Second Edition

An introduction to
ARTIFICIAL INTELLIGENCE
Alan Dix with Janet Finlay

Topics

These are some of the topics I’m considering for the new edition, but this may change as things develop – so please let me know what you think!

deep learning – multi-level NN, reinforcement learning, adversarial learning

dimension reduction – classic factorial and similarity matrix decompositions,  restricted Boltzmann machines, random vectors

explainable AI – argumentation, deep learning and black-box ML, sensitivity/perturbation analysis

human-centred AI – bias (gender, ethnic), need for transparency, augmentation vs replacement, IUI, designing interactions to aid ML

big data – recommenders, surprising power (e.g. language), map-reduce, limits and links to HPC, large network analysis

in the world – more robots, autonomous vehicles, smart homes, smart cities

temporal AI – recurrent NN, HMM, applications: language, activity recognition, security, continuous data (e.g. GAs for ODEs)

human-like computing – one shot learning, higher-level AI, links to explainability

practicalities – data cleaning, reconciliation, mixed methods

plus –  spiking NN, algebraic topology and other new techniques, ontologies and sem-web for knowledge representation, update ethics/legal/philosophical

case studies – small (1-2 page) boxes or in text – maybe over time add longer case studies to web: onCue, GA for submarine design, musicology data, AlphaGo, Watson, QbB, find security example, predictive policing?