Unite.AI

OCTOBER 11, 2024

While large language models (LLMs) like GPT-3 and Llama are impressive in their capabilities, they often need more information and more access to domain-specific data. Retrieval-augmented generation (RAG) solves these challenges by combining LLMs with information retrieval. This integration allows for smooth interactions with real-time data using natural language, leading to its growing popularity in various industries.

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Large Language Models BERT Chatbots Algorithm 162

AssemblyAI

OCTOBER 11, 2024

AssemblyAI is now integrated with Langflow , a powerful low-code platform for building generative AI applications.  Langflow is a visual framework for building multi-agent and RAG (Retrieval-Augmented Generation) applications. It is open-source, Python-powered, fully customizable, and LLM and vector store agnostic. It allows for easy manipulation of AI building blocks, enabling developers to quickly prototype and turn their ideas into real-world solutions.

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LLM Python Generative AI AI 162

Analytics Vidhya

OCTOBER 11, 2024

Introduction The rise of large language models (LLMs), such as OpenAI’s GPT and Anthropic’s Claude, has led to the widespread adoption of generative AI (GenAI) products in enterprises. Organizations across sectors are now leveraging GenAI to streamline processes and increase the efficiency of their workforce. Integrating LLM agents into an organization requires thoughtful planning and […] The post Step-by-Step Guide to Integrate LLM Agents in an Organization appeared first on Analyti

LLM 167

LLM Large Language Models OpenAI Generative AI 167

Unite.AI

OCTOBER 11, 2024

The path to AI isn’t a sprint – it’s a marathon, and businesses need to pace themselves accordingly. Those who run before they have learned to walk will falter, joining the graveyard of businesses who tried to move too quickly to reach some kind of AI finish line. The truth is, there is no finish line. There is no destination at which a business can arrive and say that AI has been sufficiently conquered.

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Data Quality AI AI Generative AI 147

Speaker: David Warren and Kevin O'Neill Stoll

Transitioning to a usage-based business model offers powerful growth opportunities but comes with unique challenges. How do you validate strategies, reduce risks, and ensure alignment with customer value? Join us for a deep dive into designing effective pilots that test the waters and drive success in usage-based revenue. Discover how to develop a pilot that captures real customer feedback, aligns internal teams with usage metrics, and rethinks sales incentives to prioritize lasting customer eng

Marktechpost

OCTOBER 11, 2024

In the rapidly evolving world of artificial intelligence, one pressing challenge that developers face is orchestrating complex multi-agent systems. These systems, involving multiple AI agents working collaboratively, often present significant difficulties in coordination, control, and scalability. Current solutions tend to be heavy, requiring extensive resource allocation, which complicates deployment and testing.

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Analytics Vidhya

OCTOBER 11, 2024

Introduction Natural Language Processing (NLP) has rapidly advanced, particularly with the emergence of Retrieval-Augmented Generation (RAG) pipelines, which effectively address complex, information-dense queries. By combining the precision of retrieval-based systems with the creativity of generative models, RAG pipelines enhance the ability to answer questions with high relevance and context, whether by extracting sections from research […] The post 15 Chunking Techniques to Build Except

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Natural Language Processing NLP Generative AI Python 110

Unite.AI

OCTOBER 11, 2024

A new research collaboration between Singapore and China has proposed a method for attacking the popular synthesis method 3D Gaussian Splatting (3DGS). The new attack method uses crafted source data to overload the available GPU memory of the target system, and to make training so lengthy as to potentially incapacitate the target server, equivalent to a denial-of-service (DOS) attack.

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Marktechpost

OCTOBER 11, 2024

Retrieval-augmented generation (RAG) has become a key technique in enhancing the capabilities of LLMs by incorporating external knowledge into their outputs. RAG methods enable LLMs to access additional information from external sources, such as web-based databases, scientific literature, or domain-specific corpora, which improves their performance in knowledge-intensive tasks.

