“ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Amazon Audible & Kindle book)

Friends, the “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) (Kindle) book is the latest entry to the “Transformative Innovation” series. AI in all of its manifestations represents a bona fide “generational change” of human society as we know it. AI, like many of the historic innovations of the past, has the unsurpassed potential to impact the human race for both good and evil.  All humanity has a vested interest in ensuring the impact of AI is positive. The alternative is unfathomable: A dystopian environment leading to the destruction of mankind in a Noahic antediluvian episode of human culture by the hands of man himself or the divine, omnipotent hand of our sovereign Creator.

This book has a “5 Star” rating on Amazon. Reader comment “If you're curious about how AI works, how it's changing the world, and what might happen next, this book is great. It's a bit like a guide that helps you understand this complex topic. It's a journey that could open your mind to new ideas and change how you see the world.”

Table of Contents:

I - Introduction

II - The Birth of Artificial Intelligence (AI)

III - AI Research and Development

IV - The AI Quantum Multiplier

V - Technological Synergy

VI - The Ethics of Artificial Intelligence

VII - The Global Race for AI Supremacy

VIII - Understanding Artificial General Intelligence (AGI)

IX - Approaches to AGI Development

X - The Era of Artificial Superintelligence (ASI)

XI - Ensuring Safety in AGI and ASI

XII  - AGI and ASI Impact on Society and the Economy

XIII - AI Singularity (“Technological Singularity”)

XIV - Looking Beyond the Singularity

XV - Conclusions

Our journey will examine the birth of AI, the pending transition to Artificial General Intelligence, the era of Artificial Super Intelligence and thoughts on the possibility of AI (or “Technological”) Singularity. Although some of these topics have verifiable, concrete answers, overall, the pendulum rapidly swings from the domain of the known to the domain of the unknown and speculative in the concluding chapters. Our commitment is to amplify the known (factual) elements of Artificial Intelligence and only when necessary delve into the realm of the unknown (subjective) aspects of AI Singularity … and beyond.

Join us for a journey that will transform your thinking and possibly your life!

Order today:

Audible (https://tinyurl.com/3pswj5sx) 

Kindle (https://www.amazon.com/dp/B0CG536WDN)   

3 Book Series: “Transformative Innovation”: 

 

1 - ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity (Audible) (Kindle)

2 - ChatGPT - The Era of Generative Conversational AI Has Begun (Audible) (Kindle) 

3 - The Race for Quantum Computing  (Audible) (Kindle) 

Thank you for dropping a brief review on this book’s Audible or Kindle page.

Regards, Genesys Digital (Amazon Author Page) https://tinyurl.com/hh7bf4m9 

Python Data Structures

Colleagues, in the “Python Data Structures” you will learn to explain the principles of data structures & how they are used, create programs that are able to read and write data from files, store data as key/value pairs using Python dictionaries, and accomplish multi-step tasks like sorting or looping using tuples.Skills you will gain address: Data Analysis, File I/O, File Management, Data Dictionary, Data Structures, Development Environment, Computer Programming Tools, Data Processing, Data Manipulation, Software Installation, Data Store, and Python Programming. Training modules include: 1) Strings - Python installed if you want to actually run the applications on your desktop or laptop. If you choose not to install Python, you can just skip to the third week and get a head start, 2) Files - You can write and test Python programs in the browser using the "Python Code Playground" in this lesson. Please read the "Using Python in this Class" material for details; real programs process much larger amounts of data by reading and writing files on the secondary storage on your computer. In this chapter we start to write our first programs that read, scan, and process real data, 3) Lists - Starting with lists we will store many values in a single variable using an indexing scheme to store, organize, and retrieve different values from within a single variable. We call these multi-valued variables "collections" or "data structures", 4) Dictionaries - The Python dictionary is one of its most powerful data structures. Instead of representing values in a linear list, dictionaries store data as key / value pairs. Using key / value pairs gives us a simple in-memory "database" in a single Python variable, and 5) Tuples - Tuples are our third and final basic Python data structure. Tuples are a simple version of lists. We often use tuples in conjunction with dictionaries to accomplish multi-step tasks like sorting or looping through all of the data in a dictionary.

