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Enhancing AI Transparency and Trust with Composite AI

Unite.AI

As organizations strive for responsible and effective AI, Composite AI stands at the forefront, bridging the gap between complexity and clarity. The Need for Explainability The demand for Explainable AI arises from the opacity of AI systems, which creates a significant trust gap between users and these algorithms.

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AI’s Inner Dialogue: How Self-Reflection Enhances Chatbots and Virtual Assistants

Unite.AI

It includes deciphering neural network layers , feature extraction methods, and decision-making pathways. These AI systems directly engage with users, making it essential for them to adapt and improve based on user interactions. These systems rely heavily on neural networks to process vast amounts of information.

Chatbots 204
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5 key areas for governments to responsibly deploy generative AI

IBM Journey to AI blog

Generative AI is emerging as a valuable solution for automating and improving routine administrative and repetitive tasks. This technology excels at applying foundation models, which are large neural networks trained on extensive unlabeled data and fine-tuned for various tasks.

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6 Free Artificial Intelligence AI Courses from Google

Marktechpost

Introduction to Generative AI: This course provides an introductory overview of Generative AI, explaining what it is and how it differs from traditional machine learning methods. Participants will learn about the applications of Generative AI and explore tools developed by Google to create their own AI-driven applications.

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The Black Box Problem in LLMs: Challenges and Emerging Solutions

Unite.AI

SHAP's strength lies in its consistency and ability to provide a global perspective – it not only explains individual predictions but also gives insights into the model as a whole. This method requires fewer resources at test time and has been shown to effectively explain model predictions, even in LLMs with billions of parameters.

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Evolving Trends in Data Science: Insights from ODSC Conference Sessions from 2015 to 2024

ODSC - Open Data Science

By 2017, deep learning began to make waves, driven by breakthroughs in neural networks and the release of frameworks like TensorFlow. Sessions on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) started gaining popularity, marking the beginning of data sciences shift toward AI-driven methods.

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AI News Weekly - Issue #350: TIME100 AI list : 100 most influential people in AI - Sep 14th 2023

AI Weekly

eweek.com Robots that learn as they fail could unlock a new era of AI Asked to explain his work, Lerrel Pinto, 31, likes to shoot back another question: When did you last see a cool robot in your home? As it relates to businesses, AI has become a positive game changer for recruiting, retention, learning and development programs.

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