1 / 10

H Computing

H Computing. Artificial Intelligence. Lesson 1 - Introduction. The development of artificial intelligence Definitions of human intelligence and artificial intelligence

ostinmannual
Download Presentation

H Computing

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. H Computing Artificial Intelligence

  2. Lesson 1 - Introduction • The development of artificial intelligence • Definitions of human intelligence and artificial intelligence • Descriptions of aspects of intelligence (including language, learning, cognitive ability, problem solving skills, memory, creativity) • Explanation of the difficulties of determining an accurate and agreed definition of intelligence. • Explanation of the inherent flaws of the Turing test as a method for determining the existence of artificial intelligence

  3. Lesson 1 Main Points • It is hard to define “intelligence” so therefore very difficult to define “artificial intelligence”. • Several areas of intelligence: ability to learn, creativity, reasoning and ability to explain, use of language, vision, problem solving, adaptability, cognitive ability, memory. • Showing intelligent behaviour does not means something is intelligent. • Description of the Turing Test.

  4. Lesson 2 – The Development of AI • Description of the need for knowledge representation techniques (including semantic nets and logic programming) • Explanation of the need for a restricted domain • Identification of languages: LISP (functional), Prolog (declarative/logic) • Description of difference between declarative and imperative languages • Explanation (with examples) of: • the success and failures of game playing programs from simple early examples to contemporary complex examples exhibiting intelligence • the successes and failures of language processing (including Eliza, SHRDLU, chatterbots and contemporary applications) • the scope and limitations of expert systems • Explanation of the effects of hardware developments (including faster processors, more memory, and increasing backing store capacity) on the field of AI • Description of the implementation and advantages of parallel processing • Description of the practical problems associated with AI despite advances in hardware/software

  5. Lesson 2 Main Points • Knowledge Representation Techniques: • Semantic Nets • Logic Programming using declarative languages - Prolog or functional language – LISP • Restricted Domain required • AI systems require knowledge. Successful systems have knowledge of a particular area. • Game playing • Required restricted domain • Simply games (tic tac toe, draughts) then chess systems developed. • Language processing • Eliza – 1966 (therapist – always asked questions taken from user previous answer) • Parry – paranoid person • SHRDLU – could respond within a limited domain (coloured shaped blocks) • Expert Systems • Some success but limited scope • Most problems stemmed from the lack of processing power and memory capacity of early computers. • Parallel processing developed – have a job broken down into parts that different processor could do at the same time.

  6. Lesson 3 – Computer Vision • Applications and uses of artificial intelligence – Computer Vision • Description of the problems of interpreting 2D images of 3D objects • Description of the stages of computer vision (image acquisition, signal processing, edge detection, object recognition, image understanding)

  7. Lesson 3 – Main Points • Need to know 5 stages of computer vision: image acquisition, signal processing, edge detection, object recognition and image understanding. • Recognise difficulties in object recognition and image understanding. • Description of applications of computer vision.

  8. Lesson 4 – Natural Language Processing • Identification of the main stages of NLP (speech recognition, natural language understanding (NLU), natural language generation, speech synthesis) • Explanation of some difficulties in NLP (including ambiguity of meaning; similar sounding words; inconsistencies in grammar of human language; changing nature of language) • Identification of applications of NLP (including automatic translation, speech driven software, NL search engines, NL database interfaces)

  9. Lesson 4 – Main Points • Understand differences between formal and natural language and the problems with natural language. • State and the describe the stages of NLP: input sound and digitise; analyse sounds; generate words; create meaningful sentences; understand meaning; generate responses; create sound output. • Applications of NLP: translation, natural language search engines, speech recognition for people unable to type,…

  10. Lesson 5 – Intelligent Robots, Smart Devices and Fuzzy Logic • Description of examples of the use of intelligent software to control devices (including car engine control systems; domestic appliances) • Intelligent robots: • Explanation of the difference between dumb and intelligent robots • Description of contemporary research and developments • Description of possible social and legal implications of the increasing use of intelligent robots • Descriptions of practical problems (including processor power, power supply, mobility, vision recognition, navigation, path planning, pick and place, and strategies used to overcome these problems)

More Related