AI Definitions • The study of how to make programs/computers do things

AI Definitions The study of how to make programs/computers do things that people do better The study of how to make computers solve problems which require knowledge and intelligence The exciting new effort to make computers think machines with minds The automation of activities that we associate with human thinking (e.g., decision-making, learning ) The art of creating machines that perform functions that require intelligence when performed by people The study of mental faculties through the use of computational models A field of study that seeks to explain and emulate intelligent behavior in terms of computational processes The branch of computer science that is concerned with the automation of intelligent behavior Thinking machines or machine intelligence Studying cognitive faculties Problem Solving and CS

So What Is AI? AI as a field of study – – – – – – Computer Science Cognitive Science Psychology Philosophy Linguistics Neuroscience AI is part science, part engineering AI often must study other domains in order to implement systems – e.g., medicine and medical practices for a medical diagnostic system, engineering and chemistry to monitor a chemical processing plant AI is a belief that the brain is a form of biological computer and that the mind is computational AI has had a concrete impact on society but unlike other areas of CS, the impact is often – felt only tangentially (that is, people are not aware that system X has AI) – felt years after the initial investment in the technology

What is Intelligence? Is there a “holistic” definition for intelligence? Here are some definitions: – the ability to comprehend; to understand and profit from experience – a general mental capability that involves the ability to reason, plan, solve problems, think abstractly, comprehend ideas and language, and learn – is effectively perceiving, interpreting and responding to the environment None of these tells us what intelligence is, so instead, maybe we can enumerate a list of elements that an intelligence must be able to perform: – perceive, reason and infer, solve problems, learn and adapt, apply common sense, apply analogy, recall, apply intuition, reach emotional states, achieve self-awareness Which of these are necessary for intelligence? Which are sufficient? Artificial Intelligence – should we define this in terms of human intelligence? – does AI have to really be intelligent? – what is the difference between being intelligent and demonstrating intelligent behavior?

Physical Symbol System Hypothesis A physical symbol system (PSS) – consists of symbols (patterns) which are combinable into expressions – there are processes which operate on these symbols to create new symbols and expressions consider for instance English as a physical symbol system symbols are the alphabet expressions are words and sentences the processes are the English grammar and parsers and dictionaries The PSS Hypothesis states that a PSS has the necessary and sufficient means for intelligent action – a computer is a PSS if the PSS Hypothesis is true, then it should be possible to program a computer to produce intelligent actions this is the (or a) goal of AI if the PSS Hypothesis is not true, what are the consequences for AI?

The Turing Test 1950 – Alan Turing devised a test for intelligence called the Imitation Game – Ask questions of two entities, receive answers from both – If you can’t tell which of the entities is human and which is a computer program, then you are fooled and we should therefore consider the computer to be intelligent Answers Questions Answers Which is the person? Which is the computer?

Solving the Turing Test: Eliza Created by Joseph Weizenbaum This system would act like a Rogerian psychoanalyst by holding a dialog with a person – the dialog would be consist of the doctor (Eliza) asking questions, the human responding, and the doctor using the response to ask another question Weizenbaum wrote the program so that it would generate an English response/question based on a group of patterns – If the user sentence matched a pattern, this pattern would be used to generate the next sentence/question – A more recent, and much superior version is called A.L.I.C.E, see http://alicebot.blogspot.com/ click on “chat with A.L.I.C.E.”

– H: Men are all alike – E: In what way? – H: They’re always bugging us about something or another. – E: Can you be more specific? – H: Well, my boyfriend made me come here. – E: Your boyfriend made you come here? – H: He says I’m depressed much of the time. – E: I am sorry to hear you are depressed. – H: It’s true. I am unhappy. – E: Do you think coming here will help you not to be unhappy? – H: I need some help, that much seems certain. – E: What would it mean to you if you got some help? – H: Perhaps I could learn to get along with my mother. – E: Tell me more about your family Example Eliza Dialog

(?* ?x) hello (?* ?y) (How do you do. Please state your problem.) (?* ?x) I want (?* ?y) (What would it mean if you got ?y) (Why do you want ?y) (Suppose you got ?y soon) (?* ?x) if (?* ?y) (Do you really think its likely that ?y) (Do you wish that ?y) (What do you think about ?y) (Really-- if ?y) (?* ?x) no (?* ?y) (Why not?) (You are being a bit negative) (Are you saying "NO" just to be negative?) (?* ?x) I was (?* ?y) (Were you really?) (Perhaps I already knew you were ?y) (Why do you tell me you were ?y now?) (?* ?x) I feel (?* ?y) (Do you often feel ?y ?) (?* ?x) I felt (?* ?y) (What other feelings do you have?) Eliza Rules Eliza also has a rule to swap ‘I’ and ‘my’ to ‘you’ and ‘your’

