Artificial intelligence (AI) is the intelligence of machines and the branch of computer science that aims to create it. Textbooks define the field as “the study and design of intelligent agents,” where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. John McCarthy, who coined the term in 1956, 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-the sapience of Homo sapiens-can be so precisely described that it can be simulated by a machine. This raises philosophical issues about the nature of the mind and limits of scientific hubris, issues which have been addressed by myth, fiction and philosophy since antiquity. Artificial intelligence has been the subject of optimism,but has also suffered setbacks 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. 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. General intelligence (or “strong AI”) is still a long-term goal of (some) research.
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AI plays a major role in the field of robotics. The word robot can refer to both physical robots and virtual software agents, but the latter are usually referred to as bots. 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. In South Africa, robot 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.
The International Organization for Standardization gives a definition of robot in 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.” This definition is used by the International Federation of Robotics, the European Robotics Research Network (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.” 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. For example, Joseph Engelberger, a pioneer in industrial robotics, once remarked: “I can’t define a robot, but I know one when I see one.” According to 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”. Merriam-Webster 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 on assembly lines because they have difficulty responding to unexpected interference. Because of this, most humans rarely encounter robots. However, domestic robots for cleaning and maintenance are increasingly common in and around homes in developed countries, particularly in Japan. Robots can also be found in the military.
Mechanical or “formal” reasoning has been developed by philosophers and mathematicians since antiquity. The study of logic led directly to the invention of the programmable digital electronic computer, based on the work of mathematician Alan Turing and others. Turing’s theory of computation suggested that a machine, by shuffling symbols as simple as “0” and “1”, could simulate any conceivable act of mathematical deduction. This, along with recent discoveries in neurology, information theory and 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 at a conference on the campus of Dartmouth College in the summer of 1956. The attendees, including John McCarthy, Marvin Minsky, Allen Newell and Herbert Simon, became the leaders of AI research for many decades. They and their students wrote programs that were, to most people, simply astonishing: computers were solving word problems in algebra, proving logical theorems and speaking English. By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense and laboratories had been established around the world. AI’s founders were profoundly optimistic about the future of the new field: Herbert Simon predicted that “machines will be capable, within twenty years, of doing any work a man can do” and Marvin Minsky 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 of expert systems, 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’s fifth generation computer 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 autonomous machines only appeared in the 20th century. The first digitally operated and programmable robot, the Unimate, was installed in 1961 to lift hot pieces of metal from a die casting machine and stack them. Today, commercial and industrial robots 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 word robot was introduced to the public by Czech writer Karel Čapek in his play R.U.R. (Rossum’s Universal Robots), published in 1920. The play begins in a factory that makes artificial people called robots, but they are closer to the modern ideas of androids, creatures who can be mistaken for humans. They can plainly think for themselves, though they seem happy to serve. At issue is whether the robots are being exploited and the consequences of their treatment. However, Karel Čapek himself did not coin the word. He wrote a short letter in reference to anetymology in the Oxford English Dictionary in which he named his brother, the painter and writer Josef Čapek, as its actual originator. In an article in the Czech journal Lidové noviny in 1933, he explained that he had originally wanted to call the creatures laboÅ™i (from Latin labor, work). However, he did not like the word, and sought advice from his brother Josef, who suggested “roboti”.
III. FIELDS OF ARTIFICIAL INTELLIGENCE
A. Combinatorial Search
Many problems in AI can be solved in theory by intelligently searching through many possible solutions: Reasoning can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from premises to conclusions, where each step is the application of an inference rule. Planning algorithms search through trees of goals and sub goals, attempting to find a path to a target goal, a process called means-ends analysis. Robotics algorithms for moving limbs and grasping objects use local searches in configuration space. Many learning algorithms use search algorithms based on optimization. Simple exhaustive searches are rarely sufficient for most real world problems: the search space (the number of places to search) quickly grows to astronomical numbers. The result is a search that is too slow 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 “pruning the search tree”). Heuristics 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 of 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 blind 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 are simulated annealing, beam search and random optimization.
