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DAI#49 – Discussing unrestricted use of Llamas, fear of artificial intelligence, and excessively simple prison escapes

In AI developments this week, OpenAI released a high-performance, cost-effective version of its flagship GPT-4o model, the GPT-4o mini. More developers are expected to opt for the mini version due to its affordable token rates and impressive performance on the MMLU benchmark. Elsewhere, Meta launched its highly anticipated Llama 3.1 405B model and upgraded 8B…

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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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EuroCropsML: A Ready-for-Analysis Machine Learning Dataset for Time Ordered Crop-Type Identification using Remote Sensing across European Agricultural Plots

Remote sensing is a crucial and innovative technology that utilizes satellite and aerial sensor technologies for the detection and classification of objects on Earth. This technology plays a significant role in environmental monitoring, agricultural management, and natural resource conservation. It enables scientists to accumulate massive amounts of data over large geographical areas and timeframes, providing…

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EVAL-LMMS: A Consolidated and Uniform Multimodal AI Evaluation Framework for Clear and Repeatable Assessments

Large Language Models (LLMs) such as GPT-4, Gemini, and Claude have exhibited striking capabilities but evaluating them is complex, necessitating an integrated, transparent, standardized and reproducible framework. Despite the challenges, no comprehensive evaluation technique currently exists, which has hampered progress in this area. However, researchers from the LMMs-Lab Team and S-Lab at NTU, Singapore, developed the…

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Unified and Standardized Multimodal AI Benchmark Framework for Clear and Consistent Evaluations: An LMMS-EVAL Overview

Fundamental large language models (LLMs) including GPT-4, Gemini and Claude have shown significant competencies, matching or surpassing human performance. In this light, benchmarks are necessary tools to determine the strengths and weaknesses of various models. Transparent, standardized and reproducible evaluations are crucial and much needed for language and multimodal models. However, the development of custom…

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To improve an AI assistant, initiate by simulating the unpredictable conduct of humans.

Researchers from MIT and the University of Washington have developed a computational model to predict human behavior while taking into account the suboptimal decisions humans often make due to computational constraints. The researchers believe such a model could help AI systems anticipate and counterbalance human-derived errors, enhancing the efficacy of AI-human collaboration. Suboptimal decision-making is characteristic…

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