Introduction & FAQs
The below lists various introductions or FAQs on Artificial Intelligence technicals including Neural Networks, Fuzzy Logic, Genetic Algorithms, Natural Language Processing and others.
Below is a list of materials that introduces the reader to the field of Artificial Intelligence. These materials are targeted at the novice AI practitioner. Students who are just learning the field may also find this material useful.
Artificial intelligence (AI) is the intelligence exhibited by machines or software. It is also the name of the academic field of study which studies how to create computers and computer software that are capable of intelligent behavior. [from Wikipedia]
Machine learning (ML) is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. Machine learning explores the construction and study of algorithms that can learn from and make predictions on data. [from Wikipedia]
Artificial Neural Networks (ANNs)In machine learning and cognitive science, artificial neural networks (ANNs) are a family of statistical learning models inspired by biological neural networks (the central nervous systems of animals, in particular the brain) and are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown. [from Wikipedia]
Deep Learning (deep machine learning, or deep structured learning, or hierarchical learning, or sometimes DL) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using model architectures, with complex structures or otherwise, composed of multiple non-linear transformations. [from Wikipedia]
Genetic Algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. [from Wikipedia]
Fuzzy logic is a form of many-valued logic that deals with approximate, rather than fixed and exact reasoning. Compared to traditional binary logic (where variables may take on true or false values), fuzzy logic variables may have a truth value that ranges in degree between 0 and 1. Fuzzy logic has been extended to handle the concept of partial truth, where the truth value may range between completely true and completely false. [from Wikipedia]
Cognitive computing (CC) makes a new class of problems computable. It addresses complex situations that are characterized by ambiguity and uncertainty; in other words it handles human kinds of problems. In these dynamic, information-rich, and shifting situations, data tends to change frequently, and it is often conflicting. The goals of users evolve as they learn more and redefine their objectives. To respond to the fluid nature of users’ understanding of their problems, the cognitive computing system offers a synthesis not just of information sources but of influences, contexts, and insights. To do this, systems often need to weigh conflicting evidence and suggest an answer that is “best” rather than “right” [from Wikipedia]
Neuromorphic engineering also known as neuromorphic computing, is a concept developed by Carver Mead, in the late 1980s, describing the use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system. In recent times the term neuromorphic has been used to describe analog, digital, and mixed-mode analog/digital VLSI and software systems that implement models of neural systems (for perception, motor control, or multisensory integration). [from Wikipedia]
General AI Introduction
- What Is Artificial Intelligence? from Stanford Uni
- Computing Machinery and Intelligence – Turing’s original 1950 article on machine intelligence, where he introduces the famous Turing Test, and started this profound multi-decade debate.
- An executive’s guide to machine learning from McKinsey.com
- Machine Learning Wiki
- A ‘Brief’ History of Neural Nets and Deep Learning Part 1 – Andrey Kurenkov’s Web World
- Marvin Minsky’s book “Society of mind” ebook Pavel Savchenko
These wiki pages give a good overview of each of the subject areas, and contain alot of additional references and links to other resources.
- Artificial Intelligence
- Artificial Neural Networks
- Deep Learning
- Machine Learning
- Fuzzy Logic
- Genetic Algorithms
- Natural Language Processing
- Introduction to Neural Networks by Leslie Smith at Stirling University
- What is a neural network? [from pnl.gov]
- What is a neural network and what can it do? [from sas.com]
- News groups comp.ai.neural-nets
- Backpropagator’s Review
- Introduction to Artificial Neural Networks – Part 1 by Lee Jacobson
- Introduction to Artificial Neural Networks – Part 2 by Lee Jacobson
- How to implement a neural network Part 1 by Peter Rlnts
- UFLDL Tutorial 1
- UFLDL Tutorial 2
- Deep Learning for NLP (without Magic)
- A Deep Learning Tutorial: From Perceptrons to Deep Networks
- Fuzzy Math Sets – Quick tutorial on fuzzy logic and sets.
- Introduction to Fuzzy Logic by Franck Dernoncourt at MIT
- Fuzzy Logic Introduction by Naranker Dulayat at Imperial College London
- FAQ for Fuzzy at CS.CMU.EDU
- Genetic Algorithms Introduction by Naranker Dulayat at Imperial College London
Below are a series of FAQs on the topic of Artificial Intelligence and Machine Learning.
Other Useful Information
Other useful information that introduces you to the area of AI.
- 10 Machine Learning Terms Explained in Simple English from aylien.com
- The State of Artificial Intelligence in Six Visuals via Medium.com
Glossary of Terms
Below is a list of common acronyms used in this directory:
- AI – Artificial Intelligence
- AGI – Artificial General Intelligence
- CC – Cognitive Computing
- ML – Machine Learning
- DL – Deep Learning
- ANN – Artificial Neural Network
- NN – same as above: ANN
- GA – Genetic Algorithms
- NLP – Natural Language Processing
- FL – Fuzzy Logic
- GPU – Graphics Processor Unit
- FPGA – Field Programmable Gate Array
- Computer Clubhouse Network – An after-school learning environment for kids to explore their own interests and learn about technology.
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