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Researchers from NASA and IBM Present INDUS: A Collection of Specific Large Language Models (LLMs) for Progressive Scientific Research.

Large Language Models (LLMs) are typically trained on large swaths of data and demonstrate effective natural language understanding and generation. Unfortunately, they can often fail to perform well in specialized domains due to shifts in vocabulary and context. Seeing this deficit, researchers from NASA and IBM have collaborated to develop a model that covers multidisciplinary…

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Scientists from the University of Toronto have unveiled a deep-learning model that surpasses the predictive capabilities of Google’s AI system for peptide structures.

Peptides are involved in various biological processes and are instrumental in the development of new therapies. Understanding their conformations, i.e., the way they fold into their specific three-dimensional structures, is critical for their functional exploration. Despite the advancements in modeling protein structures, like with Google's AI system AlphaFold, the dynamic conformations of peptides remain challenging…

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Gibbs Diffusion (GDiff): An Innovative Bayesian Noisy Data Filtering Technique for Use in Image Cleaning and Cosmological Studies.

The advancement of deep generative models has brought new challenges in denoising, specifically in blind denoising where noise level and covariance are unknown. To tackle this issue, a research team from Ecole Polytechnique, Institut Polytechnique de Paris, and Flatiron Institute developed a novel method called the Gibbs Diffusion (GDiff) approach. The GDiff approach is a fresh…

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A group of researchers from Tencent AI Lab have unveiled their AI Paper which delves into Persona-Hub, an aggregation of one billion varied personas designed to broaden the scope of synthetic data.

Training large language models (LLMs) hinges on the availability of diverse and abundant datasets, which can be created through synthetic data generation. The conventional methods of creating synthetic data - instance-driven and key-point-driven - have limitations in diversity and scalability, making them insufficient for training advanced LLMs. Addressing these shortcomings, researchers at Tencent AI Lab have…

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MultiOn AI’s Retrieve API revolutionizes autonomous web information retrieval by offering real-time processing and unmatched precision. This breakthrough allows developers to create sophisticated web agents and applications.

MultiOn AI has recently unveiled its latest development, the Retrieve API. This innovative autonomous web information retrieval API is designed to transform how businesses and developers extract and utilize data from the web. The API is an enhancement of the previously introduced Agent API and offers an all-encompassing solution for autonomous web browsing and data…

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GPT4All 3.0: A New Definition of Local AI Interaction Balancing Privacy and Efficiency

In the quick-paced field of artificial intelligence (AI), GPT4All 3.0, a milestone project by Nomic, is revolutionizing how large language models (LLMs) are accessed and controlled. As corporate control over AI intensifies, there emerges a higher demand for locally-run, open-source alternatives that prioritize user privacy and control. Addressing this demand, GPT4All 3.0 provides a comprehensive…

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Kyutai Discloses Moshi as Open Source: A Live Native Multimodal Foundation AI Model Capable of Speaking and Listening

In a significant reveal that has shaken the world of technology, Kyutai introduced Moshi, a pioneering real-time native multimodal foundation model. This new AI model emulates and exceeds some functionalities previously demonstrated by OpenAI’s GPT-4o. Moshi understands and delivers emotions in various accents, including French, and can simultaneously handle two audio streams, allowing it to…

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FI-CBL: A Stochastic Approach for Perceptual Machine Learning Applying Specialist Guidelines

Concept-based learning (CBL) is a machine learning technique that involves using high-level concepts derived from raw features to make predictions. It enhances both model interpretability and efficiency. Among the various types of CBLs, the concept-based bottleneck model (CBM) has gained prominence. It compresses input features into a lower-dimensional space, capturing the essential data and discarding…

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Scientists from the University of Wisconsin-Madison have suggested an adjustment method that uses a meticulously created artificial dataset consisting of numerical key-value retrieval assignments.

Large Language Models (LLMs) like GPT-3.5 Turbo and Mistral 7B often struggle to maintain accuracy while retrieving information from the middle of long input contexts, a phenomenon referred to as "lost-in-the-middle". This complication significantly hampers their effectiveness in tasks requiring the processing and reasoning over long passages, such as multi-document question answering (MDQA) and flexible…

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WildGuard: A Versatile, Lightweight Monitoring Instrument for Evaluating User-LLM Interaction Security

Safeguarding user interactions with Language Models (LLMs) is an important aspect of artificial intelligence, as these models can produce harmful content or fall victim to adversarial prompts if not properly secured. Existing moderating tools, like Llama-Guard and various open-source models, focus primarily on identifying harmful content and assessing safety but suffer from shortcomings such as…

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