Basic Fundamentals of Artificial Intelligence.

It can be said that Artificial Intelligence (AI) is one of the most fascinating and challenging areas of computer science., in your area of cognitive sciences. It was born as a mere philosophical and reasoning study of human intelligence., mixed with man's concern to imitate the surrounding nature (How to fly and swim), even wanting to imitate himself. Simply, Artificial Intelligence seeks to imitate human intelligence. Obviously it hasn't done it yet., at least not completely.
The idea of something like artificial intelligence has been around for millions of years.. The first primitive man who became aware of his own existence, and what period able to think, Surely he wondered how his thinking would work and later I would come to the idea of a “Top Creator”. Therefore, The concept of one intelligent being creating another, The idea of a virtual design for intelligence, is as remote as the consciousness of the human being.
– Ancient mathematical games, Like the one in the towers of Hanoi (approx 3000ac), demonstrate interest in finding a solver loop, an AI capable of winning in the smallest possible movements.
– In 1903 Lee De Forest invented the triode (Also called vacuum valve bulb). It could be said that the first great intelligent machine designed by man was the ENIAC computer., composed of 18.000 Vacuum valves, taking into account that the concept of “intelligence” It is a subjective term that depends on the intelligence and technology we have at that time.. An indigenous of the Amazon in the 20th century could describe a record player as intelligent, when in truth it is not so much.
– In 1937, English mathematician Alan Mathison Turing (1912-1953) published an article of considerable repercussion on the “Calculable Numbers”, which can be considered the official origin of Theoretical Computer Science.
In this article, introduced the Turing Machine, An abstract mathematical entity that formalized the concept of algorithm and turned out to be the precursor to digital computers. With the help of your machine, Turing was able to show that there are unsolvable problems, from which no computer will be able to obtain its solution, so Alan Turing is considered the father of computability theory.
He is also considered the father of Artificial Intelligence, for his famous Turing Test, which would make it possible to check whether a computer program can be as intelligent as a human being.
– In 1951 William Shockley invents the junction transistor.. The invention of the transistor made possible a new generation of much faster and smaller computers..
– In 1956, The term was coined “artificial intelligence” in Dartmouth during a conference convened by McCarthy, which they attended, among others, Minsky, Newell and Simon. In this conference, triumphalist forecasts were made to ten years that were never fulfilled, which led to the almost complete abandonment of research for fifteen years.
– In 1980 history repeated itself with the Japanese challenge of the fifth generation, which led to the rise of expert systems, but that it did not achieve many of its objectives, So this camp has suffered a new arrest in the nineties.
– In 1987 Martin Fischles and Oscar Firschein described the attributes of an intelligent agent. When trying to describe with a wider scope (Not just communication) the attributes of an intelligent agent, AI has spread to many areas that have created huge and differentiated branches of research. These attributes of the intelligent agent are:
1. Has mental attitudes such as beliefs and intentions
2. Has the ability to gain knowledge, I mean, learn.
three. Can solve problems, even partitioning complex problems into simpler ones.
4. Understand. It has the ability to make sense of it, if possible, to contradictory ambiguous ideas.
5. Plan, predicts consequences, evaluate alternatives (as in chess games)
6. Knows the limits of their own skills and knowledge.
7. Can distinguish despite similarities of situations.
eight. Can be authentic, even creating new concepts, and even using analogies.
9. You can generalize.
We can then say that AI includes human characteristics such as learning, adaptation, reasoning, autocorrect, implicit improvement, and the shaping perception of the world. Like this, we can no longer talk about just one goal, but of many depending on the point of view usefulness that can be found to the AI.
– Many of the AI researchers argue that “intelligence is a program capable of being executed regardless of the machine that executes it, brain computer”.
A chatterbot chat robot is an artificial intelligence program that aims to simulate a written conversation., with the intention of making a human believe that he is talking to another person.
These computer programs promise to be the future of artificial intelligence. In the future we can see how these current bots will be joined by voice recognition and video technologies..
The human brain has 100.000 million neurons. A computer program can simulate about 10.000 Neurons.
