An Ancient Wish to Forge the Gods

ARTIFICIAL INTELLIGENCE

TERM PAPERON


SUBMITTED TO:AMITY SCHOOL 0F ENGINEERING & TECHNOLOGY

GUIDED BY: SUBMITTED BY:Dr. ANJANA SRIVASTAVA PARAMJYOT S. CHADHACHEMISRY DEPARTMENT A4717009014

E&I ASET

AMITY UNIVERSITY, UTTAR PRADESH

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ACKNOWLEDGEMENT

I, Paramjyot S. Chadha, student of Amity University, Uttar Pradesh, enrolled in the course B.TECH - Electronics & Instrumentation (2009 – 2013), 3rd Semester (Section ECE-4) (Enroll. No: A4717009014), hereby acknowledge and express my gratitude for the constant support,encouragement and fairness of Dr. Anjana Srivastava for carefully guiding me into the successful completion of this Term Paper.It gives me immense pleasure to place on record the esteemed faculty academics and my parents for their invaluable and constructive comments that has given a great impetus to the completion of this term paper.

NAMAN ARORAB.Tech- E&I3-ECE-4 (Y)Enroll. No: A4717009025

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CERTIFICATE

This is to certify that Mr.Naman Arora, student of B.Tech. in Electronics & Instrumentation has carried out the work presented in the project of the Term paper entitled "HELICOPTERS” as a part of First year programme of Bachelor of Technology in Electronics & Instrumentation from Amity School of Engineering and Technology, Amity University, Noida, Uttar Pradesh under my supervision.

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Contents Page NumberMain page 1Abstract 3Introduction to AI 5History 7Branches of AI 9AI in Our Lives 11Artificial Intelligence & Robotics:

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AI! A Boon or a curse……?

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Conclusion 17Bibliography 18

INDEX

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Abstract

Artificial intelligence may be defined as branch of Computer Science that is concerned with the automation of intelligent behavior. The principles of AI includes the data structures used in knowledge representation, the algorithms needed to apply that knowledge and the languages and programming techniques used in their implementation.

AI has always been more concerned with expanding the capabilities of computer science with defining its limits. Keeping this exploration grounded in sound theoretical principles is one of the challenges facing AI researches at present.

Another early foray in to AI focused on the sort of problem solving that we do every day often called ‘Common Sense Reasoning‘. To investigate this sort of reasoning, Newell, Shaw and Simon built the general problem solver (GPS), which they applied to several

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Artificial intelligence in its very direct concern has been applied to all the areas of legacy - medicine, psychology, biology, astronomy, geology – and many problems of scientific endeavors.

As AI research progressed and techniques for handling larger amounts of world knowledge were developed, some progress was made on the tasks just described and new tasks could reasonably be attempted.


common tasks as well as to perform symbolic manipulations of logical expressions.

Fig. 1

These include perception, natural language understanding and problem solving in specialized domains such as medical diagnosis and chemical analysis

Due to AI in medicine, programs have been developed that analyze the disease symptoms, medical history, and laboratory test results of a patient, and then suggest a diagnosis to the physician. The diagnostic program is an example of so-called expert systems—programs designed to perform tasks in specialized areas as a human would. Expert systems take computers a step beyond straightforward programming, being based on a technique called rule-based inference, in which pre-established rule systems are used to process the data.

INTRODUCTION TO AIAI is the science and engineering of making intelligent machines, especially intelligent computer programs, which envisages the nature of human thoughts & sophisticated computing systems.

AI coordinates and correlates psychological and physiological research in to the nature of human thoughts and technological development of sophisticated computing systems.

Artificial Intelligence, a term that in its broadest sense would indicate the ability of an artifact to perform the same kinds of functions that characterizes the human thought. The possibility of developing some such artifact has intrigued human beings since ancient time.

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The revised version of AI can be defined as ‗AI is the study of the mechanisms underlying intelligent behavior through the construction and evaluation of artifacts that attempt to enact those mechanisms.’ On this definition, Artificial Intelligence is less a theory about the mechanisms underlying intelligence and more on empirical methodology for constructing and testing possible models for supporting such a theory. It must be noted that our revised definition does not define intelligence; rather it proposes a coherent role for Artificial Intelligence in exploring the nature and expression of intelligent phenomena.