Evolutionary computation 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, selecting only the fittest to survive each generation (refining the guesses). Forms of evolutionary computation include swarm intelligence algorithms (such as ant colony or particle swarm optimization) and evolutionary algorithms
B. Neural Network
A neural network is an interconnected group of nodes, akin to the vast network of neurons in the human brain. The study of artificial neural networks began in the decade before the field AI research was founded, in the work of Walter Pitts and Warren McCullough. Other important early researchers were Frank Rosenblatt, who invented the perception and Paulwerbos who developed the back propagation algorithm.The main categories of networks are acyclic or feed forward neural networks (where the signal passes in only one direction) and recurrent neural networks (which allow feedback). Among the most popular feed forward networks are perceptions, multi-layer perceptions and radial basis networks. Among recurrent networks, the most famous is the Hopfield net, a form of attractor network, which was first described by John Hopfield in 1982. Neural networks can be applied to the problem of intelligent control(for robotics) or learning, using such techniques as Hebbian learning and competitive learning.Jeff Hawkins argues that research in neural networks has stalled because it has failed to model the essential properties of the neocortex, and has suggested a model (Hierarchical Temporal Memory) that is based on neurological research.
There is no established unifying theory or paradigm that guides AI research. Researchers disagree about many issues. A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence, by studying psychology or neurology? Or is human biology as irrelevant to AI research as bird biology is to aeronautical engineering? Can intelligent behavior be described using simple, elegant principles (such as logic or optimization)? Or does it necessarily require solving a large number of completely unrelated problems? 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: Strong AI and AI-complete Most researchers hope that their work will eventually be incorporated into a machine with general Intelligence (known as strong AI),combining all the skills above and exceeding human abilities at most or all of them. A few believe that anthropomorphic features like artificial consciousness or an artificial brain may be required for such a project. Eliezer Yudkowsky has argued for the importance of friendly artificial intelligence, to mitigate the risks of an uncontrolled intelligence explosion. The Singularity Institute for Artificial Intelligence is dedicated to creating such an AI. Many of the problems above are considered AI-complete: to solve one problem, you must solve them all. For example, even a straightforward, specific task like machine translation 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). Machine translation, therefore, is believed to be AI-complete: it may require strong AI to be done as well as humans can do it.
Intelligent agents must be able to set goals and achieve them. 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 utility (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. 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 planning uses the cooperation and competition of many agents to achieve a given goal. Emergent behavior such as this is used bye volutionary algorithms and swarm intelligence.
Machine learning has been central to AI research from the beginning. Unsupervised learning is the ability to find patterns in a stream of input. Supervised learning includes both classification and numerical 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. In reinforcement learning the agent is rewarded for good responses and punished for bad ones. These can be analyzed in terms of decision theory, using concepts like utility. The mathematical analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory
G. Motion And Manipulation
The field of robotics is closely related to AI. Intelligence is required for robots to be able to handle such tasks as object manipulation and navigation, with sub-problems of localization (knowing where you are), mapping (learning what is around you) and motion planning (figuring out how to get there).
H. Knowledge Representation
Knowledge representation and knowledge engineering 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; situations, events, states and time; causes and effects; knowledge about knowledge (what we know about what other people know); and many other, less well researched domains. A complete representation of “what exists” is an ontology (borrowing a word from traditional philosophy), of which the most general are called upper ontologies.
I. Natural Language Processing
Natural language processing 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 include information retrieval (or text mining) and machine translation.
IV. 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:
A. Outer Space
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 Home
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 Robots
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
Automated harvesters can cut and gather crops. Robotic dairies are available allowing operators to feed and milk their cows remotely.
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.
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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 ¥1 million ($8,400) to develop. But earlier versions developed by Yamamoto over the past 10 years cost upwards of ¥20 million in government development grants.
H. Disaster Areas
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. (See The Terminator, Runaway, Blade Runner, Robocop, the Replicators in Stargate, the Cylons in Battlestar Galactica, The Matrix, THX-1138, and 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 are 2001: A Space Odyssey, 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. Frankenstein (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 Landa 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 and Kenji Urada. Robert Williams was struck by a robotic arm at a casting plant in Flat Rock, Michigan on January 25, 1979. 37-year-old 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 a 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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