Portal:Artificial intelligence

Four approaches summarize past attempts to define the field:

  • The study of systems that think like humans.
  • The study of systems that think rationally.
  • The study of systems that act like humans.
  • The study of systems that act rationally.

Of these approaches, the former two are considered to be "white-box" approaches because they require our analysis of intelligence to be based on the rationale for the behaviour rather than the behaviour itself. The latter two are considered "black-box" approaches because they operationalize intelligence by measuring performance over a task domain. We prefer the latter two because they allow for quantitative comparisons between systems rather than requiring a qualitative comparison of rationales. We realize that the ultimate performance of a system will depend heavily on the task domain that it is situated in, and this motivates our preference for studying activity (behaviour) rather than thought (rationale).

Although the third approach, (known as cognitive modelling), is of great importance to cognitive scientists, we concern ourselves with the fourth approach. Of the four, this approach allows to consider the performance of a theoretical system that yields the behaviour optimally suited to achieve its goals, given the information available to it.

This approach motivates us to provide a model for our intelligent systems known as the intelligent agent.

See: Learning Projects and the Wikiversity:Learning model.

Learning materials and learning projects are located in the main Wikiversity namespace. Simply make a link to the name of the learning project (learning projects are independent pages in the main namespace) and start writing! We suggest the use of the learning project template (use "subst:Learning project boilerplate" on the new page, inside the double curved brackets {{}}).

Learning materials and learning projects can be used by multiple departments. Cooperate with other departments that use the same learning resource. Understanding AI as a field of Computer Science involves a thorough understanding of the following topics:

Remember, Wikiversity has adopted the "learning by doing" model for education. Lessons should center on learning activities for Wikiversity participants. We learn by doing.

Select a descriptive name for each learning project.

Applied project


Research projects



Your Deep Learning Journey
From Model to Production
Data Ethics
Under the Hood: Training a Digit Classifier
Image Classification
Other Computer Vision Problems
Training a State-of-the-Art Model
Collaborative Filtering Deep Dive
Tabular Modeling Deep Dive
NLP Deep Dive: RNNs
Data Munging with fastai's Mid-Level API
A Language Model from Scratch
Convolutional Neural Networks
Application Architectures Deep Dive
The Training Process
A Neural Net from the Foundations
CNN Interpretation with CAM
A fastai Learner from Scratch
Concluding Thoughts
Appendix: Jupyter Notebook 101
Natural Language Processing (NLP) course
Artificial Intelligence: A Modern Approach, companion to the popular textbook


Artificial intelligence
Artificial neural networks
Machine learning
Deep learning
Generative artificial intelligence
Applications of artificial intelligence
Large language models
Natural language processing
Existential risk from artificial general intelligence
Deep belief network
Speech recognition
List of artificial intelligence projects
List of datasets for machine learning research

See also


Open source software