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Explainable Artificial Intelligence (XAI) with Python

#artificialintelligence

Our reliance on artificial intelligence models is increasing day by day, and it's also becoming equally important to explain how and why AI This course provides detailed insights into the latest developments in Explainable Artificial Intelligence (XAI). Our reliance on artificial intelligence models is increasing day by day, and it's also becoming equally important to explain how and why AI makes a particular decision. Recent laws have also caused the urgency about explaining and defending the decisions made by AI systems. This course discusses tools and techniques using Python to visualize, explain, and build trustworthy AI systems. This course covers the working principle and mathematical modeling of LIME (Local Interpretable Model Agnostic Explanations), SHAP (SHapley Additive exPlanations) for generating local and global explanations.


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#artificialintelligence

This course provides detailed insights into the latest developments in Explainable Artificial Intelligence (XAI). Our reliance on artificial intelligence models is increasing day by day, and it's also becoming equally important to explain how and why AI makes a particular decision. Recent laws have also caused the urgency about explaining and defending the decisions made by AI systems. This course discusses tools and techniques using Python to visualize, explain, and build trustworthy AI systems. This course covers the working principle and mathematical modeling of LIME (Local Interpretable Model Agnostic Explanations), SHAP (SHapley Additive exPlanations) for generating local and global explanations.


Eighth International Workshop on Qualitative Reasoning about Physical Systems

AI Magazine

The Eighth International Workshop on Qualitative Reasoning about Physical Systems (QR '94) was held on 7-10 June 1994 in Nara, Japan. Fifty-three people participated, and 34 papers were presented in either oral or poster sessions. The papers either addressed core issues of qualitative reasoning or extended the field along three axes: (1) cognitive modeling, (2) mathematical sophistication, and (3) application. Mita's selfmaintenance copier and IBM's mechanism design and analysis using configuration spaces were demonstrated, convincing the participants of the promising role of qualitative-reasoning techniques in engineering and manufacturing domains. Since the first workshop in 1987, the workshop site has altered between the United States and Europe.


Alternatives to algebraic modeling for complex data: topological modeling via Gunnar Carlsson

@machinelearnbot

For many, mathematical modeling is exclusively about algebraic models, based on one form or another of regression or on differential equation modeling in the case of dynamical systems. However, this is too restrictive a point of view. For example, a clustering algorithm can be regarded as a modeling mechanism applicable to data where linear regression simply isn't applicable. Hierarchical clustering can also be regarded as a modeling mechanism, where the output is a dendrogram and contains information about the behavior of clusters at different levels of resolution. Kohonen self-organizing maps can similarly be regarded in this way.


Contrastive Counterfactual Visual Explanations With Overdetermination

arXiv.org Artificial Intelligence

A novel explainable AI method called CLEAR Image is introduced in this paper. CLEAR Image is based on the view that a satisfactory explanation should be contrastive, counterfactual and measurable. CLEAR Image explains an image's classification probability by contrasting the image with a corresponding image generated automatically via adversarial learning. This enables both salient segmentation and perturbations that faithfully determine each segment's importance. CLEAR Image was successfully applied to a medical imaging case study where it outperformed methods such as Grad-CAM and LIME by an average of 27% using a novel pointing game metric. CLEAR Image excels in identifying cases of "causal overdetermination" where there are multiple patches in an image, any one of which is sufficient by itself to cause the classification probability to be close to one.