Artificial Mathematical IntelligenceArtificial Mathematical Intelligence



This volume discusses the theoretical foundations of a new inter- and intra-disciplinary meta-research discipline, which can be succinctly called cognitive metamathematics, with the ultimate goal of achieving a global instance of concrete ...

Author: Danny A.J. Gómez Ramírez

Publisher: Springer

ISBN: 3030502724

Category:

Page: 259

View: 177

This volume discusses the theoretical foundations of a new inter- and intra-disciplinary meta-research discipline, which can be succinctly called cognitive metamathematics, with the ultimate goal of achieving a global instance of concrete Artificial Mathematical Intelligence (AMI). In other words, AMI looks for the construction of an (ideal) global artificial agent being able to (co-)solve interactively formal problems with a conceptual mathematical description in a human-style way. It first gives formal guidelines from the philosophical, logical, meta-mathematical, cognitive, and computational points of view supporting the formal existence of such a global AMI framework, examining how much of current mathematics can be completely generated by an interactive computer program and how close we are to constructing a machine that would be able to simulate the way a modern working mathematician handles solvable mathematical conjectures from a conceptual point of view. The thesis that it is possible to meta-model the intellectual job of a working mathematician is heuristically supported by the computational theory of mind, which posits that the mind is in fact a computational system, and by the meta-fact that genuine mathematical proofs are, in principle, algorithmically verifiable, at least theoretically. The introduction to this volume provides then the grounding multifaceted principles of cognitive metamathematics, and, at the same time gives an overview of some of the most outstanding results in this direction, keeping in mind that the main focus is human-style proofs, and not simply formal verification. The first part of the book presents the new cognitive foundations of mathematics’ program dealing with the construction of formal refinements of seminal (meta-)mathematical notions and facts. The second develops positions and formalizations of a global taxonomy of classic and new cognitive abilities, and computational tools allowing for calculation of formal conceptual blends are described. In particular, a new cognitive characterization of the Church-Turing Thesis is presented. In the last part, classic and new results concerning the co-generation of a vast amount of old and new mathematical concepts and the key parts of several standard proofs in Hilbert-style deductive systems are shown as well, filling explicitly a well-known gap in the mechanization of mathematics concerning artificial conceptual generation.

Artificial Intelligence and Symbolic Mathematical ComputingArtificial Intelligence and Symbolic Mathematical Computing



This volume contains the papers, updated in some cases, presented at the first AISMC (Artificial Intelligence and Symbolic Mathematical Computations)conference, held in Karlsruhe, August 3-6, 1992.

Author: Jacques Calmet

Publisher: Springer Science & Business Media

ISBN: 3540573224

Category:

Page: 305

View: 130

This volume contains the papers, updated in some cases, presented at the first AISMC (Artificial Intelligence and Symbolic Mathematical Computations)conference, held in Karlsruhe, August 3-6, 1992. This was the first conference to be devoted to such a topic after a long period when SMC made no appearance in AI conferences, though it used to be welcome in the early days of AI. Some conferences were held recently on mathematics and AI, but none was directly comparable in scope to this conference. Because of the novelty of the domain, authors were given longer allocations of time than usual in which to present their work. As a result, extended and fruitful discussions followed each paper. The introductory chapter in this book, which was not presented during the conference, reflects in many ways the flavor of these discussions and aims to set out the framework for future activities in this domain of research. In addition to the introduction, the volume contains 20 papers.

Mathematical Models for Handling Partial Knowledge in Artificial IntelligenceMathematical Models for Handling Partial Knowledge in Artificial Intelligence



The volume is composed of the invited and contributed papers presented at the Workshop on Mathematical Models for Handling Partial Knowledge in Artificial Intelligence, held at the Ettore Majorana Center for Scientific Culture of Erice ...

