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How AI can decipher dolphin communication

OpenAIMay 6, 202512Views0Likes0Comments
Sharing DolphinGemma with the research community Recognizing the value of collaboration in scientific discovery, we’re planning to share DolphinGemma as an open model this summer. While trained on Atlantic spotted dolphin sounds, we anticipate its potential utility for researchers studying other cetacean species, like bottlenose or spinner dolphins. Fine-tuning may be required for different species'…

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The CNN That Challenges ViT

Data ScienceMay 6, 20259Views0Likes0Comments
Introduction The invention of ViT (Vision Transformer) causes us to think that CNNs are obsolete.  But is this really true? It is widely believed that the impressive performance of ViT comes primarily from its transformer-based architecture. However, researchers from Meta argued that it’s not entirely true. If we take a closer look at the architectural design,…

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UniME: A Two-Stage Framework for Enhancing Multimodal Representation Learning with MLLMs

AI NewsMay 1, 202515Views0Likes0Comments
The CLIP framework has become foundational in multimodal representation learning, particularly for tasks such as image-text retrieval. However, it faces several limitations: a strict 77-token cap on text input, a dual-encoder design that separates image and text processing, and a limited compositional understanding that resembles bag-of-words models. These issues hinder its effectiveness in capturing nuanced,…

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Meta AI Introduces Token-Shuffle: A Simple AI Approach to Reducing Image Tokens in Transformers

AI NewsApril 26, 202519Views0Likes0Comments
Autoregressive (AR) models have made significant advances in language generation and are increasingly explored for image synthesis. However, scaling AR models to high-resolution images remains a persistent challenge. Unlike text, where relatively few tokens are required, high-resolution images necessitate thousands of tokens, leading to quadratic growth in computational cost. As a result, most AR-based multimodal…

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Researchers at Physical Intelligence Introduce π-0.5: A New AI Framework for Real-Time Adaptive Intelligence in Physical Systems

RoboticsApril 26, 202522Views0Likes0Comments
Designing intelligent systems that function reliably in dynamic physical environments remains one of the more difficult frontiers in AI. While significant advances have been made in perception and planning within simulated or controlled contexts, the real world is noisy, unpredictable, and resistant to abstraction. Traditional AI systems often rely on high-level representations detached from their…

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