In the latter sense, the term AI has been applied to computer systems and programs capable of performing tasks more complex than straightforward programming, although still far from the realm of actual thought. The most important fields of research in this area are information processing, pattern recognition, game-playing computers, and applied fields such as medical diagnosis. Current research in information processing deals with programs that enable a computer to understand written or spoken information and to produce summaries, answer specific questions, or redistribute information to users interested in specific areas of this information. Essential to such programs is the ability of the system to generate grammatically correct sentences and to establish linkages between words, ideas, and associations with other ideas.

Fig. 2

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This robotic hand designed using the concept of AI is capable of performing the delicate task of picking up and holding an egg without breaking it. A tactile array sensor located on the right half of its gripping mechanism sends information to the robot's control computer about the pressure the robotic hand exerts; given this information, the control computer instructs the robotic hand to loosen, tighten, or maintain the current gripping force. This feedback loop repeats continuously, enabling the robotic hand to stay in between the two extremes of dropping and crushing the egg.


History

Fig. 3-Talos of Crete

The seeds of modern AI were planted by classical philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. This work culminated in the invention of the programmable digital computer in the 1940s, a machine based on the abstract essence of mathematical reasoning. This device and the ideas behind it inspired a handful of scientists to begin seriously discussing the possibility of building an electronic brain.

The field of artificial intelligence research was founded at a conference on the campus of Dartmouth College in the summer of 1956. Those who attended would become the

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The history of artificial intelligence began in antiquity, with myths, stories and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen; as Pamela McCorduck writes, AI began with "an ancient wish to forge the gods."

http://en.wikipedia.org/wiki/Dartmouth_College

http://en.wikipedia.org/wiki/Artificial_intelligence

http://en.wikipedia.org/wiki/Computer

http://en.wikipedia.org/wiki/Pamela_McCorduck

http://en.wikipedia.org/wiki/Ancient_history


leaders of AI research for decades. Many of them predicted that a machine as intelligent as a human being would exist in no more than a generation and they were given millions of dollars to make this vision come true.

American computer scientist John McCarthy coined the term "artificial intelligence" (AI). In 1959 he developed LISP (List-oriented computer programming language), which becomes the standard language for AI research.

Alan Turing, He was the first to decide that AI was best researched by programming computers rather than by building machines. He put forward a test known as Turing Test; for determining whether or not machine intelligence can converse like a human. The Turing test measures the performance of an allegedly intelligent machine against that of a human being, arguably the best and only standard for intelligent behavior, which he mentioned as an Imitation Game.

During the World War II, the need for intelligent automated machines which can have a niche of applications paved path for the field of AI and the research on intelligent machines started.

Once thinking had come to be regarded as a form of computation, its formalization and eventual mechanization were obvious next steps. In the seventeenth century, Gottfried Wilhelm Von Leibniz, with his Calculus Philosophicus introduced the first system of formal logic as well as constructed a machine for automating its calculation. Euler in the eighteenth century with his analysis of connectedness of bridges joining the river banks and islands of the city Konigsberg introduced the study of representations that abstractly capture the structure of relation ships in the world.

In the 1990‘s information scientists developed an AI computer program that allows non experts to use their own natural language to retrieve information from databases that use more complicated programming languages. This allowed more people to find information—such as business data or medical records—that previously only a few computer experts could retrieve.

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BRANCHES OF AI

Logical AI: What a program knows about the world in general the facts of the specific situation in which it must act, and its goals are all represented by sentences of some mathematical logical language. The program decides what to do by inferring that certain actions are appropriate for achieving its goals.

Pattern Recognition: When a program makes observations of some kind, it is often programmed to compare what it sees with a pattern. For example, a vision program may try to match a predefined pattern of eyes and a nose in a scene in order to find a face. More complex patterns, e.g. in a natural language text, in a chess position, or in the history of some event are also studied. These more complex patterns require quite different methods than do the simple patterns that have been studied the most.