Enroll today (teams and execs are welcome): https://imp.i384100.net/oq1ydm 

Recommended Reading:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) (Kindle)

3 - “The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age” (Audible) (Kindle)

Much success in your AI career, AI Academy (please subscribe and share with you colleagues)

Deep Learning and Reinforcement Learning

Colleagues, in the “Deep Learning and Reinforcement Learning” training program you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning. Once you have developed a few  Deep Learning models, the course will focus on Reinforcement Learning, a type of Machine Learning that has caught up more attention recently. Although currently Reinforcement Learning has only a few practical applications, it is a promising area of research in AI that might become relevant in the near future. After this course, if you have followed the courses of the IBM Specialization in order, you will have considerable practice and a solid understanding in the main types of Machine Learning which are: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning. Gain high-demand skills involving Machine Learning Methods, Deep Learning, Unsupervised Learning, Artificial Neural Networks, Machine Learning, Convolutional Neural Networks, Artificial Intelligence, Image Analysis, Transfer Learning, Fine-tuning, Reinforcement Learning, Model Optimization, Computer Vision, Generative Model Architectures, and Recurrent Neural Networks (RNNs). You will obtain hands-on experience with Generative Adversarial Networks (GANs), Generative AI, Autoencoders, and Keras (Neural Network Library). Training modules include: Introduction to Neural Networks, 2) Back Propagation and Keras, 3) Neural Network Optimizers, 4) Convolutional Neural Networks, 5) Transfer Learning, 6) Neural Networks (RNNs) and Long-Short Term Memory Networks (LSTM), 7) Autoencoders, and 8) Generative Models and Applications of Deep Learning.

Enroll today (teams and execs are welcome): https://imp.i384100.net/RG3Ed7

Recommended Reading:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) (Kindle)

3 - “The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age” (Audible) (Kindle)

Much success in your AI career, AI Academy (please subscribe and share with you colleagues)

Unsupervised Machine Learning: Unlocking the Potential of Data (MIT)

Colleagues, the “Unsupervised Machine Learning: Unlocking the Potential of Data” from the MIT Sloan School of Management is designed to help business decision makers leverage opportunities created by unsupervised ML. Understanding how to leverage uncurated data, managers and technology leads will be able to plan new data acquisition protocols. IT and tech professionals looking for up-to-date developments in unsupervised learning will benefit from this program material, as will data scientists and analysts looking to strengthen their knowledge of the application of computer vision technologies. Skills-based training modules include: 1) The potential of data, 2) Learning and leveraging representations, 3) Generative models, 4) AI building blocks, 5) Adapting AI tools, and 6) Challenges and the future. 

Enroll today (teams and execs are welcome): https://edx.sjv.io/L0R3xj 

Recommended Reading:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) (Kindle)

3 - “The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age” (Audible) (Kindle)

Much success in your AI career, AI Academy (please subscribe and share with you colleagues)

Build AI Agents with GitHub Copilot (training)

Colleagues, the “Build AI Agents with GitHub Copilot” program is designed to help developers harness the power of GitHub Copilot to build AI agents more efficiently and effectively. By combining foundational knowledge of AI agents with hands-on practice, the course takes a practical, project-based approach--guiding learners from understanding core concepts and setting up their environment to building, testing, and refining real AI agents with Copilot's assistance. Along the way, participants will explore best practices, compare Copilot with other tools, and reflect on responsible AI development, ensuring they leave with both the technical skills and strategic insight to accelerate their productivity with AI-assisted coding. In this Beginner to Intermediate level course you will grasp the fundamentals of AI agents and their components, configure GitHub Copilot for your development environment, write, test, and refine AI agent code with Copilot's assistance, build a complete working AI agent step by step, extend AI agents with external APIs and libraries, use Copilot to generate, refactor, and improve code quality, compare Copilot with other AI coding tools for a broader perspective, and apply ethical and responsible practices in AI-assisted development. Skill-based training modules include: 1) Understand AI Agents and GitHub Copilot - Understand what an AI agent is, Explore key components of code-based AI agents, Recognize the role of GitHub Copilot in AI agent development, Compare GitHub Copilot with other AI coding tools, Identify use cases for AI agents with Copilot, 2) Set Up Your Development Environment - Install GitHub Copilot, Configure Copilot settings and preferences, Use advanced configurations, Set up an AI agent project, Write and run, 3) Build and Extend AI Agents with Copilot - Implement a simple rule-based agent, Make and execute decisions, Control agent behavior with generative AI, Create effective AI agent prompts, Integrate multiple components, and 4) Apply Best Practices and Next Steps - Make AI agent code reusable, Using structured outputs, and Learn best practices for creating safe agents.

Enroll today (teams and execs are welcome): https://tinyurl.com/yc6swmwu

Recommended Reading:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) (Kindle)

3 - “The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age” (Audible) (Kindle)

Much success in your AI career, AI Academy (please subscribe and share with you colleagues)

Advanced Learning Algorithms (training)

Colleagues, in the “Advanced Learning Algorithms” program you will learn to build and train a neural network with TensorFlow to perform multi-class classification. Apply best practices for machine learning development so that your models generalize to data and tasks in the real world. And use decision trees and tree ensemble methods, including random forests and boosted trees. Gain highly marketable skills in Decision Tree Learning, Applied Machine Learning, Machine Learning Algorithms, Logistic Regression, Deep Learning, Model Evaluation, Model Training, Transfer Learning, Model Optimization,  Random Forest Algorithm, Data Ethics, Regression Analysis, Responsible AI, Supervised Learning, and Artificial Neural Networks. You will also acquire hands-on experience with Tensorflow and Classification Algorithms. 

