Marktechpost

MARCH 28, 2024

The primary issues revolve around allocating computational resources, maintaining interaction context, and integrating agents with varying capabilities and functions. Traditional approaches often lead to bottlenecks and underutilization of resources, undermining these intelligent systems’ potential efficiency and effectiveness.

Large Language Models 141

Large Language Models LLM Artificial Intelligence Artificial Intelligence 141

Marktechpost

MAY 10, 2024

Existing research in language modeling prominently features the Transformer architecture, known for its self-attention mechanism that effectively processes word sequences regardless of distance. Microsoft Research and Tsinghua University researchers have introduced a novel architecture, You Only Cache Once (YOCO), for large language models.

Large Language Models 128

Large Language Models Auto-complete BERT Machine Learning 128

Marktechpost

MAY 3, 2024

This paper highlights the most influential open-source LLMs like Mistral’s sparse Mixture of Experts model Mixtral-8x7B, Alibaba Cloud’s multilingual Qwen1.5 Researchers from Nexa AI introduce Octopus v4, a robust approach that utilizes functional tokens to integrate multiple open-source models, each optimized for specific tasks.

Artificial Intelligence 133

Artificial Intelligence Artificial Intelligence Large Language Models Natural Language Processing 133

Marktechpost

JANUARY 15, 2024

One persistent challenge is the translation of low-resource languages, which often need more substantial data for training robust models. In low-resource languages, the need for more data limits the effectiveness of traditional models. Check out the Paper. All credit for this research goes to the researchers of this project.

Large Language Models 131

Large Language Models Data Scarcity Computational Linguistics Natural Language Processing 131

Marktechpost

MARCH 5, 2024

The core issue is the immense demand for computational resources inherent in traditional LLMs. This scenario has spurred research into alternative architectures that promise comparable effectiveness with a fraction of the resource usage. employs a novel approach utilizing 1-bit ternary parameters for every model weight.

Large Language Models 134

Large Language Models LLM AI Developer AI Development 134

Marktechpost

APRIL 25, 2024

Researchers at the National Renewable Energy Laboratory (NREL) recognized this problem and developed a new tool called Sup3rCC to address it. By simulating future climate conditions, Sup3rCC helps bridge the gap between energy planning and climate research. Check out the Paper and Blog.

Machine Learning 105

Machine Learning ML Artificial Intelligence Artificial Intelligence 105

Marktechpost

FEBRUARY 14, 2024

Neural operators, specifically the Fourier Neural Operators (FNO), have revolutionized how researchers approach solving partial differential equations (PDEs), a cornerstone problem in science and engineering. The research’s core problem lies in optimizing neural operator training to make it more feasible for real-world applications.

AI 134

AI AI ML 134

Marktechpost

MARCH 18, 2024

In a new AI research paper , Google researchers introduced a pre-trained scorer model, Cappy , to enhance and surpass the performance of large multi-task language models. The paper aims to resolve challenges faced in the large language models (LLMs). Check out the Paper.

Machine Learning 126

Machine Learning Natural Language Processing Large Language Models LLM 126

Marktechpost

APRIL 30, 2024

Charts have become indispensable tools for visualizing data in information dissemination, business decision-making, and academic research. As the volume of multimodal data grows, a critical need arises for automated chart comprehension, which has garnered increasing attention from the research community. Check out the Paper.

Large Language Models 103

Large Language Models Data Analysis Python Automation 103

Marktechpost

MARCH 23, 2024

The approach in the current research, underpinned by the belief that larger models yield more powerful representations, has led to the development of gigantic vision models. It raises a pertinent question about the sustainability and efficiency of this approach, especially in a domain where computational resources are invaluable.

Computer Vision 135

Computer Vision Artificial Intelligence Artificial Intelligence Robotics 135

Marktechpost

JANUARY 16, 2024

This research paper studies the impact low-cost MT has on the web and on large multi-lingual language models (LLMs). Prior works have identified MT in the web corpora, but only a few have used multi-way parallelism in their study, and the authors of this research paper have used the same in their work. respectively).

Artificial Intelligence 111

Artificial Intelligence Artificial Intelligence ML AI Modeling 111

Marktechpost

MARCH 14, 2024

This transformation, which you, as academic researchers and professionals in AI and machine learning, have played a significant role in, has spurred many AI applications, from enhancing conversational agents to automating complex text analysis tasks. Check out the Paper. Also, don’t forget to follow us on Twitter.

