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Applications

Revealing Hidden Bias in AI: An In-depth Examination of Language Variant Discrimination

Today's increasingly pervasive artificial intelligence (AI) technologies have given rise to concerns over the perpetuation of historically entrenched human biases, particularly within marginalized communities. New research by academics from the Allen Institute for AI, Stanford University, and the University of Chicago exposes a worrying form of bias rarely discussed before: Dialect Prejudice against speakers of…

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Introducing SaulLM-7B: An Innovative Extensive Language Model for Legal Sector

Recent advancements in large language models (LLMs), which have revolutionized fields like healthcare, translation, and code generation, are now being leveraged to assist the legal domain. Legal professionals often grapple with extensive, complex documents, emphasizing the need for a dedicated LLM. To address this, researchers from several prestigious institutions—including Equall.ai, MICS, CentraleSupélec, and Université Paris-Saclay—have…

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Apple’s AI Report Explores the Complexities of Machine Learning: Evaluating Vision-Language Models using Raven’s Progressive Matrices

Vision-Language Models (VLMs) provide state-of-the-art performance across a spectrum of vision-language tasks, including captioning, object localization, commonsense reasoning, and vision-based coding, amongst others. Recent studies, such as one undertaken by Apple, showed that these models excel in extracting text from images and interpreting visual data, including tables and charts. However, when tested on complex tasks…

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Observing and Listening: Merging the Spheres of Sight and Sound through Artificial Intelligence

Artificial Intelligence (AI) researchers have developed an innovative framework to produce visually and audibly cohesive content. This advancement could help overcome previous difficulties in synchronizing video and audio generation. The framework uses pre-trained models like ImageBind, which links different data types into a unified semantic space. This function allows ImageBind to provide feedback on the…

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01.AI has unveiled the Yi Model Family, a range of models that are proficient in various languages and have multi-dimensional abilities. These models are capable of illustrating superior multimodal functionalities.

The 01.AI research team has introduced the Yi model family of Artificial Intelligence (AI) designed to bridge the gap between human language and visual perception. Uniquely, this model doesn't simply parse text or images individually; it combines both, demonstrating an unprecedented degree of multi-modal understanding. This ground-breaking technology's purpose is to mirror and extend human…

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DeepSeek-AI Launches DeepSeek-VL: A Publicly Accessible Vision-Language (VL) System Crafted for Practical Vision and Language Comprehension Uses.

The boundary between the visual world and the realm of natural language has become a crucial frontier in the fast-changing field of artificial intelligence. Vision-language models, which aim to unravel the complicated relationship between images and text, are important developments for various applications, including enhancing accessibility and providing automated assistance in diverse industries. However, creating models…

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Revealing the Simplicity in Complexity: The Straightforward Depiction of Ideas in Extensive Language Models

In the ever-evolving sphere of artificial intelligence, the study of large language models (LLMs) and how they interpret and process human language has provided valuable insights. Contrary to expectation, these innovative models represent concepts in a simple and linear manner. To demystify the basis of linear representations in LLMs, researchers from the University of Chicago…

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Transforming Text into Imagery: The Game-Changing Collaboration between AWS AI Labs and the University of Waterloo through MAGID.

A new multimodal system, created by scientists from the University of Waterloo and AWS AI Labs, uses text and images to create a more engaging and interactive user experience. The system, known as Multimodal Augmented Generative Images Dialogues (MAGID), improves upon traditional methods that have used static image databases or real-world sources, which can pose…

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Introducing Modeling Collaborator: A Revolutionary Artificial Intelligence system enabling anyone to train vision models through straightforward language interactions and less effort.

Computer vision traditionally concentrates on acknowledging universally agreed concepts like animals, vehicles, or specific objects. However, real-world applications often need to identify variable subjective concepts like predicting emotions, determining aesthetic appeal, or regulating content. What is considered "unsafe" content or "gourmet" food differs greatly among individuals, hence the increasing demand for user-centric training frameworks that…

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“Thought Enhancement via Retrieval (TER): An AI Instruction Approach that Unifies Thought Sequence (TS) Instructions and Retrieval Enhanced Generation (REG) to Resolve the Difficulties Associated with Long-Term Reasoning and Generation Tasks.”

Artificial Intelligence researchers are continuously striving to create models that can think, reason, and generate outputs similar to the way humans solve complex problems. However, Large Language Models (LLMs), the current best attempt at such a feat, often struggle to maintain factual accuracy, especially in tasks that require a series of logical steps. This lack…

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Pioneering Advances in AI: The Role of Multimodal Large Language Models in Transforming Age and Gender Prediction

The evolution of Multimodal Large Language Models (MLLMs) has been significant, particularly those models that blend language and vision modalities (LVMs). There has been growing interest in applying MLLMs in various fields like computer vision tasks and integrating them into complex pipelines. Despite some models like ShareGPTV performing well in data annotation tasks, their practical…

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This Chinese AI research showcases MathScale: an expandable machine learning approach for generating superior mathematical reasoning data with cutting-edge language models.

Large language models (LLMs) like GPT-3 have proven to be powerful tools in solving various problems, but their capacity for complex mathematical reasoning remains limited. This limitation is partially due to the lack of extensive math-related problem sets in the training data. As a result, techniques like Instruction Tuning, which is designed to enhance the…

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