Measuring trends in Artificial Intelligence The AI Index is an independent initiative at the Stanford Institute for Human-Centered Artificial Intelligence (HAI), led by the AI Index Steering Committee, an interdisciplinary group of experts from across academia and industry. The annual report tracks, collates, distills, and visualizes data relating to artificial intelligence, enabling decision-makers to take meaningful action to advance AI responsibly and ethically with humans in mind. The AI Index collaborates with many different organizations to track progress in artificial intelligence.

Here are some explainable AI features that can help to understand why an AI came to a decision. This post will be contantly updated. Keep coming back for more! Advanced AI explainability for PyTorch: https://github.com/jacobgil/pytorch-grad-cam This project helps us understand how the AI calculates based on visual cues. This is really good to understand the AI. Project is MIT licensed. AOmniXAI: A Library for Explainable AI: https://github.

Prof. Richard Buggs an evolutionary biologist and molecular ecologist talks about how Darwinian tree of life may be wrong and insists that we need to look deeper into the fact that we cannot explain everything with evolutionary biology and may be it is time that we take another look at the Biblical view of a designer. You can watch this brilliant talk on YouTube Read more: https://richardbuggs.com/2022/12/02/public-lecture-trees-of-life-do-they-exist/ https://richardbuggs.