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Applications

Symbolic Learning in AI Agents: A Framework for Machine Learning that Simultaneously Enhances All Symbolic Elements within an AI Agent Structure.

Language models have undergone significant developments in recent years which has revolutionized artificial intelligence (AI). Large language models (LLMs) are responsible for the creation of language agents capable of autonomously solving complex tasks. However, the development of these agents involves challenges that limit their adaptability, robustness, and versatility. Manual task decomposition into LLM pipelines is…

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Strengthening Firm Denial Training in LLMs: A Previous Time Modification Assault and Possible Protective Measures

Large Language Models (LLMs) like GPT-3.5 and GPT-4 are cutting-edge artificial intelligence systems that generate text which is nearly indistinguishable from that created by humans. These models are trained using enormous volumes of data that enables them to accomplish a variety of tasks from answering complex questions to writing coherent essays. However, one significant challenge…

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The Neo4j LLM Knowledge Graph Builder: An AI Mechanism that Constructs Knowledge Graphs from Disorganized Data

Artificial Intelligence (AI) is making strides in the data analysis sphere, with teams of researchers developing new applications to convert unstructured data into usable information. Recently, one such application was introduced, known as the Neo4j LLM Knowledge Graph Builder. This tool leverages powerful machine learning models to transform unstructured data into a comprehensive knowledge graph,…

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The Launch of Nephilim v3 8B: A Groundbreaking AI Solution for Combining Models to Improve Roleplay and Creativity

Hugging Face has introduced two new innovative models named llama-3-Nephilim-v3-8B and llama-3-Nephilim-v3-8B-GGUF. Despite not being explicitly trained for roleplays, these models have demonstrated outstanding proficiency in this area, illuminating the possibilities of "found art" strategies in the domain of artificial intelligence (AI) development. To create these models, several pre-trained language models were converged. The merger was…

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Apple’s artificial intelligence has launched an open-source language model trained on 2.5 trillion tokens using open datasets, with a capacity of 7 billion.

Language models have become an integral part of natural language processing, assisting in tasks like text generation, translation, and sentiment analysis. Their efficiency and accuracy, however, greatly rely on quality training datasets. Creating such datasets can be a complex process, involving the elimination of irrelevant or harmful content, removal of duplicates, and the selection of…

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The Athene-Llama3-70B Unveiled: A Non-Specific Weight LLM Developed with RLHF, Grounded on Llama-3-70B-Instruct.

Nexusflow has recently launched Athene-Llama3-70B, a high-performance open-weight chat model that's been fine-tuned from Meta AI's earlier model, Llama-3-70B. The improvement in terms of performance is quite significant with the new model achieving an impressive Arena-Hard-Auto score of 77.8%, surpassing models like GPT-4o and Claude-3.5-Sonnet. This is a substantial improvement from Llama-3-70B-Instruct, the predecessor which…

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ZebraLogic: An AI Benchmark Created for Assessing Language Models through Logical Puzzles

The article introduces a benchmark known as ZebraLogic, which assesses the logical reasoning capabilities of large language models (LLMs). Using Logic Grid Puzzles, the benchmark measures how well LLMs can deduce unique value assignments for a set of features given specific clues. The unique value assignment task mirrors those that are often found in assessments…

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The DiT-MoE: An Updated Edition of the DiT Framework for Creating Images

In recent years, diffusion models have emerged as powerful assets in various fields including image and 3D object creation. Renowned for their proficiency in managing denoising assignments, these models can effectively transform random noise into the targeted data distribution. But their deployment triggers high computational costs, mainly because these deep networks are dense, which means…

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How can Casual Logic Enhance Formal Evidence Validation? This AI Research Presents an AI Structure for Learning to Integrate Casual Ideas with the Phases of Formal Validation.

Researchers from the Language Technologies Institute at Carnegie Mellon University and the Institute for Interdisciplinary Information Sciences at Tsinghua University have developed a groundbreaking framework - Lean-STaR - that bridges informal human reasoning with formal proof generation to improve machine-driven theorem proving. This research seeks to utilize the potential of integrating natural language thought processes…

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Assessing the Stability and Equality of Instruction-Calibrated Language Models in Healthcare Endeavors: Insights into Performance Fluctuation and Demographic Equitability.

Language Learning Models (LLMs) that are capable of interpreting natural language instructions to complete tasks are an exciting area of artificial intelligence research with direct implications for healthcare. Still, theypresent challenges as well. Researchers from Northeastern University and Codametrix conducted a study to evaluate the sensitivity of various LLMs to different natural language instructions specifically…

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Investigating the Influence of ChatGPT’s AI Features and Human-like Characteristics on Improving Knowledge and User Contentment in the Professional Workplace Settings

ChatGPT, an AI system by OpenAI, is making waves in the artificial intelligence field with its advanced language capabilities. Capable of performing tasks such as drafting emails, conducting research, and providing detailed information, such tools are transforming the way office tasks are conducted. They contribute to more efficient and productive workplaces. As with any technological…

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Together AI is introducing a groundbreaking inference stack, which is poised to redefine performance standards in generative AI.

Together AI has introduced a new inference stack, marking a significant breakthrough in AI inference. This new stack has a decoding speed which is four times faster than the open-source vLLM, and outperforms industry-leading commercial solutions such as Amazon Bedrock, Azure AI, Octo AI, and Fireworks by a margin of 1.3x to 2.5x. The new…

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