In this video I'll discuss the Crypt Arithmetic Problem in AI. Cryptarithmetic Tutorials and Problems. Seven/7 Problem Characteristics of. Critical Thinking in Everyday Life. Wherever possible take problems one by one. State the problem as clearly. Critical Thinking in Everyday Life: 9 Strategies. Critical Thinking: A Literature Review. Consumer Behaviour Customer is profit. Indian consumer and his characteristics. Intelligence - Wikipedia. Intelligence has been defined in many different ways including as one's capacity for logic, understanding, self- awareness, learning, emotional knowledge, planning, creativity and problem solving. ![]() It can be more generally described as the ability to perceive information, and to retain it as knowledge to be applied towards adaptive behaviors within an environment or context. Intelligence is most widely studied in humans, but has also been observed in non- human animals and in plants. Artificial intelligence is intelligence in machines. It is commonly implemented in computer systems using program software. Within the discipline of psychology, various approaches to human intelligence have been adopted. The psychometric approach is especially familiar to the general public, as well as being the most researched and by far the most widely used in practical settings. A form of this verb, intellectus, became the medieval technical term for understanding, and a translation for the Greek philosophical term nous. This term was however strongly linked to the metaphysical and cosmological theories of teleologicalscholasticism, including theories of the immortality of the soul, and the concept of the Active Intellect (also known as the Active Intelligence). This entire approach to the study of nature was strongly rejected by the early modern philosophers such as Francis Bacon, Thomas Hobbes, John Locke, and David Hume, all of whom preferred the word . Expert Systems and Applied Artificial Intelligence. Characteristics of AI Systems. How the AI Field Evolved. Planning Principles and Practices Victoria Transport Policy Institute, and. Everything on About.com Religion & Spirituality. Agnosticism & Atheism. It is not merely book learning, a narrow academic skill, or test- taking smarts. Rather, it reflects a broader and deeper capability for comprehending our surroundings. Although these individual differences can be substantial, they are never entirely consistent: a given person's intellectual performance will vary on different occasions, in different domains, as judged by different criteria. Although considerable clarity has been achieved in some areas, no such conceptualization has yet answered all the important questions, and none commands universal assent. Indeed, when two dozen prominent theorists were recently asked to define intelligence, they gave two dozen, somewhat different, definitions. Intelligence enables humans to remember descriptions of things and use those descriptions in future behaviors. It is a cognitive process. It gives humans the cognitive abilities to learn, form concepts, understand, and reason, including the capacities to recognize patterns, comprehend ideas, plan, problem solve, and use language to communicate. Intelligence enables humans to experience and think. Note that much of the above definition applies also to the intelligence of non- human animals. In animals. These researchers are interested in studying both mental ability in a particular species, and comparing abilities between species. They study various measures of problem solving, as well as numerical and verbal reasoning abilities. Some challenges in this area are defining intelligence so that it has the same meaning across species (e. Stanley Coren's book, The Intelligence of Dogs is a notable book on the topic of dog intelligence. Cephalopods appear to exhibit characteristics of significant intelligence, yet their nervous systems differ radically from those of backboned animals. Vertebrates such as mammals, birds, reptiles and fish have shown a fairly high degree of intellect that varies according to each species. The same is true with arthropods. The general factor of intelligence, or g factor, is a psychometric construct that summarizes the correlations observed between an individual. First described in humans, the g factor has since been identified in a number of non- human species. Instead, intelligence is measured using a variety of interactive and observational tools focusing on innovation, habit reversal, social learning, and responses to novelty. Studies have shown that g is responsible for 4. These values are similar to the accepted variance in IQ explained by g in humans (4. If this is accepted as definitive of intelligence, then it includes the artificial intelligence of robots capable of . Plants are not limited to automated sensory- motor responses, however, they are capable of discriminating positive and negative experiences and of 'learning' (registering memories) from their past experiences. They are also capable of communication, accurately computing their circumstances, using sophisticated cost. Intelligence can be defined as a person. It is also associated with school performance, IQ, logic, abstract thought, self- awareness, emotional knowledge, memory, planning, creativity, and problem solving. Culture can be defined as a way of life that influences our views, experiences, and engagement with our lives and the world around us. It is shaped by the political, social, and environmental contexts in which we live. Together these form part of the sociocultural theory, coined by Lev Vygotsky. The sociocultural theory investigates . More specifically, culture shapes intelligence. Intelligence and culture is most widely studied in humans. There are not any known studies that exam the culture and intelligence of non- human or plant life in the same way. These are psychological terms that are most easily identified in humans. The sociocultural theory closely relates to intelligence and culture. Lev Vygotsky was the first researcher to define the sociocultural theory. The theory proposes that children learn a larger part of their cognitive abilities from social interactions with adults or older children and people. He distinctly defines this as the Zone of Proximal Development. Older people provide scaffolding, or tools that help children improve their cognitive abilities. Successful intelligence incorporates the socio- cultural environment and people. Different cultures value different things and have different experiences. This will greatly influence what they need to succeed in their world. Sternberg (2. 