Artificial intelligenceÂ (AI) is theÂ intelligenceÂ of machines and the branch ofÂ computer scienceÂ that aims to create it. Textbooks define the field as "the study and design ofÂ intelligent agents,"Â where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success.Â John McCarthy, who coined the term in 1956,Â defines it as "the science and engineering of making intelligent machines. The field was founded on the claim that a central property of humans, intelligence-theÂ sapienceÂ ofÂ Homo sapiens-can be so precisely described that it can be simulated by a machine.Â This raises philosophical issues about the nature of theÂ mindÂ and limits of scientificÂ hubris, issues which have been addressed byÂ myth,Â fictionÂ andÂ philosophyÂ since antiquity.Â Artificial intelligence has been the subject of optimism,but has also suffered setbacksÂ and, today, has become an essential part of the technology industry, providing the heavy lifting for many of the most difficult problems in computer science. AI research is highly technical and specialized, deeply divided into subfields that often fail to communicate with each other.Â Subfields have grown up around particular institutions, the work of individual researchers, the solution of specific problems, longstanding differences of opinion about how AI should be done and the application of widely differing tools. The central problems of AI include such traits as reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects.Â General intelligence (or "strong AI") is still a long-term goal of (some) research.
AI plays a major role in the field of robotics. The wordÂ robotÂ can refer to both physical robots andÂ virtualÂ software agents, but the latter are usually referred to asÂ bots.Â There is no consensus on which machines qualify as robots, but there is general agreement among experts and the public that robots tend to do some or all of the following: move around, operate a mechanical limb, sense and manipulate their environment, and exhibit intelligent behaviour, especially behaviour which mimics humans or other animals. There is conflict about whether the term can be applied to remotely operated devices, as the most common usage implies, or solely to devices which are controlled by their software without human intervention. InÂ South Africa,Â robotÂ is an informal and commonly used term for a set of traffic lights. It is difficult to compare numbers of robots in different countries, since there are different definitions of what a "robot" is.
Fig. 1 Example of an image of a Robot
TheÂ International Organization for StandardizationÂ gives a definition of robot inÂ ISO 8373: "an automatically controlled, reprogrammable, multipurpose, manipulator programmable in three or more axes, which may be either fixed in place or mobile for use in industrial automation applications."Â This definition is used by the International Federation of Robotics, theÂ European Robotics Research NetworkÂ (EURON), and many national standards committees. The Robotics Institute of America (RIA) uses a broader definition: a robot is a "re-programmable multi-functional manipulator designed to move materials, parts, tools, or specialized devices through variable programmed motions for the performance of a variety of tasks."Â The RIA subdivides robots into four classes: devices that manipulate objects with manual control, automated devices that manipulate objects with predetermined cycles, programmable and servo-controlled robots with continuous point-to-point trajectories, and robots of this last type which also acquire information from the environment and move intelligently in response. There is no one definition of robot which satisfies everyone, and many people have their own.Â For example,Â Joseph Engelberger, a pioneer in industrial robotics, once remarked: "I can't define a robot, but I know one when I see one."Â According toÂ Encyclopaedia Britannica, a robot is "any automatically operated machine that replaces human effort, though it may not resemble human beings in appearance or perform functions in a humanlike manner".Â Merriam-WebsterÂ describes a robot as a "machine that looks like a human being and performs various complex acts (as walking or talking) of a human being", or a "device that automatically performs complicated often repetitive tasks", or a "mechanism guided by automatic controls. Modern robots are usually used in tightly controlled environments such as onÂ assembly linesÂ because they have difficulty responding to unexpected interference. Because of this, most humans rarely encounter robots. However,Â domestic robotsÂ for cleaning and maintenance are increasingly common in and around homes in developed countries, particularly inÂ Japan. Robots can also be found in theÂ military.
Mechanical orÂ "formal" reasoningÂ has been developed by philosophers and mathematicians since antiquity. The study of logic led directly to the invention of theÂ programmable digital electronic computer, based on the work ofÂ mathematicianÂ Alan TuringÂ and others. Turing'sÂ theory of computationÂ suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction.Â This, along with recent discoveries inÂ neurology,Â information theoryÂ andÂ cybernetics, inspired a small group of researchers to begin to seriously consider the possibility of building an electronic brain.