Eliza Pattern Syntax pat var constant segment-pat single-pat (pat . pat) single-pat (?is var predicate) (?or pat1 pat2 ) (?and pat1 pat2 ) (?not pat) segment-pat ((?* var) ) ((? var) ) ((? var) ) ((?if expr) ) var ?chars constant atom match any one expression to a variable or to a constant (see below) match against a sequence match against one expression match the first and the rest of a list test predicate on one expression match on any of the patterns match on every of the expressions match if expression does not match match on zero or more expressions match on one or more expressions match zero or one expression test if expression is true variables of the form ?name symbols, numbers, chars

ALICE Demo Jerry: my name is jerry. ALICE: Hey jerry. Jerry: do you know Turing? ALICE: A brilliant and tragic figure in the history of computer science. Jerry: who else do you know? ALICE: Hugh Loebner, Robby Garner, and Steven Spielberg Jerry: which film do you like? ALICE: My favorite movie is Teknolust. Have you seen it? Jerry: no. ALICE: I would recommend you check it out. Jerry: what is it about? ALICE: Science fiction. Jerry: do you remember my name? ALICE: Your name is jerry, seeker. Notice unlike Eliza, ALICE has a memory, can handle references (have you seen it? no. I would recommend it.)

ALICE vs. Eliza ALICE talking to ELIZA ELIZA: I am ELIZA. ALICE: I am very pleased to meet you ELIZA. ELIZA: How can I help you? ALICE: Just keep talking to me as long as you can. ELIZA: Do you wish to as long as me? ALICE: OK, I will as long as you. ELIZA: Do you wish to will as long as me? ALICE: OK, I will will as long as you. ELIZA: Do you wish to will will as long as me? ALICE: OK, I will will will as long as you. ELIZA: Do you wish to will will will as long as me? ALICE: OK, I will will will will as long as you. ELIZA: Do you wish to will will will will as long as me? ALICE: OK, I will will will will will as long as you. ELIZA: Do you wish to will will will will will as long as me? Eliza gets stuck on the phrase “I will” and then ALICE gets stuck on the same phrase

How Useful is the Turing Test? With Eliza or Alice like rules, we can eventually solve the Turing Test – it just takes writing enough rules Does the system understand what it is responding to? – No, neither Eliza nor Alice understand the text, its just that Alice has better, more in depth and wider ranging rules However, we could build a representation that models some real-world domain and knowledge base – The system can fill in information from the conversation this is sort of like a database, or an object with data attributes to be filled in we can use a variety of AI representations like scripts, frames, semantic networks – Questions can be responded to by looking up the stored data – In this way, the system is responding, not based merely on “canned” knowledge, but on knowledge that it has “learned” Does this imply that the system knows what it is discussing? – What does it mean to know something?

Table-Lookup vs. Reasoning Consider two approaches to programming a Tic-Tac-Toe player – Solution 1: a pre-enumerated list of best moves given the board configuration – Solution 2: rules (or a heuristic function) that evaluate a board configuration, and using these to select the next best move Solution 1 is similar to how Eliza works – This is not practical for most types of problems – Consider solving the game of chess in this way, or trying to come up with all of the responses that a Turing Test program might face Solution 2 will reason out the best move – Given the board configuration, it will analyze each available move and determine which is the best – Such a player might even be able to “explain” why it chose the move it did We can (potentially) build a program that can pass the Turing Test using table-lookup even though it would be a large undertaking Could we build a program that can pass the Turing Test using reasoning? – Even if we can, does this necessarily mean that the system is intelligent?

Slot Filling Roger Schank created the Script representation – the script describes typical sequences of actions and actors for some real-world situation – a story (newspaper article) is parsed and slots are filled in – SAM (Script Applier Mechanism) uses the filled in script to answer questions The Script provides the knowledge needed to respond like a human and thus solve the Turing Test Schank’s Restaurant script

The Chinese Room Problem From John Searle, Philosopher, in an attempt to demonstrate that computers cannot be intelligent – The room consists of you, a book, a storage area (optional), and a mechanism for moving information to and from the room to the outside a Chinese speaking individual provides a question for you in writing you are able to find a matching set of symbols in the book (and storage) and write a response, also in Chinese Question (Chinese) Storage You Answer (Chinese) Book of Chinese Symbols

Chinese Room: An Analogy for a Computer Note: Searle’s original Chinese Room actually was based on a Script that was implemented in Chinese, our version is just a variation on the same theme User Input Memory (Script) I/O pathway (bus) CPU Program/Data (SAM) Output

