artificial intelligence problems and solutions pdf

Artificial Intelligence Problems And Solutions Pdf

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In the field of artificial intelligence , the most difficult problems are informally known as AI-complete or AI-hard , implying that the difficulty of these computational problems, assuming intelligence is computational, is equivalent to that of solving the central artificial intelligence problem—making computers as intelligent as people, or strong AI.

Problem Solving Techniques in Artificial Intelligence (AI)

Artificial intelligence AI is intelligence demonstrated by machines , unlike the natural intelligence displayed by humans and animals , which involves consciousness and emotionality. The distinction between the former and the latter categories is often revealed by the acronym chosen.

Leading AI textbooks define the field as the study of " intelligent agents ": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.

As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect. Artificial intelligence was founded as an academic discipline in , and in the years since has experienced several waves of optimism, [13] [14] followed by disappointment and the loss of funding known as an " AI winter " , [15] [16] followed by new approaches, success and renewed funding.

The traditional problems or goals of AI research include reasoning , knowledge representation , planning , learning , natural language processing , perception and the ability to move and manipulate objects. Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability and economics.

The AI field draws upon computer science , information engineering , mathematics , psychology , linguistics , philosophy , and many other fields. The field was founded on the assumption that human intelligence "can be so precisely described that a machine can be made to simulate it". These issues have been explored by myth , fiction and philosophy since antiquity. In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power , large amounts of data , and theoretical understanding; and AI techniques have become an essential part of the technology industry , helping to solve many challenging problems in computer science, software engineering and operations research.

The study of mechanical or "formal" reasoning began with philosophers and mathematicians in antiquity. The study of mathematical logic led directly to Alan Turing 's theory of computation , which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction. This insight, that digital computers can simulate any process of formal reasoning, is known as the Church—Turing thesis.

Turing proposed changing the question from whether a machine was intelligent, to "whether or not it is possible for machinery to show intelligent behaviour". The field of AI research was born at a workshop at Dartmouth College in , [42] where the term "Artificial Intelligence" was coined by John McCarthy to distinguish the field from cybernetics and escape the influence of the cyberneticist Norbert Wiener.

Marvin Minsky agreed, writing, "within a generation They failed to recognize the difficulty of some of the remaining tasks. Progress slowed and in , in response to the criticism of Sir James Lighthill [51] and ongoing pressure from the US Congress to fund more productive projects, both the U. The next few years would later be called an " AI winter ", [15] a period when obtaining funding for AI projects was difficult.

In the early s, AI research was revived by the commercial success of expert systems , [52] a form of AI program that simulated the knowledge and analytical skills of human experts. By , 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. Mead and Mohammed Ismail. In the late s and early 21st century, AI began to be used for logistics, data mining , medical diagnosis and other areas.

In , in a Jeopardy! AlphaGo was later improved, generalized to other games like chess, with AlphaZero ; [66] and MuZero [67] to play many different video games , that were previously handled separately, [68] in addition to board games.

Other programs handle imperfect-information games; such as for poker at a superhuman level, Pluribus poker bot [69] and Cepheus poker bot. According to Bloomberg's Jack Clark, was a landmark year for artificial intelligence, with the number of software projects that use AI within Google increased from a "sporadic usage" in to more than 2, projects.

Clark also presents factual data indicating the improvements of AI since supported by lower error rates in image processing tasks. By , Natural Language Processing systems such as the enormous GPT-3 then by far the largest artificial neural network were matching human performance on pre-existing benchmarks, albeit without the system attaining commonsense understanding of the contents of the benchmarks.

Computer science defines AI research as the study of " intelligent agents ": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. A typical AI analyzes its environment and takes actions that maximize its chance of success. Goals can be explicitly defined or induced. If the AI is programmed for " reinforcement learning ", goals can be implicitly induced by rewarding some types of behavior or punishing others.

AI often revolves around the use of algorithms. An algorithm is a set of unambiguous instructions that a mechanical computer can execute. A simple example of an algorithm is the following optimal for first player recipe for play at tic-tac-toe : [81].

Many AI algorithms are capable of learning from data; they can enhance themselves by learning new heuristics strategies, or "rules of thumb", that have worked well in the past , or can themselves write other algorithms.

Some of the "learners" described below, including Bayesian networks, decision trees, and nearest-neighbor, could theoretically, given infinite data, time, and memory learn to approximate any function , including which combination of mathematical functions would best describe the world.