Algorithm 131

Algorithm Machine Learning NLP Automation 131

Marktechpost

APRIL 26, 2024

This problem is exacerbated by the models’ dense activation patterns, which demand substantial computational resources. Existing research includes approaches like quantization, notably explored in BinaryBERT, and pruning techniques to enhance model efficiency. Check out the Paper.

Machine Learning 126

Machine Learning Large Language Models Neural Network ML 126

Marktechpost

FEBRUARY 11, 2024

This narrative centers on the intricate balance between developing expansive language models imbued with the capacity for deep understanding and generation of human language and the practical considerations of deploying these models efficiently, especially in environments constrained by computational resources. Researchers from Apple Inc.

Large Language Models 134

Large Language Models AI AI ML 134

Marktechpost

NOVEMBER 25, 2023

The crux, highlighted in the research, lies in the inefficiency of traditional neural networks and their escalating environmental footprint. As snnTorch adoption grows, so does the need for educational resources. Eshraghian aims to provide a resource he wished he had during his coding journey.

Neural Network 129

Neural Network Python Deep Learning Artificial Intelligence 129

Marktechpost

APRIL 18, 2024

Researchers have recently seen a surge of interest in image-and-language representation learning, aiming to capture the intricate relationship between visual and textual information. Efforts to scale CLIP, including FLIP and other methods, aim to improve efficiency and scalability, though the focus remains on large computational resources.

ML 105

ML Computer Vision Artificial Intelligence Artificial Intelligence 105

Marktechpost

MAY 5, 2024

The large language models (LLMs) research domain emphasizes aligning these models with human preferences to produce helpful, unbiased, and safe responses. Researchers at NVIDIA introduced NeMo-Aligner , a novel tool designed to streamline the training process for large-scale LLMs using reinforcement learning.

Large Language Models 119

Large Language Models Algorithm NLP LLM 119

Marktechpost

JANUARY 20, 2024

Traditional training methodologies, often reliant on expansive datasets, substantially burden computational resources, creating a notable barrier for researchers with limited access to high-powered computing infrastructures. In practical terms, this translates to substantial cost savings in computational resources and time.

Artificial Intelligence 135

Artificial Intelligence Artificial Intelligence Machine Learning Computer Vision 135

Marktechpost

FEBRUARY 22, 2024

A team of researchers from the Renmin University of China addresses the challenge of automating table-related tasks by focusing on instruction-tuning, prompting, and agent-based approaches in the context of LLMs. Check out the Paper. All credit for this research goes to the researchers of this project.

Large Language Models 131

Large Language Models Automation ML 131

Marktechpost

APRIL 27, 2024

This type of forecasting is instrumental in enhancing decision-making processes and optimizing resource allocation over long periods. However, making accurate long-term forecasts is complex due to the inherently unpredictable nature of the datasets involved and the substantial computational resources required for processing them.

Convolutional Neural Networks 132

Convolutional Neural Networks Neural Network ML Artificial Intelligence 132

Marktechpost

APRIL 30, 2024

With billions of parameters, these models demand extensive computational resources for operation. As an invaluable resource, this survey equips researchers with insights into PEFT algorithms and their system implementations, offering detailed analyses of recent progressions and practical uses. Check out the Paper.

Large Language Models 133

Large Language Models Categorization Natural Language Processing Algorithm 133

Marktechpost

NOVEMBER 16, 2023

Researchers from MIT investigated the scaling behavior of large chemical language models, focusing on both generative pre-trained transformers (GPT) for chemistry (ChemGPT) and graph neural network force fields (GNNs). Check out the Paper. All credit for this research goes to the researchers of this project.

Deep Learning 105

Deep Learning Neural Network Computer Vision AI 105

Marktechpost

APRIL 25, 2024

In order to address hallucinations and to implement an enterprise application that translates natural language requirements into workflows, a team of researchers from ServiceNow has created a system that makes use of Retrieval-Augmented Generation (RAG), a method that is known to improve the caliber of structured outputs produced by GenAI systems.

LLM 125

LLM Machine Learning Large Language Models Artificial Intelligence 125

Marktechpost

MAY 10, 2024

In a recent study, a team of researchers from Imperial College London and Dell introduced StyleMamba, an effective framework for transferring picture styles that uses text prompts to direct the stylization process while maintaining the original image content. Check out the Paper. Also, don’t forget to follow us on Twitter.

ML 130

ML Large Language Models Artificial Intelligence Artificial Intelligence 130

Marktechpost

MAY 10, 2024

However, the training of these models poses significant challenges, including high resource requirements and long training times due to the complexity of the computations involved. Previous research has explored techniques like loss-scaling and mixed-precision strategies to reduce memory usage and enhance training efficiency for large models.

