Chapter 10: Artificial Intelligence Outline Introduction Main tasks Knowledge representation Recognition tasks Reasoning tasks Social Issues Applications Software Virtual Machine Hardware Algorithmic Foundations

Introduction Artificial Intelligence (AI) The part of computer science that exploits human intelligence in deriving computer algorithms The more we know how human intelligence works, the more “intelligent” computer-based solutions can be achieved. Is the machine “intelligence” not enough? No … Because we still have a multitude of problems that cannot be solved by a computer at all. Because we still have a multitude of (unfortunately practice-close) problems that are solvable but need years or hundreds of years to come to a result. Because humans are “more intelligent” than computers in a variety of situations.

Introduction Turing Test A. Turing proposed in 1950s a method to test the intelligence of machines Human interrogates two entities Entity 1: Human Entity 2: Computer Interrogator is not allowed to see where an answer comes from (e.g. answers are printed prior to inspection) Test: If as a result of the questioning, the interrogator is not able to determine which answer originates from the computer and which answer originates from the human, then the computer has passed the Turing intelligence test.

Introduction Examples: Problems with Turing Test: Is your partner in a chess game a computer or a human? Are you communicating (e.g. by email) with a computer or humans? Problems with Turing Test: Measures intelligence of computers based on human intelligence. This kind of comparisons are no more of interest, since computer should support humans and NOT replace them. Also: What about interrogator’s intelligence? What types of questions are representative ones? …

Introduction Roughly speaking there were two phases in the history of AI Initial euphoric phase with great expectations, which were not realized. (science fiction remained science fiction!) Current phase with more realistic expectations and with valuable results. Observe: Artificial: may mean apparent, non-genuine, mimed, … Intelligence: may mean thought-based information, the right information in the right place/time, …

Main Tasks Let us focus on three human tasks: Computational tasks Adding columns of numbers Sorting a list of numbers Search a given name in telephone book Manage the payroll of a company … Recognition tasks Recognizing your best friend Understanding the spoken word Finding the tennis ball in the grass in your backyard Reasoning tasks Planning what to wear today Deciding on the strategic directions of a company in the next five years Running an alarm after an earthquake

Main Tasks Humans and computational tasks: Humans can follow algorithmic steps in order to come to a result But results should be found very quickly: Humans make mistakes Get bored And become sloppy Computers and computational tasks Computers are the specialists in this domain (and only this domain?) They don’t get bored and don’t become sloppy They are very fast in following stepwise instructions This is why we emphasized the step-by-step instruction processing so much in the early chapters  computers are better than humans in performing computation tasks provided that an efficient step-by-step solution (algorithm) is used

Main Tasks Humans and recognition tasks We, humans, are good in recognition tasks We exploit our sensory-recognition-motor skills very effectively We receive information through our senses (hearing, seeing) We can recognize the information we “sensed” And we usually response to the received information by “doing” something (e.g. movement) Compare: an infant (a few weeks old) is able to recognize his/her mom’s face!!, BUT the same person will need in general at least six/seven years in order to perform basic arithmetic operations

Main Tasks Computers are not that good in recognition tasks How do we recognize things? This topic is shared by different science disciplines Consider recognizing your best friend: You have a “database” of pictures in your brain When you see your best friend, you match his/her picture against your “file” If the match was successful you will probably laugh and select a specific manner of communication (e.g. language) Moreover: You can recognize your best friend’s sister even if you have never seen her before Thus you don’t need exact or complete information for recognition! Computers are not that good in recognition tasks

Main Tasks Humans and reasoning tasks We use also here a large storehouse of information (e.g. experience) This information consists not only of immediate facts like images but also of cause-and-effect rules Example: wearing a coat in a winter day You decide to wear a coat because you know by experience that you would be uncomfortable without a coat Reasoning steps I don’t want to be cold If it is winter and I don’t wear a coat, then I will be cold It is winter now Conclusion: I will wear a coat To mimic reasoning steps using a computer is a challenging task We, humans, can often come to a conclusion even if we do not have enough information, we may be ambiguous, and exploit intuition

Knowledge Representation From the previous assessment it follows that AI tries to improve computer ability for solving recognition and reasoning problems Likewise, it was mentioned that for these tasks to be achieved, information bases (e.g. picture databases) are needed Thus: we need to tackle the problem of representing information (knowledge) in a computer system Knowledge: Facts or truths about some topic Rules for gaining new facts

