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CMU Researchers Introduce MultiModal Graph Learning (MMGL): A New Artificial Intelligence Framework for Capturing Information from Multiple Multimodal Neighbors with Relational Structures Among Them
Multimodal graph learning is a multidisciplinary field that combines machine learning, graph theory, and data fusion to address complex problems involving diverse data sources. It can generate descriptive captions for images, improve retrieval accuracy, and enhance perception in autonomous vehicles. Researchers at Carnegie Mellon University propose a framework for multimodal graph learning that captures information…
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5 Ideas to Foster Data Scientists/Analysts Engagement Without Suffocating in Meetings
The author outlines five essential touchpoints for finding a balance between focus time and collaboration within a data science or data analytics team. These touchpoints include a morning standup meeting, a Friday “Work In Progress” presentation, a monthly Data Science team meeting, individual one-on-one meetings, and a department team meeting. These touchpoints foster focus, communication,…
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Synergy of LLM and GUI, Beyond the Chatbot
This text introduces a new approach to combining conversational AI and graphical user interface (GUI) interaction in mobile apps. It describes the concept of a Natural Language Bar that allows users to interact with the app using their own language. The article provides examples and implementation details for developers. The Natural Language Bar can be…
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Reshaping the Model’s Memory without the Need for Retraining
Large language models (LLMs) have become widely used, but they also pose ethical and legal risks due to the potentially problematic data they have been trained on. Researchers are exploring ways to make LLMs forget specific information or data. One method involves fine-tuning the model with the text to be forgotten, penalizing the model when…
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Oh, you meant “manage change”?
This text explores different perspectives on change in a data organization. Alex, the CDO, focuses on driving business value and staying ahead of market shifts, while Jamie, a data engineer, is more concerned with day-to-day challenges and keeping things running smoothly. The article emphasizes the importance of transparency, collaboration, and standardization in managing change effectively.…
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KAIST Researchers Propose SyncDiffusion: A Plug-and-Play Module that Synchronizes Multiple Diffusions through Gradient Descent from a Perceptual Similarity Loss
Researchers from KAIST have introduced SYNCDIFFUSION, a module that aims to improve the generation of panoramic images using pretrained diffusion models. The module addresses the problem of visible seams when stitching together multiple images. It synchronizes multiple diffusions using gradient descent based on a perceptual similarity loss. Experimental results show that SYNCDIFFUSION outperforms previous techniques,…
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Common-Knowledge Effect: A Harmful Bias in Team Decision Making
Teams often make worse decisions than individuals because they rely too heavily on widely understood data and ignore information possessed by only a few team members. Research has consistently shown that teams spend too much time discussing information they all already know, leading to poor decision-making.
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The 4 Degrees of Anthropomorphism of Generative AI
Chatbots and AI are often seen as human-like, with users treating them as companions. This anthropomorphism has a functional role, as users believe AI will perform better, and a connection role, to enhance the user experience. A usability study of ChatGPT identified two new behaviors for managing length and detail: accordion editing and apple picking.
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Meet ScaleCrafter: Unlocking Ultra-High-Resolution Image Synthesis with Pre-trained Diffusion Models
Researchers have developed ScaleCrafter, a method that enables the generation of ultra-high-resolution images using pre-trained diffusion models. By dynamically adjusting the convolutional receptive field, ScaleCrafter addresses issues like object repetition and incorrect object topologies. It also introduces innovative strategies like dispersed convolution and noise-damped classifier-free guidance. The method has been successfully applied to a text-to-video…
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6 Magic Commands for Jupyter Notebooks in Python Data Science
Jupyter Notebooks are widely used in Python-based Data Science projects. Several magic commands enhance the notebook experience. These commands include “%%ai” for conversing with machine learning models, “%%latex” for rendering mathematical expressions, “%%sql” for executing SQL queries, “%run” for running external Python files, “%%writefile” for quick file creation, and “%history -n” for retrieving previous commands.…