00. Kenya on their knowledge of natural herbal medicine. Many in this area of Kenya do not have Westernized schooling or strive for a Westernized education. Therefore, Sternberg (2. They also discuss how Western children may have knowledge of the herbal medicines, but it would not be as extensive as the Kenyan children. This demonstrates different forms of intelligence in different contexts. One is not better than the other, and the type of knowledge that these children have is beneficial for their environment. Intelligence is moldable by culture. When we combine intelligence and the sociocultural influence, we see that culture has a significant impact on cognitive development and thus school and learning. Siegler and Alibali (2. For example, the Korean and American children spent less of their time in formal and informal lessons and work than those in Russia and Estonia (Tudge et al., 1. In addition, the book continues to discuss cultural norms influence child development and their abilities to perform certain tasks. This can also apply to intelligence in a school and learning context if culture is truly influential. Stevenson and colleagues. They examined Japanese, Taiwanese, and American mothers. The children took reading and mathematical tests, and the United States children performed worse than the Taiwanese and Japanese children. Researchers found that the mothers. For example, the Asian mothers were more likely to help them with their homework. Therefore, definitions and the value of intelligence can be different across cultures. Several other studies explore and define the relation between intelligence and culture. The first study by Greenfield and Quiroz (2. Latino immigrant parents and European American parents. More specifically, they examined how the different culture valued personal achievement for their children. Their research found that Latino families had more familistic values, family before outsiders, whereas European Americans had more individualistic values. The interviews consisted of conflict scenarios about family reactions school performance and the importance of family. There were 7. 4% of Latino parents that believed the child should be able to leave school to care for his brother at home and only 3. European American parents believed this. These results imply that there are differences in values of family life and culture that influence children. The main point was that maternal education influences this difference. People in poverty are less likely to have a degree from higher education. The children will only learn from their environment and interactions with people in their neighborhood and family members. This creates a cultural difference in the value of intelligence and education. Brooks- Gunn and colleagues (1. The results indicated that US parents valued incremental theory of intelligence the most, Chinese parents encouraged their kids the most and were most persistent, and New Zealand parents had more significant levels of frustration. The more parents supported incremental learning the more the children were persistent on the task. The main claim that the researchers made was that Asian parents motivate their children to learn in a different way than Western parents do. This study was not so much about levels of intelligence than the way that culture shapes learning and intelligence. It, evidently, varies across cultures. Lastly, Wentzel (1. Parents and culture had an influence on children. The main idea was that parents set their expectations for their children through their confidence in them, the nature of children. Additionally, the studies imply that socio- culture plays one of the biggest roles in school achievement, educational motivation, learning abilities, and thus intelligence. That is, these children value what their parents, community, or culture values. This also shapes the way that they learn, the way that they approach problems, and how they value learning and certain educational skills. Artificial intelligence - Wikipedia. Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, the field of AI research defines itself as the study of . As machines become increasingly capable, mental facilities once thought to require intelligence are removed from the definition. For instance, optical character recognition is no longer perceived as an exemplar of . Many tools are used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics. The AI field draws upon computer science, mathematics, psychology, linguistics, philosophy, neuroscience and artificial psychology. The field was founded on the claim that human intelligence . With his Calculus ratiocinator, Gottfried Leibniz extended the concept of the calculating machine (Wilhelm Schickard engineered the first one around 1. Since the 1. 9th century, artificial beings are common in fiction, as in Mary Shelley's Frankenstein or Karel . In the 1. 9th century, George Boole refined those ideas into propositional logic and Gottlob Frege developed a notational system for mechanical reasoning (a . Around the 1. 94. Alan Turing's theory of computation suggested that a machine, by shuffling symbols as simple as . This insight, that digital computers can simulate any process of formal reasoning, is known as the Church. Shaw (RAND), presented the first true artificial intelligence program, the Logic Theorist. This spurred tremendous research in the domain: computers were winning at checkers, solving word problems in algebra, proving logical theorems and speaking English. Progress slowed and in 1. Sir James Lighthill and ongoing pressure from the US Congress to fund more productive projects, both the U. S. The next few years would later be called an . By 1. 98. 5 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. The Kinect, which provides a 3. D body. Clark also presents factual data indicating that error rates in image processing tasks have fallen significantly since 2. Other cited examples include Microsoft's development of a Skype system that can automatically translate from one language to another and Facebook's system that can describe images to blind people. The general problem of simulating (or creating) intelligence has been broken down into sub- problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention. The search for more efficient problem- solving algorithms is a high priority. 