"Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.
https://en.wikipedia.org/wiki/Machine_learning
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This article provides an overview of feature selection in machine learning, detailing methods to maximize model accuracy, minimize computational costs, and introduce a novel method called History-based Feature Selection (HBFS).
Researchers from the University of California San Diego have developed a mathematical formula that explains how neural networks learn and detect relevant patterns in data, providing insight into the mechanisms behind neural network learning and enabling improvements in machine learning efficiency.
Creativity and a Jetson Orin Nano Super can help hobbyists build accessible robots that can reason and interact with the world. The article discusses building a robot using accessible hardware like Arduino and Raspberry Pi, eventually upgrading to more capable hardware like the Jetson Orin Nano Super to run a large language model (LLM) onboard.
Discussion on the challenges and promises of deep learning for outlier detection in various data modalities, including image and tabular data, with a focus on self-supervised learning techniques.
An explanation of the differences between encoder- and decoder-style large language model (LLM) architectures, including their roles in tasks such as classification, text generation, and translation.
A detailed explanation of the Transformer model, a key architecture in modern deep learning for tasks like neural machine translation, focusing on components like self-attention, encoder and decoder stacks, positional encoding, and training.
A detailed guide on creating a text classification model with Hugging Face's transformer models, including setup, training, and evaluation steps.
An article explaining why and how beginners in machine learning should read academic papers, highlighting the vast amount of information available on arXiv and the benefits of engaging with these papers for learning and staying updated.
MIT researchers developed a system that uses large language models to convert AI explanations into narrative text that can be more easily understood by users, aiming to help with better decision-making about model trustworthiness.
The system, called EXPLINGO, leverages large language models (LLMs) to convert machine-learning explanations, such as SHAP plots, into easily comprehensible narrative text. The system consists of two parts: NARRATOR, which generates natural language explanations based on user preferences, and GRADER, which evaluates the quality of these narratives. This approach aims to help users understand and trust machine learning predictions more effectively by providing clear and concise explanations.
The researchers hope to further develop the system to enable interactive follow-up questions from users to the AI model.
David Ferrucci, the founder and CEO of Elemental Cognition, is among those pioneering 'neurosymbolic AI' approaches as a way to overcome the limitations of today's deep learning-based generative AI technology.
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