Inference: From some facts, others can be inferred. Mathematical logical deduction is adequate for some purposes, but new methods of non-monotonic inference have been added to logic since the 1970s. The simplest kind of non-monotonic reasoning is default reasoning in which a conclusion is to be inferred by default, but the conclusion can be withdrawn if there is evidence to the contrary. For example, when we hear of a bird, we man infer that it can fly, but this conclusion can be reversed when we hear that it is a penguin. It is the possibility that a conclusion may have to be withdrawn that constitutes the non-monotonic character of the reasoning. Ordinary logical reasoning is monotonic in that the set of conclusions that can the drawn from a set of premises is a monotonic

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increasing function of the premises. Circumscription is another form of non-monotonic reasoning.

Learning from experience: Programs do that. The approaches to AI based on connectionism and neural nets specialize in that. There is also learning of laws expressed in logic. Programs can only learn what facts or behaviors their formalisms can represent, and unfortunately learning systems are almost all based on very limited abilities to represent information.

Ontology: Ontology is the study of the kinds of things that exist. In AI, the programs and sentences deal with various kinds of objects, and we study what these kinds are and what their basic properties are. Emphasis on ontology begins in the 1990‘s.

Epistemology: The development of AI has been shaped by a number of important challenges and questions. Natural language understanding, planning, reasoning in uncertain situations and machine learning are all typical of those types of problems that capture some essential aspect of intelligent behavior. More importantly, intelligent systems operating in each of these domains require knowledge of purpose, practice and performance in situated and socially embedded contexts. To accomplish this we must examine the epistemological commitment of a program that is intended to be ―intelligent‖. In one sentence we can say that Epistemology is study of the kinds of knowledge that are required for solving problems.

Heuristics: A heuristic is a way of trying to discover something or an idea embedded in a program. The term is used variously in AI. Heuristic functions are used in some approaches to search to measure how far a node in a search tree seems to be from a goal. Heuristic predicates that compare two nodes in a search tree to see if one is better than the other, i.e. constitutes an advance toward the goal, and may be more useful.

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AI IN OUR LIVES:

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In business:

Banks, brokerages and insurance companies have been relying on various AI tools for over two decades.

Fraud Detection: AI is the Key technology in banking systems, Credit card providers, telephone companies, mortgage lenders and banks employ AI to detect fraud. NASDAQ stock market monitor will identify potential insider trading and fraud against investors.

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Handwriting recognition in millions of PDA`S (Personal Digital Assistant); speech recognition is widely deployed in business applications.

A team of Robots beat human`s in stimulated financial trading.

Computer Giant IBM `S BOTS made 7% more cash than humans in trading commodities such as pork bellies and gold. Fig.4- A team of Robots

beat human`s in stimulated financial trading.


Prescribing decision support systems (PDSS): Drug-drug interactions and dosage error.

AI Helpmate robot will independently navigate through hospital corridors, delivering meal trays, paperwork, and supplies. In the absence of doctor; even it behaves as a doctor in some situations. The robot employs multiple sensors to safely navigate and work in close proximity to people.

Game Playing:

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In Medical field:

Breakthrough for Alzheimer`s patients

Alerts and remainders: ECG or Pulse Oximeter

Diagnostic assistance: diagnoses based on the patient‘s data presented to

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. It can be best seen through the animated figures in action battling with brain and brawn. Doom, Duke Nukem, Grand PRIX, Half life are games that have

Fig. 5- AI Helpmate

Fig 6-Game playing robots


Speech Recognition: In the 1990‘s, computer speech recognition reached a practical level for limited purposes. Thus United Airlines has replaced its keyboard tree for flight information by a system using speech recognition of flight numbers and city names. It is quite convenient. On the other hand, while it is possible to instruct some computers using speech, most users have gone back to the keyboard and the mouse as still more convenient.

Understanding Natural Language: Just getting a sequence of words into a computer is not enough. Parsing sentences is not enough either. The computer has to be provided with an understanding of the domain the text is about, and this is presently possible only for very limited domains.

