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New Neural Model Enables AI-to-AI Linguistic Communication

Unite.AI

Central to this advancement in NLP is the development of artificial neural networks, which draw inspiration from the biological neurons in the human brain. These networks emulate the way human neurons transmit electrical signals, processing information through interconnected nodes.

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This AI Research Shares a Comprehensive Overview of Large Language Models (LLMs) on Graphs

Marktechpost

The well-known Large Language Models (LLMs) like GPT, BERT, PaLM, and LLaMA have brought in some great advancements in Natural Language Processing (NLP) and Natural Language Generation (NLG). The post This AI Research Shares a Comprehensive Overview of Large Language Models (LLMs) on Graphs appeared first on MarkTechPost.

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AI News Weekly - Issue #343: Summer Fiction Reads about AI - Jul 27th 2023

AI Weekly

techcrunch.com The Essential Artificial Intelligence Glossary for Marketers (90+ Terms) BERT - Bidirectional Encoder Representations from Transformers (BERT) is Google’s deep learning model designed explicitly for natural language processing tasks like answering questions, analyzing sentiment, and translation.

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Sub-Quadratic Systems: Accelerating AI Efficiency and Sustainability

Unite.AI

AI models like neural networks , used in applications like Natural Language Processing (NLP) and computer vision , are notorious for their high computational demands. Models like GPT and BERT involve millions to billions of parameters, leading to significant processing time and energy consumption during training and inference.

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GraphStorm 0.3: Scalable, multi-task learning on graphs with user-friendly APIs

AWS Machine Learning Blog

We also released a comprehensive study of co-training language models (LM) and graph neural networks (GNN) for large graphs with rich text features using the Microsoft Academic Graph (MAG) dataset from our KDD 2024 paper. GraphStorm provides different ways to fine-tune the BERT models, depending on the task types. Dataset Num.

BERT 107
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Google AI Proposes Easy End-to-End Diffusion-based Text to Speech E3-TTS: A Simple and Efficient End-to-End Text-to-Speech Model Based on Diffusion

Marktechpost

This model consists of two primary modules: A pre-trained BERT model is employed to extract pertinent information from the input text, and A diffusion UNet model processes the output from BERT. It is built upon a pre-trained BERT model. The BERT model takes subword input, and its output is processed by a 1D U-Net structure.

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Can Transformer Blocks Be Simplified Without Compromising Efficiency? This AI Paper from ETH Zurich Explores the Balance Between Design Complexity and Performance

Marktechpost

The research presents a study on simplifying transformer blocks in deep neural networks, specifically focusing on the standard transformer block. The study examines the simplification of transformer blocks in deep neural networks, focusing specifically on the standard transformer block.