- Mon. Oct 7th, 2024
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MIT Researchers Developed SmartEM: An AI Technology that Takes Electron Microscopy to the Next Level by Seamlessly Integrating Real-Time Machine Learning into the Imaging Process
Understanding the intricate networks in animal brains has been a big challenge for scientists, especially when studying diseases like Alzheimer’s. Traditional methods could have been faster and cheaper. Before SmartEM,…
This AI Paper from Google DeepMind Studies the Gap Between Pretraining Data Composition and In-Context Learning in Pretrained Transformers
Researchers from Google DeepMind explore the in-context learning (ICL) capabilities of large language models, specifically transformers, trained on diverse task families. However, their study needs to work on out-of-domain tasks,…
Google AI Introduces AltUp (Alternating Updates): An Artificial Intelligence Method that Takes Advantage of Increasing Scale in Transformer Networks without Increasing the Computation Cost
In deep learning, Transformer neural networks have garnered significant attention for their effectiveness in various domains, especially in natural language processing and emerging applications like computer vision, robotics, and autonomous…
This AI Research Unveils LSS Transformer: A Revolutionary AI Approach for Efficient Long Sequence Training in Transformers
A new AI research has introduced the Long Short-Sequence Transformer (LSS Transformer), an efficient distributed training method tailored for transformer models with extended sequences. It segments long sequences among GPUs,…
Researchers from China Introduce CogVLM: A Powerful Open-Source Visual Language Foundation Model
Models of visual language are strong and flexible. Next, token prediction may be used to create a variety of vision and cross-modality tasks, such as picture captioning, visual question answering,…
Google DeepMind Researchers Propose a Framework for Classifying the Capabilities and Behavior of Artificial General Intelligence (AGI) Models and their Precursors
The recent development in the fields of Artificial Intelligence (AI) and Machine Learning (ML) models has turned the discussion of Artificial General Intelligence (AGI) into a matter of immediate practical…
This AI Paper Introduces Neural MMO 2.0: Revolutionizing Reinforcement Learning with Flexible Task Systems and Procedural Generation
Researchers from MIT, CarperAI, and Parametrix.AI introduced Neural MMO 2.0, a massively multi-agent environment for reinforcement learning research, emphasizing a versatile task system enabling users to define diverse objectives and…
Researchers from MIT and NVIDIA Developed Two Complementary Techniques that could Dramatically Boost the Speed and Performance of Demanding Machine Learning Tasks
Researchers from MIT and NVIDIA have formulated two techniques that accelerate the processing of sparse tensors (Tensors serve as fundamental data structures in machine learning models, acting as multi-dimensional arrays…
A Team of UC Berkeley and Stanford Researchers Introduce S-LoRA: An Artificial Intelligence System Designed for the Scalable Serving of Many LoRA Adapters
A team of UC Berkeley and Stanford researchers have developed a new parameter-efficient fine-tuning method called Low-Rank Adaptation (LoRA) for deploying LLMs. S-LoRA was designed to enable the efficient deployment…
Researchers from Cambridge have Developed a Virtual Reality Application Using Machine Learning to Give Users the ‘Superhuman’ Ability to Open and Control Tools in Virtual Reality
Hotkeys are keyboard shortcuts typically found in traditional desktop applications. A team of researchers from the University of Cambridge explores what makes for a suitable alternative to hotkeys in a…