Introduction to Artificial Intelligence

Artificial intelligence (AI) has been a topic of high interest in this day and age. AI has emerged through the early nineties and continues to grow at an unprecedented rate. The idea of having machines that are able to process certain cognition to come to a decision without the intervention of humans is the ultimate idea that is being pursued. Though the stage in which AI is able to completely outperform humans in its cognitive skills is yet to be achieved, there has been remarkable progress towards that area. This chapter aims to provide a brief introduction about AI and the area covered under the topic. Various algorithms are used in programming AI on machines such as evolutionary algorithms, genetic algorithms, and swarm intelligence. AI encompasses machine learning, which will be further discussed in this chapter. Furthermore, the impact of AI on society and futuristic predictions the chapter reviews

CS 156: Introduction to Artificial Intelligence Course Redesign

Poster summarizing course redesign activities for CS 156: Introduction to Artificial Intelligence.https://scholarworks.sjsu.edu/davinci_itcr2014/1016/thumbnail.jp

An Introduction to Artificial Intelligence

This chapter explores the evolution of artificial intelligence, starting with the first ideas of Alan Turing, going through the promises of its inception, and landing in our current state, when AI invokes a sense of power and awe. Next, the chapter will provide a summary of different technologies related to AI and machine learning, such as deep neural networks, to help the reader distinguish different terminologies. The chapter will end with a discussion of some potential tendencies concerning how AI may be used or evolve in the near future, and some questions about the technology in the long term

Introduction to artificial intelligence, media and regulation

Non peer reviewe

Unifying an Introduction to Artificial Intelligence Course through Machine Learning Laboratory Experiences

This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering

Introduction to ‘Artificial intelligence in failure analysis of transportation infrastructure and materials'

Transportation infrastructures, including roads, bridges, tunnels, stations, airports and subways, play fundamental roles in modern society. Engineering failures of transportation infrastructures may result in significant damage to the public. The traditional methods are to monitor, store and analyse the information during the infrastructure and material design, testing, construction, numerical simulations, evaluation, operation, maintenance and preservation, using mechanistic-based, material-based and statistics-based approaches. In recent decades, artificial intelligence (AI) has drawn the attention of many researchers and has been used as a powerful tool to understand and analyse the engineering failures in transportation infrastructure and materials. AI has the advantages of conveniently characterizing infrastructure materials in multi-scale, extracting failure information from images and cloud points, evaluating performance from the signals of sensors, predicting the long-term performance of infrastructure based on big data and optimizing infrastructure maintenance strategies, etc. In the future, AI techniques will be more effective and promising for data collection, transmission, fusion, mining and analysis, which will help engineers quickly detect, analyse and finally prevent the engineering failures of transportation infrastructure and materials. This theme issue presents the latest developments of AI in failure analysis of transportation infrastructure and materials

Identifying Appropriate Games for the Missouri S& T Introduction to Artificial Intelligence Course & Tournament

The CS 347: Introduction to Artificial Intelligence (Al) class and the following Al versus human tournaments have shown that some testing of a game should be done before it is used as an educational vehicle, such as whether it provides a fair and challenging contest in a tournament. Until now, very little work has been done to study how well a game would perform in a tournament with both human and Al players before holding the tournament itself. This research identifies several possible ways a game can be ill-suited to this class and/or tournament from previous experience and describes and utilizes a general test schema that can be applied to any turn-based two-player game to quantify a game\u27s suitability in each of these respects

An Introduction to Artificial Intelligence and Legal Reasoning: Using xTalk to Model the Alien Tort Claims Act and Torture Victim Protection Act

This paper presents an introduction to artificial intelligence for legal scholars and includes a computer program that determines the existence of jurisdiction, defences, and applicability of the Alien Tort Claims Act and Torture Victims Protection Act. The paper includes a discussion of the limits and implications of computer programming in formal representations of the law. Concluding that formalization of the law reveals implicit weaknesses in reductionist legal theories, this paper emphasizes the limitations in practice of such theories