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Applications

SpeechAlign: Improving Speech Synthesis through Human Input to Increase Realism and Expressivity in Tech-Based Communication

Speech synthesis—the technological process of creating artificial speech—is no longer a sci-fi fantasy but a rapidly evolving reality. As interactions with digital assistants and conversational agents become commonplace in our daily lives, the demand for synthesized speech that accurately mimics natural human speech has escalated. The main challenge isn't simply to create speech that sounds…

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The Illusion of “Zero-Shot”: The Restraint of Limited Data on Multimodal AI

In the field of Artificial Intelligence (AI), "zero-shot" capabilities refer to the ability of an AI system to recognize any object, comprehend any text, and generate realistic images without being explicitly trained on those concepts. Companies like Google and OpenAI have made advances in multi-modal AI models such as CLIP and DALL-E, which perform well…

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Stanford and MIT researchers have unveiled the Stream of Search (SoS): A Machine Learning structure, designed to allow language models to learn how to resolve issues by conducting searches in language without relying on any external assistance.

To improve the planning and problem-solving capabilities of language models, researchers from Stanford University, MIT, and Harvey Mudd have introduced a method called Stream of Search (SoS). This method trains language models on search sequences represented as serialized strings. It essentially presents these models with a set of problems and solutions in the language they…

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A collaborative team from MIT and Stanford introduced the Search of Stream (SoS), a machine learning structure that allows language models to learn problem-solving skills through linguistic searching without the need for external assistance.

Language models (LMs) are a crucial segment of artificial intelligence and can play a key role in complex decision-making, planning, and reasoning. However, despite LMs having the capacity to learn and improve, their training often lacks exposure to effective learning from mistakes. Several models also face difficulties in planning and anticipating the consequences of their…

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This AI Research Presents ReasonEval: An Innovative Machine Learning Approach for Assessing Mathematical Logic Beyond Precision

The complexity of mathematical reasoning in large language models (LLMs) often exceed the capabilities of existing evaluation methods. These models are crucial for problem-solving and decision-making, particularly in the field of artificial intelligence (AI). Yet the primary method of evaluation – comparing the final LLM result to a ground truth and then calculating overall accuracy…

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The University of Cambridge’s researchers have suggested AnchorAL: an innovative method of machine learning for active learning in tasks involving unbalanced classification.

Generative language models in the field of natural language processing (NLP) have fuelled significant progression, largely due to the availability of a vast amount of web-scale textual data. Such models can analyze and learn complex linguistic structures and patterns, which are subsequently used for various tasks. However, successful implementation of these models depends heavily on…

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Meta has unveiled a machine learning (ML) method that enables holistic solutions to networking issues across various layers, including Bandwidth Expansion (BWE).

Meta has developed a machine-learning (ML) model to improve the efficiency and reliability of real-time communication (RTC) across its various apps. Developing this ML-based solution is an answer to the limitations of existing bandwidth estimation (BWE) and congestion control methods, such as the Google Congestion Controller (GCC) used in WebRTC, which relies on hand-tuned parameters…

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AutoWebGLM: An Automated Web Navigation Agent, Superior to GPT-4, Based on ChatGLM3-6B

Large Language Models (LLMs) have taken center stage in many intelligent agent tasks due to their cognitive abilities and quick responses. Even so, existing models often fail to meet demands when negotiating and navigating the multitude of complexities on webpages. Factors such as versatility of actions, HTML text-processing constraints, and the intricacy of on-the-spot decision-making…

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Sigma: Altering Views on AI with Multiple-Modal Semantic Segmentation via a Siamese Mamba Network for Improved Comprehension of the Environment

The field of semantic segmentation in artificial intelligence (AI) has seen significant progress, but it still faces distinct challenges, especially imaging in problematic conditions such as poor lighting or obstructions. To help bridge these gaps, researchers are looking into various multi-modal semantic segmentation techniques that combine traditional visual data with additional information sources like thermal…

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CT-LLM: A Compact LLM Demonstrating the Important Move to Prioritize Chinese Language in LLM Development

Natural Language Processing (NLP) has traditionally centered around English language models, thereby excluding a significant portion of the global population. However, this status quo is being challenged by the Chinese Tiny LLM (CT-LLM), a groundbreaking development aimed at a more inclusive era of language models. CT-LLM, innovatively trained on the Chinese language, one of the…

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