This book aims to be the first comprehensive textbook on the application and use of artificial intelligence (AI) in, and for, games. Our hope is that the book will be used by educators and students of graduate or advanced undergraduate courses on game AI as well as game AI practitioners at large.
Archives: Resources
Resource description
Stereotypes in Language & Computational Language Models
Combined knowledge from linguistics, psychology, and natural language processing. In one project they study whether and how generic sentences — which typically express (perhaps stereotypical) generalisations — should be interpreted, and whether corresponding implicit generalisations can be found in computational language models trained by (huge) linguistic corpora. In the other project, the main object of… Continue reading Stereotypes in Language & Computational Language Models
Understanding and mitigating bias in AI automated systems
“The AI community has been focusing on developing fixes for harmful bias and discrimination, through so-called ‘debiasing algorithms’ that either try to fix data for known or expected biases, or constrain the outcomes of a given predictive model to produce ‘fair’ outcomes. We argue that creating more AI solutions to fix harmful biases in data… Continue reading Understanding and mitigating bias in AI automated systems
Building Cultural AI
“Biases in data can be both explicit and implicit. A simple two-word phrase can carry strong contestations, and entire research fields, such as post-colonial studies, are devoted to them. However, these sometimes subtle (and sometimes not so subtle) differences in voice are as yet not often found in the results of automatic analyses or datasets… Continue reading Building Cultural AI
Synthetic Media & News
If you are up to date with the news on technology, you may be interested to learn that ChatGPT has crossed 1M+ users in just five days. Top 10 things you can do with it? Generate code, track fitness, debug code, make it your personal assistant, create a marketing plan in less than 3o minutes,… Continue reading Synthetic Media & News
Deep Autoencoders
This lecture overviews Deep Autoencoders that has many applications in image denoising, classification, generation and in object pose estimation. It covers the following topics in detail: unsupervised learning, autoencoder principles, deep autoencoder types (Sparse/variational/ Convolutional/Adversarial Autoencoders) and their applications.