Artificial Intelligence

Artificial intelligence (AI) is the intelligence by machines or software.

Subfields of AI

Subfields of AI:

Knowledge Represntation and Reasoning (KR&R)
- Symbolic AI
Machine Learning (ML)
- Parametric AI
- Natural Language Processing (NLP)
- Deep Learning
Large Language Model (LLM)

Knowledge Representation and Reasoning (KR&R)

Symbolic AI

Symbolic AI is a subpart of KR&R. It is based on how human intelligence is managed symbollically. It is an alternative to parametric AI.

Part of symbolic learning are:

Semantic Networks
Frames
Rules
Restrictions

Aproximated reasoning: fuzzy logic, MYCIN, Dempster-Shafer

MI, OPS-5, ART-IM, RETE algorithm

Semantic Networks

Semantic networks were developed by Quillian. You can read more about them at Wikipedia.

Frames

Frames were proposed by Minsky in 1976. You can read more about them at Wikipedia.

Rules

Restrictions

Machine Learning

Federated learning or collaborative learning is a machine learning technique in a setting where multiple entities (often called clients) collaboratively train a model while keeping their data decentralized, rather than centrally stored.

MLOps ease the implementation and machine learning model management in production.

You can find this post about machine learning frameworks.

Data mining is quite related to artificial intelligence. You can read this post about data mining.

Parametric AI

Parametric AI is based on how human intelligence works physically.

Artificial Neural Networks

Artificial neural networks can be considered a data mining method.

You can read this post about artificial neural networks.

Genetic Algorithms

Deep Learning

Deep learning is a type of machine learning that uses artificial neural networks to learn from data.

Large Language Model (LLM)

Large Language Models (LLM) is a language model notable for its ability to achieve general-purpose language understanding and generation.

You can read more about it on this post.

GenIA

Stable Diffusion is a GenIA algorithm to generate images.

Stable Diffusion at Wikipedia

Agentic IA

An IA agent is a system that receives information and takes decision autonomously.

Agentic IA implies the use of IA agents in system.

Distributed AI

Distributed AI goals:

Ensuring agents acts coherently when taking decision or making actions.
Enabling agent reasoning about other agents actions and plans.
Developing platforms for multiagent systems and development methodologies.

Natural Language Processing (NLP)

Natural Language Processing (NLP) helps computers communicate with humans in their own language and scales other language-related tasks.

Task of NLP include text classification, sentiment analysis, machine translation, named entity recognition, part-of-speech tagging and question answering.

There are different approaches to NLP. The earliest ones (1970s) were rule-based or statistical-based, and are used for parsing, tokenization, or syntactic analysis. The more modern are based on machine learning and LLMs.

Types of AI

Types of AI:

Decision support systems
Expert systems
Knowledge bank
Neural networks