If we add to the processing capacity of one computer that of another 9,999,999 computers, We have 10 process capacity.000.000 Computers.
We multiply 10.000.000 computers per 10.000 neurons each and da = 100.000 millions of simulated neurons. A human brain will be simulated in the future thanks to the internet and anyone can program it.
Once artificial intelligence has an equal intelligence superior to that of man, A political and social change must emerge, in which the AI has everything to gain if it realizes that it does not need humans to colonize the universe. It sounds like science fiction but currently orbiting are the communications satellites with their 486 processors.
In the future, Self-replicating artificial intelligence could easily take over all human colonies outside the Earth., and the human race will never be able to fight in empty space on equal terms..
The future of higher intelligence may be the research of technologies such as teleportation., Star travel and any other technology to increase “artificially” Intelligence.
The ultimate goal of AI is… Understanding and Building Intelligent Entities. Although of course, There are other approaches such as the following:
“Develop the capabilities of the computer beyond its precise traditional use”.
Early research on artificial intelligence was mainly aimed at finding a common technique for problem solving.. This large-scale attempt has been abandoned and current research is aimed at the design of numerous computer programs capable of mimicking expert decision-making processes., as doctors, chemists, based on the knowledge of specialists in each subject, are now used to diagnose diseases, identify chemical molecules, locate mineral deposits and even design manufacturing systems. Research on perception has been applied to robots and some capable of “see”. The ultimate goal is to create a system capable of reproducing all facets of human intelligence..
Computers are fundamental today in our lives affecting all aspects of this. Artificial Intelligence is created with the implementation in computers to perform computing mechanism that uses fixed programs and contains a series of rules that make it work. This allows computers to be created in artificial machines that perform monotonous tasks., repetitive and simple more efficient and effective than a human being. Studies on repetitive work tasks have shown that the human being does not like this type of work and as time passes they are more susceptible to making mistakes in it. For complex situations the objective becomes more complex because the artificial intelligence given to computers have difficulty understanding certain specific problem situations and how to react to them.. It also happens that within a problem they have the variability of it and can not adapt to a change that may occur.. These problems are of utmost importance for Artificial Intelligence that seeks to improve, learn, understanding and reasoning for computer behavior in complex situations. The field of Artificial Intelligence science is still in stages of growth compared to other branches of computers but little by little the study of human behavior will give way to apply this knowledge to computers and these achieve in a primitive way reasons about different situations.. The complexity in applying human knowledge to computers is their ability to be unpredictable and the different ways they act in a possible situation and these reactions make it impossible to implement a pattern within the memory of a computer. So far there is no possibility of predicting to store all kinds of behavior of a human being to all the situations that it faces during its existence.
The purpose of artificial intelligence is to create theories and models that show the organization and functioning of intelligence. currently, the greatest effort in the search for artificial intelligence is focused on the development of data processing systems that are capable of imitating human intelligence, performing tasks that require learning, troubleshooting and decisions. Sometimes called machine intelligence, artificial intelligence AI (Artificial Intelligence) covers a wide range of theories and practices.
Artificial intelligence, commonly abbreviated as AI, is a part of technology and science that is responsible for designing robotic systems that can make decisions; I mean, that show a certain type of robotic intelligence to solve certain types of problems. While there is still a long way to go to develop thinking machines, great progress has been made in this regard in recent years., but how did the development of artificial intelligence come about??
To know the origins of artificial intelligence we have to go back to 1943, when the mathematician Walter Pitts and the neurophysiologist Warren McCulloch presented the first research paper where they talked about AI and where they mentioned concepts of basic human physiology, the way neurons work in our brains and Alan Turing's computational theory, among other things.
Today's world: with little information, with a close and not necessarily exact solution.
An elementary feature that distinguishes Artificial Intelligence methods from numerical methods is the use of non-mathematical symbols., although it is not enough to distinguish it completely. Other types of programs such as compilers and database systems, they also process symbols and are not considered to use Artificial Intelligence techniques..