Knowledge Representation How to represent knowledge? Natural language: For example: “Spot is a brown dog, and, like any dog, has four legs and a tail. Also, like any dog, Spot is a mammal, which means Spot is warm-blooded.” Notice that these “strings” have a meaning and have to be treated as such Formal language: x: entity e.g. a dog A(x): x has attribute A Above example: Dog(Spot) Brown(Spot) For all x: Dog(x)  FourLegs(x) For all x: Dog(x)  hasTail(x) For all x: Dog(x)  isMammal(x) For all x: isMammal(x)  isWarmBlooeded(x)

Knowledge Representation Pictorial representation: Give the picture of Spot showing that it is brown and has four legs and a tail +: additional information can be contained -: attributes warm-blooded and mammal cannot be represented, additional text is necessary Graphical representation Semantic net has Warm blood Mammal 4 legs is a has Dog has tail instance is color brown Spot

Knowledge Representation What type of representation to use: It depends on the application domain E.g. for computer vision, rather pictorial Semantic nets are extensible and therefore are appropriate for capturing non-complete knowledge Natural languages are not as exact as formal languages. General criteria for representation methods: Adequateness Efficiency Extensibility Appropriateness

Recognition Tasks AI sometimes tries to mimic the way of human thinking ( brain functions) But how does our brain function? 1011-1012 neurons Stimuli “enter” a neuron though dendrites Stimuli “exit” a neuron through axons Axons connect to other dendrites by synapses A neuron gathers stimuli from dendrites If the sum of signals is higher than a threshold, the neuron fires; it sends a new signal down its axon and affects other neurons in its neighborhood

Recognition Tasks Neural Network Computer scientists have developed (artificial) neural networks to simulate the work of our brain in order to solve problems related to recognition Hardware or software implementation Each node represents the nucleus of a neuron Each node has a specific threshold value Each node has a number of weighted input lines; the dendrites Each node has a number of output lines; the axons/synapses  If sum of weighted inputs >= threshold, then output is activated Weight 1 Node (Nucleus) Weight Weight 2 Weight 3

Recognition Tasks Example: recognizing two character patterns: Node: Class A Input: all shapes of an “A” Output: to “Different” and “Equivalent” nodes Node: Class B Input: all shapes of a “B” Examples: Inputs: A and A  Equivalent is activated Inputs: B and B  Equivalent is activated Inputs: A and B  Different is activated How to come to the correct weights:  training Training phase (done automatically by a “trainer” module): Network is fed by an initial set of input with known output Weights are iteratively adjusted until actual output is close enough to desired output Compare: training a dog until it “hardwires” things in its brain

Recognition Tasks Example network A 2 2 Class A 1 2 Different 3 A 2 A -2 2 B 2 Class B 1 2 Equivalent -3 B -2 2 B

Recognition Tasks Neural networks have been applied in a wide range of areas: Handwriting recognition Speech recognition Recognizing bad credit risks in loans Predicting the odds of cancer susceptibility Limited visual recognition Segmenting magnetic resonance images in medicine Adapting mirror shapes for astronomical observations Discovering a good routing algorithm in a computer network

Reasoning Tasks How do humans reason in a logical way? Example: Triage center in a hospital Understanding the situation Capability of staff Availability of resources Older experiences … Conclusion: Patient A: first priority Patient B: second priority In AI, rule-based systems (or expert systems) are used to emulate this kind of reasoning

Reasoning Tasks A rule-based system consists of: Knowledge base: A knowledge base and An inference engine Knowledge base: Set of facts and rules about a special domain Examples (compare Prolog facts and rules): F1: Lincoln was president during Civil War F2: Kennedy was president before Nixon F3: FDR was president before Kennedy R1: if X was president before Y, then X precedes Y R2: if X was president before Z and Z precedes Y, then X precedes Y

Reasoning Tasks Inference Engine: How to generate new facts from the knowledge base Example 1: F2: Kennedy was president before Nixon R1: if X was president before Y, then X precedes Y F2 and R1 lead to a new fact F4: F4: Kennedy precedes Nixon Example 2: F3: FDR was president before Kennedy R2: if X was president before Z and Z precedes Y, then X precedes Y F3, F4, and R2 lead to a new fact F5: F5: FDR precedes Nixon

Reasoning Tasks Inference Engine: Basic meta-rule we are using to infer new knowledge is: Given the fact A and the rule if A then B, then infer a new fact B. Using this meta-rule, called modus ponens, new facts can be “learned” from older ones. These new facts may let other rules in the knowledge to be used in order to generate further new facts, and so on. The process of inference is repeated until no new rules can be inferred. Two policies for inference: Forward chaining: Match facts to the if-branches of rules (like our examples) Backward chaining: Match facts to the then-branches of rules