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. The most general are called upper ontologies, which attempt to provide a foundation for all other knowledge. For example, if a bird comes up in conversation, people typically picture an animal that is fist sized, sings, and flies. None of these things are true about all birds. John Mc. Carthy identified this problem in 1. Almost nothing is simply true or false in the way that abstract logic requires. AI research has explored a number of solutions to this problem. Research projects that attempt to build a complete knowledge base of commonsense knowledge (e. Cyc) require enormous amounts of laborious ontological engineering. For example, a chess master will avoid a particular chess position because it . These are intuitions or tendencies that are represented in the brain non- consciously and sub- symbolically. As with the related problem of sub- symbolic reasoning, it is hoped that situated AI, computational intelligence, or statistical AI will provide ways to represent this kind of knowledge. Emergent behavior such as this is used by evolutionary algorithms and swarm intelligence. 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 is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change. In reinforcement learning. The agent uses this sequence of rewards and punishments to form a strategy for operating in its problem space. These three types of learning 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. A sufficiently powerful natural language processing system would enable natural language user interfaces and the acquisition of knowledge directly from human- written sources, such as newswire texts. Some straightforward applications of natural language processing include information retrieval, text mining, question answering. Increases in processing speeds and the drop in the cost of data storage makes indexing large volumes of abstractions of the user's input much more efficient. Perception. A few selected subproblems are speech recognition. Intelligence is required for robots to be able to handle such tasks as object manipulation. It is an interdisciplinary field spanning computer sciences, psychology, and cognitive science. While the origins of the field may be traced as far back as to early philosophical inquiries into emotion, the more modern branch of computer science originated with Rosalind Picard's 1. The machine should interpret the emotional state of humans and adapt its behaviour to them, giving an appropriate response for those emotions. Emotion and social skills. First, it must be able to predict the actions of others, by understanding their motives and emotional states. Related areas of computational research are Artificial intuition and Artificial thinking. General intelligence. For example, even a straightforward, specific task like machine translation requires that the machine read and write in both languages (NLP), follow the author's argument (reason), know what is being talked about (knowledge), and faithfully reproduce the author's intention (social intelligence). A problem like machine translation is considered . In order to reach human- level performance for machines, one must solve all the problems. Researchers disagree about many issues.? Or is human biology as irrelevant to AI research as bird biology is to aeronautical engineering?? Or does it necessarily require solving a large number of completely unrelated problems?? Computational psychology is used to make computer programs that mimic human behavior. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as W. Grey Walter's turtles and the Johns Hopkins Beast. Many of these researchers gathered for meetings of the Teleological Society at Princeton University and the Ratio Club in England. The research was centered in three institutions: Carnegie Mellon University, Stanford and MIT, and each one developed its own style of research. John Haugeland named these approaches to AI . Approaches based on cybernetics or neural networks were abandoned or pushed into the background. Their research team used the results of psychological experiments to develop programs that simulated the techniques that people used to solve problems. This tradition, centered at Carnegie Mellon University would eventually culminate in the development of the Soar architecture in the middle 1. Roger Schank described their . A number of researchers began to look into . This coincided with the development of the embodied mind thesis in the related field of cognitive science: the idea that aspects of the body (such as movement, perception and visualization) are required for higher intelligence. Computational intelligence and soft computing. Interest in neural networks and . Other soft computing approaches to AI include fuzzy systems, evolutionary computation and many statistical tools. The application of soft computing to AI is studied collectively by the emerging discipline of computational intelligence. These tools are truly scientific, in the sense that their results are both measurable and verifiable, and they have been responsible for many of AI's recent successes. The shared mathematical language has also permitted a high level of collaboration with more established fields (like mathematics, economics or operations research). Stuart Russell and Peter Norvig describe this movement as nothing less than a . There is an ongoing debate about the relevance and validity of statistical approaches in AI, exemplified in part by exchanges between Peter Norvig and Noam Chomsky. Integrating the approaches. The simplest intelligent agents are programs that solve specific problems. More complicated agents include human beings and organizations of human beings (such as firms). The paradigm gives researchers license to study isolated problems and find solutions that are both verifiable and useful, without agreeing on one single approach. An agent that solves a specific problem can use any approach that works . The paradigm also gives researchers a common language to communicate with other fields. The intelligent agent paradigm became widely accepted during the 1. A hierarchical control system provides a bridge between sub- symbolic AI at its lowest, reactive levels and traditional symbolic AI at its highest levels, where relaxed time constraints permit planning and world modelling. A few of the most general of these methods are discussed below. Search and optimization. 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.
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