Author: Giulianella Coletti

Publisher: Springer Science & Business Media

ISBN: 9781489914248

Category:

Page: 308

View: 311

Knowledge acquisition is one of the most important aspects influencing the quality of methods used in artificial intelligence and the reliability of expert systems. The various issues dealt with in this volume concern many different approaches to the handling of partial knowledge and to the ensuing methods for reasoning and decision making under uncertainty, as applied to problems in artificial intelligence. The volume is composed of the invited and contributed papers presented at the Workshop on Mathematical Models for Handling Partial Knowledge in Artificial Intelligence, held at the Ettore Majorana Center for Scientific Culture of Erice (Sicily, Italy) on June 19-25, 1994, in the framework of the International School of Mathematics "G.Stampacchia". It includes also a transcription of the roundtable held during the workshop to promote discussions on fundamental issues, since in the choice of invited speakers we have tried to maintain a balance between the various schools of knowl edge and uncertainty modeling. Choquet expected utility models are discussed in the paper by Alain Chateauneuf: they allow the separation of perception of uncertainty or risk from the valuation of outcomes, and can be of help in decision mak ing. Petr Hajek shows that reasoning in fuzzy logic may be put on a strict logical (formal) basis, so contributing to our understanding of what fuzzy logic is and what one is doing when applying fuzzy reasoning.

Mathematical Methods in Artificial IntelligenceMathematical Methods in Artificial Intelligence



The text emphasizes the main mathematical tools for representing and manipulating knowledge symbolically. These are various forms of logic for qualitative knowledge, and probability and related concepts for quantitative knowledge.

Author: Edward A. Bender

Publisher: Wiley-IEEE Computer Society Press

ISBN: 0818672005

Category:

Page: 656

View: 995

Mathematical Methods in Artificial Intelligence introduces the student to the important mathematical foundations and tools in AI and describes their applications to the design of AI algorithms. This useful text presents an introductory AI course based on the most important mathematics and its applications. It focuses on important topics that are proven useful in AI and involve the most broadly applicable mathematics. The book explores AI from three different viewpoints: goals, methods or tools, and achievements and failures. Its goals of reasoning, planning, learning, or language understanding and use are centered around the expert system idea. The tools of AI are presented in terms of what can be incorporated in the data structures. The book looks into the concepts and tools of limited structure, mathematical logic, logic-like representation, numerical information, and nonsymbolic structures. The text emphasizes the main mathematical tools for representing and manipulating knowledge symbolically. These are various forms of logic for qualitative knowledge, and probability and related concepts for quantitative knowledge. The main tools for manipulating knowledge nonsymbolically, as neural nets, are optimization methods and statistics. This material is covered in the text by topics such as trees and search, classical mathematical logic, and uncertainty and reasoning. A solutions diskette is available, please call for more information.

Artificial Mathematical IntelligenceArtificial Mathematical Intelligence



Chapter 1 Global Introduction to the Artificial Mathematical Intelligence General Program 1.1 A Quite Revolutionary “Artificial Mathematical” Vision More ...

Author: Danny A. J. Gómez Ramírez

Publisher: Springer Nature

ISBN: 9783030502737

Category:

Page: 259

View: 402

This volume discusses the theoretical foundations of a new inter- and intra-disciplinary meta-research discipline, which can be succinctly called cognitive metamathematics, with the ultimate goal of achieving a global instance of concrete Artificial Mathematical Intelligence (AMI). In other words, AMI looks for the construction of an (ideal) global artificial agent being able to (co-)solve interactively formal problems with a conceptual mathematical description in a human-style way. It first gives formal guidelines from the philosophical, logical, meta-mathematical, cognitive, and computational points of view supporting the formal existence of such a global AMI framework, examining how much of current mathematics can be completely generated by an interactive computer program and how close we are to constructing a machine that would be able to simulate the way a modern working mathematician handles solvable mathematical conjectures from a conceptual point of view. The thesis that it is possible to meta-model the intellectual job of a working mathematician is heuristically supported by the computational theory of mind, which posits that the mind is in fact a computational system, and by the meta-fact that genuine mathematical proofs are, in principle, algorithmically verifiable, at least theoretically. The introduction to this volume provides then the grounding multifaceted principles of cognitive metamathematics, and, at the same time gives an overview of some of the most outstanding results in this direction, keeping in mind that the main focus is human-style proofs, and not simply formal verification. The first part of the book presents the new cognitive foundations of mathematics’ program dealing with the construction of formal refinements of seminal (meta-)mathematical notions and facts. The second develops positions and formalizations of a global taxonomy of classic and new cognitive abilities, and computational tools allowing for calculation of formal conceptual blends are described. In particular, a new cognitive characterization of the Church-Turing Thesis is presented. In the last part, classic and new results concerning the co-generation of a vast amount of old and new mathematical concepts and the key parts of several standard proofs in Hilbert-style deductive systems are shown as well, filling explicitly a well-known gap in the mechanization of mathematics concerning artificial conceptual generation.