Skill-based training modules address: 1) Neural Networks - learn about neural networks and how to use them for classification tasks. You'll use the TensorFlow framework to build a neural network with just a few lines of code. Then, dive deeper by learning how to code up your own neural network in Python, "from scratch". Optionally, you can learn more about how neural network computations are implemented efficiently using parallel processing (vectorization); 2) Neural Networks - you'll learn how to train your model in TensorFlow, and also learn about other important activation functions (besides the sigmoid function), and where to use each type in a neural network. You'll also learn how to go beyond binary classification to multiclass classification (3 or more categories). Multiclass classification will introduce you to a new activation function and a new loss function. Optionally, you can also learn about the difference between multiclass classification and multi-label classification. You'll learn about the Adam optimizer, and why it's an improvement upon regular gradient descent for neural network training, 3) Advice for Applying Machine Learning -  learn best practices for training and evaluating your learning algorithms to improve performance. This will cover a wide range of useful advice about the machine learning lifecycle, tuning your model, and also improving your training data, and 4) Decision Trees -  you'll learn about a practical and very commonly used learning algorithm the decision tree. You'll also learn about variations of the decision tree, including random forests and boosted trees (XGBoost). 

Enroll today (teams and execs are welcome): https://imp.i384100.net/POKMKj

Recommended Reading:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) (Kindle)

3 - “The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age” (Audible) (Kindle)

Much success in your AI career, AI Academy (please subscribe and share with you colleagues)

Data Structures and Algorithms (training)

Colleagues, in the “Data Structures and Algorithms” program you will sharpen your problem-solving skills in this Nanodegree program. Practice over 100 algorithm and data structure challenges, learn Python-based techniques, and prepare for interviews with mentor guidance and real coding scenarios. Key learning modules include: 1) Data Structures, 2) Arrays and linked lists to trees and hash maps. Strengthen your coding logic, recursion skills, and ability to solve real programming - Recursive algorithmsPython treesPython arraysData structuresHash maps, 3) Basic Algorithms -  basic algorithms used in programming, Tree searchSorting algorithmsDivide and conquer algorithmsTree algorithms, 4) Advanced Algorithms - Graph algorithmsDepth-first searchGraph data structureA, 5) Search Algorithm - Python programming syntax and concepts, Breadth-first searchGreedy algorithmsDynamic programming, 6) Introduction to Python Programming, and 7) Basic Python.

Enroll today (teams and execs are welcome): https://tinyurl.com/3wxmn5k5

Recommended Reading:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) (Kindle)

3 - “The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age” (Audible) (Kindle)

Much success in your AI career, AI Academy (please subscribe and share with you colleagues)

IBM AI Engineering Professional Certificate

Colleagues, in the “IBM AI Engineering Professional Certificate” program you will learn to describe machine learning, deep learning, neural networks, and ML algorithms like classification, regression, clustering, and dimensional reduction. Implement supervised and unsupervised machine learning models using SciPy and ScikitLearn. Deploy machine learning algorithms and pipelines on Apache Spark. Build deep learning models and neural networks using Keras, PyTorch, and TensorFlow. Acquire high-demand skills in Computer Vision, Data Science, Fine-tuning, Generative AI Agents, Generative Model Architectures, Large Language Modeling, LLM Application, Machine Learning, Prompt Patterns, Retrieval-Augmented Generation, Supervised Learning, and Unsupervised Learning. You will also gain hands-on experience with Apache Spark, Generative AI, Keras (Neural Network Library), Prompt Engineering, PySpark, Python Programming, PyTorch (Machine Learning Library), and Vector Databases. Training modules cover: 1) Machine Learning with Python, 2) Introduction to Deep Learning & Neural Networks with Keras, 3) Deep Learning with Keras and Tensorflow, 4) Introduction to Neural Networks and PyTorch, 5) Deep Learning with PyTorch, 6) AI Capstone Project with Deep Learning, 7) Generative AI and LLMs: Architecture and Data Preparation, 8) Gen AI Foundational Models for NLP & Language Understanding, 9) Generative AI Language Modeling with Transformers, 10) Generative AI Engineering and Fine-Tuning Transformers, 11) Generative AI Advanced Fine-Tuning for LLMs, 12) Fundamentals of AI Agents Using RAG and LangChain, and 13) Project: Generative AI Applications with RAG and LangChain.

Enroll today (teams and executives are welcome): https://imp.i384100.net/nXRPy6

Recommended Reading:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) (Kindle)

3 - “The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age” (Audible) (Kindle)

Much success in your AI career, AI Academy (please subscribe and share with you colleagues)