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Machine learning

Developing and confirming robust AI-operated systems using thorough and adaptable methods.

Researchers from the Massachusetts Institute of Technology's (MIT) Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed an algorithm to mitigate the risks associated with using neural networks in robots. The complexity of neural network applications, while offering greater capability, also makes them unpredictable. Current safety and stability verification techniques, called Lyapunov functions, do not…

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Mistral AI has launched Mathstral 7B and the Math Fine-Tuning Base, scoring 56.6% on MATH and a 63.47% on MMLU, revolutionizing the process of mathematical discovery.

Mistral AI has unveiled the new Mathstral model, an innovation designed specifically for mathematical reasoning and scientific discovery. The model, named Mathstral as an homage to Archimedes on the occasion of his 2311th anniversary, comprises a vast 7 billion parameters and a 32,000-token context window, and is made available under the Apache 2.0 license. The Mathstral…

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This AI Article Presents TelecomGPT: A Dedicated Large Language Model for Improved Efficiency in Telecommunication-Related Chores.

Telecommunication, the transmission of information over distances, is fundamental in our modern world, enabling the channeling of voice, data, and video via technologies including radio, television, satellite and the internet to support global connectivity and data exchange. But while innovations in the field continue to improve the speed, reliability, and efficiency of communication systems, existing…

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This AI Article Presents TelecomGPT: A Specialized Large Language Model for Improved Efficiency in Telecommunication Assignments

Telecommunications is a field involving the transmission of information over distances to facilitate communication. It uses various technologies such as radio, television, satellite, and the internet for voice, data, and video transmission and plays a fundamental role in societal and economic functions. However, Large Language Models (LLMs) that are typically used in the field lack specialised…

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Researchers at UCSD have presented a variational inference framework, referred to as MCD, for determining the primary causal models and tracking down the mixing probability for every single data piece.

Researchers are grappling with how to identify cause and effect in diverse time-series data, where a single model can't account for various causal mechanisms. Most traditional methods used for casual discovery from this type of data typically presume a uniform causal structure across the entire dataset. However, real-world data is often highly complex and multi-modal,…

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An improved, more efficient method to prohibit an AI chatbot from producing harmful responses.

Researchers from Improbable AI Lab at MIT and the MIT-IBM Watson AI Lab have developed a technique to enhance the safety measures implemented in AI chatbots to prevent them from providing toxic or dangerous information. They have improved the process of red-teaming, where human testers trigger unsafe or dangerous context to teach AI chatbot to…

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The AI technique dramatically accelerates the prediction of thermal characteristics of materials.

An international team of researchers, including members from MIT (Massachusetts Institute of Technology), has developed a machine learning-based approach to predict the thermal properties of materials. This understanding could help improve energy efficiency in power generation systems and microelectronics. The research focuses on phonons - subatomic particles that carry heat. Properties of these particles affect…

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Microsoft’s research team has crafted SheetCompressor: A cutting-edge AI framework designed for encoding that efficiently compresses spreadsheets for LLMs.

Spreadsheet analysis is crucial for managing and interpreting data in the extensive two-dimensional grids used in tools like MS Excel and Google Sheets. However, the large, complex grids often exceed the token limits of large language models (LLMs), making it difficult to process and extract meaningful information. Traditional methods struggle with the size and complexity…

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Transforming Cell Analysis: Advanced Phenotyping Made Possible by Integrating Artificial Intelligence and Mass Spectrometry with Deep Visual Proteomics.

Deep Visual Proteomics (DVP) is a groundbreaking approach for analyzing cellular phenotypes, developed using Biology Image Analysis Software (BIAS). It combines advanced microscopy, artificial intelligence, and ultra-sensitive mass spectrometry, considerably expanding the ability to conduct comprehensive proteomic analyses within the native spatial context of cells. The DVP method involves high-resolution imaging for single-cell phenotyping, artificial…

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Transforming Cell Study: Advanced Phenotyping through the Integration of Artificial Intelligence and Mass Spectrometry in Deep Visual Proteomics

Deep Visual Proteomics (DVP) is a groundbreaking method that combines high-end microscopy, AI, and ultra-sensitive mass spectrometry for comprehensive proteomic analysis within the native spatial context of cells. By utilizing AI to identify different cell types, this technology allows an in-depth study of individual cells, increasing the precision and effectiveness of cellular phenotyping. The DVP workflow…

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An improved, quicker method to restrict an AI chatbot from delivering harmful replies.

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An improved and quicker method to stop an AI chatbot from providing harmful reactions.

Artificial intelligence (AI) advancements have led to the creation of large language models, like those used in AI chatbots. These models learn and generate responses through exposure to substantial data inputs, opening the potential for unsafe or undesirable outputs. One current solution is "red-teaming" where human testers generate potentially toxic prompts to train chatbots to…

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