Source metadata
- Type: Non-technical introduction
- Author: Tom Taulli
- Published: 2019, Apress
Key takeaways
- Provides a broad conceptual frame for AI foundations, data, machine learning, deep learning, NLP, robots, and implementation concerns.
- Is useful for explaining the nested relationship between AI, machine learning, and deep learning to readers who are new to the area.
- Emphasises that data quality, implementation context, and organisational readiness matter as much as model choice.
- Is not game-specific, so it works best as background support for technical or design pages rather than as a primary game-AI authority.
Notable claims
- The book repeatedly treats AI progress as inseparable from improvements in data availability, compute, and tooling.
- It presents deep learning as a subset of machine learning rather than a synonym for AI.
- It frames implementation as a socio-technical challenge, not just a model-building problem.
Relevance
Directly informs:
Supports:
Open questions raised
- Should the wiki eventually have a small “AI background for designers” synthesis page distinct from the more technical game-AI pages?
- How much of this book belongs in the programming section versus in broader discussions of AI-assisted production?
Links
machine-learning-games · overview-artificial-intelligence-in-games · generative-ai-game-dev