QLISP

Interactive programming environments , Interactive programming environments , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

This paper explores both the design and classroom usage of a visual and interactive programming environment. This environment, named JavaTown, provides a visual representation of the inner workings of object-oriented programs, in which on-screen characters are literally depicted as carrying messages to each other and remembering values. JavaTown was created to aid in the introduction of some of the most elusive concepts in the CS1/CS2 curriculum: object references, parameter passing, variable scope, recursion, and linked lists.

Artificial Intelligence (AI) has received significant attention in recent years. It is being adopted to provide solutions to medicine, engineering, education, government and several other domains. To analyze the state-of-theart of research in AI, we present a systematic literature review focusing on the Evolution of AI programming languages. Our search returned 7,604 documents; after reviewing these documents, 78 which were relevant for this study were retained. Our research revealed that the prevalence of AI programming language by volume of publications had experienced peaks and valleys between 1963 and 2018; however, between 2015 to 2020, related publications have been experiencing peaks. During the review period, the PROLOG programming language received the most attention in about 49% of publications; this was followed by LISP, which received almost 22%. The remaining attention was shared between Logic and Object-Oriented Programming (LOOP), ARCHLOG, Epistemic Ontology Language with Constraints (EOLC), Python, C++, ADA and JAVA. However, the predominant AI programming language in recent AI software is C/C++, which takes 70% of the modern AI libraries analyzed in this study. Python is used in 60% of the modern AI libraries analyzed. Their prevalence is as a result of their speed, portability and ease of coding, making them effective in developing trending AI libraries such as TensorFlow and Keras.

In the dynamic field of programming education, integrating artificial intelligence (AI) tools has started to play a significant role in enhancing learning experiences. This paper presents a case study conducted during a foundational programming course for first-year students in higher education, where students were encouraged to utilize generative artificial intelligence programming copilot extensions in their programming IDE and browser-based generative AI tools as supportive AI tools. The primary objective was to observe the impact of AI on the learning curve and the overall educational experience.Key findings suggest that the introduction of AI tools significantly altered the learning experience for students. Many who initially struggled with grasping elementary programming concepts found that AI support made understanding basic programming concepts much easier, enhancing their confidence and skills. This was particularly evident in the reduced levels of anxiety typically associated with early programming learning, as the AI copilot provided a non-judgmental, always-available source for clarifying doubts, including queries that students might hesitate to ask in a traditional classroom setting.Notably, some students leveraged the AI to generate similar exercise problems, reinforcing their understanding and skills. The AI's capability to address basic queries also freed up the instructor's time, allowing for more personalized student guidance in more advanced problems. This shift in the instructional dynamic further contributed to a learning environment where students felt more comfortable engaging with complex topics, thereby reducing the psychological barriers often linked with early-stage programming education.The course's structure, enriched by AI, enabled students to delve into more complex programming constructs earlier than traditional curricula would allow. For instance, students were tasked with simulating basic e-commerce operations, such as user registration, product browsing, and cart functionalities. These practical challenges naturally introduced advanced concepts like external data storage, unit testing, and user interface design, which are typically reserved for more advanced courses. With the help of generative AI programming copilot tools, students at any programming skill level were able to develop nearly functional complex structures. Interestingly, even when their projects were not fully functional, students remained motivated. Instead of feeling discouraged by these imperfect outcomes, they showed resilience and a keen interest in understanding and improving their code. This reaction is a significant shift from traditional learning settings, where unfinished or flawed projects often lead to increased anxiety or a drop in motivation.Furthermore, the AI's proactive suggestions inspired students to explore beyond the curriculum. Advanced learners delved into databases, cryptography libraries in Python, and even more advanced user interface design, ensuring that they remained engaged and challenged. This elementary course, enhanced by generative AI tools, also inspired students to learn other programming languages since they now learned that individual learning is more available with the aid of generative AI.In conclusion, the integration of AI in programming education offers a promising avenue for enhancing both the learning experience and outcomes. This case study underscores the potential of AI to revolutionize traditional teaching methodologies, fostering a more dynamic, responsive, and inclusive learning environment.This paper handles the results, possibilities and challenges of AI empowered education in programming. It also gives practical examples as well as future research perspectives.

We introduce Plotting Agent, a system that enables users to directly manipulate plots using natural language instructions within an interactive programming environment. It maps language to plot updates. We formulate this problem as a slot-based task-oriented dialog problem, which we tackle with a sequence-to-sequence model. Practitioners such as data scientists broadly defined, and researchers interested in Natural Language Interfaces (NLIs) may find our demo useful.

