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 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:

• Artificial intelligence
• History of AI
• Intelligent Agents
• Search techniques
• Constraint Satisfaction
• Knowledge Representation and Reasoning
• Reasoning under Uncertainty
• Theory-based Semantics
• Decision Making
• Learning and Neural Networks
• Bots and automatons

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

Wikipedia

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

• Artificial intelligence eschewal policy

External links

• Numenta Platform for Intelligent Computing (NuPIC)
• 'WIKIPEDIA AND ARTIFICIAL INTELLIGENCE: AN EVOLVING SYNERGY' - A workshop
• Common-sense Computing @ MIT Media Lab Project page
• MLOps Wiki - A glossary of machine learning terms
• AI Research - A collection of research-based articles in AI space

Documentation, manuals

https://platform.openai.com/

https://huggingface.co/docs

Courses

https://www.deeplearning.ai/

Fastfook

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

Hugging Face Natural Language Processing

Natural Language Processing (NLP) course

transformer models

using transformers: pipeline, tokenizer, AutoModel, decoding, padding, attention mask

fine-tuning a pretrained model: preprocessing, map, dataset, dynamic padding, batch, collate function, train, predict, evaluate, accelerate

sharing models and tokenizers: hub, model card

the datasets library: batch, DataFrame, validation, splitting, embedding, FAISS

the tokenizers library: grouping, QnA, normalizers, pre-tokenization, models,trainers: BPE, WordPiece, Unigram, post processors, decoders

main nlp tasks: token classification, metrics, perplexity, translation, summarization,

how to ask for help

building and sharing demos new

Artificial Intelligence: A Modern Approach, companion to the popular textbook

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