Unite.AI

SEPTEMBER 18, 2024

The field of artificial intelligence is evolving at a breathtaking pace, with large language models (LLMs) leading the charge in natural language processing and understanding. 405B: The most powerful model with 405 billion parameters Llama 3.1 70B: A balanced model offering strong performance Llama 3.1

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Unite.AI

APRIL 26, 2024

Large Language Models (LLMs) have revolutionized the field of natural language processing (NLP) by demonstrating remarkable capabilities in generating human-like text, answering questions, and assisting with a wide range of language-related tasks. LLMs based on prefix decoders include GLM130B and U-PaLM.

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Unite.AI

NOVEMBER 28, 2024

Graph AI: The Power of Connections Graph AI works with data represented as networks, or graphs. Graph Neural Networks (GNNs) are a subset of AI models that excel at understanding these complex relationships. Graph AI is already being used in: Drug discovery: Modeling molecule interactions to predict therapeutic potential.

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IBM Journey to AI blog

APRIL 4, 2023

The AI Commentary feature is a generative AI built from a large language model that was trained on a massive corpus of language data. The world’s eyes were first opened to the power of large language models last November when a chatbot application dominated news cycles.

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Flipboard

MARCH 28, 2025

One of the most frustrating things about using a large language model is dealing with its tendency to confabulate information , hallucinating answers that are not supported by its training data.

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Unite.AI

MARCH 9, 2025

Companies like Tesla , Nvidia , Google DeepMind , and OpenAI lead this transformation with powerful GPUs, custom AI chips, and large-scale neural networks. Deep learning and neural networks excel when they can process vast amounts of data simultaneously, unlike traditional computers that process tasks sequentially.

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Unite.AI

OCTOBER 27, 2024

The ecosystem has rapidly evolved to support everything from large language models (LLMs) to neural networks, making it easier than ever for developers to integrate AI capabilities into their applications. is its intuitive approach to neural network training and implementation. environments.

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Unite.AI

OCTOBER 24, 2023

Large Language Models (LLMs) have carved a unique niche, offering unparalleled capabilities in understanding and generating human-like text. While this huge scale fuels their performance, it simultaneously births challenges, especially when it comes to model adaptation for specific tasks or domains.

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Unite.AI

FEBRUARY 12, 2025

The fast progress in AI technologies like machine learning, neural networks , and Large Language Models (LLMs) is bringing us closer to ASI. This development in technological capability offers significant opportunities but also several challenges.

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Unite.AI

MARCH 19, 2025

While no AI today is definitively conscious, some researchers believe that advanced neural networks , neuromorphic computing , deep reinforcement learning (DRL), and large language models (LLMs) could lead to AI systems that at least simulate self-awareness.

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AI News

DECEMBER 7, 2023

He outlined key attributes of neural networks, embeddings, and transformers, focusing on large language models as a shared foundation. Neural networks — described as probabilistic and adaptable — form the backbone of AI, mimicking human learning processes.

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Unite.AI

NOVEMBER 19, 2024

Today, machine learning and neural networks build on these early ideas. This automation speeds up the model development process and sets the stage for systems that can optimize themselves with minimal human guidance. These algorithms replicated natural evolutionary process, enabling solutions to improve over time.

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Flipboard

MARCH 18, 2025

In November last year, reports indicated that OpenAI researchers discovered that the upcoming version of its GPT large language model displayed significantly less improvement , and in some cases, no improvements at all than previous versions did over their predecessors. Of course, the writing had been on the wall before that.

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Unite.AI

OCTOBER 16, 2023

The underpinnings of LLMs like OpenAI's GPT-3 or its successor GPT-4 lie in deep learning, a subset of AI, which leverages neural networks with three or more layers. These models are trained on vast datasets encompassing a broad spectrum of internet text.

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Flipboard

APRIL 2, 2025

Large language models think in ways that dont look very human. Their outputs are formed from billions of mathematical signals bouncing through layers of neural networks powered by computers of unprecedented power and speed, and most of that activity remains invisible or inscrutable to AI researchers.

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Unite.AI

NOVEMBER 28, 2023

Recently, text-based Large Language Model (LLM) frameworks have shown remarkable abilities, achieving human-level performance in a wide range of Natural Language Processing (NLP) tasks. This approach trains large language models to more effectively follow open-ended user instructions.

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Unite.AI

DECEMBER 4, 2023

In the ever-evolving domain of Artificial Intelligence (AI), where models like GPT-3 have been dominant for a long time, a silent but groundbreaking shift is taking place. Small Language Models (SLM) are emerging and challenging the prevailing narrative of their larger counterparts.

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Unite.AI

DECEMBER 17, 2024

By combining the power of neural networks with the logic of symbolic AI, it could solve some of the reliability problems generative AI faces. It combines two strengths: neural networks that recognize patterns and symbolic AI that uses logic to reason. This is where neurosymbolic AI can help.

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Marktechpost

APRIL 25, 2024

Are you curious about the intricate world of large language models (LLMs) and the technical jargon that surrounds them? LLM (Large Language Model) Large Language Models (LLMs) are advanced AI systems trained on extensive text datasets to understand and generate human-like text.

