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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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Researchers from Harvard introduce ReXrank: A Publicly Available Ranking System for AI-Driven Radiology Report Creation using Chest X-Ray Pictures.

Harvard researchers have launched ReXrank, an open-source leaderboard that aims to improve artificial intelligence (AI)-powered radiology report generation. This development could revolutionize healthcare AI, especially concerning chest X-ray image interpretation. ReXrank aims to provide a comprehensive, objective evaluation framework for advanced AI models, encouraging competition and collaboration among researchers, clinicians, and AI enthusiasts and accelerating…

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Harvard scholars introduce ReXrank: A publicly accessible ranking system for AI-based creation of radiology reports from chest X-ray pictures.

Harvard researchers have won the medical AI field's attention with their launch of ReXrank, an open-source leaderboard promoting the advancement of AI-driven radiology report generation, particularly in chest X-ray imaging. This unveiling implicates changes in healthcare AI and is designed to bring a transparent and full-picture evaluation framework. ReXrank makes use of a variety of datasets…

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Self-Route: An Easy and Efficient AI Technique that Directs Inquiries to RAG or Long Context LC, drawing on the Model’s Self-Evaluation Capability

Large Language Models (LLMs) like GPT-4 and Gemini-1.5 have revolutionized the field of natural language processing, significantly enhancing text processing applications such as summarization and question answering. However, the long context management required for these applications presents challenges due to computational limitations and cost implications. Recent research has been exploring ways to balance performance and…

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Release Announcement: The Mistral-Large-Instruct-2407, a multilingual AI featuring a 128K context and proficiency in over 80 programming languages, has been launched. With an MMLU (Machine Learning Understanding) score of 84.0% and HumanEval score of 92%, along with solid 93% performance on the GSM8K test, this represents a significant advancement.

AI firm Mistral AI has launched the Mistral Large 2 model, its latest flagship AI model. The new iteration offers significant improvements on its predecessor, with considerable ability in code generation, mathematics, reasoning, and advanced multilingual support. Furthermore, Mistral Large 2 offers enhanced function-calling capabilities and is designed to be cost-efficient, high-speed, and high-performance. Users can…

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Imposter.AI: Revealing Tactics for Adversarial Assaults to Highlight Weaknesses in Sophisticated High Volume Language Models

Large Language Models (LLMs), widely used in automation and content creation, are vulnerable to manipulation by adversarial attacks, leading to significant risk of misinformation, privacy breaches, and enabling criminal activities. According to research led by Meetyou AI Lab, Osaka University and East China Normal University, these sophisticated models are open to harmful exploitation despite safety…

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MIT’s recent AI research indicates that an individual’s perceptions of an LLM significantly influence its efficiency and are critical to its implementation.

MIT and Harvard researchers have highlighted the divergence between human expectations of AI system capabilities and their actual performance, particularly in large language models (LLMs). The inconsistent ability of AI to match human expectations could potentially erode public trust, thereby obstructing the broad adoption of AI technology. This issue, the researchers emphasized, escalates the risk…

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