Intelligence & Artificial Intelligence You must have a pre-prepared sentence or two to spout about what is a description of intelligence.. And what is a description of Artificial Intelligence

Difficulties of AI  Seemingly ‘intelligent’ activities were easy to write ‘algorithms’ (programs) for e.g. chess; 200/300 rules for university calculus  However it is the simple problems which prove hard to program in conventional languages – e.g. the difference between a male and female face, understanding human speech  These problems are not suited to the conventional ‘sequential’ programming approach – the Von Neumann model. To escape limitations of conventional computer methods, techniques such as neural nets are used

Early AI: in the 1960 till 1975  Early people were: Alan Turing (Turing test – learn the definition) John McCarthy (summoned the ‘Dartmouth conference’ 1956, where the term AI came about. The Von Neumann model was not working. Researchers began to think of ‘Neural Networks’ (more later)  Eliza developed by Joseph Weisenbaum (1966) An example of use of language processing, but used ‘non- directive’ rather than normal talk.  SHRDLU, also demonstrating language processing, was developed by Terry Winograd (1970)  Research CHATTERBOTS for yourself chatterbots.net/ see others page 19  Knowledge representation Languages were developed; examples are LISP (LISt Processing), and PROLOG (PROgramming LOGic).

Turing Test for AI  a human judge engages in a natural language conversation with one human and one machine, each of which try to appear human. All participants are placed in isolated locations. If the judge cannot reliably tell the machine from the human, the machine is said to have passed the test. In order to test the machine's intelligence rather than its ability to render words into audio, the conversation is limited to a text-only channel such as a computer keyboard and screen.conversationhumancomputer keyboardscreen

Vonn Neumann model of computer processing The model describes computers which are designed with programs and data stored in addressable memory, and each instruction is executed in sequence. Compare it to the different parallel processing

Eliza ELIZA is a computer program by Joseph Weizenbaum, designed in 1966, which parodied a therapist, largely by rephrasing many of the patient's statements as questions and posing them to the patient. Thus, for example, the response to "My head hurts" might be "Why do you say your head hurts?" The response to "My mother hates me" might be "Who else in your family hates you?" computer programJoseph Weizenbaum1966therapist

NLP Natural language processing (NLP) is a field of computer science concerned with the interactions between computers and human (natural) languages. Eliza and SHRDLU used early attempts at NLPcomputer science

SHRDLU SHRDLU was an early natural language understanding computer program, developed by Terry Winograd at MIT from The user instructed SHRDLU to move various objects around in a small "blocks world" containing various basic objects: blocks, cones, balls, etc.natural language understandingcomputer programTerry WinogradMIT

Early AI: From 1975 on  ‘Domain Specific’ means concentrating on one small area of AI; most research areas have been like this since 1975  The best example is EXPERT SYSTEMS  Expert Systems started They were written using Knowledge representation Languages (KRLs) They worked in a narrow field of knowledge – a narrow domain Early examples were  MYCIN, a medical one which diagnosed blood infections  Prospector, geological – identified sites for mineral extraction

Other areas of ‘Domain Specific’ fields of AI  Developments we will get ‘answer ready on’ Natural Language Processing (NLP)  We will have a separate note on this Machine vision  We will have a separate note on this  We will skim over Intelligent robots Tutoring systems Fuzzy logic

Hardware & Software Advances that have facilitated improved research in AI  Improved processor power  Increased memory  Peripheral advances (e.g. backing storage)  Communications & the internet  Programming languages more efficient, more purpose suited  Etc.

Parallel Processing  This is when a system has more than one processor  E.g. a current supercomputer might be referred to as a 8K machine meaning it has over 8000 processor chips, not 8Kb of RAM or cache  Neural nets are good examples  However with even a relatively low number of processors inter- communication of data gets hopelessly complicated – remember example in video of neural net  A large task is broken up into many smaller ones, done simultaneously, therefore large task done quicker  Suited to languages like prolog; there are also specialised parallel processing programming languages