The field of AI research was founded atÂ a conferenceÂ on the campus ofÂ Dartmouth CollegeÂ in the summer of 1956.Â The attendees, including John McCarthy,Â Marvin Minsky,Â Allen NewellÂ andÂ Herbert Simon, became the leaders of AI research for many decades.Â They and their students wrote programs that were, to most people, simply astonishing:Â computers were solving word problems in algebra, proving logical theorems and speaking English.Â By the middle of the 1960s, research in the U.S. was heavily funded by theÂ Department of DefenseÂ and laboratories had been established around the world.Â AI's founders were profoundly optimistic about the future of the new field:Â Herbert Simon predicted that "machines will be capable, within twenty years, of doing any work a man can do"Â andÂ Marvin MinskyÂ agreed, writing that "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved".
In the early 1980s, AI research was revived by the commercial success ofÂ expert systems,Â a form of AI program that simulated the knowledge and analytical skills of one or more human experts. By 1985 the market for AI had reached over a billion dollars. At the same time, Japan'sÂ fifth generation computerÂ project inspired the U.S and British governments to restore funding for academic research in the field.
Stories of artificial helpers and companions and attempts to create them have a long history but fully autonomousÂ machines only appeared in the 20th century. The firstÂ digitallyÂ operated and programmable robot, theÂ Unimate, was installed in 1961 to lift hot pieces of metal from a die casting machine and stack them. Today, commercial andÂ industrial robotsÂ are in widespread use performing jobs more cheaply or with greater accuracy and reliability than humans. They are also employed for jobs which are too dirty, dangerous or dull to be suitable for humans. Robots are widely used in manufacturing, assembly and packing, transport, earth and space exploration, surgery, weaponry, laboratory research, and mass production of consumer and industrial goods. The wordÂ robotÂ was introduced to the public by Â CzechÂ writerÂ Karel ÄŒapekÂ in his playÂ R.U.R. (Rossum's Universal Robots), published inÂ 1920.Â The play begins in aÂ factoryÂ that makes artificial people calledÂ robots, but they are closer to the modern ideas ofÂ androids, creatures who can be mistaken for humans. They can plainly think for themselves, though they seem happy to serve. At issue is whether theÂ robotsÂ are beingÂ exploitedÂ and the consequences of their treatment . However, Karel ÄŒapek himself did not coin the word. He wrote a short letter in reference to anetymologyÂ in theÂ Oxford English DictionaryÂ in which he named his brother, the painter and writer Josef ÄŒapek, as its actual originator.Â In an article in the Czech journalÂ Lidové novinyÂ in 1933, he explained that he had originally wanted to call the creaturesÂ laboÅ™iÂ (fromÂ LatinÂ labor, work). However, he did not like the word, and sought advice from his brother Josef, who suggested "roboti".
III. FIELDS OF ARTIFICIAL INTELLIGENCE
Many problems in AI can be solved in theory by intelligently searching through many possible solutions:Â ReasoningÂ can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads fromÂ premises to Â conclusions, where each step is the application of anÂ inference rule.Â PlanningÂ algorithms search through trees of goals and sub goals, attempting to find a path to a target goal, a process calledÂ means-ends analysis.Â RoboticsÂ algorithms for moving limbs and grasping objects useÂ local searchesÂ in configuration space.Â ManyÂ learningÂ algorithms use search algorithms based onÂ optimization. Simple exhaustive searchesÂ are rarely sufficient for most real world problems: theÂ search spaceÂ (the number of places to search) quickly grows toÂ astronomicalÂ numbers. The result is a search that isÂ too slowÂ or never completes. The solution, for many problems, is to use "heuristics" or "rules of thumb" that eliminate choices that are unlikely to lead to the goal (called "pruningÂ theÂ search tree").Â HeuristicsÂ supply the program with a "best guess" for what path the solution lies on.A very different kind of search came to prominence in the 1990s, based on the mathematical theory ofÂ optimization. For many problems, it is possible to begin the search with some form of a guess and then refine the guess incrementally until no more refinements can be made. These algorithms can be visualized as blindÂ hill climbing: we begin the search at a random point on the landscape, and then, by jumps or steps, we keep moving our guess uphill, until we reach the top. Other optimization algorithms areÂ simulated annealing,Â beam searchÂ andÂ random optimization.
