The document provides an overview of artificial intelligence and key developments in the field, including: 1. It discusses early definitions of intelligence and issues with defining AI, as well as tests like the Turing Test. 2. Early developments in AI focused on game playing to demonstrate problem solving abilities within limited domains. 3. Research then shifted to language processing with programs like ELIZA, which could hold basic conversations, and knowledge representation using semantic nets and logic programming.

1. Artificial Intelligence Mr Arthur

2. Aims of Lesson 1 What is Intelligence?? Why is there so much confusion to its meaning

3. What is Intelligence???? “Intelligence is the name we give to the data processing activity of entities which respond to information with behaviour which appears to be intended to be optional with respect to pre-set goals” “Intelligence involves Knowing and Choosing”

4. Intelligence?? Many researchers feel that intelligence has the following features The Ability to: Learn and adapt from experience. Use knowledge to make decisions. Problem solve. Show Creativity Handle and manipulate language “ Artificial intelligence is concerned with building machines that can act and react appropriately, adapting their response to the demands of situation.”

5. Confusion to its Meaning?? It is a subject which is developing all the time, It is a subject which involves computer scientists, biologists, psychologists etc and they all have a different perspective It is difficult to define human intelligence, let alone artificial intelligence.

6. Aims of Lesson 2 Test for Identifying Intelligence Early Developments in AI Game Playing

7. Testing Intelligence Turing Test Alan Turing, a British mathematician developed a test in 1950 to determine if a program was intelligent: It has the following features: A human tester was connected to 2 terminals and asked a series of questions. One of the terminals had another human making the responses and the other used a computer program to make the responses If the human tester could not distinguish between the human responder and the program response then the software was said to be intelligent.

8. Problems with Turing Test It needed a fairly limited problem domain i.e. the area of the knowledge had to be small. Did the program pretend to forget things or make errors in order to fool the human tester?

9. Early Developments in AI (1940-65) In the beginning the focus of AI research was on modelling the human brain. Research shifted to using games like noughts and crosses, chess etc to create “AI” systems These games were effective as they had limited problem domains. The games also had a number of rules that were easy to define.

10. Aims of Lesson 3 Last Lesson Definition of intelligence Why can experts not agree on the definition of AI The Turing Test Early Developments in AI = Game Playing Today’s Lesson Language Processing ELIZA SHRDLU PARRY Chatterbots

11. Language Processing (1965-1975) In 1965 Researchers agreed that game playing programs could not pass the Turing test The focus shifted to language processing ELIZA (1966) 1 st language processing program Responded to users inputs by asking questions based on previous responses

12. Language Processing (1965-1975) PARRY (1972) Parry modelled a conversation with a paranoid person This seems odd but the program was created by a psychiatrist SHRDLU (1970) The program could interpret verbal commands to move coloured blocks "Move the red block behind the green one" It could understand and carry out the instruction Chatterbots??

13. Aims of Lesson 4 Last Lesson Definition of intelligence Why can experts not agree on the definition of AI The Turing Test Early Developments in AI = Game Playing Language Processing ELIZA SHRDLU PARRY Chatterbots Today’s Lesson Knowledge representation Semantic Nets Logic programming List Prolog Developments in Hardware AI questions on Forum

14. Knowledge Representation Semantic nets Diagrams which show how the information in a system is interlinked They can get very large and out of hand if you try to model a lot of knowledge

15. Knowledge Representation Logic Programming Implemented using declarative languages. Declarative Languages They have no fixed start or end point Consist of collection of facts and rules Use goal directed problem solving Prolog/Lisp

16. Knowledge Representation Early AI developments were hampered by the programming techniques used human conversations cannot easily be represented by an algorithm A framework for representing knowledge' was a crucial moment for AI semantic nets logic programming

17. Aims of Lesson 5 Last Lesson Definition of intelligence Why can experts not agree on the definition of AI The Turing Test Early Developments in AI = Game Playing Language Processing ELIZA SHRDLU PARRY Chatterbots Knowledge representation Semantic Nets Logic programming List Prolog Today’s Lesson Developments in Hardware Applications of AI Intro to Natural Language Processing Uses of NLP NLP Steps

18. Developments in Hardware Faster processors More memory Increased backing storage capacity Computers being developed with multiple processors (parallel processing where a task can be processed simultaneously on different processors)

19. Uses of Natural Language Processing Customer query lines Vue cinema Bank of Scotland O2 Language translation e.g. English to French Speech-driven word processors Command and control systems – giving verbal instructions to save, print etc

