<OL> <LI>Describes seven novel variations of the Turing test. </LI> <LI>Outlines an original definition of consciousness described as implementable specifications. </LI> <LI>Introduces the concept of Mathematics as a "genetically" transmissible cognitive medium. </LI></OL> This book presents a groundbreaking journey into the world of Generative AI technology and offers an in-depth look at the prospect of AI achieving consciousness. The book navigates through various historical and modern perspectives on AI, from ancient myths to the Turing Test to the latest in technological advancements. It covers the theoretical and practical aspects of creating a conscious AI, including the specifications for synthetic consciousness and the integration of AI with human cognition. The book questions whether generative AI can meet the traditional criteria of consciousness and how this might be realized.

Artificial Intelligence, Cognitive Science, Ethics, and Artificial Consciousness

artificial intelligence; cognition; consciousness; data science; machine learning; generative AI

The Quest for Artificial Intelligence

The Creation of a Conscious Machine

Jean E. Tardy is a systems analyst and expert in artificial intelligence. Following a rigorous top-down process, he developed a detailed system architecture to implement synthetic consciousness in autonomous agents.

Jean E. Tardy is a Systems Analyst and expert in Artificial Intelligence. Following a rigorous top-down process, Jean developed a detailed System Architecture to implement Synthetic Consciousness in autonomous agents. "The Creation of a Conscious Machine" completed the first stage of this process. It surveys past attempts at achieving AI, including the Turing Test, and specifies the design choices and implementation strategies that must be followed to successfully build machines that are conscious.

To order printed versions, visit: <A href="https://styluspub.presswarehouse.com/client/MLI">https://styluspub.presswarehouse.com/client/MLI</A>

MLI Generative AI Series

This book invites readers to explore the intricate world where AI, consciousness, and human cognition intersect. This groundbreaking book considers the profound differences between man and machine, challenging existing notions in AI and cognitive science. It argues that the key to understanding intelligence lies not in software, but in the hardware of our brain – a complex biochemical system far removed from current AI architectures. Through a deep examination of time, perception, language, and the nature of thought, the book presents a compelling case for the indispensability of biology and consciousness in cognition. To achieve this, to engineer this, will indeed be a challenge for AI.

Artificial Intelligence, Machine Learning, Cybernetics, Cognitive, Science, Conscience Theory

information theory; biology; philosophy; machine learning; data science; artificial intelligence; cognition; consciousness

Consciousness and Ecological General Intelligence

The Challenge to AI

Stephen Robbins holds a PhD in educational psychology and is a former university professor specializing in perception and cognition. He spent many years in industry as a software application architect and project manager.

This book provides a comprehensive group of topics covering the details of the Transformer architecture, BERT models, and the GPT series, including GPT-3 and GPT-4. Spanning across ten chapters, it begins with foundational concepts such as the attention mechanism, then tokenization techniques, explores the nuances of Transformer and BERT architectures, and culminates in advanced topics related to the latest in the GPT series, including ChatGPT. Key chapters provide insights into the evolution and significance of attention in deep learning, the intricacies of the Transformer architecture, a two-part exploration of the BERT family, and hands-on guidance on working with GPT-3. The concluding chapters present an overview of ChatGPT, GPT-4, and visualization using generative AI. In addition to the primary topics, the book also covers influential AI organizations such as DeepMind, OpenAI, Cohere, Hugging Face, and more. Readers will gain a comprehensive understanding of the current landscape of NLP models, their underlying architectures, and practical applications. Features companion files with numerous code samples and figures from the book.

natural language processing; expert systems; visualization; AI; artificial intelligence; deep learning; machine learning; computer science

Including ChatGPT and Prompt Engineering

Transformer, BERT, and GPT

Oswald Campesato (San Francisco, CA) specializes in Deep Learning, Python, and GPT-4. He is the author/co-author of over thirty-five books including Python 3 Using ChatGPT / GPT-4, NLP for Developers, and Artificial Intelligence, Machine Learning and Deep Learning (all Mercury Learning).

