Artificial intelligence
Major goals Knowledge reasoning Planning Machine learning Natural language processing Computer vision Robotics Artificial general intelligence
Approaches Symbolic Deep learning Bayesian networks Evolutionary algorithms
Philosophy Ethics Existential risk Turing test Chinese room Control problem Friendly AI
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Technology Applications Projects Programming languages
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Books

1. The Quest for Artificial Intelligence: A History of Ideas and Achievements
   Nils J. Nilsson
   

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2. Artificial Intelligence: A Modern Approach
   Peter Norvig
   

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3. Practical Artificial Intelligence For Dummies
   Kristian Hammond
   

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4. AI: A Guide to Intelligent Systems
   Michael Negnevitsky
   

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5. Artificial Intelligence For Games
   Ian Millington
   

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6. Computing Machinery and Intelligence
   A.M.Turing
   

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7. Mind, Computing Machinery and Intelligence
   A.M.Turing
   

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8. Life 3.0: Being Human in the Age of Artificial Intelligence
   Max Tegmark
   

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9. Our Final Invention: Artificial Intelligence and the End of the Human Era
   James Barrat
   

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10. Deep Learning with R
    Mr. Joseph J Allaire
    

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11. Superintelligence: Paths, Dangers, Strategies
    Nick Bostrom
    

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12. Virtual Reality: Human Computer Interaction
    Xin-Xing Tang
    

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13. Clever Algorithms: Nature-Inspired Programming Recipes
    Jason Brownlee
    

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14. A Course in Machine Learning
    Hal Daumé III
    

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15. Python Machine Learning
    Sebastian Raschka
    

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16. Essentials of Metaheuristics
    Sean Luke
    

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17. Common LISP: A Gentle Introduction to Symbolic Computation
    David S. Touretzky
    

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18. Planning Algorithms
    Steven M. LaValle
    

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19. Simply Logical Intelligent Reasoning by Example
    Peter Flach
    

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20. Logic For Computer Science: Foundations of Automatic Theorem Proving
    Jean H. Gallier
    

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21. From Bricks to Brains: The Embodied Cognitive Science of LEGO Robots
    Michael R.W. Dawson
    

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22. Artificial Intelligence: Foundations of Computational Agents
    David L. Poole
    

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Papers

• On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
• Black Box Explanation by Learning Image Exemplars in the Latent Feature Space
• Visualizing and Understanding Convolutional Networks
• Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
• Understanding Deep Image Representations by Inverting Them
• Striving for Simplicity: The All Convolutional Net
• Object Detectors Emerge in Deep Scene CNNs
• Understanding intermediate layers using linear classifier probes
• An unexpected unity among methods for interpreting model predictions
• Visualizing Deep Neural Network Decisions: Prediction Difference Analysis
• Axiomatic Attribution for Deep Networks
• Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations
• Learning Important Features Through Propagating Activation Differences
• Network Dissection: Quantifying Interpretability of Deep Visual Representations
• SmoothGrad: removing noise by adding noise
• Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions
• Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
• The (Un) reliability of saliency methods
• Towards better understanding of gradient-based attribution methods for Deep Neural Networks
• Using KL-divergence to focus Deep Visual Explanation
• TED: Teaching AI to Explain its Decisions
• ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
• Counterfactual Visual Explanations
• 'Why did you do that?' Explaining black box models with Inductive Synthesis
• A Simple Saliency Method That Passes the Sanity Checks
• Interpretable Image Recognition with Hierarchical Prototypes
• Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks
• Anchors: High-Precision Model-Agnostic Explanations
• Understanding Deep Neural Networks For Regression In Leaf Counting
• Explanation by Progressive Exaggeration
• Interpretable Explanations of Black Boxes by Meaningful Perturbation
• Understanding Black-box Predictions via Influence Functions
• Sanity Checks for Saliency Maps
• Towards Robust Interpretability with Self-Explaining Neural Networks
• This Looks Like That: Deep Learning for Interpretable Image Recognition
• A Unified Approach to Interpreting Model Predictions
• "Why Should I Trust You?" Explaining the Predictions of Any Classifier
• Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
• Bias Also Matters: Bias Attribution for Deep Neural Network Explanation
• Understanding Neural Networks Through Deep Visualization
• Interpretable Convolutional Neural Networks
• Learning Deep Features for Discriminative Localization