Evolutionary computationÂ uses a form of optimization search. For example, they may begin with a population of organisms (the guesses) and then allow them to mutate and recombine,Â selectingÂ only the fittest to survive each generation (refining the guesses). Forms ofÂ evolutionary computationÂ includeÂ swarm intelligenceÂ algorithms (such asÂ ant colonyÂ orÂ particle swarm optimization) andÂ evolutionary algorithms
A neural network is an interconnected group of nodes, akin to the vast network ofÂ neuronsÂ in theÂ human brain. The study ofÂ artificial neural networksÂ began in the decade before the field AI research was founded, in the work ofÂ Walter PittsÂ andÂ Warren McCullough. Other important early researchers wereÂ Frank Rosenblatt, who invented theÂ perceptionÂ andÂ Paulwerbos who developed theÂ back propagationÂ algorithm.The main categories of networks are acyclic orÂ feed forward neural networksÂ (where the signal passes in only one direction) andÂ recurrent neural networksÂ (which allow feedback). Among the most popular feed forward networks areÂ perceptions,Â multi-layer perceptionsÂ andÂ radial basis networks.Â Among recurrent networks, the most famous is theÂ Hopfield net, a form of attractor network, which was first described byÂ John HopfieldÂ in 1982.Â Neural networks can be applied to the problem ofÂ intelligent control(for robotics) orÂ learning, using such techniques asÂ Hebbian learningÂ andÂ competitive learning.Jeff HawkinsÂ argues that research in neural networks has stalled because it has failed to model the essential properties of theÂ neocortex, and has suggested a model (Hierarchical Temporal Memory) that is based on neurological research.
Fig. 2 Schematic Diagram of a Neural Network
There is no established unifying theory orÂ paradigmÂ that guides AI research. Researchers disagree about many issues.Â A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence, by studying psychologyÂ orÂ neurology? Or is human biology as irrelevant to AI research as bird biology is toÂ aeronautical engineering?Â Can intelligent behavior be described using simple, elegant principles (such asÂ logicÂ orÂ optimization)? Or does it necessarily require solving a large number of completely unrelated problems?Â Can intelligence be reproduced using high-level symbols, similar to words and ideas? Or does it require "sub-symbolic" processing?
D. General Intelligence
Main articles:Â Strong AIÂ andÂ AI-complete Most researchers hope that their work will eventually be incorporated into a machine withÂ general Intelligence (known asÂ strong AI),combining all the skills above and exceeding human abilities at most or all of them.Â A few believe thatÂ anthropomorphicÂ features likeÂ artificial consciousnessÂ or an artificial brainÂ may be required for such a project.Â Eliezer YudkowskyÂ has argued for the importance ofÂ friendly artificial intelligence, to mitigate the risks of an uncontrolled intelligence explosion. The Singularity Institute for Artificial IntelligenceÂ is dedicated to creating such an AI. Many of the problems above are consideredÂ AI-complete: to solve one problem, you must solve them all. For example, even a straightforward, specific task likeÂ machine translationÂ requires that the machine follow the author's argument (reason), know what is being talked about (knowledge), and faithfully reproduce the author's intention (social intelligence).Â Machine translation, therefore, is believed to be AI-complete: it may requireÂ strong AIÂ to be done as well as humans can do it.
Intelligent agents must be able to set goals and achieve them.Â They need a way to visualize the future (they must have a representation of the state of the world and be able to make predictions about how their actions will change it) and be able to make choices that maximize the utilityÂ (or "value") of the available choices.In classical planning problems, the agent can assume that it is the only thing acting on the world and it can be certain what the consequences of its actions may be.Â However, if this is not true, it must periodically check if the world matches its predictions and it must change its plan as this becomes necessary, requiring the agent to reason under uncertainty.Multi-agent planningÂ uses theÂ cooperationÂ andÂ competitionÂ of many agents to achieve a given goal.Â Emergent behaviorÂ such as this is used bye volutionary algorithmsÂ andÂ swarm intelligence.
Machine learningÂ has been central to AI research from the beginning.Â Unsupervised learningÂ is the ability to find patterns in a stream of input.Â Supervised learningÂ includes bothÂ classificationÂ and numericalÂ regression. Classification is used to determine what category something belongs in, after seeing a number of examples of things from several categories. Regression takes a set of numerical input/output examples and attempts to discover a continuous function that would generate the outputs from the inputs. InÂ reinforcement learningÂ the agent is rewarded for good responses and punished for bad ones. These can be analyzed in terms ofÂ decision theory, using concepts likeÂ utility. The mathematical analysis of machine learning algorithms and their performance is a branch ofÂ theoretical computer scienceÂ known as computational learning theory
G. Motion And Manipulation
The field ofÂ roboticsÂ is closely related to AI. Intelligence is required for robots to be able to handle such tasks as object manipulationÂ andÂ navigation, with sub-problems ofÂ localizationÂ (knowing where you are),Â mappingÂ (learning what is around you) andÂ motion planningÂ (figuring out how to get there).
