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Proposition by Emergence AI for Agent-E: A Web Agent with Autonomous Web Navigation Improves by 20% to Achieve a Success Rate of 73.2%

Autonomous web navigation deals with the development of AI agents used in automating complex online tasks from data mining to booking delivery services. This helps in enhancing productivity by automating certain tasks in both consumer and enterprise domains. Traditional web agents working on such complex web tasks are usually inefficient and prone to errors due…

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The introduction of Agent-E, a web navigating system from Emergence AI, boasts a success rate of 73.2%. Moreover, it exhibits a 20% enhancement in independent web navigation.

Autonomous web navigation, which involves using AI agents to perform complex online tasks, is growing in significance. Presently, these AI agents are typically used for tasks such as data retrieval, form submissions, and more sophisticated activities like finding cheap flights or booking accommodations. Utilizing large language models (LLMs) and other AI methodologies, the aim of…

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The Effect of Dubious Research Methods on the Assessment of Machine Learning (ML) Models

Evaluating the performance of Artificial Intelligence (AI) and Machine Learning (ML) models is crucial, especially with the advent of Large Language Models (LLMs). These evaluations help to assess the abilities of these models and establish reliable systems based on their capabilities. However, certain practices, termed as Questionable Research Practices (QRPs), frequently compromise the authenticity and…

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The Influence of Dubious Research Methods on the Assessment of Machine Learning (ML) Models

Artificial Intelligence and Machine Learning are rapidly advancing fields, and a crucial aspect of their progress is the evaluation of model performance, particularly with the advent of Large Language Models (LLMs). However, the integrity of these evaluations is often compromised by what are known as Questionable Research Practices (QRPs), which can severely inflate published results…

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FLUTE: A CUDA Kernel Formulated for Compound Quantized Matrix Multiplications to Speed Up LLM Inference

Large Language Models (LLMs) face several deployment challenges including latency issues triggered by memory bandwidth constraints. To mitigate such problems, researchers have resorted to applying weight-only quantization, a technique that compresses the parameters of LLMs to lower precision. Nevertheless, to effectively implement weight-only quantization, it is necessary to employ mixed-type matrix-multiply kernels that can manage,…

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The Intersection of Theory of Mind and Language Models: Conceptualizing Minds for Sophisticated Multi-Agent Activities

Artificial intelligence (AI) is continually evolving, with a significant challenge being the creation of systems that can effectively collaborate in dynamic environments. One area of focus in this regard is multi-agent reinforcement learning (MARL), which aims to teach agents to interact and adapt in these settings. However, these methods struggle with complexity and adaptability, especially…

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The Eindhoven University of Technology has published a revolutionary Deep Learning Paper, introducing Nerva: A New Sparse Neural Network Library that significantly improves efficiency and performance.

Deep learning's exceptional performance across a wide range of scientific fields and its utilization in various applications have been proven. However, these models often come with many parameters that require a substantial amount of computational power for training and testing. The improvement of these models has been a primary focus of advancement in the field,…

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Transforming the Understanding of Visual-Language: Integration of Specialist Knowledge and Self-Augmentation in VILA 2.

The realm of language models has seen tremendous growth thanks to transformative scaling efforts and applications such as OpenAI's GPT series. Innovations like Transformer-XL have broadened context windows, while models like Mistral, Falcon, Yi, DeepSeek, DBRX, and Gemini extended the reach of these capabilities. Parallel to these, visual language models (VLMs) have also observed similar…

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IBM scientists have unveiled AI-Hilbert, a novel machine learning structure designed for scientific exploration that combines algebraic geometry and mixed-integer optimization.

A recent research study by teams at Imperial College Business School, Samsung AI, and IBM has proposed an innovative solution for scientific discovery, using a framework that they call AI-Hilbert. The system is designed to discover natural laws by modeling axioms and laws as polynomials. The research leverages binary variables and logical constraints to solve…

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This AI Article Presents AssistantBench and SeePlanAct: Standard and Agent for Sophisticated Web-Related Tasks

Artificial intelligence (AI) developing systems often encounter several challenges like performing tasks that require human intellect, such as managing complex tasks and interacting with dynamic environments. This necessitates finding and synthesizing information from the web accurately and reliably. Current models face this difficulty, hence pointing out the need for more advanced AI systems. Existing solutions…

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