Artificial Intelligence (AI)

What is AI?

•Artificial Intelligence (AI) is usually defined as the science of making computers do things that require intelligence when done by humans.

Application of AI - Games

•You can buy machines that can play master level chess for a few hundred dollars. There is some AI in them, but they play well against people mainly through brute force computation--looking at hundreds of thousands of positions.

•To beat a world champion by brute force and known reliable heuristics requires being able to look at 200 million positions per second.

Application of AI - Heuristics

• Relating to or using a problem-solving technique in which the most appropriate solution of several found by alternative methods is selected at successive stages of a program for use in the next step of the program.

• Heuristic methods are used to speed up the process of finding a satisfactory solution, where an exhaustive search is impractical. Examples of this method include using a "rule of thumb", an educated guess, an intuitive judgment, or common sense.

Can Machines Really Beat Humans?

•Gary Kasparov versus Deep Blue

•Watson on Jeopardy

AI in Games and Film

Using biology to make better animation

Big dog reflexes

Application of AI - Voice Recognition

• The 1990s saw the first commercialization of spoken language understanding systems. Computers can now understand and react to humans speaking in a natural manner in ordinary languages within a limited domain.

• Basic and applied research in signal processing, computational linguistics and artificial intelligence have been combined to open up new possibilities in human-computer interfaces.

Application of AI -Understanding Natural

Language• Simple inquiries about bank balance, movie

schedules, and phone call transfers can already be handled by telephone-speech recognizers.

• Voice activated data entry is particulary useful in medical or darkroom applications, where hands and eyes are unavailable, or in hands-busy or eyes-busy command and control applications.

• Speech could be used to provide more accessibility for the handicapped (wheelchairs, robotic aids, etc.) and to create high-tech

amenities (intelligent houses, cars, etc.)

Application of AI -Understanding Natural

Language •The value to our society of being able

to communicate with computers in everyday "natural" language cannot be overstated.

•Imagine asking your computer "Does this candidate have a good record on the environment?" or "When is the next televised National League baseball game?" Then receiving a response.

Chatbots•Chatbot - An artificial living entity

designed to have conversations with real human beings. That can be a text conversation via computers, a spoken conversation or even a non-verbal conversation.

•Chatbots may also be referred to as a conversational agent, virtual agent / assistant.

Chatbots• Chatbots can be found on screens, in the

virtual world, but also in the real world, projected (with holographic projection, for example) or as physically talking and responding puppets, toys and robots.

• They currently appear most often on the web and in Instant Messenger programs like Windows Live Messenger (previously MSN Messenger) where a chatbot is in the friend list of the user.

Turing-Test• Sometimes it’s hard to discover if a chatting

partner is a real person or a chatbot. A well-known test that can be done is the so-called Turing Test. This test determines how well a chatbot is capable of appearing like a real person.

• All bots are now (and in the years to come will still be) easily uncovered, but over the next decades they will definite get smarter and finally we’ll recognize human being by their giving us silly answers as opposed to the much smarter chatbots.

Turing Test• Alan Turing proposed an operational test of

intelligence as a replacement for the philosophical question, "Can machines think?"

• Based on a imitation game between a human and a machine where an interrogator, who cannot see either, attempts to tell the difference.

• Turing thought machines would be able to pass the test but, so far, has been proven wrong.

Self-aware Robots

Using biology to make better animation

Big dog reflexes

Chatbots in the future

• In the long run (think in terms of 30 years) chatbots will be so smart that we can have conversations with them (in real speech).

• Then they will give (extremely) intelligent answers: even more intelligent than we could expect from normal people. We’ll also be unable to distinguish their appearance, including their movements, gestures and intonation from ordinary human beings.

Application of AI - Computer Vision

• Vision involves both the acquisition and processing of visual information. AI powered technologies have made possible such astounding achievements as vehicles that are able to safely steer themselves along our superhighways, and computers that can recognize and interpret facial expressions.

• Just consider the complexity of the analysis that your brain must engage in before determining that something as apparently simple as the fact that the black squares on a chess board are not holes, but rather part of the surface, and you'll have an idea of how sophisticated vision systems must be in order to reliably perform their objectives.

Application of AI - Expert Systems

•The primary goal of expert systems research is to make expertise available to decision makers and technicians who need answers quickly.

•There is never enough expertise to go around; certainly it is not always available at the right place and the right time.

Expert System - Definition

• An expert system is a computer program that simulates the judgement and behavior of a human or an organization that has expert knowledge and experience in a particular field.

• Typically, such a system contains a knowledge base containing accumulated experience and a set of rules for applying the knowledge base to each particular situation that is described to the program.

• Sophisticated expert systems can be enhanced with additions to the knowledge base or to the set of rules.

Domains

•In software engineering, domain refers to a field of study that defines a set of common requirements, terminology, and functionality for any software program constructed to solve a problem in that field.

•Today's expert systems deal with domains of narrow specialization.

Examples of Expert System Domains

• Diagnosis e.g. to recommend remedies to illnesses, trouble-shoot electronic or mechanical problems.

• Repair e.g diagnosing the problem in the automotive repair field and suggesting a plan for the repair of the item.

• Interpretation e.g. analysing data to determine its significance by comparing its data and its usefulness to models of the real world. Often used in exploration for mineral, gas and oil deposits as well as in surveillance, image analysis and speech understanding.

• Prediction e.g. systems used as a method to “guess” at the possible outcomes of observed situations, usually providing a probability factor. This is used often in weather forecasting.

• Monitoring and Control - e.g. where speed of decision making is vitally important like in the nuclear energy industry and air traffic control.