H. Knowledge Representation
Knowledge representationÂ andÂ knowledge engineeringÂ are central to AI research. Many of the problems machines are expected to solve will require extensive knowledge about the world. Among the things that AI needs to represent are: objects, properties, categories and relations between objects;Â situations, events, states and time;Â causes and effects;Â knowledge about knowledge (what we know about what other people know);Â and many other, less well researched domains. A complete representation of "what exists" is anÂ ontologyÂ (borrowing a word from traditionalÂ philosophy), of which the most general are calledÂ upper ontologies.
I. Natural Language Processing
Natural language processingÂ gives machines the ability to read and understand the languages that humans speak. Many researchers hope that a sufficiently powerful natural language processing system would be able to acquire knowledge on its own, by reading the existing text available over the internet. Some straightforward applications of natural language processing includeÂ information retrievalÂ (orÂ text mining) andÂ machine translation.
APPLICATIONS OF ROBOTS
Robotics has been of interest to mankind for over one hundred years. However our perception of robots has been influenced by the media and Hollywood.
One may ask what robotics is about? In my eyes, a robots' characteristics change depending on the environment it operates in. Some of these are:
Manipulative arms that are controlled by a human are used to unload the docking bay of space shuttles to launch satellites or to construct a space station
B. The Intelligent HomeÂ
Automated systems can now monitor home security, environmental conditions and energy usage. Door and windows can be opened automatically and appliances such as lighting and air conditioning can be pre programmed to activate. This assists occupants irrespective of their state of mobility.
Robots can visit environments that are harmful to humans. An example is monitoring the environment inside a volcano or exploring our deepest oceans. NASA has used robotic probes for planetary exploration since the early sixties.
D. Military RobotsÂ
Airborne robot drones are used for surveillance in today's modern army. In the future automated aircraft and vehicles could be used to carry fuel and ammunition or clear minefields
Â Fig. 3 Robots used in Modern Army
Automated harvesters can cut and gather crops. Robotic dairies are available allowing operators to feed and milk their cows remotely.
Fig. 4 Robots in the field of Agriculture
F. The Car Industry
Robotic arms that are able to perform multiple tasks are used in the car manufacturing process. They perform tasks such as welding, cutting, lifting, sorting and bending. Similar applications but on a smaller scale are now being planned for the food processing industry in particular the trimming, cutting and processing of various meats such as fish, lamb ,beef.
Under development is a robotic suit that will enable nurses to lift patients without damaging their backs. Scientists in Japan have developed a power-assisted suit which will give nurses the extra muscle they need to lift their patients- and avoid back injuries. The suit was designed by Keijiro Yamamoto, a professor in the welfare-systems engineering department at Kanagawa Institute of Technology outside Tokyo. It will allow caregivers to easily lift bed-ridden patients on and off beds. In its current state the suit has an aluminium exoskeleton and a tangle of wires and compressed-air lines trailing from it. Its advantage lies in the huge impact it could have for nurses. In Japan, the population aged 14 and under has declined 7% over the past five years to 18.3 million this year. Providing care for a growing elderly generation poses a major challenge to the government.
Robotics may be the solution. Research institutions and companies in Japan have been trying to create robotic nurses to substitute for humans. Yamamoto has taken another approach and has decided to create a device designed to help human nurses.
In tests, a nurse weighing 64 kilograms was able to lift and carry a patient weighing 70 kilograms. The suit is attached to the wearer's back with straps and belts. Sensors are placed on the wearer's muscles to measure strength. These send the data back to a microcomputer, which calculates how much more power is needed to complete the lift effortlessly.
The computer, in turn, powers a chain of actuators - or inflatable cuffs - that are attached to the suit and worn under the elbows, lower back and knees. As the wearer lifts a patient, compressed air is pushed into the cuffs, applying extra force to the arms, back and legs. The degree of air pressure is automatically adjusted according to how much the muscles are flexed. A distinct advantage of this system is that it assists the wearers knees, being only one of its kind to do so.