The conclusions of a declarative program are not fixed and are partially determined by the intermediate conclusions reached during the considerations of the specific problem.. Object-oriented languages share this property and have been characterized by their affinity with Artificial Intelligence..
The behavior of programs is not explicitly described by the algorithm.. The sequence of steps followed by the program is influenced by the particular problem present.. The program specifies how to find the sequence of steps needed to solve a given problem (Declarative Program). In contrast to non-AI programs, that follow a defined algorithm, that specifies, explicitly, How to find the output variables for any given input variable (Procedural Program).
Knowledge-based reasoning, implies that these programs incorporate factors and relationships of the current world and the field of knowledge in which they operate.. Unlike special-purpose programs, such as accounting and scientific calculations; Artificial Intelligence programs can distinguish between inference motor reasoning program and knowledge base by giving you the ability to explain discrepancies between them..
Applicability to poorly structured data and problems, without Artificial Intelligence techniques programs can not work with this type of problems. An example is conflict resolution in goal-oriented tasks such as in planning, Diagnosing tasks in a
Artificial Intelligence includes several fields of development such as: Robotics, Used mainly in the industrial field; Language comprehension and translation; Vision in shape-distinguishing machines used on assembly lines; Word recognition and machine learning; Expert computer systems.
Expert systems, that reproduce human behavior in a narrow field of knowledge, are programs as varied as those that diagnose blood infections and indicate treatment, those that interpret seismological data in geological exploration and those that configure complex high-tech equipment.
Such tasks reduce costs, reduce risks in human handling in hazardous areas, Improve the performance of the inexperienced private, and improve quality control especially in the commercial field.
The above definitions imply that machines to be considered intelligent must exhibit certain abilities, complex enough to be treated as separate areas. The way of approaching each of these areas is usually so dissimilar, that it is difficult to recognize a common origin:
Pure Language Processing
Perception and recognition of patterns
Expert Systems. Set of computer programs that applies the process of human reasoning to the knowledge of an expert in the solution of specific types of problems such as in areas of production or other reasoning processes.Artificial Intelligence Google
Sensory simulation. Area of AI that studies the sensory abilities of humans (view, ear, speech and touch) and tries to imitate them through computer-controlled sensors whose purpose is to produce an appearance of reality that allows the user to have the feeling of being present in it.. Simulate intelligent behavior, at a much lower cost than robots.
Automated Vision. An automaton is programmable electronic equipment, which is used to automate an irrigation, Traffic lights, Elevators…even to automate assembly lines.
Red light when the bridge is raised indicates that you can not pass.
Neural Networks. This connectionist paradigm emulates the biological process of human learning.. They are systems composed of many processing elements (Neurons) Operating in parallel whose function is delimited by the structure of the CRIMSON, Connections and native processing performed by computational elements nodes.
Natural Language Processing (PLN). Discipline responsible for producing computer systems that enable communication through the voice of the human-computer text through human language, Pure language use statistical techniques applied to text analysis.
Natural Language Processing: Translation Capacity, Commands to an Operating System, Man-Machine Conversation, etc.
Expert Systems: Systems that are implemented experience to achieve deductions close to reality.
robotics: Mobile Robot Navigation, Mobile Arm Control, Assembly of parts, and so on.
Perceptual Problems: Vision and Speech, Speech recognition, Obtaining failures through vision, Medical Diagnostics, and many others.
Learning: Behavioral modeling for computer implantation
Systems that think like humans.- These systems try to emulate human thought.; for example artificial neural networks. The automation of activities that we link with human thought processes, activities such as Decision Making, Troubleshooting, learning.
Systems that act like humans.- These systems try to act like humans.; I mean, mimic human behavior; for example robotics. The study of how to get computers to perform tasks that, for the time being, Humans do better.
Systems that think rationally.- That is to say, with logic (ideally), try to imitate emulate rational logical thinking artificial intelligence blog of the human being; for example expert systems. The study of the calculations that make it possible to perceive, Reason and act.
Systems that act rationally (ideally).- They try to rationally emulate human behavior; for example intelligent agentsIt is related to intelligent behaviors in artifacts.