This book is designed to show readers the concepts of Python 3 programming and the art of data visualization. It also explores cutting-edge techniques using ChatGPT/GPT-4 in harmony with Python for generating visuals that tell more compelling data stories. Chapter 1 introduces the essentials of Python, covering a vast array of topics from basic data types, loops, and functions to more advanced constructs like dictionaries, sets, and matrices. In Chapter 2, the focus shifts to NumPy and its powerful array operations, leading into data visualization using prominent libraries such as Matplotlib. Chapter 6 includes Seaborn's rich visualization tools, offering insights into datasets like Iris and Titanic. Further, the book covers other visualization tools and techniques, including SVG graphics, D3 for dynamic visualizations, and more. Chapter 7 covers information about the main features of ChatGPT and GPT-4, as well as some of their competitors. Chapter 8 contains examples of using ChatGPT in order to perform data visualization, such as charts and graphs that are based on datasets (e.g., the Titanic dataset). Companion files with code, datasets, and figures are available for downloading. From foundational Python concepts to the intricacies of data visualization, this book is ideal for Python practitioners, data scientists, and anyone in the field of data analytics looking to enhance their storytelling with data through visuals. It's also perfect for educators seeking material for teaching advanced data visualization techniques.

programming; data visualization; Python; NumPy; Matplotlib; graphics; data science; data analytics

Python 3 and Data Visualization Using ChatGPT /GPT-4

<OL> <LI>Extensive Python 3.x code samples generated using ChatGPT and GPT-4, covering diverse programming tasks and challenges. </LI> <LI>Comprehensive exploration of data visualization techniques using popular Python libraries such as Matplotlib and Seaborn. </LI></OL> This resource is designed to bridge the gap between theoretical understanding and practical application, making it a useful tool for software developers, data scientists, AI researchers, and tech enthusiasts interested in harnessing the power of GPT-4 in Python environments. The book contains an assortment of Python 3.x code samples that were generated by ChatGPT and GPT-4. Chapter 1 provides an overview of ChatGPT and GPT-4, followed by a chapter which contains Python 3.x code samples for solving various programming tasks in Python. Chapter 3 contains code samples for data visualization, and Chapter 4 contains code samples for linear regression. The final chapter covers visualization with Gen AI (Generative AI) and DALL-E. Companion files with source code and figures are available for downloading.

ChatGPT, GPT-4, Python, AI, Data Visualization

programming; software developers; data science; Python; artificial intelligence; AI; data visualization

GPT-4 for Developers

This book is intended primarily for people who want to learn both Python 3 and how to use ChatGPT with Python. Chapter One begins with an introduction to fundamental aspects of Python programming, including various data types, number formatting, Unicode and UTF-8 handling, and text manipulation techniques. Later, the book covers loops, conditional logic, and reserved words in Python. You will also see how to handle user input, manage exceptions, and work with command-line arguments. Next, the text transitions to the realm of Generative AI, discussing its distinction from Conversational AI. Popular platforms and models, including ChatGPT, GPT-4, and their competitors, are presented to give readers an understanding of the current AI landscape. The book also sheds light on the capabilities of ChatGPT, its strengths, weaknesses, and potential applications. In addition, you will learn how to generate a variety of Python 3 code samples via ChatGPT using the “Code Interpreter” plugin. Code samples and figures from the book are available for downloading. In essence, the book provides a modest bridge between the worlds of Python programming and AI, aiming to equip readers with the knowledge and skills to navigate both domains confidently.

programming; AI; data science; data analysis; Python

Python 3 using ChatGPT/GPT-4

Oswald Campesato (San Francisco, CA) specializes in Deep Learning, Python, and GPT-4. He is the author/co-author of over thirty-five books including Python 3 Data Visualization Using ChatGPT / GPT-4, NLP for Developers, and Artificial Intelligence, Machine Learning and Deep Learning (all Mercury Learning).

<UL> <LI>Encourages readers to compare and contrast hand-written code with ChatGPT-generated code. This approach fosters discussions on code efficiency, readability, and maintainability, enhancing understanding of programming paradigms and techniques. </LI></UL> This book is designed for those new to Java and interested in understanding how ChatGPT/GPT-4 can enhance programming. It offers a unique approach to learning Java, combining traditional hand-written code with cutting-edge ChatGPT-generated examples. The book covers the basics of Java programming and development environments, including understanding recursion, strings, arrays, fundamental data structures, algorithm analysis, queues and stacks, and follows with the role of ChatGPT in generating, explaining, and debugging code. Companion files with source code and figures available for downloading. It’s an essential resource for those starting Java programming and for anyone curious about the applications of ChatGPT in coding.