In practice, it is seldom possible to consider every possibility, because of the phenomenon of " combinatorial explosion ", where the time needed to solve a problem grows exponentially. Much of AI research involves figuring out how to identify and avoid considering a broad range of possibilities unlikely to be beneficial. The earliest and easiest to understand approach to AI was symbolism such as formal logic : "If an otherwise healthy adult has a fever, then they may have influenza ".

A second, more general, approach is Bayesian inference : "If the current patient has a fever, adjust the probability they have influenza in such-and-such way". A fourth approach is harder to intuitively understand, but is inspired by how the brain's machinery works: the artificial neural network approach uses artificial " neurons " that can learn by comparing itself to the desired output and altering the strengths of the connections between its internal neurons to "reinforce" connections that seemed to be useful.

These four main approaches can overlap with each other and with evolutionary systems; for example, neural nets can learn to make inferences, to generalize, and to make analogies. Some systems implicitly or explicitly use multiple of these approaches, alongside many other AI and non-AI algorithms; the best approach is often different depending on the problem. Learning algorithms work on the basis that strategies, algorithms, and inferences that worked well in the past are likely to continue working well in the future.

These inferences can be obvious, such as "since the sun rose every morning for the last 10, days, it will probably rise tomorrow morning as well".

Learners also work on the basis of " Occam's razor ": The simplest theory that explains the data is the likeliest. Therefore, according to Occam's razor principle, a learner must be designed such that it prefers simpler theories to complex theories, except in cases where the complex theory is proven substantially better.

Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as overfitting. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data, but penalizing the theory in accordance with how complex the theory is.

A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses.

Modifying these patterns on a legitimate image can result in "adversarial" images that the system misclassifies. This enables even young children to easily make inferences like "If I roll this pen off a table, it will fall on the floor".

Humans also have a powerful mechanism of " folk psychology " that helps them to interpret natural-language sentences such as "The city councilmen refused the demonstrators a permit because they advocated violence" A generic AI has difficulty discerning whether the ones alleged to be advocating violence are the councilmen or the demonstrators [91] [92] [93]. This lack of "common knowledge" means that AI often makes different mistakes than humans make, in ways that can seem incomprehensible.

For example, existing self-driving cars cannot reason about the location nor the intentions of pedestrians in the exact way that humans do, and instead must use non-human modes of reasoning to avoid accidents. The cognitive capabilities of current architectures are very limited, using only a simplified version of what intelligence is really capable of.

For instance, the human mind has come up with ways to reason beyond measure and logical explanations to different occurrences in life. What would have been otherwise straightforward, an equivalently difficult problem may be challenging to solve computationally as opposed to using the human mind.

This gives rise to two classes of models: structuralist and functionalist. The structural models aim to loosely mimic the basic intelligence operations of the mind such as reasoning and logic. The functional model refers to the correlating data to its computed counterpart. The overall research goal of artificial intelligence is to create technology that allows computers and machines to function in an intelligent manner. 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. Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.

These algorithms proved to be insufficient for solving large reasoning problems because they experienced a "combinatorial explosion": they became exponentially slower as the problems grew larger. They solve most of their problems using fast, intuitive judgments. Knowledge representation [] and knowledge engineering [] are central to classical AI research.

Some "expert systems" attempt to gather explicit knowledge possessed by experts in some narrow domain. In addition, some projects attempt to gather the "commonsense knowledge" known to the average person into a database containing extensive knowledge about the world.

Among the things a comprehensive commonsense knowledge base would contain 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 representation of "what exists" is an ontology : the set of objects, relations, concepts, and properties formally described so that software agents can interpret them. The semantics of these are captured as description logic concepts, roles, and individuals, and typically implemented as classes, properties, and individuals in the Web Ontology Language. Such formal knowledge representations can be used in content-based indexing and retrieval, [] scene interpretation, [] clinical decision support, [] knowledge discovery mining "interesting" and actionable inferences from large databases , [] and other areas.

Intelligent agents must be able to set goals and achieve them. In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions.

This calls for an agent that can not only assess its environment and make predictions but also evaluate its predictions and adapt based on its assessment. Multi-agent planning uses the cooperation and competition of many agents to achieve a given goal.

Emergent behavior such as this is used by evolutionary algorithms and swarm intelligence. Machine learning ML , a fundamental concept of AI research since the field's inception, [d] is the study of computer algorithms that improve automatically through experience.