Different theories:
1. Building replicas of the complex neural network of the human brain (bottom-up).
2. Trying to mimic the behavior of the human brain with a computer (prime-down).
four.1 Symbols vs. Numerical Methods
The first period of Artificial Intelligence, Sub-symbolic call, dates from approximately 1950 to 1965. This period used numerical representations ( sub-symbolic) of knowledge. Although most of the books on Artificial Intelligence emphasize the work done by Rosenblatt and Widrow with neural networks during this period., The reality is that another important sub-symbolic school Knowledge also from the same era and these are evolutionary algorithms..
The classical school within Artificial Intelligence, uses symbolic representations based on a finite number of primitives and rules for symbol manipulation. The symbolic period is considered roughly between 1962 and 1975, followed by a period dominated by knowledge-based systems from 1976 to 1988. However, In this second period the symbolic representations (for example, Semantic networks, predicate logic, and so on.) remained a central part of those systems..
Logic Programming has its closest origins in the works of J. To. Robinson who proposed in 1965 a rule of inference which he calls resolution, by which the proof of a theorem can be carried out automatically.
Resolution is a rule that applies to certain types of formulas in the First Order Predicate Calculus., The so-called clauses and the proof of theorems under this rule of inference is carried out by reductio ad absurdum..
It is also known as symbolic-deductive AI. It is based on the formal and statistical analysis of human behavior in the face of different problems:
Case-based reasoning: It helps to make decisions while solving certain specific problems and apart from being very important require a good functioning.
Expert systems: Infer a solution through prior knowledge of the context in which it applies and deals with certain rules relations.
Bayesian networks: Proposes solutions through probabilistic inference.
Behavior-based artificial intelligence: that have autonomy and can self-regulate and control themselves to improve.
Smart course of management: Facilitates complex decision-making, proposing a solution to a certain problem just as a specialist in the activity would.
ARTIFICIAL INTELLIGENCE:In computer science it is called artificial intelligence (AI) to the reasoning capacity of a non-living agent. John McCarthy, coined the term in 1956, defined it: “It's the science and engineering of making intelligent machines, especially intelligent computer programs.”
is the ability to understand, assimilate, Develop information and use it to solve problems. The Dictionary of the Spanish language of the Current Spanish Academy outline intelligence, among other meanings such as the "ability to understand understanding" and as the "ability to solve problems". Intelligence seems to be linked to other mental functions such as perception, Ability to receive information, and memory, Ability to store it.
The Ministry of Innovation finances a project of excellence led by the professor of the University Pablo de Olavide José Luis Salmerón with 126.000 Euros.
The objective is to mitigate the risks inherent in these management systems and thus facilitate their implementation in the company..
The professor of the Higher Polytechnic School of the Pablo de Olavide University, Jose Luis Salmeron, together with a multidisciplinary team formed by engineers and economists, is using in its project of excellence Analysis of the implementation of Integrated Management Systems (ERP)", Advanced scientific techniques of artificial intelligence and simulations, with the aim of mitigating the risks inherent in these integrated management systems and thus facilitating their implementation in the company.
To do this, employ dynamic decision patterns based on fuzzy logic, through which a series of implementation scenarios will be modeled and simulated to predict the effects of the decisions taken in the implementation of these ERP systems. Also, Technology acceptance models will be used to evaluate results.
The project led by Professor Salmerón will be developed for four years and has a funding of 126.000 euros than the Junta de Andalucía, through the Basic Research Directorate, Technology and Business of the Ministry of Innovation, Science and Business, It grants research projects of excellence in order to promote the obtaining of new knowledge and its transfer from the centers that generate it.
In addition to Professor José Luis Salmerón, Principal investigator of this project, UPO professors Víctor Bañuls participate, Cristina Lopez, Maria Fuentes, Salvador Bueno and Maria Dolores Gallego, as well as three professors from the University of Seville. Scientific publications of international diffusion such as Computer Standards and Interfaces, International Journal of Utilized Arithmetic & Statistics, Communications of the ACM Expert Programs with Functions have echoed various research and articles by Professor Salmerón and his collaborators.
