The Race for Quantum Computing: Who will achieve a strategic competitive advantage … China, Japan or the US? (Amazon audio & ebook)

Colleagues, according to the Future Forward: Exploring Tomorrow’s report entitled “China's Quantum Leap: The Internet Revolution of 2024” the PRC has taken a major step forward in the race for quantum supremacy. If this claim is valid the implications for technological transformation are enormous.

For a deeper dive on this matter read “The Race for Quantum Computing” (Audible) (Kindle) that clearly defines the global race for quantum computing among China, Japan and the United States. Moreover, it will identify the impact and consequences of winning this technological race … along with the strategic advantages that await the nation that wins this competition. These advantages include positive innovations in science, technology and finance. In addition, the outcome of this high stakes contest will have an immeasurable impact on military and intelligence gathering along with cybersecurity and encryption. Quantum computing is the next big technological frontier, and the race to build the first working quantum computer is heating up. A quantum computer is a type of computer that uses quantum bits, or qubits, instead of traditional bits, which allows for faster, more complex computations. This ability has the potential to revolutionize industries from finance to pharmaceuticals, and countries around the world are vying for supremacy in this field. In this article, we will explore the importance of winning the race for quantum computing and the implications for the global technological landscape.

The race for quantum computing is not just limited to the private sector, however. Governments around the world are also investing heavily in quantum research and development. In 2018, the US passed the National Quantum Initiative Act  (Daphne Leprince - February 9, 2019), which provides $1.2 billion over five years to support quantum research and development. China is also investing heavily in quantum technology, with the government aiming to build a $10 billion quantum research facility by 2024.

Access the Transformative Innovation book series on Amazon today:

 

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

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

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

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

“Transformative Innovation” Audio and eBook series make wonderful New Year’s gifts!

Transformative Innovation series:

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) 

Much success. Order today, Genesys Digital (Amazon Author Page) https://tinyurl.com/hh7bf4m9 

Artificial Intelligence - Discover the Top 3 Career & Earnings Growth Strategies for 2025

Colleagues, on average an AI Software Engineer earns $161k USD in the US according to Glassdoor. The first strategy is to Get Certified: A high quality cert from a reputable vendor or professional association can boost your income by 10%+. Topping our list is the “Generative AI for Everyone” program taught by AI pioneer Andrew Ng (437k students enrolled). Second is the Artificial Intelligence A-Z: Build 7 AI + LLM & ChatGPT, the most popular AI course on Udemy. Coming in third is Introduction to Artificial Intelligence with Python from HarvardX with some 1.9m enrollees. Second, Get Published: Write a series of posts or articles on Best Practices, Industry Trends and Emerging Technologies, and publish on DevPlan’s GCP portal, Medium, LinkedIn Articles, Reddit or Technology.org. Then reate a “project portfolio” on GitHub or LinkedIn that showcases your hands-on expertise. A high quality portfolio proves your expertise to future employers and gives you a competitive advantage over other job candidates. Then reate a “project portfolio” on GitHub or LinkedIn that showcases your hands-on prowess. A high quality portfolio proves your expertise to future employers and gives you a competitive advantage over other job candidates. And third, Get Connected: Professional networking and referrals are the most effective method for landing your dream job - within your current employer or at a new company (rather than simply applying for jobs online and standing in the Human Resources queue with the masses). Join and participate in 2-3 high profile groups or professional associations. Examples include: Hugging Face, Discord and Reddit’s AI sub-Reddit.

Get started today by enrolling in one or more training-certification programs:

• Generative AI for Everyone (https://imp.i384100.net/9gKrZ4) 

• Artificial Intelligence A-Z: Build 7 AI + LLM & ChatGPT (https://tinyurl.com/yc3t9v7y)

• Introduction to Artificial Intelligence with Python (http://edx.sjv.io/qz4bKq) 

Success awaits you! Lawrence E. Wilson - AI Academy (share & subscribe)

Machine Learning A-Z: AI, Python & R + ChatGPT Prize

Colleagues, in the “Machine Learning A-Z: AI, Python & R + ChatGPT Prize” program you will learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included. Master Machine Learning on Python & R. Have a great intuition of many Machine Learning models. Make accurate predictions and powerful analysis. Make robust Machine Learning models. Create strong added value to your business. Use Machine Learning for personal purposes. Handle specific topics like Reinforcement Learning, NLP and Deep Learning along with advanced techniques like Dimensionality Reduction. Learn which Machine Learning model to choose for each type of problem. Skill-based training modules include: 1) Data Preprocessing, 2) Regression, 3) Classification, 4) Clustering, 5) Association Rule Learning, 6) Reinforcement Learning, 7) Natural Language Processing, 8) Deep Learning, 9) Dimensionality Reduction, and 10) Model Selection & Boosting.

Enroll today (teams & executives are welcome): https://tinyurl.com/bdzbu74u 

Download your free AI-ML-DL - Career Transformation Guide.

For your listening-reading pleasure:

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

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

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

Much career success, Lawrence E. Wilson - AI Academy (share with your team)

Supervised Machine Learning: Regression and Classification

Colleagues, in the “Supervised Machine Learning: Regression and Classification” program you will build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn, and train supervised machine learning models for prediction & binary classification tasks, including linear regression & logistic regression. Develop high-demand skills in Linear Regression, Regularization to Avoid Overfitting, Logistic Regression for Classification, Gradient Descent, and Supervised Learning. The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. Training modules: Week 1: Introduction to Machine Learning - Applications of machine learning, Supervised learning, Supervised learning, Unsupervised learning, Jupyter Notebooks, Linear regression model, Cost function formula and intuition, Visualizing the cost function, Gradient descent, Implementing gradient descent, Learning rate, Gradient descent for linear regression, and Running gradient descent; Week 2: Regression with multiple input variables - learn some methods for improving your model's training and performance, such as vectorization, feature scaling, feature engineering and polynomial regression. At the end of the week, you'll get to practice implementing linear regression in code; and Week 3: Classification - predict categories using the logistic regression model. You'll learn about the problem of overfitting, and how to handle this problem with a method called regularization. You'll get to practice implementing logistic regression with regularization. 

Enroll today (teams & execs welcome): https://imp.i384100.net/555bxN

Download your free AI-ML-DL - Career Transformation Guide.

For your listening-reading pleasure:

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

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

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

Much career success, Lawrence E. Wilson - AI Academy (share with your team)

Machine Learning Specialization

Colleagues, the Machine Learning Specialization taught by Andrew Ng is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. BreakIntoAI with Machine Learning Specialization. Master fundamental AI concepts and develop practical machine learning skills in the beginner-friendly, 3-course program. Gain high demand skills in Logistic Regression, Artificial Neural Network, Linear Regression, Decision Trees and Recommender Systems. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. This 3-course Specialization is an updated version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression. 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. Build and use decision trees and tree ensemble methods, including random forests and boosted trees. Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection. Build recommender systems with a collaborative filtering approach and a content-based deep learning method. Build a deep reinforcement learning model. Skill-based lessons include: 1) Supervised Machine Learning: Regression and Classification, 2) Advanced Learning Algorithms and 3) Unsupervised Learning, Recommenders, Reinforcement Learning.

Enroll today (teams & execs welcome): https://tinyurl.com/yc5c8snp 

Download your free AI-ML-DL - Career Transformation Guide.

For your listening-reading pleasure:

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

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

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

Much career success, Lawrence E. Wilson - AI Academy (share with your team)