LLM 124

LLM Machine Learning Large Language Models Natural Language Processing 124

Marktechpost

APRIL 23, 2024

However, deploying LDMs on resource-limited devices remains challenging due to significant compute requirements, particularly from the Unet component. Researchers have explored various compression techniques for LDMs to address this challenge, aiming to reduce computational overhead while maintaining performance.

AI Researcher 105

AI Researcher AI Research Natural Language Processing Computer Vision 105

Marktechpost

MAY 8, 2024

Traditional models often require substantial resources, which hinders practical application and scalability. Existing research in large language models (LLMs) includes foundational frameworks like GPT-3 by OpenAI and BERT by Google, utilizing traditional Transformer architectures. Check out the Paper and Github.

Large Language Models 109

Large Language Models Natural Language Processing BERT AI 109

Marktechpost

APRIL 11, 2024

Recently, a wide range of architectures have proved their ability and performance in achieving vision tasks on resource constraint devices, e.g., pruning ViT architectures helps obtain smaller and faster CLIP models. Check out the Paper and Github. Check out the Paper and Github. Also, don’t forget to follow us on Twitter.

ML 133

ML AI AI Computer Vision 133

Marktechpost

DECEMBER 24, 2023

This paper (from a team of researchers from the University of Massachusetts Amherst, Columbia University, Google, Stanford University, and New York University) is a significant contribution to the ongoing discourse surrounding LLM safety, as it meticulously explores the intricate dynamics of these models during the finetuning process.

Machine Learning 116

Machine Learning Large Language Models Algorithm LLM 116

Marktechpost

APRIL 15, 2024

The researchers from UC Berkeley propose the concept of “ post-facto LLM validation ” as an alternative to “pre-facto LLM validation.” They highlight the vision of autonomous LLM-powered systems and outline open research questions. Check out the Paper and Github. Million AI Audience?

LLM 139

LLM ML Large Language Models AI 139

Marktechpost

MARCH 18, 2024

Integrating Transformer architectures aims to address this limitation but hinders high computational costs, making it unsuitable for resource-constrained healthcare settings. To conclude, the researchers have introduced LightM-UNet, a lightweight network that integrates Mamba. Check out the Paper and Github.

Software Engineer 126

Software Engineer Artificial Intelligence Artificial Intelligence AI 126

Marktechpost

MARCH 4, 2024

Despite the expanding interest in this area, limited resources hinder extensive research. The researchers from Massachusetts Institute of Technology, Stanford University, and others propose a conceptual framework to unify diverse data selection methods, particularly focusing on model pretraining. Check out the Paper.

Large Language Models 132

Large Language Models Categorization AI AI 132

Marktechpost

MARCH 23, 2024

HuggingFace Researchers introduce Quanto to address the challenge of optimizing deep learning models for deployment on resource-constrained devices, such as mobile phones and embedded systems. Current methods for quantizing PyTorch models have limitations, including compatibility issues with different model configurations and devices.

Deep Learning 144

Deep Learning Python Large Language Models Automation 144

Towards AI

FEBRUARY 5, 2024

However, a recent paper describes how the latter was put to work for the former, allowing researchers to access large-scale computing resources in an unprecedented way that entailed lower costs and much higher flexibility than traditional alternatives. From research to projects and ideas. Published via Towards AI

AI 121

AI AI Data Science Machine Learning 121

Marktechpost

MARCH 20, 2024

CNNs have been lauded for their ability to extract local features efficiently, laying the groundwork for nuanced image analysis without demanding extensive computational resources. A study by researchers from The University of Sydney introduces EfficientVMamba, a model that redefines efficiency in computer vision tasks.

Convolutional Neural Networks 105

Convolutional Neural Networks Computer Vision Neural Network AI 105

Marktechpost

FEBRUARY 26, 2024

While pre-training requires significant computational resources, fine-tuning adds comparatively less new information to the model, making it more compressible. This approach paves the way for more efficient model deployment and resource utilization in machine learning applications. Check out the Paper and Github.

Machine Learning 133

Machine Learning Large Language Models AI AI 133

Marktechpost

DECEMBER 16, 2023

A recent publication in the Proceedings of the IEEE serves as a documentation of the snnTorch coding library and an educational resource tailored for students and programming enthusiasts keen on delving into brain-inspired AI. What sets snnTorch apart is its code and the comprehensive educational resources curated alongside its development.

Neural Network 113

Neural Network Deep Learning Python Artificial Intelligence 113