Artificial Intelligence EnginesArtificial Intelligence Engines



In this richly illustrated book, key neural network learning algorithms are explained informally first, followed by detailed mathematical analyses.

Author: James V Stone

Publisher:

ISBN: 0956372813

Category:

Page: 216

View: 331

The brain has always had a fundamental advantage over conventional computers: it can learn. However, a new generation of artificial intelligence algorithms, in the form of deep neural networks, is rapidly eliminating that advantage. Deep neural networks rely on adaptive algorithms to master a wide variety of tasks, including cancer diagnosis, object recognition, speech recognition, robotic control, chess, poker, backgammon and Go, at super-human levels of performance. In this richly illustrated book, key neural network learning algorithms are explained informally first, followed by detailed mathematical analyses. Topics include both historically important neural networks (e.g. perceptrons), and modern deep neural networks (e.g. generative adversarial networks). Online computer programs, collated from open source repositories, give hands-on experience of neural networks, and PowerPoint slides provide support for teaching. Written in an informal style, with a comprehensive glossary, tutorial appendices (e.g. Bayes' theorem), and a list of further readings, this is an ideal introduction to the algorithmic engines of modern artificial intelligence.

Pro Deep Learning with TensorFlowPro Deep Learning with TensorFlow



This book will allow you to get up to speed quickly using TensorFlow and to optimize different deep learning architectures. All of the practical aspects of deep learning that are relevant in any industry are emphasized in this book.

Author: Santanu Pattanayak

Publisher: Apress

ISBN: 9781484230961

Category:

Page: 398

View: 543

Deploy deep learning solutions in production with ease using TensorFlow. You'll also develop the mathematical understanding and intuition required to invent new deep learning architectures and solutions on your own. Pro Deep Learning with TensorFlow provides practical, hands-on expertise so you can learn deep learning from scratch and deploy meaningful deep learning solutions. This book will allow you to get up to speed quickly using TensorFlow and to optimize different deep learning architectures. All of the practical aspects of deep learning that are relevant in any industry are emphasized in this book. You will be able to use the prototypes demonstrated to build new deep learning applications. The code presented in the book is available in the form of iPython notebooks and scripts which allow you to try out examples and extend them in interesting ways. You will be equipped with the mathematical foundation and scientific knowledge to pursue research in this field and give back to the community. What You'll Learn Understand full stack deep learning using TensorFlow and gain a solid mathematical foundation for deep learning Deploy complex deep learning solutions in production using TensorFlow Carry out research on deep learning and perform experiments using TensorFlow Who This Book Is For Data scientists and machine learning professionals, software developers, graduate students, and open source enthusiasts

Gene Expression ProgrammingGene Expression Programming



The book includes a self-contained introduction to this new exciting field of computational intelligence. This second edition has been revised and extended with five new chapters.

Author: Candida Ferreira

Publisher: Springer

ISBN: 9783540328490

Category:

Page: 480

View: 834

This book describes the basic ideas of gene expression programming (GEP) and numerous modifications to this powerful new algorithm. It provides all the implementation details of GEP so that anyone with elementary programming skills will be able to implement it themselves. The book includes a self-contained introduction to this new exciting field of computational intelligence. This second edition has been revised and extended with five new chapters.