Java Basics, ChatGPT, Strings, Arrays, Queues, Stacks

programming; neural networks; AI; artificial intelligence; Java; data science; machine learning

JAVA Basics Using ChatGPT/GPT-4

Oswald Campesato (San Francisco, CA) specializes in Deep Learning, Python, and GPT-4. He is the author/co-author of over forty books including Python 3 Using ChatGPT / GPT-4, NLP for Developers, and Artificial Intelligence, Machine Learning and Deep Learning (all Mercury Learning).

This book provides a bridge between the worlds of Python 3 programming and Generative AI, aiming to equip readers with the skills to navigate both domains with confidence. It begins with an introduction to fundamental aspects of Python programming, which include various data types, number formatting, Unicode and UTF-8 handling, and text manipulation techniques. In addition, you will learn about loops, functions, data structures, NumPy, Pandas, conditional logic, and reserved words in Python. Further chapters show how to handle user input, manage exceptions, and work with command-line arguments. The text then transitions to the realm of Generative AI, discussing its distinction from Conversational AI. Popular platforms and models, including Bard (now called “Gemini”) and its competitors, are presented to give readers an understanding of the current AI landscape. The book discusses the capabilities of Bard, its strengths, weaknesses, and potential applications. Finally, you will learn how to generate a variety of Python 3 code samples via Bard.

Python; Artificial Intelligence; Google; Gemini

Coding with Bard

Google Gemini for Python

Oswald Campesato (San Francisco, CA) specializes in Deep Learning, Python, and GPT-4. He is the author/co-author of over forty books including Python 3 Data Visualization Using ChatGPT / GPT-4, NLP for Developers, and Artificial Intelligence, Machine Learning and Deep Learning (all Mercury Learning).

This book is ideal for individual investors, financial analysts, portfolio managers, and anyone interested in gaining a deeper understanding of stock market investing. It demystifies the complexities of the stock market, offering insightful strategies and practical advice for investors of all levels. Key topics include laying the groundwork of investing through research, numerous buying strategies, techniques for selling to maximize returns and minimize losses, and using AI integration to master the nuances of risk management. The book covers a wide array of topics to equip you with the knowledge and tools necessary for successful stock investing. FEATURES: <ul> <li> Explores the impact of artificial intelligence on stock market investing and how to leverage it for better decision-making </li> <li> Covers foundational concepts to advanced topics to provide a thorough understanding of stock market dynamics </li> <li> Discusses numerous buying and selling techniques for maximizing returns and minimizing losses </li> </ul>

stock market; stocks; investing; risk management; AI; portfolio; financial analysis; economics

A Practical Guide to Successful Buying, Selling, and AI Risk Management

Navigating the Stock Market

Arshad Khan is an adjunct instructor at the University of California, San Diego and previously co-authored the best-selling title　Stock Investing for Everyone: Tools For Investing Like The Pros. He has also authored titles on business intelligence, data warehousing, and SAP.

This book offers a comprehensive guide to leveraging Python-based data visualization techniques with the innovative capabilities of Google Gemini. Tailored for individuals proficient in Python seeking to enhance their visualization skills, it explores essential libraries like Pandas, Matplotlib, and Seaborn, along with insights into the innovative Gemini platform. With a focus on practicality and efficiency, it delivers a rapid yet thorough exploration of data visualization methodologies, supported by Gemini-generated code samples. Companion files with source code and figures are available for downloading. FEATURES: <ul> <li> Covers Python-based data visualization libraries and techniques </li> <li> Includes practical examples and Gemini-generated code samples for efficient learning </li> <li> Integrates Google Gemini for advanced data visualization capabilities </li> <li> Sets up a conducive development environment for a seamless coding experience </li> <li> Includes companion files for downloading with source code and figures </li> </ul>

Python; Bard; Gemini; Google; Pandas; Matplotlib; Seaborn; data visualization; machine learning; data science; computer science

Python 3 Data Visualization Using Google Gemini

Oswald Campesato specializes in Deep Learning, Python, and GPT-4. He is the author/co-author of over forty books including Python 3 Data Visualization Using ChatGPT / GPT-4, NLP for Developers, and Artificial Intelligence, Machine Learning and Deep Learning (all Mercury Learning).