Specific examples of Expert System Domains

• Tax Advisor. Asked questions about its users' financial state and advised on how to minimise tax while maximising investment.

• A program written for Texas Instruments that planned where to site transistors on silicon chips so as to achieve the most compact and easy to fabricate layout.

• Wheat Counsellor from ICI chemicals. The program advised farmers on the diseases of their crops, and on treatments.

How are domains that are suitable for expert systems identified?

•The end users must want the system and have a vested interest in its success.

The importance of knowledge base

• The knowledge base stores all the facts and rules about a particular problem domain. It makes these available to the inference engine in a form that it can use.

• The facts may be in the form of background information built into the system or facts that are input by the user during a consultation.

• The rules include both the production rules that apply to the domain of the expert system and the heuristics or rules-of-thumb that are provided by the domain expert in order to make the system find solutions more efficiently by taking short cuts.

Who creates the knowledge base?

• The most important aspect of a knowledge base is the quality of information it contains. The best knowledge bases have carefully written articles that are kept up to date, an excellent information retrieval system (search engine), and a carefully designed content format and classification structure.

• The knowledge engineer builds, maintains and develops the knowledge based systems. They have a big responsibility in the ‘health’, progress, work, durability and many other factors of the expert system.

Inference Engines•The inference engine is the program

that locates the appropriate knowledge in the knowledge base, and infers new knowledge by applying logical processing and problem-solving strategies.

•This "engine" tries to act like a human brain. This results in instructions with If…Then statements – IF Grades are dropping, THEN study harder.

The need for Fuzzy Logic

•The concept computers operating as inference engines is quite simple:

•Computers normally only have two possible values for a certain variable – 1 (true or on) and 0 (false or off).

•Consider the following: Bob has a house with two rooms, a kitchen and a bedroom.

The need for Fuzzy Logic

•If Bob is in the kitchen, the variable "kitchen" is 1 and "bedroom" is 0, and if Bob is in the bedroom, "bedroom" is 1 and "kitchen" is 0.

•We can now explain where he is in his house at all times using only ones and zeroes… if he is in either of the two rooms.

The need for Fuzzy Logic

•What happens if he is standing in the doorway between the rooms? This is where fuzzy logic comes in.

Fuzzy Logic

•Fuzzy logic allows decimal places for true and false equations; thus, if bob sits in the bedroom and sticks his toe into the kitchen, "bedroom" can be 0.95 and "kitchen" can be 0.05. Thus, we can now explain the location of bob anywhere in the house.

Thus...• Fuzzy logic is a mathematical technique

that deals with imprecise data and problems that have more than one solution.

• An example of a machine that uses fuzzy logic is an industrial clothes-washing machine. Using fuzzy logic systems, these machines detected and adapted to patterns of water movement during a wash cycle in order to decrease the consumption of water and increase efficiency.

Problems with Expert Systems

• It is not easy to gather the data and rules for the computer.

• They are inflexible, have no ‘common sense’ and have no human warmth (empathy).

• We can never be sure they are programmed correctly so they might make dangerous mistakes.

• Expert systems cannot acquire and adapt to new knowledge without reprogramming

Commonsense Knowledge

• One of the fundamental problems encountered in AI has become known as the commonsense knowledge problem.

• While researchers were aware that in an AI system, knowledge would have to be explicitly represented, they did not anticipate the vast amount of implicit knowledge we all share about the world and ourselves.

Commonsense Knowledge

•Designers of AI systems did not consider producing rules like "If President Clinton is in Washington, then his left foot is also in Washington," or

•"If a father has a son, then the son is younger than the father and remains younger for his entire life."

•This is implicit knowledge and we never stop to state this explicitly.

Commonsense Knowledge

•It is clear that commonsense knowledge represents an enormous hurdle for the development of general purpose intelligent systems.

•It is clear that not all human knowledge is represented in such an explicit or declarative form. Imagine writing everything we know formulated as a series of if-then rules?

Commonsense Knowledge

•One of the most important aspects of commonsense knowledge is that it is gained through experience.

•If computers were to develop commonsense knowledge, then this would have huge implications for storage requirements.

Machine Learning•Machine learning is associated with

the development of techniques that allow a computer to learn things.

•In general, machine learning is understood as a subtopic of artificial intelligence and can be divided further into two categories, inductive and deductive. Machine learning is generally used to help with data mining and statistical analysis.

Pattern Recognition

•One of the techniques developed in machines to enable them to learn things is pattern recognition.

•This is is the act of the computer taking in raw data and taking an action based on the category of the data. A good example of pattern recognition software is the fingerprint based security system. The system works by recognizing a person's fingerprint to that of a fingerprint stored in its memory.

Processing Power• Since the objective of AI development is to

make a multifunctional duplicate of the human brain, it can be said that the machinery which run the AI will need the same processing power as the human brain.

• Considering that humans absorb and process abundant information in the forms of images, memory, smell, feelings, and time, we can see that it will take high processing power to create an equivalently powerful artificial mind.

Parallel Processing

•This involves the simultaneous use of more than one CPU or processor core to execute a program or multiple computational threads. Ideally, parallel processing makes programs run faster because there are more engines (CPUs or cores) running it.

Parallel Processing

•Many AI applications have real-time constraints e.g. real-time speech understanding, warning and navigation systems.

•Thus, high speed processing requirements need to be met.

Parallel Processing

•The human brain can perform tasks remarkably well using slow neurons working in parallel.

•With carefully considered architecture, the use of parallel processors in computers, we perhaps can achieve similar in AI.