Artificial Intelligence By ExampleArtificial Intelligence By Example



This book is packed with some of the smartest and easy-peasy examples through which you will learn the fundamentals of AI. You will have acquired the foundation of AI and understood the practical case studies in this book.

Author: Denis Rothman

Publisher: Packt Publishing Ltd

ISBN: 9781788990028

Category:

Page: 490

View: 180

Be an adaptive thinker that leads the way to Artificial Intelligence Key Features AI-based examples to guide you in designing and implementing machine intelligence Develop your own method for future AI solutions Acquire advanced AI, machine learning, and deep learning design skills Book Description Artificial Intelligence has the potential to replicate humans in every field. This book serves as a starting point for you to understand how AI is built, with the help of intriguing examples and case studies. Artificial Intelligence By Example will make you an adaptive thinker and help you apply concepts to real-life scenarios. Using some of the most interesting AI examples, right from a simple chess engine to a cognitive chatbot, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and IoT, and develop emotional quotient in chatbots using neural networks. You will move on to designing AI solutions in a simple manner rather than get confused by complex architectures and techniques. This comprehensive guide will be a starter kit for you to develop AI applications on your own. By the end of this book, will have understood the fundamentals of AI and worked through a number of case studies that will help you develop business vision. What you will learn Use adaptive thinking to solve real-life AI case studies Rise beyond being a modern-day factory code worker Acquire advanced AI, machine learning, and deep learning designing skills Learn about cognitive NLP chatbots, quantum computing, and IoT and blockchain technology Understand future AI solutions and adapt quickly to them Develop out-of-the-box thinking to face any challenge the market presents Who this book is for Artificial Intelligence by Example is a simple, explanatory, and descriptive guide for junior developers, experienced developers, technology consultants, and those interested in AI who want to understand the fundamentals of Artificial Intelligence and implement it practically by devising smart solutions. Prior experience with Python and statistical knowledge is essential to make the most out of this book.

First Symposium on Artificial Intelligence for Mathematics Education Book of Abstracts AI4ME 2020 First Symposium on Artificial Intelligence for Mathematics Education Book of Abstracts AI4ME 2020



This book of abstracts gathers the summaries of the talks presented at the symposium, as well as the conclusions of each of the four thematic groups.

Author: Philippe R. Richard

Publisher: Ed. Universidad de Cantabria

ISBN:

Category:

Page: 58

View: 455

The digital revolution that we have experienced since the last quarter of the twentieth century has had some influence, yet to be analysed and extended, on the way mathematics is made, taught and learned. While the rate of innovation in these technologies is growing exponentially, the potential impact of most information technologies on mathematical education remains to be fully exploited. In particular, several authoritative voices point out that the technology that will most likely transform education in the coming years is artificial intelligence (AI). Interestingly, today AI is mainly associated with technologies to automate tasks and lower costs, thus serving primarily the interests of the political-administrative, industrial and commercial world. In this scenario, the world of education and, more specifically, didactics, appears at best as a mere user of AI techniques developed in other fields, forgetting that AI should play a much more relevant role here, serving the human being who is doing his work as a mathematician or who is learning mathematics. The AI4ME symposium at the International Centre for Mathematical Meetings (CIEM) in Castro Urdiales is a space for research and reflection to better understand the interconnected challenges of instrumental learning of mathematics and instrumental mathematics, taking advantage of the achievements and opportunities of Artificial Intelligence for Mathematical Education. This book of abstracts gathers the summaries of the talks presented at the symposium, as well as the conclusions of each of the four thematic groups.

Computer Science and Artificial IntelligenceComputer Science and Artificial Intelligence



Chapter 1 presents the panel's definitions of these and related terms. Chapter 2 presents the panel's general observations and recommendations regarding AI and HCI.