20. Problems with NLP Ambiguity of words and phrases I saw a man eating fish I saw a man hitting a boy with a stick Changing English language i.e. bouncebackability Accents and dialects i.e. I ken what you are talking about!! Similar sounding words and phrases e.g. furry boots are you fae?? Inconsistencies with grammar

21. Aims of Lesson 6 Last Lesson Definition of intelligence Why can experts not agree on the definition of AI The Turing Test Early Developments in AI = Game Playing Language Processing ELIZA SHRDLU PARRY Chatterbots Knowledge representation Semantic Nets Logic programming List Prolog Developments in Hardware Applications of AI Intro to Natural Language Processing Today’s Lesson Steps in the NLP process Moral, Legal issues with NLP

22. Main Stages in NLP 1. Speech Recognition 2. Natural Language Recognition 3. Natural Language Generation 4. Speech Synthesis

23. NLP Process Speech Recognition Get sound input using microphone, sample and convert to digital data Segment the sound into recognisable sounds (phonemes) Natural Language Recognition Put these sounds together to form words, sentences etc “ Understand" the meaning of these sentences Natural Language Generation Create an appropriate response Speech Synthesis Convert the response into intelligible sound output

24. Moral and Legal Issues Moral NLP has benefited blind or disabled users who could not access computers In military applications, it could be used in weapon systems Legal Mistranslations leading to commercial problems Possible wrong diagnosis

25. Aims of Lesson 7 Last Lesson Definition of intelligence Why can experts not agree on the definition of AI The Turing Test Early Developments in AI = Game Playing Language Processing ELIZA SHRDLU PARRY Chatterbots Knowledge representation Semantic Nets Logic programming List Prolog Developments in Hardware Applications of AI Natural Language Processing Today’s Lesson Vision Systems Applications of Vision Systems Vision System Process

26. Vision Systems The aim is to develop systems which can see, make sense of what they see, and react accordingly. Uses Security systems, recognising faces at airports Inspection of manufactured goods judging quality of production Vision systems on automated cars Visual stock control systems Control of AGVs Artificial Visual Sensing for the blind

27. Vision Systems Image Acquisition Signal Processing Edge detection

28. Vision Systems Object recognition (Pattern Matching) Its a Tin of Soup! Image Understanding

29. Vision System Process Image acquisition Capturing the image and converting to digital Signal processing improving the resolution, removing distortion Edge detection Identifying edges of image Object recognition Comparing the objects with known objects. Image understanding How do objects relate to each other? What do they mean?

30. Aims of Lesson 8 Last Lesson Applications of AI Natural Language Processing Uses Problems Steps in NLP process Moral, Legal issues Vision Systems Uses 5 steps in process Today’s Lesson Expert Systems Examples in everyday life Advantages of Expert Systems Disadvantages of expert systems

31. Expert Systems An expert system is a computer program which uses a knowledge base of facts and rules populated by a human expert The expert system will give reliable advice on a limited area of expertise, and can interact and explain it reasoning to the user (justification facilities) Examples MYCIN to diagnose blood disorders Legal advice Chemical analysis, DENDRAL was designed to identify unknown substances Car mechanic expert system BABY used to monitor premature babies

32. Advantages of Expert Systems Available to access 24/7 Expert System will never get tired It will never have an “Off day” You can have multiple copies of the expert system Can combine the knowledge of many experts No human emotion involved No possibility of expert system retiring No barrier due to poor communication skills

33. Disadvantages of Expert Systems No common sense is used If the expert system makes an error who is repsonsible? Developer? Company using it? Less trust in expert system?

34. Aims of Lesson 9 Last Lesson Applications of AI Natural Language Processing Uses Problems Steps in NLP process Moral, Legal issues Vision Systems Uses 5 steps in process Expert Systems Examples in everyday life Advantages of Expert Systems Disadvantages of expert systems Today’s Lesson Expert Systems Parts of an Expert System Expert System Shells Moral/Legal issues

35. Parts of an Expert System User Interface This allows the user to input information into the Expert System and get responses in a language that the user understands Knowledge base Consists of all the facts and rules about the topic This information will have been provided by a domain experts Converted into an appropriate form by a knowledge engineer. Inference engine , This searches the knowledge base, comes to conclusions and generates questions to the user

