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.

Learning projects
See: Learning Projects and the 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

 * Building an artificial neural network using reinforcement learning strategies

Research projects

 * Neural Symbolic Learning and Reasoning

Readings

 * https://www.deeplearning.ai/
 * https://github.com/fastai/fastbook/
 * 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
 * ResNets
 * 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
 * transformer models
 * using transformers: pipeline, tokenizer, AutoModel, decoding, padding, attention mask
 * fine-tuning a pretrained model: preprocessing, dataset, dynamic padding, batch, collate function, train, predict, evaluate, accelerate
 * sharing models and tokenizers: hub, model card
 * the datasets library
 * the tokenizers library
 * main nlp tasks
 * how to ask for help
 * building and sharing demos new
 * Artificial Intelligence: A Modern Approach, companion to the popular textbook

Wikipedia

 * Deep belief network
 * Speech recognition
 * List of artificial intelligence projects
 * List of datasets for machine learning research
 * Deep belief network
 * Speech recognition
 * List of artificial intelligence projects
 * List of datasets for machine learning research
 * Deep belief network
 * Speech recognition
 * List of artificial intelligence projects
 * List of datasets for machine learning research
 * List of datasets for machine learning research

Open source software

 * EVO - 3D artificial life simulator
 * MindForth artificial mind for robots
 * Open Source 3D Vision Library
 * Texai English Lexicon, Fluid Construction Grammar, and RDF Entity Manager

People

 * --NicholasTurnbull 04:12, 14 November 2008 (UTC)

Papers

 * Papers @ CALO: Cognitive Agent that Learns and Organizes

Fachbereich Virtuelle Wissenskonstruktion fr:Département:Intelligence artificielle Introdução à Inteligência Artificial Факультет искусственного интеллекта