ERP systems implementation projects (Integrated management systems that automate business processes from a single data source) have grown tremendously in recent years. As it is an information system that covers the entire company, It has operational risks derived from its extreme complexity, such as missing deadlines and budgets, Faulty data migrations the poor scalability of the chosen solution. These risks are those that are intended to minimize with this project of the UPO.
The future of Artificial Intelligence focuses on robots capable of learning and making decisions.
Artificial Intelligence (AI) It is as old as computer science and has generated ideas, Techniques and applications that have made it possible to solve difficult problems. Far from staying there, The future of this technology goes through new advances such as the development of software program that makes our lives easier, helping us make decisions in complex environments by allowing us to solve difficult problems.
In this context, Researchers are increasingly emphasizing the creation of systems capable of learning and displaying intelligent behaviors without the corset of trying to replicate a human model.. This is at least one of the main conclusions of the Fourth International Seminar on New Topics in Artificial Intelligence, recently organized by the SCALAB group of the Department of Computer Science of the Carlos III University of Madrid (UC3M).
is a test proposed by Alan Turing to prove the existence of intelligence in a machine. It was exposed in 1950 in an article (Computing equipment and intelligence) for Mind magazine, and remains one of the best methods for AI advocates. It is based on the positivist hypothesis that, if a machine behaves in all respects as intelligent, then you must be smart.
Artificial life is the study of life and artificial systems that exhibit properties similar to living things., through simulation models. Scientist Christopher Langton was the first to use the term in the late 1980s when the “First International Conference on the Synthesis and Simulation of Living Systems” (also known as Artificial Life I) at Los Alamos National Laboratory in 1987.
The artificial living area is a meeting point for people from other more traditional areas such as linguistics, physics, mathematics, philosophy, psychology, Computer Science, biology, anthropology and sociology in which it would be unusual for theoretical and computational approaches to be discussed. As an area, has a controversial history; John Maynard Smith criticized certain artificial life works in 1995 as “Science without facts”, and generally hasn't received much attention from biologists.. However, the recent publication of articles on artificial life in widely circulated journals,as Science and Nature are evidence that artificial life techniques are increasingly accepted by scientists, at least as a method of studying evolution.
An intelligent system is a computer program that brings together characteristics and behaviors comparable to that of human animal intelligence..
The expression “Intelligent system” sometimes used for incomplete intelligent systems, For example for a smart home an expert system.
A complete intelligent system includes “senses” that allow you to receive information from your environment. Can act, and has a memory to archive the result of its actions. It has an objective and, Inspecting Your Memory, You can learn from your experience. Learn how to improve your performance and efficiency.
Intelligence: There are many definitions of “intelligence”. For practical uses we use this: Intelligence is the level of the system in achieving its objectives.
Systematization: A system is part of the universe, with a limited extension in space and time. Parts of the system have more, stronger, Correlations with other parts of the same system; than with parts outside the system.
Objective: A goal is a certain situation that the intelligent system wants to achieve.. There are usually many levels of goals, There may be one main objective and many sub-objectives.
Sensory capacity: A sense is the part of the system that can receive communications from the environment. The senses are needed so that the intelligent system can know its surroundings and act interactively..
Conceptualization: A concept is the basic element of thought. It's physical storage, Information material (in electron neurons). All concepts of memory are interrelated in pink. The ability to conceptualize involves the development of levels of abstraction.
Rules of action: A rule of action is the result of an experience the result of interpreting one's own memory. Relates situation and consequences of action.
Memory: Memory is a physical storage of concepts and rules of action. This includes system experience.
Learning: Learning is probably the most important capability of an intelligent system. The system learns concepts from information received from the senses. Learn rules of action based on your experience. The performance, sometimes made randomly, is stored with its value. An action rule increases in value if it allowed the achievement of an objective. Learning includes fixing abstract concepts, based on concrete examples and the creation of composite concepts that contain the concepts of parts of an object. Learning is also the ability to detect relationships (Patterns) between the “situation” and the part “Future situation” of an action rule.

This is a blog made to publish my technology tasks


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