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AI Researcher AI Research LLM AI 133

Unite.AI

OCTOBER 11, 2024

Significant advancements in large language models (LLMs) have inspired the development of multimodal large language models (MLLMs). Early MLLM efforts, such as LLaVA, MiniGPT-4, and InstructBLIP, demonstrate notable multimodal understanding capabilities. To integrate LLMs into multimodal domains, these studies explored projecting features from a pre-trained modality-specific encoder, such as CLIP, into the input space of LLMs, enabling multimodal understanding and reasoning within the transforme

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Large Language Models LLM Natural Language Processing Categorization 130

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NVIDIA

OCTOBER 11, 2024

In 1999, fans lined up at Blockbuster to rent chunky VHS tapes of The Matrix. Y2K preppers hoarded cash and canned Spam, fearing a worldwide computer crash. Teens gleefully downloaded Britney Spears and Eminem on Napster. But amid the caffeinated fizz of turn-of-the-millennium tech culture, something more transformative was unfolding. The release of NVIDIA’s GeForce 256 twenty-five years ago today, overlooked by all but hardcore PC gamers and tech enthusiasts at the time, would go on to lay the

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Neural Network Deep Learning AI AI 128

AWS Machine Learning Blog

OCTOBER 11, 2024

Agentic workflows are a fresh new perspective in building dynamic and complex business use case-based workflows with the help of large language models (LLMs) as their reasoning engine. These agentic workflows decompose the natural language query-based tasks into multiple actionable steps with iterative feedback loops and self-reflection to produce the final result using tools and APIs.

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LLM Chatbots Natural Language Processing Large Language Models 127

Marktechpost

OCTOBER 11, 2024

Information Retrieval (IR) systems for search and recommendations often utilize Learning-to-Rank (LTR) solutions to prioritize relevant items for user queries. These models heavily depend on user interaction features, such as clicks and engagement data, which are highly effective for ranking. However, this reliance presents significant challenges. User Interaction data can be noisy and sparse, especially for newer or less popular items, resulting in cold start problems where these items are rank

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Neural Network Algorithm Data Quality ML 116

Extreme Tech

OCTOBER 11, 2024

The company appears to be mostly finished with its second-generation 'Battlemage' GPUs, even though they've yet to launch in discrete form.

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Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?

Business Intelligence

Marktechpost

OCTOBER 11, 2024

Generating accurate and aesthetically appealing visual texts in text-to-image generation models presents a significant challenge. While diffusion-based models have achieved success in creating diverse and high-quality images, they often struggle to produce legible and well-placed visual text. Common issues include misspellings, omitted words, and improper text alignment, particularly when generating non-English languages such as Chinese.

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Artificial Intelligence Artificial Intelligence ML Computer Vision 115

Extreme Tech

OCTOBER 11, 2024

Sadly, room-temperature superconductors still aren't a thing. But one rare-earth mineral gets close. Sort of.

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Marktechpost

OCTOBER 11, 2024

In the rapidly evolving landscape of artificial intelligence, the quality and quantity of data play a pivotal role in determining the success of machine learning models. While real-world data provides a rich foundation for training, it often faces limitations such as scarcity, bias, and privacy concerns. These challenges can hinder the development of accurate and reliable AI systems.

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Data Scarcity Neural Network Natural Language Processing Machine Learning 111

Extreme Tech

OCTOBER 11, 2024

Intel even says AMD's 7800X3D will be slightly faster in gaming than its own just-announced Core Ultra 200 series.

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Cloud Development Environments (CDEs) are changing how software teams work by moving development to the cloud. Our Cloud Development Environment Adoption Report gathers insights from 223 developers and business leaders, uncovering key trends in CDE adoption. With 66% of large organizations already using CDEs, these platforms are quickly becoming essential to modern development practices.

Marktechpost

OCTOBER 11, 2024

Large language models (LLMs) have emerged as powerful tools capable of performing complex tasks beyond text generation, including reasoning, tool learning, and code generation. These advancements have sparked significant interest in developing LLM-based language agents to automate scientific discovery processes. Researchers are exploring the potential of these agents to revolutionise data-driven discovery workflows across various disciplines.