To order printed versions, visit: <A href="<a>https://styluspub.presswarehouse.com/client/MLI</a>https://styluspub.presswarehouse.com/client/MLI">https://styluspub.presswarehouse.com/client/MLI</A>

This book is designed to bridge the gap between theoretical knowledge and practical application in the fields of Python programming, machine learning, and the innovative use of ChatGPT-4 in data science. The book is structured to facilitate a deep understanding of several core topics. It begins with a detailed introduction to Pandas, a cornerstone Python library for data manipulation and analysis. Next, it explores a variety of machine learning classifiers from kNN to SVMs. In later chapters, it discusses the capabilities of GPT-4, and how its application enhances traditional linear regression analysis. Finally, the book covers the innovative use of ChatGPT in data visualization. This segment focuses on how AI can transform data into compelling visual stories, making complex results accessible and understandable. It includes material on AI apps, GANs, and DALL-E. Companion files are available for downloading with code and figures from the text.

Python; Machine Learning; ChatGPT-4; Pandas; Classifiers; Linear Regression; Data Visualization; AI; Data Science; Programming

Python 3 and Machine Learning Using ChatGPT/GPT-4

Oswald Campesato (San Francisco, CA) specializes in Deep Learning, Python, Data Science, and generative AI. He is the author/co-author of over forty books including Google Gemini for Python, Data Cleaning, and GPT-4 for Developers (all Mercury Learning).

This book provides a structured framework for exploring various aspects of prompt engineering for ChatGPT, from foundational principles to advanced techniques, real-world applications, and ethical considerations. It aims to guide readers in effectively harnessing the capabilities of ChatGPT through well-crafted prompts to achieve their goals. The digital age has ushered in a new era of communication, one where the boundaries between human and machine are becoming increasingly blurred. Artificial Intelligence (AI) technology, in its relentless evolution, has given rise to remarkable language models that can understand and generate human-like text. "Prompt Engineering for ChatGPT," demystifies the intricacies of this ground breaking technology, offering insights and strategies to harness its capabilities.

Prompt Engineering; ChatGPT; GPT-4; Natural Language Processing; AI Applications; Contextual Prompts; Advanced AI; AI Plugins; Effective Interactions; Ethical AI

Crafting Effective Interactions  and Building GPT Apps

Prompt Engineering Using ChatGPT

Mehrzad Tabatabaian (PhD, PEng) is currently Chair of the BCIT School of Energy Research Committee. He has published several papers for scientific journals and has written textbooks on multiphysics modelling and thermodynamics. He holds several registered patents in the energy field and teaches courses in renewable energy.

<UL> <LI>Comprehensive guide to prompt engineering for ChatGPT, covering foundational principles and advanced techniques. </LI> <LI>Real-world examples and case studies showcasing the impact of well-crafted prompts </LI> </UL> <UL> </UL>

The series is crafted to provide developers, programmers, instructors, and AI enthusiasts with critical insights and tools needed to stay abreast of the rapid advancements in artificial intelligence. It covers a broad spectrum of topics including emerging AI models like ChatGPT and Google Gemini, ethical considerations in AI development, Python programming enhancements, effective marketing strategies in the AI context, and innovative data visualization techniques.

The series is crafted to provide developers, programmers, instructors, and AI enthusiasts with critical insights and tools needed to stay abreast of the rapid...

MLI Generative AI Series

Mercury Learning and Information

Prompt Engineering Using ChatGPT

Crafting Effective Interactions and Building GPT Apps

Python 3 and Machine Learning Using ChatGPT/GPT-4

Python 3 Data Visualization Using Google Gemini

Navigating the Stock Market

A Practical Guide to Successful Buying, Selling, and AI Risk Management

Google Gemini for Python

Coding with Bard

The Challenge to AI

Consciousness and Ecological General Intelligence

The Creation of a Conscious Machine

The Quest for Artificial Intelligence

Transformer, BERT, and GPT

Including ChatGPT and Prompt Engineering

Prompt Engineering Using ChatGPT

Crafting Effective Interactions and Building GPT Apps

Python 3 and Machine Learning Using ChatGPT/GPT-4

Python 3 Data Visualization Using Google Gemini

Navigating the Stock Market

A Practical Guide to Successful Buying, Selling, and AI Risk Management

Google Gemini for Python

Coding with Bard

The Challenge to AI

Consciousness and Ecological General Intelligence

The Creation of a Conscious Machine

The Quest for Artificial Intelligence

Transformer, BERT, and GPT

Including ChatGPT and Prompt Engineering