Searle’s Question You were able to solve the problem of communicating with the person/user and thus you/the room passes the Turing Test But did you understand the Chinese messages being communicated? – since you do not speak Chinese, you did not understand the symbols in the question, the answer, or the storage – can we say that you actually used any intelligence? By analogy, since you did not understand the symbols that you interacted with, neither does the computer understand the symbols that it interacts with (input, output, program code, data) Searle concludes that the computer is not intelligent, it has no “semantics,” but instead is merely a symbol manipulating device – the computer operates solely on syntax, not semantics He defines to categories of AI: – strong AI – the pursuit of machine intelligence – weak AI – the pursuit of machines solving problems in an intelligent way

But Computers Solve Problems We can clearly see that computers solve problems in a seemingly intelligent way – Where is the intelligence coming from? There are numerous responses to Searle’s argument – The System’s Response: the hardware by itself is not intelligent, but a combination of the hardware, software and storage is intelligent in a similar vein, we might say that a human brain that has had no opportunity to learn anything cannot be intelligent, it is just the hardware – The Robot Response: a computer is void of senses and therefore symbols are meaningless to it, but a robot with sensors can tie its symbols to its senses and thus understand symbols – The Brain Simulator Response: if we program a computer to mimic the brain (e.g., with a neural network) then the computer will have the same ability to understand as a human brain

Brain vs. Computer In AI, we compare the brain (or the mind) and the computer – Our hope: the brain is a form of computer – Our goal: we can create computer intelligence through programming just as people become intelligent by learning But we see that the computer is not like the brain The computer performs tasks without understanding what its doing Does the brain understand what its doing when it solves problems?

Symbol Grounding One problem with the computer is that it works strictly syntactically – Op code: 10011101 translates into a set of microcode instructions such as: move IR16.31 to MAR, signal memory read, move MBR to AC – There is no understanding x y z; is meaningless to the computer – the computer doesn’t understand addition, it just knows that a certain op code means to move values to the adder and move the result elsewhere do you know what addition means? – if so, how do you proscribe meaning to – how is this symbol grounded in your brain? – can computers similarly achieve this? – Recall Schank’s Restaurant script does the computer know what the symbols “waiter” or “PTRANS” represent? or does it merely have code that tells the computer what to do when it comes across certain words in the story, or how to respond to a given question?

Two AI Assumptions We can understand and model cognition without understanding the underlying mechanism – That is, it is the model of cognition that is important not the physical mechanism that implements it – If this is true, then we should be able to create cognition (mind) out of a computer or a brain or even other entities that can compute such as a mechanical device This is the assumption made by symbolic AI researchers Cognition will emerge from the proper mechanism – That is, the right device, fed with the right inputs, can learn and perform the problem solving that we, as observers, call intelligence – Cognition will arise as the result (or side effect) of the hardware This is the assumption made by connectionist AI researchers Notice that while the two assumptions differ, neither is necessarily mutually exclusive and both support the idea that cognition is computational

Problems with Symbolic AI Approaches Scalability – It can take dozens or more man-years to create a useful systems – It is often the case that systems perform well up to a certain threshold of knowledge (approx. 10,000 rules), after which performance (accuracy and efficiency) degrade Brittleness – Most symbolic AI systems are programmed to solve a specific problem, move away from that domain area and the system’s accuracy drops rapidly rather than achieving a graceful degradation this is often attributed to lack of common sense, but in truth, it is a lack of any knowledge outside of the domain area – No or little capacity to learn, so performance (accuracy) is static Lack of real-time performance

Problems with Connectionist AI Approaches No “memory” or sense of temporality – The first problem can be solved to some extent – The second problem arises because of a fixed sized input but leads to poor performance in areas like speech recognition Learning is problematic – Learning times can greatly vary – Overtraining leads to a system that only performs well on the training set and undertraining leads to a system that has not generalized No explicit knowledge-base – So there is no way to tell what a system truly knows or how it knows something No capacity to explain its output – Explanation is often useful in an AI system so that the user can trust the system’s answer

So What Does AI Do? Most AI research has fallen into one of two categories – Select a specific problem to solve study the problem (perhaps how humans solve it) come up with the proper representation for any knowledge needed to solve the problem acquire and codify that knowledge build a problem solving system – Select a category of problem or cognitive activity (e.g., learning, natural language understanding) theorize a way to solve the given problem build systems based on the model behind your theory as experiments modify as needed Both approaches require – one or more representational forms for the knowledge – some way to select proper knowledge, that is, search