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Snorkel AI

JANUARY 22, 2025

The neural network architecture of large language models makes them black boxes. Neither data scientists nor developers can tell you how any individual model weight impacts its output; they often cant reliably predict how small changes in the input will change the output. Lets dive in. Fine-tuning.

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Flipboard

MARCH 21, 2025

Previously, the latest AI model that powers Claude could only rely on data absorbed during its neural network training process, having a "knowledge cutoff" of October 2024. On Thursday, Anthropic introduced web search capabilities for its AI assistant Claude, enabling the assistant to access current information online.

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Marktechpost

JUNE 18, 2024

Two notable research papers contribute to this development: “Bayesian vs. PAC-Bayesian Deep Neural Network Ensembles” by University of Copenhagen researchers and “Deep Bayesian Active Learning for Preference Modeling in Large Language Models” by University of Oxford researchers.

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Unite.AI

APRIL 2, 2025

Training and running large language models (LLMs) requires vast computational power and equally vast amounts of energy. Why AI Needs a Radical Overhaul With AI systems like ChatGPT, Claude, and Gemini becoming increasingly sophisticated, the underlying hardware is being pushed to its breaking point.

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Unite.AI

JANUARY 18, 2025

While Central Processing Units (CPUs) and Graphics Processing Units (GPUs) have historically powered traditional computing tasks and graphics rendering, they were not originally designed to tackle the computational intensity of deep neural networks.

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The MLOps Blog

JANUARY 23, 2025

TL;DR Multimodal Large Language Models (MLLMs) process data from different modalities like text, audio, image, and video. Compared to text-only models, MLLMs achieve richer contextual understanding and can integrate information across modalities, unlocking new areas of application. How do multimodal LLMs work?

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Unite.AI

FEBRUARY 28, 2024

Large language models (LLMs) like GPT-4, DALL-E have captivated the public imagination and demonstrated immense potential across a variety of applications. Modern LLMs like OpenAI's GPT-3 contain upwards of 175 billion parameters, several orders of magnitude more than previous models.

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Flipboard

FEBRUARY 18, 2025

With the recent explosion of interest in large language models (LLMs), they often seem almost magical. But lets demystify them.I wanted to step back

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Unite.AI

NOVEMBER 22, 2024

However, the complexity of advanced AI models, particularly large language models (LLMs), makes it difficult to understand how they arrive at those decisions. It helps explain how AI models, especially LLMs, process information and make decisions. Gemma Scope acts like a window into the inner workings of AI models.

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Unite.AI

DECEMBER 3, 2024

Here is why this matters: Moves beyond template-based responses Advanced pattern recognition capabilities Dynamic style adaptation in real-time Integration with existing language model strengths Remember when chatbots first appeared? Could we see neural networks specifically designed for dynamic adaptation?

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Unite.AI

SEPTEMBER 24, 2023

The models were then evaluated based on whether their assessments resonated with human choices. When the models were pitted against each other, the ones based on transformer neural networks exhibited superior performance compared to the simpler recurrent neural network models and statistical models.

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Unite.AI

OCTOBER 17, 2024

During training, each row of data as it passes through the network–called a neural network–modifies the equations at each layer of the network so that the predicted output matches the actual output. As the data in a training set is processed, the neural network learns how to predict the outcome.

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Unite.AI

FEBRUARY 19, 2025

Large language models (LLMs) like OpenAIs o3 , Googles Gemini 2.0 , and DeepSeeks R1 have shown remarkable progress in tackling complex problems, generating human-like text, and even writing code with precision. But do these models actually reason , or are they just exceptionally good at planning ?

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Marktechpost

APRIL 26, 2024

Graph Machine Learning (Graph ML), especially Graph Neural Networks (GNNs), has emerged to effectively model such data, utilizing deep learning’s message-passing mechanism to capture high-order relationships. Alongside topological structure, nodes often possess textual features providing context.

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NYU Center for Data Science

NOVEMBER 22, 2024

However, CDS Research Scientist Ravid Shwartz-Ziv is experimenting with a different approach, coordinating multiple research projects through a Discord server where anyone interested can contribute to exploring connections between large language models and information theory. The initiative began with a Twitter post. “I

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Unite.AI

JUNE 20, 2024

Large Language Models (LLMs) are capable of understanding and generating human-like text, making them invaluable for a wide range of applications, such as chatbots, content generation, and language translation. Large Language Models (LLMs) are a type of neural network model trained on vast amounts of text data.

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Marktechpost

JULY 22, 2024

Large Language Models (LLMs) have revolutionized natural language processing, demonstrating remarkable capabilities in various applications. Transformer architecture has emerged as a major leap in natural language processing, significantly outperforming earlier recurrent neural networks.

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Flipboard

NOVEMBER 7, 2024

Modern artificial neural network (ANN) models, like large language models, demonstrate impressive success when tested on abstract reasoning problems. The nature of abstract reasoning is a matter of debate.

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