Interactive programming environments

This chapter describes an in-depth analysis of the errors made by novice programmers learning to write programs in a language called SOLO. It then analyzes the underlying causes of these errors, based upon in-depth analyses of session transcripts. The relative importance of different types of error is very hard to assess, particularly across different languages, programmer skill levels, and types of programming environment. The trap philosophy allows the user to enter nearly-correct lines of a program, and then relies on the user-interface to make sense of obvious errors. J. B. H. duBoulay looked in depth at programming errors made by school teachers learning the language LOGO. Naturally, users of interactive computing environments, especially beginners, are destined to make some mistakes. Semantic and conceptual errors can be characterized by the presence of ill-formed plans in a programmer's emerging repertoire of skills.

Studies have been made on computer systems based on the user model reflecting the behavior of the users, aiming at the human interface which is easy to use. Recently, it has been proposed that individual models be constructed for the users, rather than a model where the only one “typical” human model represents the user in general. A case has not been observed, however, where the individual model is utilized fully in the interactive computer system which actually requires such an approach. From such a viewpoint, this paper proposes a generation of suitable messages utilizing the individual user model, by restricting the problem to the generation of the messages from the computer side in the construction of a program in the interactive programming environment. The features of the user model which will be useful in such a problem, are pointed out. The points in this study are that the skill of the user concerning the system is separated into the general understanding level and the knowledge of the individual functions; utilization of the information obtained by the operating system and for the language processor; and object‐oriented user model whose contents are always updated. A human interface was implemented experimentally which indicates the individual user, the error message from Lisp interpreter according to each situation. We have conducted an experiment by using the interface. The usefulness of our method was demonstrated by the result of experiment.

In order to develop courses related to artificial intelligence and mathematics, this study provides plans for the composition and operation of courses related to artificial intelligence and mathematics in college general mathematics by analyzing the mathematics curriculum, artificial intelligence and mathematics-related research, and selecting learning elements and contents. The content needed to deal with the principles of artificial intelligence in mathematics is mainly related to calculus, probability and statistics, and linear algebra. Such mathematical learning content is one of the functions of artificial intelligence. Since it is necessary for analysis and expression, neural networks, clustering, unsupervised learning, and optimization, these contents must be included in artificial intelligence and mathematics-related courses in college general mathematics. In addition, in courses related to artificial intelligence and mathematics, it is difficult for students to learn various topics such as linear algebra and multi-variable calculus, which are essential as basic mathematics for understanding machine learning, all in one semester. Thus, in this study, I propose that we divide the course into two subjects, <Artificial Intelligence Mathematics 1> and <Artificial Intelligence Mathematics 2>, so that college students can select and take the courses according to their abilities and needs. Lastly, while theory-oriented classes are essential for understanding the principles of artificial intelligence in artificial intelligence and mathematics-related courses, applied learning is also crucial. This includes learning the basics of Python, a widely used programming language, for data analysis, data organization, and preprocessing. Additionally, applying these skills in real-life projects involving data analysis, artificial intelligence, and deep learning is necessary for comprehensive education.

Alien is a generator for interactive language-specific programming environments. The system makes a language definition environment available to the language definer and supports him with an interactive generator. Using these tools every syntactic unit of a language can be made available. This can be done by a method of stepwise testing, correction and expansion. Fully defined environments can easily be restricted to subsets of a language.This paper introduces a method making a large software product executable on small UNIX machines as well as a survey of the system's components. These are designed to make the implementation on PCs effective and to develop a powerful generator for programming environments. Therefore the results are of interest to all projects in the programming environment field.ALIEN has been implemented in C running on Siemens PC-X, PC-X10, PC-MX-2 machines and Atari PC. Implementation on Apollo and SUN workstations is still in progress and on IBM PC is planned.The project is supported by the Stiftung Volkswagen-werk under grant 1/61 602.

A large variety of tools for program development are available today. The tools tend to become more and more sophisticated, but are usually isolated utilities that have to be applied one by one. An alternative to the tool-box approach is an integrated programming environment utilizing powerful interaction facilities.The design and a prototype implementation of an integrated programming environment is described and the qualities of such a system are discussed. One important design goal was to obtain an environment which has an execution efficiency comparable to that of a system based on a conventional compiler, and a reasonable interaction speed. For this reason the system is based on incremental translation to native code. Another goal was to obtain incremental execution, i.e. the possibility to modify an executing program. The properties of this mechanism and its consequences are investigated.