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Automation Large Language Models Python LLM 111

AWS Machine Learning Blog

OCTOBER 11, 2024

Customers across all industries are experimenting with generative AI to accelerate and improve business outcomes. Generative AI is used in various use cases, such as content creation, personalization, intelligent assistants, questions and answers, summarization, automation, cost-efficiencies, productivity improvement assistants, customization, innovation, and more.

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Metadata Generative AI LLM Data Ingestion 95

Marktechpost

OCTOBER 11, 2024

While writing the code for any program or algorithm, developers can struggle to fill gaps in incomplete code and often make mistakes while trying to fit new pieces into existing code snippets or structures. These challenges arise from the difficulty of fitting the latest code with the prior and following parts, especially when the broader part of the context is not taken into consideration.

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AWS Machine Learning Blog

OCTOBER 11, 2024

Modern organizations increasingly depend on robust cloud infrastructure to provide business continuity and operational efficiency. Operational health events – including operational issues, software lifecycle notifications, and more – serve as critical inputs to cloud operations management. Inefficiencies in handling these events can lead to unplanned downtime, unnecessary costs, and revenue loss for organizations.

AI 91

AI AI Automation Chatbots 91

Speaker: Simran Kaur, Founder & CEO at Tattva Health Inc.

The healthcare landscape is being revolutionized by AI and cutting-edge digital technologies, reshaping how patients receive care and interact with providers. In this webinar led by Simran Kaur, we will explore how AI-driven solutions are enhancing patient communication, improving care quality, and empowering preventive and predictive medicine. You'll also learn how AI is streamlining healthcare processes, helping providers offer more efficient, personalized care and enabling faster, data-driven

AI

Marktechpost

OCTOBER 11, 2024

Multimodal Situational Safety is a critical aspect that focuses on the model’s ability to interpret and respond safely to complex real-world scenarios involving visual and textual information. It ensures that Multimodal Large Language Models (MLLMs) can recognize and address potential risks inherent in their interactions. These models are designed to interact seamlessly with visual and textual inputs, making them highly capable of assisting humans by understanding real-world situations and provi

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AI Modeling Large Language Models Categorization AI 105

Extreme Tech

OCTOBER 11, 2024

Microsoft acknowledges the issue and promises a fix in the upcoming update.

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Marktechpost

OCTOBER 11, 2024

The problem that this research seeks to address lies in the inherent limitations of existing large language models (LLMs) when applied to formal theorem proving. Current models are often trained or fine-tuned on specific datasets, such as those focused on undergraduate-level mathematics, but struggle to generalize to more advanced mathematical domains.

Large Language Models 105

Large Language Models ML AI AI 105

NVIDIA

OCTOBER 11, 2024

The autonomous driving industry is shaped by rapid technological advancements and the need for standardization of guidelines to ensure the safety of both autonomous vehicles (AVs) and their interaction with human-driven vehicles. At the NVIDIA AI Summit this week in Washington, D.C., industry experts shared viewpoints on this AV safety landscape from regulatory and technology perspectives.

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Software Development AI AI Explainability 84

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Marktechpost

OCTOBER 11, 2024

Graphical User Interface (GUI) agents are crucial in automating interactions within digital environments, similar to how humans operate software using keyboards, mice, or touchscreens. GUI agents can simplify complex processes such as software testing, web automation, and digital assistance by autonomously navigating and manipulating GUI elements. These agents are designed to perceive their surroundings through visual inputs, enabling them to interpret the structure and content of digital interf

Large Language Models 104

Large Language Models Artificial Intelligence Artificial Intelligence Automation 104

Extreme Tech

OCTOBER 11, 2024

The boards are now available for pre-order, even though the CPUs have yet to be tested by independent sites.

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Marktechpost

OCTOBER 11, 2024

Text-to-Audio (TTA) and Text-to-Music (TTM) generation have seen significant advancements in recent years, driven by audio-domain diffusion models. These models have demonstrated superior audio modeling capabilities compared to generative adversarial networks (GANs) and variational autoencoders (VAEs). However, diffusion models face the challenge of long inference times due to their iterative denoising process.

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