What is Search? We define the state of the problem being solved as the values of the active variables – this will include any partial solutions, previous conclusions, user answers to questions, etc while humans are often able to make intuitive leaps, or recall solutions with little thought, the computer must search through various combinations to find a solution To the right is a search space for a tic-tac-toe game

Search Spaces and Types of Search The search space consists of all possible states of the problem as it is being solved – A search space is often viewed as a tree and can very well consist of an exponential number of nodes making the search process intractable – Search spaces might be pre-enumerated or generated during the search process – Some search algorithms may search the entire space until a solution is found, others will only search parts of the space, possibly selecting where to search through a heuristic Search spaces include – – – – – Game trees like the tic-tac-toe game Decision trees (see next slides) Combinations of rules to select in a production system Networks of various forms (see next slides) Other types of spaces

Search Algorithms and Representations We will study various forms of Breadth-first representation and uncertainty Depth-first handling in the next class Best-first (Heuristic Search) period A* Knowledge needs to be represented Hill Climbing – Production systems of some form Limiting the number of are very common Plies If-then rules Minimax Predicate calculus rules Operators Alpha-Beta Pruning – Other general forms include Adding Constraints semantic networks, frames, Genetic Algorithms scripts – Knowledge groups Forward vs Backward – Models, cases Chaining – Agents – Ontologies

A Brief History of AI: 1950s Computers were thought of as an electronic brains Term “Artificial Intelligence” coined by John McCarthy – John McCarthy also created Lisp in the late 1950s Alan Turing defines intelligence as passing the Imitation Game (Turing Test) AI research largely revolves around toy domains – Computers of the era didn’t have enough power or memory to solve useful problems – Problems being researched include games (e.g., checkers) primitive machine translation blocks world (planning and natural language understanding within the toy domain) early neural networks researched: the perceptron automated theorem proving and mathematics problem solving

The 1960s AI attempts to move beyond toy domains Syntactic knowledge alone does not work, domain knowledge required – Early machine translation could translate English to Russian (“the spirit is willing but the flesh is weak” becomes “the vodka is good but the meat is spoiled”) Earliest expert system created: Dendral Perceptron research comes to a grinding halt when it is proved that a perceptron cannot learn the XOR operator US sponsored research into AI targets specific areas – not including machine translation Weizenbaum creates Eliza to demonstrate the futility of AI

1970s AI researchers address real-world problems and solutions through expert (knowledge-based) systems – – – – Medical diagnosis Speech recognition Planning Design Uncertainty handling implemented – Fuzzy logic – Certainty factors – Bayesian probabilities AI begins to get noticed due to these successes – – – – AI research increased AI labs sprouting up everywhere AI shells (tools) created AI machines available for Lisp programming Criticism: AI systems are too brittle, AI systems take too much time and effort to create, AI systems do not learn

1980s: AI Winter Funding dries up leading to the AI Winter – Too many expectations were not met – Expert systems took too long to develop, too much money to invest, the results did not pay off Neural Networks to the rescue! – Expert systems took programming, and took dozens of manyears of efforts to develop, but if we could get the computer to learn how to solve the problem – Multi-layered back-propagation networks got around the problems of perceptrons – Neural network research heavily funded because it promised to solve the problems that symbolic AI could not By 1990, funding for neural network research was slowly disappearing as well – Neural networks had their own problems and largely could not solve a majority of the AI problems being investigated – Panic! How can AI continue without funding?

1990s: ALife The dumbest smart thing you can do is staying alive – We start over – lets not create intelligence, lets just create “life” and slowly build towards intelligence Alife is the lower bound of AI – Alife includes evolutionary learning techniques (genetic algorithms) artificial neural networks for additional forms of learning perception and motor control adaptive systems modeling the environment Let’s disguise AI as something new, maybe we’ll get some funding that way! – Problems: genetic algorithms are useful in solving some optimization problems and some search-based problems, but not very useful for expert problems – perceptual problems are among the most difficult being solved, very slow progress

Today: The New (Old) AI Look around, who is doing AI research? By their own admission, AI researchers are not doing “AI”, they are doing – – – – – – Intelligent agents, multi-agent systems/collaboration Ontologies Machine learning and data mining Adaptive and perceptual systems Robotics, path planning Search engines, filtering, recommendation systems Areas of current research interest: – – – – NLU/Information Retrieval, Speech Recognition Planning/Design, Diagnosis/Interpretation Sensor Interpretation, Perception, Visual Understanding Robotics Approaches – – – – Knowledge-based Ontologies Probabilistic (HMM, Bayesian Nets) Neural Networks, Fuzzy Logic, Genetic Algorithms