Change the Address | Aleksander Kadiev | TEDxNBU

The Meaning Gap: Your Agent Is Correct. Your Deployment Is Not.

Do Multilingual Embedding Models Really Retrieve African Language Content Well? A Yoruba Case Study

Kimi K3: Alucinante! Analizamos a fondo el mejor modelo open source (Ep. 163)

Kimi k3: Mind-blowing! An in-depth analysis of the best open-source model (Ep. 163)

École d'été | 8 juin 2026: Natural Language Inference from Aristotle to AI par Ian Pratt-Hartmann

Summer School | 8 June 2026: Natural Language Inference from Aristotle to AI by Ian Pratt-Hartmann

Behind the Scenes: Introduction to Artificial Intelligence with Brian Yu - Chapter 5 - Communicating

Talk by Jacob Andreas (Massachusetts Institute of Technology)

I Turned Claude Into the Ultimate Second Brain

I Tested Every Claude Code Feature, These 12 Are the Best

Claude Code Dynamic Workflows Clearly Explained

J'ai étudié les LLM pendant 6 mois (et j'ai enfin compris comment ChatGPT fonctionne)

I Studied LLMs for 6 Months (And Finally Figured Out How ChatGPT Works)

Nicolas Boffi - Flow map language models

Kast : créez vos persos et parlez-leur !

Kast: create your own personas and talk to them!

Unitree IPO, Multi-Stream LLMs, Soluciones para grandes contextos

Unitree IPO, Multi-Stream LLMs, Solutions for Large Contexts

[ИАД, весна 2026] Математические методы анализа текстов. Лекция 3: Lang.Modeling, Seq2Seq, Attention

[IAD, spring 2026] Mathematical methods of text analysis. Lecture 3: Lang.Modeling, Seq2Seq, Attention

[ИАД, весна 2026] Математические методы анализа текстов. Лекция 12: Alignment

[IAD, Spring 2026] Mathematical Methods for Text Analysis. Lecture 12: Alignment

Generative AI L10: semantic properties, evaluation and societal biases of word embeddings

Generative AI L9: Skipgram, CBOW (full softmax variant, negative sampling variant), GloVe

Generative AI L8: Word embedding concept, training data preparation, prediction based encodings

Understanding LLM Capabilities on Large-scale Multilingual Real-World Clinical Data

MIT 6.S191: Language Models and New Frontiers

Building Realistic Voice Agents Has Never Been Easier

Generative AI L7: Word synonymy, similarity, relatedness, one hot vectors, term frequency, tf-idf

Generative AI L6: Morphological units, word formation processes, lexical semantics, ambiguity

[ИАД, весна 2026] Математические методы анализа текстов. Лекция 10: IR, RAG

[IAD, Spring 2026] Mathematical Methods of Text Analysis. Lecture 10: IR, RAG

Generative AI L5: Tokenization (Word piece, unigram tokenization), language related terminologies

Generative AI L4: Types of tokenization (word level, character level, subword level), BPE algorithm

Mikhail Gromov - 3/4 Generation, Transformation, Transmission, Memorization, Storage and (...)

Generative AI L2: Evolution of Natural Language Processing

Stanford CS336 Language Modeling from Scratch | Spring 2026 | Lecture 1: Overview, Tokenization

This One Plugin Just 10x’d Claude Code

Why You're Still Better Than AI at Editing Documents - CS50 Tech Talk

“Cross-lingual Transfer for Machine Translation” - Nate Robinson

“LLM-powered exploratory text analysis at scale” - Mian Zhong

NotebookLM trouve les sources, Claude fait le travail — voici comment les connecter

NotebookLM finds the sources, Claude does the work — here's how to connect them

Leveraging Large Speech Language Models as Evaluators for Expressive Speech - Bismarck Odoom