Author: National Research Council

Publisher: National Academies Press

ISBN: 9780309058315

Category:

Page: 29

View: 911

The focus of this report is on artificial intelligence (AI) and human-computer interface (HCI) technology. Observations, conclusions, and recommendations regarding AI and HCI are presented in terms of six grand challenge areas which serve to identify key scientific and engineering issues and opportunities. Chapter 1 presents the panel's definitions of these and related terms. Chapter 2 presents the panel's general observations and recommendations regarding AI and HCI. Finally, Chapter 3 discusses computer science, AI, and HCI in terms of the six selected "grand challenge" areas and three time horizons, that is, short term (within the next 2 years), midterm (2 to 6 years), and long term (more than 6 years from now) and presents additional recommendations in these areas.

Artificial Intelligence and Symbolic ComputationArtificial Intelligence and Symbolic Computation



This book constitutes the refereed proceedings of the 7th International Conference on Artificial Intelligence and Symbolic Computation, AISC 2004, held in Linz, Austria in September 2004.

Author: Bruno Buchberger

Publisher: Springer Science & Business Media

ISBN: 9783540232124

Category:

Page: 294

View: 984

This book constitutes the refereed proceedings of the 7th International Conference on Artificial Intelligence and Symbolic Computation, AISC 2004, held in Linz, Austria in September 2004. The 17 revised full papers and 4 revised short papers presented together with 4 invited papers were carefully reviewed and selected for inclusion in the book. The papers are devoted to all current aspects in the area of symbolic computing and AI: mathematical foundations, implementations, and applications in industry and academia.

Artificial and Mathematical Theory of ComputationArtificial and Mathematical Theory of Computation



This book can prove valuable to engineers and researchers of electrical, computer, and mechanical engineering, as well as, for computer programmers and designers of industrial processes.

Author: Vladimir Lifschitz

Publisher: Academic Press

ISBN: 9780323148313

Category:

Page: 490

View: 834

Artificial and Mathematical Theory of Computation is a collection of papers that discusses the technical, historical, and philosophical problems related to artificial intelligence and the mathematical theory of computation. Papers cover the logical approach to artificial intelligence; knowledge representation and common sense reasoning; automated deduction; logic programming; nonmonotonic reasoning and circumscription. One paper suggests that the design of parallel programming languages will invariably become more sophisticated as human skill in programming and software developments improves to attain faster running programs. An example of metaprogramming to systems concerns the design and control of operations of factory devices, such as robots and numerically controlled machine tools. Metaprogramming involves two design aspects: that of the activity of a single device and that of the interaction with other devices. One paper cites the application of artificial intelligence pertaining to the project "proof checker for first-order logic" at the Stanford Artificial Intelligence Laboratory. Another paper explains why the bisection algorithm widely used in computer science does not work. This book can prove valuable to engineers and researchers of electrical, computer, and mechanical engineering, as well as, for computer programmers and designers of industrial processes.

Artificial Intelligence in MathematicsArtificial Intelligence in Mathematics



This book offers a revelatory glimpse into the future--when science, social science, and social administration will be based on the complementary interplay between artificial intelligence, mathematics, and statistics.

Author: Senior Research Fellow in Design Jeffrey Johnson

Publisher: Oxford University Press, USA

ISBN: UOM:39015032190533

Category:

Page: 328

View: 176

This book offers a revelatory glimpse into the future--when science, social science, and social administration will be based on the complementary interplay between artificial intelligence, mathematics, and statistics. Comprised of contributions from a broad range of leading scientists and researchers, the book outlines how artificial intelligence supplies insights into the nature of complex problems, mathematics offers a rich language for presenting systems and methods for investigating them rigorously, and statistics provides the interface between theory and data from both observation and experiment. Students and researchers in applied mathematics, artificial intelligence, and statistics interested in the growing integration of computer technologies and modern mathematical breakthroughs will want to read this important new book.

Artificial Intelligence with UncertaintyArtificial Intelligence with Uncertainty



This book develops a framework that shows how uncertainty in Artificial Intelligence (AI) expands and generalizes traditional AI. It explores the uncertainties of knowledge and intelligence.

