Table of contents : Preface 1 The Evolution and Pitfalls of AI The basic idea behind AI The evolution of AI Philosophy Logic Mathematics Cognitive science A short history of AI Subfields of AI ML Computer vision Natural language processing Robotics Knowledge representation Problem-solving and reasoning Planning Evolutionary computing The pitfalls of AI Is AI limitless? How important is the data? Can we get training data? Have we got good data? Can a high-performance AI still fail? Summary 2 The Rise and Fall of Symbolic AI Defining Symbolic AI Humans, symbols, and signs Enabling machine intelligence through symbols The concept of intelligence Towards Symbolic AI From symbols and relations to logic rules The fall of Symbolic AI Symbolic AI today Expert systems Natural language processing Constraint satisfaction Explainable AI The sub-symbolic paradigm Summary Further reading 3 The Neural Networks Revolution Artificial neural networks modeling the human brain A simple artificial neural network Introducing popular neural network architectures Recurrent neural networks Competitive networks Hopfield networks Delving into deep neural networks Convolutional neural networks Long short-term memory networks Autoencoders Deep belief networks Generative networks Transformers The rise of data The complexities and limitations of neural networks Summary 4 The Need for Explainable AI What is XAI? Why do we need XAI? XAI case studies The state-of-the-art models in XAI Accumulated Local Effects Anchors Contrastive Explanation Method Counterfactual instances Explainable Boosting Machine Global Interpretation via Recursive Partitioning Integrated gradients Local interpretable model-agnostic explanations Morris Sensitivity Analysis Partial dependence plot Permutation importance Protodash SHapley Additive exPlanations Summary 5 Introducing Neuro-Symbolic AI – the Next Level of AI The idea behind NSAI Modeling human intelligence – insights from child psychology The ingredients of an NSAI system The symbolic ingredient The neural ingredient The neuro-symbolic blend Exploring different architectures of NSAI Neuro-Symbolic Concept Learner Neuro-symbolic dynamic reasoning Dissecting the NLM architecture Summary Further reading 6 A Marriage of Neurons and Symbols – Opportunities and Obstacles The benefits of combining neurons and symbols Data efficiency High accuracy Transparency and interpretability The challenges of combining neurons and symbols Knowledge and symbolic representation Multi-source knowledge reasoning Dynamic reasoning Query understanding for knowledge reasoning Research gaps in neuro-symbolic computing Summary 7 Applications of Neuro-Symbolic AI Application 1 – health – computational drug Application details Problem statement The role of NSAI Application 2 – education – student strategy prediction Application details Problem statement The role of NSAI Application 3 – finance – bank loan risk assessment Application details Problem statement The role of NSAI Summary Further reading 8 Neuro-Symbolic Programming in Python Environment and data setup Solution 1 – logic tensor networks Loading the dataset Modifying the dataset Creating train and test datasets Defining our knowledge base and NN architecture Defining our predicate, connectives, and quantifiers Setting up evaluation parameters Training the LTN model Analyzing the results Solution 2 – prediction stacking Experiment setup and loading the data Data preparation Training our NSAI model Analyzing the results Prediction interpretability and logic tracing Summary Further reading 9 The Future of AI Looking at fringe AI research Small data Novel network architectures New ways of learning Evolution of attention mechanisms World model Hybrid models Exploring future AI developments Quantum computing Neuromorphic engineering Brain-computer interaction Bracing for the rise of AGI Preparing for singularity Popular media Exploring the expert views Singularity challenges Summary Further reading Index Other Books You May Enjoy