Computer Vision: The world is composed of three-dimensional objects, but the inputs to the human eye and computers' TV cameras are two dimensional. Some useful programs can work solely in two dimensions, but full computer vision requires partial three-dimensional information that is not just a set of two-dimensional views. At present there are only limited ways of representing three-dimensional information directly, and they are not as good as what humans evidently use.

Automated Reasoning and Theorem Proving: It is the one of the fruitful branch of the field. Theorem-proving research was responsible for much of the early work in formalizing search algorithms and developing formal representation languages such as the predicate calculus and the programming language.

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Artificial Intelligence & Robotics:

Since the 1980‘s artificial intelligence (AI) has been the primary focus of research activity for information scientists. Artificial intelligence refers to a machine's capacity to mimic human thought and behavior. Using findings of information science research, scientists have created Robots with AI that can understand spoken language and can make logical decisions.

Perhaps the most dramatic changes in future robots will arise from their increasing ability to reason. The field of artificial intelligence is moving rapidly from university laboratories to practical application in industry, and machines are being developed that can perform cognitive tasks, such as strategic planning and learning from experience. Increasingly, diagnosis of failures in aircraft or satellites, the management of a battlefield, or the control of a large factory will be performed by intelligent computers.

One of the most controversies of AI is that it will open the door to computers that think faster than the human brain, giving machines a superior edge. As AI, robotics and nanotechnology combine to relieve humans of doing tasks that machines can do better, faster and cheaper, some believe we may be paving the way to our own destruction. Will nations secretly create armies of AI-enhanced, nano-augmented (think bionic) super soldiers to fight wars? Will politicians opt for AI-enhancements, Nano-augmentation? Who will it be available to, and are we as a race headed towards total dependency on machinery to the extent it becomes part of our biology? Will there be equity or will a new class divide be created, similar to that depicted in ‗Gattica’? If we do not embrace AI-enhancement and nano-augmentation will intelligent machines ultimately decide we are unnecessary?

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AI! A Boon or a curse……?

There are probably as many opinions about the ultimate goal of artificial intelligence research as there are AI researchers. The answer to this question has been controversial for decades within the field of AI research. The ultimate answer to our question is that there is no single answer. Each researcher—indeed, each observer of the field of AI research—is likely to answer our question differently.

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Fig 7-An inventor plays a duet with his robotic creation, Wabot-2, at the Tokyo Exposition. Building this kind of robot is a challenging task because the dexterity of the human hand is perhaps the most difficult function to recreate mechanically. Although Wabot-2‘s performance may not be emotional, with an electronic scanning eye and quality components, the technical accuracy will be extremely high.


There are those who believe—sometimes with great passion—that technologists will one day be able to build a computer with all the cognitive, memory, and emotional capabilities of the human brain. These people are sometimes called the ―strong AI proponents. A few of these ―strong advocates believe that computers will someday be more intelligent than human beings. It is common to hear such researchers say that this is the ultimate frontier of science.

On the other hand, there are other AI researchers who think that research into how human minds work can be useful in building better computer systems, regardless of whether we pursue a goal of full machine intelligence. In other words, these ―weak‖ AI proponents believe that human cognition and its applicability to computers is an interesting research field in itself, and a field that may help make computers easier to use, more useful to people, and better at what computers are good at doing. The products of this research may not resemble human intelligence. Some ―weak‖ AI proponents say that computers are obviously superior to human beings at some tasks, and it‘s the job of AI research to figure out how to optimize those capabilities, instead of making computers more like people.

AN AMAZING BREAKTHROUGH IN AI

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Fig 8-Dr. Cynthia Breazeal plays with Kismet, the robot that mimics and responds to human emotions. Some roboticists study how humans think, work together and communicate so they can apply that to robots. This new field of human-robot interaction is led mostly by women.