Author: Deyi Li

Publisher: CRC Press

ISBN: 9781498776271

Category:

Page: 290

View: 623

This book develops a framework that shows how uncertainty in Artificial Intelligence (AI) expands and generalizes traditional AI. It explores the uncertainties of knowledge and intelligence. The authors focus on the importance of natural language – the carrier of knowledge and intelligence, and introduce efficient physical methods for data mining amd control. In this new edition, we have more in-depth description of the models and methods, of which the mathematical properties are proved strictly which make these theories and methods more complete. The authors also highlight their latest research results.

Artificial intelligence When do machines take over Artificial intelligence When do machines take over



This book is a plea for technology design: AI must prove itself as a service in society.

Author: Klaus Mainzer

Publisher: Springer Nature

ISBN: 9783662597170

Category:

Page: 279

View: 708

Everybody knows them. Smartphones that talk to us, wristwatches that record our health data, workflows that organize themselves automatically, cars, airplanes and drones that control themselves, traffic and energy systems with autonomous logistics or robots that explore distant planets are technical examples of a networked world of intelligent systems. Machine learning is dramatically changing our civilization. We rely more and more on efficient algorithms, because otherwise we will not be able to cope with the complexity of our civilizing infrastructure. But how secure are AI algorithms? This challenge is taken up in the 2nd edition: Complex neural networks are fed and trained with huge amounts of data (big data). The number of necessary parameters explodes exponentially. Nobody knows exactly what is going on in these "black boxes". In machine learning we need more explainability and accountability of causes and effects in order to be able to decide ethical and legal questions of responsibility (e.g. in autonomous driving or medicine)! Besides causal learning, we also analyze procedures of tests and verification to get certified AI-programs. Since its inception, AI research has been associated with great visions of the future of mankind. It is already a key technology that will decide the global competition of social systems. "Artificial Intelligence and Responsibility" is another central supplement to the 2nd edition: How should we secure our individual liberty rights in the AI world? This book is a plea for technology design: AI must prove itself as a service in society.

Python Artificial Intelligence Projects for BeginnersPython Artificial Intelligence Projects for Beginners



This book demonstrates AI projects in Python covering modern techniques that make up the world of Artificial Intelligence.

Author: Dr. Joshua Eckroth

Publisher: Packt Publishing Ltd

ISBN: 9781789538243

Category:

Page: 162

View: 588

Build smart applications by implementing real-world artificial intelligence projects Key Features Explore a variety of AI projects with Python Get well-versed with different types of neural networks and popular deep learning algorithms Leverage popular Python deep learning libraries for your AI projects Book Description Artificial Intelligence (AI) is the newest technology that’s being employed among varied businesses, industries, and sectors. Python Artificial Intelligence Projects for Beginners demonstrates AI projects in Python, covering modern techniques that make up the world of Artificial Intelligence. This book begins with helping you to build your first prediction model using the popular Python library, scikit-learn. You will understand how to build a classifier using an effective machine learning technique, random forest, and decision trees. With exciting projects on predicting bird species, analyzing student performance data, song genre identification, and spam detection, you will learn the fundamentals and various algorithms and techniques that foster the development of these smart applications. In the concluding chapters, you will also understand deep learning and neural network mechanisms through these projects with the help of the Keras library. By the end of this book, you will be confident in building your own AI projects with Python and be ready to take on more advanced projects as you progress What you will learn Build a prediction model using decision trees and random forest Use neural networks, decision trees, and random forests for classification Detect YouTube comment spam with a bag-of-words and random forests Identify handwritten mathematical symbols with convolutional neural networks Revise the bird species identifier to use images Learn to detect positive and negative sentiment in user reviews Who this book is for Python Artificial Intelligence Projects for Beginners is for Python developers who want to take their first step into the world of Artificial Intelligence using easy-to-follow projects. Basic working knowledge of Python programming is expected so that you’re able to play around with code