Unite.AI

JANUARY 23, 2025

Consequently, the foundational design of AI systems often fails to include the diversity of global cultures and languages, leaving vast regions underrepresented. Bias in AI typically can be categorized into algorithmic bias and data-driven bias. Explainable AI tools make spotting and correcting biases in real time easier.

Algorithm 113

Algorithm AI Development AI Developer AI 113

AI Weekly

OCTOBER 12, 2023

techspot.com Applied use cases Study employs deep learning to explain extreme events Identifying the underlying cause of extreme events such as floods, heavy downpours or tornados is immensely difficult and can take a concerted effort by scientists over several decades to arrive at feasible physical explanations. "I'll get more," he added.

Robotics 264

Robotics Large Language Models Deep Learning Artificial Intelligence 264

Marktechpost

OCTOBER 15, 2024

XAI, or Explainable AI, brings about a paradigm shift in neural networks that emphasizes the need to explain the decision-making processes of neural networks, which are well-known black boxes. Additionally, a metric can be categorized into three types: ground_truth, downstream_evaluation, or heuristic.

Explainability 57

Explainability Explainable AI Python Neural Network 57

ODSC - Open Data Science

OCTOBER 24, 2023

It easily handles a mix of categorical, ordinal, and continuous features. Yet, I haven’t seen a practical implementation tested on real data in dimensions higher than 3, combining both numerical and categorical features. All categorical features are jointly encoded using an efficient scheme (“smart encoding”).

Categorization 98

Categorization Data Science Neural Network Algorithm 98

IBM Journey to AI blog

FEBRUARY 9, 2024

Prior to the current hype cycle, generative machine learning tools like the “Smart Compose” feature rolled out by Google in 2018 weren’t heralded as a paradigm shift, despite being harbingers of today’s text generating services.

AI 298

AI AI Generative AI Artificial Intelligence 298