🤖How do embeddings help Generative AI?

🧠They turn information into vectors that power:
🔹 Semantic search
🔹 RAG applications
🔹 Smart recommendations
🔹 Context retrieval

🚀Explore the tools, benefits & challenges!
👉Read the full article: www.linkedin.com/pulse/how-do...

#GenAI #AI #Embeddings #RAG

EmbeddingGemma 2：テキスト・画像・音声・動画を統合するオープンな軽量マルチモーダル埋め込みモデル

Google DeepMindが発表した「EmbeddingGemma 2」は、テキスト・画像・音声・動画を単一の空間にマッピングする7.4億パラメータのオープンな軽量マルチモーダル埋め込みモデルです。

#Multimodal #Embeddings #OpenSource #EdgeAI #RAG

Google DeepMind Releases EmbeddingGemma 2, a Local Multimodal Embedding for Text, Image, Audio and Video

#ai #embeddings #google #multimodal

New #J2C Certification:

The Linear Geometry of Interpretable Tokens: Jailbreaking Attacks and Defenses for Unlearned Diff...

Siyi Chen, Yimeng Zhang, Sijia Liu, Qing Qu

https://openreview.net/forum?id=CwGv55bbj4

#embeddings #embedding #tokens

Gradient-based Sample Selection for Faster Bayesian Optimization

Qiyu Wei, Haowei Wang, Zirui Cao, Songhao Wang, Richard Allmendinger, Mauricio A Álvarez

Action editor: Jeff Phillips

https://openreview.net/forum?id=Ysr1zUeuxz

#optimization #surrogate #embeddings

🌎 Welcome LGND as a Bronze Sponsor of #FOSS4GNA

LGND builds #AI that understands the planet — turning #satellite imagery into searchable #embeddings so you can query the Earth in plain language, find similar features, and track change. No model training needed.
Learn more:
www.foss4gna.org/202...

В последнее время довольно много работаю над agent-memory-cpp.

Это C++ библиотека для быстрой, компактной и встраиваемой памяти AI-агентов: хранить embeddings локально и искать по ним без отдельного vector DB сервиса.

#cpp #VectorSearch #VectorDatabase #Embeddings #ANN #AI

Un millón de #Embeddings de 1.024 dimensiones en float32: ~4 GB. Casi nunca hace falta tanto.

Matryoshka a 256 + binaria: ~32 MB, 128 veces menos. Y con rescoring recuperas buena parte de la calidad.

Mide contra el original: el ahorro que vale es el que no se nota en tu #RAG.

#PostgreSQL #in-process with #vectorsearch extensions directly #embedded in your #Python app. #OpenSource Apache 2.0 lic

- Similarly search: #pgvector + #pgvectorscale, high-performance storage
- #FullText search: #pg_textsearch, #BM25 and #ranking
#AI #LLM #Embeddings #Agents #RAG

New arXiv work shows scaling T5Gemma embeddings improves continuous diffusion language model performance, though highly discriminative raw embeddings can hurt sampling. Authors search for more diffusible embedding…

#GenerativeAI #DiffusionModels #LLM #Embeddings
https://arxiv.org/abs/2610.01016

Así se armó una app RAG con Gemini gratis, sin vector DB

Conocé el nuevo hito en IA: una app que implementa RAG con Gemini para respuestas más precisas y rápidas. Descubrí cómo funciona ahora mismo.

#ragcongemini #embeddings #geminiapi #nextjs #retrievalaugmentedgeneration

🚨 Confused by AI terms as a developer?
Clear guide to tokens, inference, embeddings, vector DBs & RAG! 🤖
📖 Guide: www.ottorinobruni.com/ai-basics-fo...
Which concept confuses you most? 👇
#dotnet #AI #RAG #Embeddings

Contractive MASO‑Generalized Predictors for Stable Latent‑Space Learning in JEPA

BELLOULATA

Action editor: Anastasios Kyrillidis

https://openreview.net/forum?id=k2Z2gPOtlq

#embeddings #embedding #encoders

Including #Embeddings in Your #Spatial #Analysis Workflows tinyurl.com/4ud5zzhs

#data #datascience #GIS #esri #arcgis #GISchat #geospatial #mapping #ArcGISPro #geosky

🚨 Confused by AI terms as a developer?
Clear guide to tokens, inference, embeddings, vector DBs & RAG! 🤖
📖 Guide: www.ottorinobruni.com/ai-basics-fo...
Which concept confuses you most? 👇
#dotnet #AI #RAG #Embeddings

Learning Structured Set Utility Functions with Contrastive Element Representations

Jayanth Bhargav, Younggil Chang, Mahsa Ghasemi, Shreyas Sundaram

Action editor: Sebastian Tschiatschek

https://openreview.net/forum?id=SZ8mOziJBx

#embeddings #embedding #regularization

ANU-RL: A New Perspective on Weakly-Supervised Representation Learning for Visual Place Recognition

Anuradha Uggi, Sumohana S. Channappayya

Action editor: Yannis Kalantidis

https://openreview.net/forum?id=mXE4OP55il

#embeddings #embedding #recognition

Random features for Grassmannian kernel approximation with bounded rank-one projections

Remi Delogne, Laurent Jacques

Action editor: Chinmay Hegde

https://openreview.net/forum?id=wq18dZJ2pA

#kernels #grassmannian #embeddings

ARC-Encoder: learning compressed text representations for large language models

Hippolyte Pilchen, Edouard Grave, Patrick Perez

Action editor: Jonathan Berant

https://openreview.net/forum?id=lU1P9dsqfn

#encoder #encoders #embeddings