Meta’s loss is Thinking Machines’ gain

Weiyao Wang spent eight years at Meta — his first job out of college — helping build multimodal perception systems and ...
Devdiscourse

Blockchain-based AI education model boosts speed, privacy and accuracy

The researchers argue that traditional centralized learning platforms are no longer equipped to handle the scale, speed, and ...
Frontiers

Multimodal input for vocabulary learning: Chinese EFL learners’ perceived effectiveness across input combinations, word types, and proficiency levels

This study investigated how Chinese learners of English perceive the effectiveness of different multimodal input for vocabulary learning. Forty participants perceived 14 combinations of visual, ...
IEEE

The Impact of Eye Movement Examples on Cognitive Processes in Standard Operating Procedure Learning: A Multimodal Human–Computer Interaction Study

Abstract: This study explores the application and effectiveness of Eye Movement Modeling Examples (EMME) in learning Standard Operating Procedures (SOP) in the manufacturing industry, where improving ...
Microsoft

Phi-4-reasoning-vision and the lessons of training a multimodal reasoning model

In this post, we share the motivations, design choices, experiments, and learnings that informed its development, as well as an evaluation of the model’s performance and guidance on how to use it. Our ...
eLife

Comprehensive Neural Representations of Naturalistic Stimuli through Multimodal Deep Learning

This study presents a valuable application of a video-text alignment deep neural network model to improve neural encoding of naturalistic stimuli in fMRI. The authors provide convincing evidence that ...
marktechpost

How to Design Complex Deep Learning Tensor Pipelines Using Einops with Vision, Attention, and Multimodal Examples

In this tutorial, we walk through advanced usage of Einops to express complex tensor transformations in a clear, readable, and mathematically precise way. We demonstrate how rearrange, reduce, repeat, ...