A number of hurdles are still faced by Yamamoto. The suit is unwieldy, the wearer can't climb stairs and turning is awkward. The design weight of the suit should be less than 10 kilograms for comfortable use. The latest prototype weighs 15 kilograms. Making it lighter is technically possible by using smaller and lighter actuators. The prototype has cost less than Â¥1 million ($8,400) to develop. But earlier versions developed by Yamamoto over the past 10 years cost upwards of Â¥20 million in government development grants.
Fig. 5 Robots in the field of Healthcare
H. Disaster AreasÂ
Surveillance robots fitted with advanced sensing and imaging equipment can operate in hazardous environments such as urban setting damaged by earthquakes by scanning walls, floors and ceilings for structural integrity.
Interactive robots that exhibit behaviours and learning ability. SONY has one such robot which moves freely, plays with a ball and can respond to verbal instructions.
V. ADVANTAGES OF ROBOTS
A. Business Benefits
Robots have the ability to consistently produce high-quality products and to precisely perform tasks. Since they never tire and can work nonstop without breaks, robots are able to produce more quality goods or execute commands quicker than their human counterparts
B. Management Benefits
Robot employees never call in sick, never waste time and rarely require preparation time before working. With robots, a manager never has to worry about high employee turnover or unfilled positions
C. Employee Benefits
Robots can do the work that no one else wants to do-the mundane, dangerous, and repetitive jobs. Common Misconception about Robots : Introducing robots into a work environment does not necessarily mean the elimination of jobs. With the addition of robots comes the need for highly-skilled, human workers.
D. Consumer Benefits
Robots produce high quality goods Since robots produce so many quality goods in a shorter amount of time than humans, we reap the benefits of cheaper goods. Since the products are produced more quickly, this significantly reduces the amount of time that we are forced to wait for products to come to the marketplace
Fears and concerns about robots have been repeatedly expressed in a wide range of books and films. A common theme is the development of a master race of conscious and highly intelligent robots, motivated to take over or destroy the human race. (SeeÂ The Terminator,Â Runaway,Â Blade Runner,Â Robocop,Â the Replicators inÂ Stargate ,Â the Cylons inÂ Battlestar Galactica,Â The Matrix,Â THX-1138, andÂ I, Robot.) Some fictional robots are programmed to kill and destroy; others gain superhuman intelligence and abilities by upgrading their own software and hardware. Examples of popular media where the robot becomes evil areÂ 2001: A Space Odyssey,Â Red Planet, ... Another common theme is the reaction, sometimes called the "uncanny valley", of unease and even revulsion at the sight of robots that mimic humans too closely.Â FrankensteinÂ (1818), often called the first science fiction novel, has become synonymous with the theme of a robot or monster advancing beyond its creator. In the TV show, Futurama, the robots are portrayed as humanoid figures that live alongside humans, not as robotic butlers. They still work in industry, but these robots carry out daily lives.
Manuel De LandaÂ has noted that "smart missiles" and autonomous bombs equipped with artificial perception can be considered robots, and they make some of their decisions autonomously. He believes this represents an important and dangerous trend in which humans are handing over important decisions to machines.
Marauding robots may have entertainment value, but unsafe use of robots constitutes an actual danger. A heavy industrial robot with powerful actuators and unpredictably complex behavior can cause harm, for instance by stepping on a human's foot or falling on a human. Most industrial robots operate inside a security fence which separates them from human workers, but not all. Two robot-caused deaths are those of Robert Williams andÂ Kenji Urada. Robert Williams was struck by a robotic arm at a casting plant inÂ Flat Rock, MichiganÂ on January 25, 1979.Â 37-year-oldÂ Kenji Urada, a Japanese factory worker, was killed in 1981; Urada was performing routine maintenance on the robot, but neglected to shut it down properly, and was accidentally pushed into aÂ grinding machine.
If the current developments are to be believed then the next wave of robots will have a supernatural resemblance with humans with the help of AI. The Indian automotive industry has finally awaken to the fact that robotics is not just about saving labour, but it also helps companies significantly to step up productivity and quality to meet the demands of international competition. Industrial robots can be involved in production industry because of its less time consumption, accuracy of work, and less labour. As globalization accelerates, robotics is increasingly vital to maintain the health of the industrial sector and keep manufacturing jobs at home. ''Now more than ever, the need to stay competitive is a driver for investing in robotics. Companies in all over the world are often faced with difficult choices: Do they send their manufacturing to low-cost producers overseas? Or, do they invest in robotics to continue making products here?'' We conclude that more companies are realizing that robotics is the better option.
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