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Artificial Intelligence

Julie Shah has been appointed as the leader of the Department of Aeronautics and Astronautics.

Julie Shah, a distinguished scholar and leader in the field of robotics and artificial intelligence (AI), has been announced as the new head of the Department of Aeronautics and Astronautics (AeroAstro) at the Massachusetts Institute of Technology (MIT) starting May 1. Recognized widely for her significant technological contributions and grasp of the social, ethical, and…

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A dataset for Artificial Intelligence paves fresh routes for identifying tornadoes.

The arrival of spring in the Northern Hemisphere brings with it the commencement of tornado season. Meteorologists use radar to track these dangerous natural phenomena, but understanding exactly when a tornado has formed or why can be a challenge. However, a new dataset may provide some answers. Known as TorNet, this dataset compiled by researchers from…

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Comparing MLPs and KANs: Assessing Efficacy in Machine Learning, Image Recognition, Natural Language Processing, and Symbolic Assignments

Multi-layer perceptrons (MLPs) are integral to modern deep learning models for their versatility in replicating nonlinear functions across various tasks. However, interpretation and scalability challenges and reliance on fixed activation functions have raised concerns about their adaptability and scalability. Researchers have explored alternative architectures to overcome these issues, such as Kolmogov-Arnold Networks (KANs). KANs have…

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Meta FAIR’s Artificial Intelligence paper presents MoMa: An Efficient Multimodal Pre-training structure that incorporates a mixture-of-experts design, specifically tailored for modality-awareness.

Multimodal AI models, which integrate diverse data types like text and images, are pivotal for tasks such as answering visual questions and generating descriptive text for images. However, optimizing model efficiency remains a significant challenge. Traditional methods, which fuse modality-specific encoders or decoders, often limit the model's ability to combine information across different data types…

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LangChain presents LangGraph Studio: The inaugural Agent IDE designed for visual representation, interaction, and troubleshooting of intricate agentic applications.

Large Language Models (LLMs) have significantly impacted the development of agentic applications, prompting the need for evolved tooling for efficient development. In response to this demand, Langchain has developed LangGraph Studio, the first Integrated Development Environment (IDE) specifically designed for agent development, and made it available in open beta. LangGraph Studio represents a powerful solution in…

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Character AI unveils Prompt Poet, a new low-code Python library that simplifies prompt design for both coders and non-tech savvy individuals.

Character.AI recently unveiled a novel library in the field of Prompt Engineering called Prompt Poet. This represents a shift from traditional 'prompt engineering' to a more meticulous and engaging 'prompt design'. The tool offers greater functionality by considering multiple elements such as conversation modes, customer personas, conversations history, and ongoing experiments. Prompt Poet offers a comprehensive…

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LLM-for-X: Improving the Efficiency and Integration of Large Language Models Across Various Uses by Streamlining Workflow Enhancements

Incorporating advanced language models such as Large Language Models (LLMs) like ChatGPT and Gemini into writing and editing workflows is rapidly becoming essential in many fields. These models can transform the processes of text generation, document editing, and information retrieval, significantly enhancing productivity and creativity by integrating robust language processing capabilities. Despite this, a problem…

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Parseltongue: A Publicly Available Browser Plug-In Made for Complex Text Handling and Displaying

Parseltongue, an open-source browser extension introduced by a team of researchers, is aimed at enhancing text manipulation and visualization. It is ideally designed for users across various fields like linguistics, red teamers, and latent space explorers. The unique tool facilitates multi-format text conversion and real-time tokenization visualization, providing insights into the distinct cognitive processes used…

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The launch of sqlite-vec v0.1.0 has been announced. This movable vector database extension is compatible with SQLite, with the capacity to support a million 128-dimension vectors. It also supports binary quantization and includes an expansive selection of SDKs.

Alex Garcia recently released sqlite-vec v0.1.0, a SQLite extension written in C that brings powerful vector search capability to the SQLite database system. Available under the MIT/Apache-2.0 dual license, the extension pairs versatility with accessibility, making it a highly valuable tool for developers across different platforms and environments. The new sqlite-vec extension enables vector search functionality…

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RAGate: Advancing Conversational AI through Adaptable Knowledge Recovery

Large Language Models (LLMs) have significantly contributed to the enhancement of conversational systems today, generating increasingly natural and high-quality responses. But with their matured growth have come certain challenges, particularly the need for up-to-date knowledge, a proclivity for generating non-factual orhallucinated content, and restricted domain adaptability. These limitations have motivated researchers to integrate LLMs with…

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To enhance the effectiveness of an AI assistant, begin by simulating the unpredictable actions of people.

Scientists from the Massachusetts Institute of Technology (MIT) and the University of Washington have developed an approach to mechanically infer the computational weaknesses of an AI or human agent by observing prior activities. This perceptible agent’s "inference budget" can be used to predict future behavior. Used in forthcoming AI structures, the technique could allow them…

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This small microchip can protect user information and promote effective processing on a mobile phone.

Health-monitoring apps that use machine learning can be helpful in managing chronic diseases and fitness goals; however, they can also be slow and use a lot of energy. This is mainly due to machine learning models being shuttled between a smartphone and a central memory server. While machine-learning accelerators are often used to streamline computations,…

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