Unsupervised learning is the ability to find patterns in a stream of input, without requiring a human to label the inputs first. Supervised learning includes both classification and numerical regression , which requires a human to label the input data first. Classification is used to determine what category something belongs in, and occurs after a program sees 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. Computational learning theory can assess learners by computational complexity , by sample complexity how much data is required , or by other notions of optimization. The agent uses this sequence of rewards and punishments to form a strategy for operating in its problem space. Natural language processing [] NLP allows machines to read and understand human language.

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 [] and machine translation. Modern statistical NLP approaches can combine all these strategies as well as others, and often achieve acceptable accuracy at the page or paragraph level.

Machine perception [] is the ability to use input from sensors such as cameras visible spectrum or infrared , microphones, wireless signals, and active lidar , sonar, radar, and tactile sensors to deduce aspects of the world. Applications include speech recognition , [] facial recognition , and object recognition.

Such input is usually ambiguous; a giant, fifty-meter-tall pedestrian far away may produce the same pixels as a nearby normal-sized pedestrian, requiring the AI to judge the relative likelihood and reasonableness of different interpretations, for example by using its "object model" to assess that fifty-meter pedestrians do not exist.

Machine Learning

Artificial Intelligence AI is the toast of every technology driven company. Integration of AI gives a business a massive amount of transformation opportunities to leverage the value chain. Adopting and integrating AI technologies is a roller-coaster ride no matter how business-friendly it may sound. As an AI technology consumer and developer, we must know about both the merits and the challenges associated with the adoption of AI. AI technologies must be accepted as a friend not as a foe. To integrate, deploy and implement AI applications in the enterprise, the organization must have the knowledge of the current AI advancement and technologies as well as its shortcomings. The lack of technical know-how is hindering the adoption of this niche domain in most of the organization.

Have you ever heard about Neuralink? It is a budding start-up company co-founded by Elon Musk that is working on some serious Artificial Intelligence integration with the human body. They have developed a chip which is an array of 96 small, polymer threads, each containing 32 electrodes and can be transplanted into the brain. This is happening in the real world and using this device, and you can connect your brain with everyday electronic devices without even touching them! Time for some serious questions: Is it really necessary? Will it be that useful?

The reflex agents are known as the simplest agents because they directly map states into actions. Unfortunately, these agents fail to operate in an environment where the mapping is too large to store and learn. Goal-based agent, on the other hand, considers future actions and the desired outcomes. Here, we will discuss one type of goal-based agent known as a problem-solving agent , which uses atomic representation with no internal states visible to the problem-solving algorithms. According to computer science, a problem-solving is a part of artificial intelligence which encompasses a number of techniques such as algorithms, heuristics to solve a problem.


Using this technique of problem decomposition, we can solve very large problems very easily. This can be considered as an intelligent behaviour. Can Solution.


Problem description and hypotheses testing in Artificial Intelligence

Available Formats. Answer: Artificial Intelligence is a branch of computer science concerned with the study and creation of systems that exhibit some form of human intelligence. Prior to the beginning of the unit, teachers should make their own robot to show the students. The program uses Machine Learning algorithm for automatized spam and low-quality.

There are two central problems concerning the methodology and foundations of Artificial Intelligence AI. One is to find a technique for defining problems in AI. The other is to find a technique for testing hypotheses in AI. There are, as of now, no solutions to these two problems.

Russell and others Artificial Intelligence AI technologies refers to any device that perceives its. Russell and Peter Norvig Some methods and examples of vector addition were given in Chapter 3.

As more companies adopt Industry 4. Semiconductor manufacturers have become more automated, and the number of process sensors and tests collecting data has increased. However, it is estimated that more than half of the data collected is never processed.

Всю ответственность я беру на. Быстрее. Хейл выслушал все это, не сдвинувшись с места и не веря своим ушам. Хватка на горле Сьюзан слегка ослабла. Стратмор выключил телефон и сунул его за пояс.

 Что ты говоришь? - засмеялся Стратмор.  - Что же ты предлагаешь. Открыть дверь и вызвать сотрудников отдела систем безопасности, я угадал. - Совершенно. Будет очень глупо, если вы этого не сделаете.

Тело его обгорело и почернело. Упав, он устроил замыкание основного электропитания шифровалки. Но еще более страшной ей показалась другая фигура, прятавшаяся в тени, где-то в середине длинной лестницы.

Скажи. Сьюзан словно отключилась от Хейла и всего окружающего ее хаоса. Энсей Танкадо - это Северная Дакота… Сьюзан попыталась расставить все фрагменты имеющейся у нее информации по своим местам.

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