Learn With Jay on MSN

Transformer encoder architecture explained simply

We break down the Encoder architecture in Transformers, layer by layer! If you've ever wondered how models like BERT and GPT process text, this is your ultimate guide. We look at the entire design of ...
Learn With Jay on MSN

BERT demystified: Explained simply for beginners

In this video, we break down BERT (Bidirectional Encoder Representations from Transformers) in the simplest way possible—no ...
EurekAlert!

CornPheno: A game-changer in corn breeding with smartphone-based phenotyping

Corn is one of the world's most important crops, critical for food, feed, and industrial applications. In 2023, corn ...
AlphaGalileo

A new era of intelligent factories: How VLMs enable smarter, safer human–robot partnerships

Vision-language models (VLMs) are rapidly changing how humans and robots work together, opening a path toward factories where machines can “see,” ...
EurekAlert!

AI boosts understanding of ocean dynamics and marine structure safety

Fluid–structure interaction (FSI) governs how flowing water and air interact with marine structures—from wind turbines to ...

Google Real-Time Translator: More Than Word-for-Word Translations

Google's real-time translator looks ahead and anticipates what is being said, explains Niklas Blum, Director Product ...

WiMi Achieves Coexistence of Lightweight Design and High Performance by Efficiently Embedding Quantum Modules into U-Net

WiMi Hologram Cloud Inc. (NASDAQ: WiMi) focuses on holographic cloud services, primarily concentrating on professional fields such as in-vehicle AR holographic HUD, 3D holographic pulse LiDAR, ...

Bolmo’s architecture unlocks efficient byte‑level LM training without sacrificing quality

Ai2 releases Bolmo, a new byte-level language model the company hopes would encourage more enterprises to use byte level ...
Scientific Research Publishing

Multimodal Digital Phenotyping for Bipolar Disorder: Robust Mood-State Classification and Early Relapse Risk Monitoring ()

Bipolar Disorder, Digital Phenotyping, Multimodal Learning, Face/Voice/Phone, Mood Classification, Relapse Prediction, T-SNE, Ablation Share and Cite: de Filippis, R. and Al Foysal, A. (2025) ...