36. Expert System Shells An expert system shell is an expert system with no knowledge base. It consists only of a user interface and an inference engine. EMYCIN - The interface was so good on this expert system that the company sold this as a shell and the user added the knowledge base

37. Legal and Moral Issues Legal If the system is wrong, who do you blame? Human expert, company who bought it, company who created it, the programmer Moral Would you like a machine to diagnose serious health problems Expert system wouldn't make a decision on what is morally correct, it would only use facts and rules It wouldn’t consider feelings Is it right to potentially make human experts redundant

38. Aims of Lesson 9 Last Lesson Applications of AI Natural Language Processing Uses Problems Steps in NLP process Moral, Legal issues Vision Systems Uses 5 steps in process Expert Systems Examples in everyday life Advantages of Expert Systems Disadvantages of expert systems Parts of an Expert System Expert System Shells Moral/Legal issues Today’s Lesson Intelligent Robots Smart Embedded Technology

39. Intelligent Robots Robots can be considered intelligent when they go beyond simple sensors and feedback (dumb robots), and display some further aspect of human-like behaviour Vision Systems The ability to learn and improve performance Robot that can walk rather than on wheels NLP response Examples Bomb disposal Deep sea exploration and rescue Space exploration Honda’s Asimo

40. Practical Problems with Intelligent Robots Provide a power supply? Implement effective 3D vision system? Implement mobility? Providing adequate processing power? Manipulating objects?

41. Smart Embedded Technology SET is where a processor is inside another device, making the device “smart” e.g. it can make decisions and learn from experiences Examples The car that can park itself without you The fridge that knows when food orders are running low and orders in more food Car engine control systems which monitor performance and inform the user when maintenance is required The tv system that works out what you might enjoy watching from past viewing patterns and records it when you are out

42. Aims of Lesson 10 Last Lesson Applications of AI Natural Language Processing Uses Problems Steps in NLP process Moral, Legal issues Vision Systems Uses 5 steps in process Expert Systems Examples in everyday life Advantages of Expert Systems Disadvantages of expert systems Parts of an Expert System Expert System Shells Moral/Legal issues Intelligent Robots Smart Embedded Technology Today’s Lesson Intro to Artificial Neural Systems

43. Artificial Neural Systems ANS is an approach to AI where the developer attempts to model the human brain The human brain is made up of billions of connections called Neutrons. A Neutron will “fire” when it gets enough input from other Neutrons. This firing of Neutrons is how the brain sends signals to the muscles

44. ANS Continued AI researchers have modelled this process using a network of artificial neutrons (perceptron). If you connect a lot of these perceptrons together you can get some fairly complex decisions being made, which might rely on dozens of inputs.

45. ANS Continued Simply speaking, a neural network is an electronic model of the brain consisting of many interconnected simple processors. Neural networks can either be built in hardware as hard wired circuitry or be implemented in software.

46. Aims of Lesson 10 Last Lesson Applications of AI Natural Language Processing Uses Problems Steps in NLP process Moral, Legal issues Vision Systems Uses 5 steps in process Expert Systems Examples in everyday life Advantages of Expert Systems Disadvantages of expert systems Parts of an Expert System Expert System Shells Moral/Legal issues Intelligent Robots Smart Embedded Technology Intro to Artificial Neural Systems Today’s Lesson Layers 3 basic stages of ANS creation Applications of ANS Advantages/Disadvantages of ANS

47. ANS Layers ANS are made up of layers with input, internal (hidden) and output layers Perceptrons in the input layer react to different stimuli which triggers perceptrons in the hidden layers If the combined output from the hidden layers passes a certain threshold, it will pass an output signal You can therefore make fairly complex decisions based on dozens of separate inputs

48. Stages on Creating ANS Initial Setup The network is created and the initial values (weights) are set for the input layer, the hidden layers and the output layers. Training the Network A number of trials are run, to test the accuracy of the original threshold values. The internal weights change, or rebalanced, so that the network gives the correct outputs. These changes may be done manually or the network may rebalance itself, Running the Network The network is used and outputs generated. These may be checked against expectations and further balancing done as the network learns and improves.

49. Applications of ANS Post Office has been using ANS to automate the reading of postcodes An ANS system to predict the stock market NASA have used ANS systems to pilot aircraft

50. Adv/Disadv of ANS Advantages They can learn and adjust to changing circumstances They have a good success rate at predicting the correct response Disadvantages The difficulty is setting them up and training them, determining why they are giving particular responses