Mark Watson Morgan Kaufmann Publishers, San Francisco, 1997, 377 pp. ISBN 1-55860-420-0, $44.95 This book is well written and organized to cover how to apply AI (Artificial Intelligence) techniques for practical Internet and Intranet tools by using the Java programming language. In the book, the author uses neural network and genetic algorithms to develop distributed systems that are applied for the Internet and Intranets, such as client/server model and information retrieval tool and so on. Part 1 begins with a good general introduction to AI. The history and survey of Artificial Intelligence research are given briefly. In addition, symbolic processing versus neural networks approaches are discussed. This author opts to use neural network to develop example Internet/Intranet systems in the book. Part 2 consists of Java programs developed for AI, neural networks, genetic algorithms, natural language processing, and expert systems. Chapter 2 illustrates Graphical User Interface (GUI) class that is derived from Applet and Canvas classes in Java, which is used in most example programs in the book. Chapter 3 introduces a frame data structure that is used as a message in distributed systems in the later chapters. Chapter 4 shows neural network classes developed in Java and its theory and implementation. Chapter 5 explains the genetic algorithms and the Java genetic algorithm classes. Chapter 6 describes how to develop and implement natural language processing classes using Java. Chapter 7 shows how to share data objects in distributed systems. For example, distributed AI agent classes are developed to share data objects, WWW data collection agent classes are shown to retrieve documents in the Internet, and small POP (Post Office Protocol) mail classes are developed to send and receive mails. Chapter 8 illustrates how to develop expert systems by using Java Jess, which is based on OPS5/CLIPS expert systems. Part 3 explains how to generate and implement the projects using the Java classes that are developed in Part 2. Chapter 9 shows the project that demonstrates the use of genetic algorithms in chapter 5 to control enemy ships in a scrolling arcade game. Chapter 10 describes the real-time handwriting recognition system developed by using the neural network classes in chapter 4. Chapter 11 illustrates a greedy algorithm to optimize neural networks that is based on the author’s hand-tuning neural training data. Chapter 12 improves neural network training data by applying genetic algorithms, to discard bad training data and to avoid discarding good training data. Chapter 13 describes the distributed natural language processing system that uses natural language processing classes in chapter 6. The data structures in chapter 2 are used to store historical data in a separate server application on any other computer in the Internet or Intranets. The final chapter provides the design and implementation of a complete agent-based information retrieval tool for the Internet. The retrieved documents are stored in ASCII or HTML format. This book is a very good reference book for anyone interested in developing systems using Java in distributed environments, especially applying AI techniques. Also, the author provides the CD-ROM that includes the source code for Java classes developed in the book. Haklin Kimm, University of Tennessee

Inheritance is a powerful concept employed in computer science, especially in artificial intelligence (AI), object-oriented programming (OOP), and object-oriented databases (OODB). In the field of AI, inheritance has been primarily used as a concise and effective means of representing and reasoning with common-sense knowledge (Thirunarayan, 1995). In programming languages and databases, inheritance has been used for the purpose of sharing data and methods, and for enabling modularity of software (re)use and maintenance (Lakshmanan & Thirunarayan, 1998). In this chapter, we present various design choices for incorporating inheritance into programming languages from an application programmer’s perspective. In contrast with the language of mathematics, which is mature and well-understood, the embodiment of object-oriented concepts and constructs in a concrete programming language is neither fixed nor universally accepted. We exhibit programs with similar syntax in different languages that have very different semantics, and different looking programs that are equivalent. We compare and contrast method inheritance, interaction of type system with method binding, constructs for method redefinition, and their implementation in widely used languages such as C++ (Stroustrup, 1997), Java (Arnold, Gosling, & Holmes, 2005), and C# (Hejlsberg, Wiltamuth, & Golde, 2006), to illustrate subtle issues of interest to programmers. Finally, we discuss multiple inheritance briefly.

The Smalltalk-80 system is an interactive graphical programming and applications environment. In the system the language, graphical display, editors, programming tools, operating system features, and application programs are integrated by treating each in a consistent object-oriented manner. The object-orientation of the Smalltalk-80 language, in which all information structure is in the form of objects and all processing is in the form of message-sending, greatly facilitates this consistency of the system.