Robots Created That Develop Emotions in Interaction With HumansLed by Dr. Lola Cañamero at the University of Hertfordshire, and in collaboration with a consortium of universities and robotic companies across Europe, these robots differ from others in the way that they form attachments, interact and express emotion through bodily expression.Developed as part of the interdisciplinary project FEELIX GROWING (Feel, Interact, eXpress: a Global approach to development with Interdisciplinary Grounding), funded by the European Commission and coordinated by Dr. Cañamero, the robots have been developed so that they learn to interact with and respond to humans in a similar way as children learn to do it, and use the same types of expressive and behavioural cues that babies use to learn to interact socially and emotionally with others.

The robots have been created through modelling the early attachment process that human and chimpanzee infants undergo with their caregivers when they develop a preference for a primary caregiver.They are programmed to learn to adapt to the actions and mood of their human caregivers, and to become particularly attached to an individual who interacts with the robot in a way that is particularly suited to its personality profile and learning needs. The more they interact, and are given the appropriate feedback and level of engagement from the human caregiver, the stronger the bond developed and the amount learned.The robots are capable of expressing anger, fear, sadness, happiness, excitement and pride and will demonstrate very visible distress if the caregiver fails to provide them comfort when confronted by a stressful situation that they cannot cope with or to interact with them when they need it."This behaviour is modelled on what a young child does," said Dr Cañamero. "This is also very similar to the way chimpanzees and other non-human primates develop affective bonds with their caregivers."This is the first time that early attachment models of human and non-human primates have been used to program robots that develop emotions in interaction with humans."We are working on non-verbal cues and the emotions are revealed through physical postures, gestures and movements of the body rather than facial or verbal expression," Dr Cañamero added.

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Fig 9-The first prototype robots capable of developing emotions as they interact with their human caregivers and expressing a whole range of emotions have been finalised by researchers.


The researchers led by Dr. Cañamero at the University of Hertfordshire are now extending the prototype further and adapting it as part of the EU project ALIZ-E, which will develop robots that learn to be carer/companion for diabetic children in hospital settings.Within this project, coordinated by Dr Tony Belpaeme of the University of Plymouth, the Hertfordshire group will lead research related to the emotions and non-linguistic behaviour of the robots. The future robot companions will combine non-linguistic and linguistic communication to interact with the children and become increasingly adapted to their individual profiles in order to support both, therapeutic aspects of their treatment and their social and emotional wellbeing.The FEELIX GROWING project has been funded by the Sixth Framework Programme of the European Commission. The other partners in the project are: Centre National de la Recherche Scientifique (France), Université de Cergy Pontoise (France), Ecole Polytechnique Fédérale de Lausanne (Switzerland), University of Portsmouth (U.K.), Institute of Communication and Computer Systems (Greece), Entertainment Robotics (Denmark), and Aldebaran Robotics (France).

CONCLUSION

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We have now surveyed the major technique of AI. From our discussion of them, it should be clear that there are two important classes of AI techniques:

Methods for representing and using knowledge

Methods for conducting heuristic search

These two aspects interact heavily with each other. The choice of a knowledge representation framework determines the kind of problem-solving methods that can be applied.

Knowledge serves two important functions in AI programs. The first is to define what can be done to solve a problem and to specify what it means to have solved the problem. We can call knowledge that does this essential knowledge. The second is to provide advice on how best to go about solving a problem efficiently. We can call such a knowledge heuristic knowledge. The goal of this has been to say enough about the use of knowledge in problem solving programs and techniques to build a real and perfect friendly AI.

‘ THE CONCLUSION NOW DIRECTLY COMES TO OUR MIND IS THAT; AI IN COLLABORATION WITH NANOTECHNOLOGY AND ROBOTICS WILL CREATE A COMPLEX IMPROVED REVOLUTION IN THE WORLD VERY SOON ’.

BIBLIOGRAPHY

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Books – ―ARTIFICIAL INTELLIGENCE By- Elaine Rich & Kevin Knight ―ARTIFICIAL INTELLIGENCE By- George F Luger

Websites - www.wisegeek.com www.encarta.msn.com

Web search - Artificial Intelligence

TV Channel - Discovery channel.

Magazine - Electronics for you

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http://www.wisegeek.com/

Documents

An Ancient Wish to Forge the Gods