Somewhat ironically, I asked ChatGPT and got a pretty comprehensive response:

Transitioning back into a technical field like AI development after a decade of working in other areas can be challenging but definitely achievable. Here’s a structured plan to help you leverage your existing CUDA and parallel computing knowledge while updating your skills to meet the demands of AI development:

1. Refresh and Update Your Knowledge
Since your background is in CUDA and parallel computing, start by refreshing these skills:

CUDA Programming: NVIDIA’s official CUDA documentation and tutorials are great starting points. Utilize resources like CUDA Programming Guide and CUDA Toolkit Documentation.

Parallel Computing: Revisit concepts and explore modern parallel computing frameworks like OpenCL and Thrust.

2. Learn AI and Machine Learning Fundamentals
Online Courses: Platforms like Coursera, edX, and Udacity offer excellent AI and ML courses. Key courses include:
Machine Learning by Andrew Ng on Coursera.
Deep Learning Specialization by Andrew Ng on Coursera.
CS231n: Convolutional Neural Networks for Visual Recognition by Stanford (available online for free).

Books:
Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
Pattern Recognition and Machine Learning by Christopher Bishop.
Mathematics for AI: Brush up on linear algebra, calculus, probability, and statistics. Khan Academy and MIT OpenCourseWare can be good resources for this.

3. Gain Practical Experience with AI Frameworks
TensorFlow and PyTorch: These are the two most popular frameworks for AI development.
Follow official tutorials and documentation: TensorFlow, PyTorch.
Engage in projects and Kaggle competitions to apply your knowledge.

4. Dive into GPU-Accelerated Machine Learning
NVIDIA’s Deep Learning SDK: Explore NVIDIA's deep learning libraries such as cuDNN, TensorRT, and RAPIDS.
Implementations: Try converting existing CPU-based ML models to run on GPUs using your CUDA skills.

5. Build Projects and a Portfolio
Personal Projects: Start small by replicating classic ML projects and gradually move to more complex ones. Document your projects on GitHub.
Kaggle Competitions: Participate in Kaggle competitions to solve real-world problems, improve your skills, and build a portfolio.

6. Stay Updated and Network
Follow AI Research: Regularly read research papers from conferences like NeurIPS, CVPR, and ICML. Websites like arXiv and Papers with Code are useful.
Join Communities: Engage with AI communities on platforms like Reddit, Stack Overflow, and specialized forums. Attend local meetups, webinars, and conferences.

7. Consider Advanced Education
Professional Certificates: Consider earning AI/ML certificates from universities or companies (e.g., Google, Microsoft).
Graduate Programs: If time and resources permit, a postgraduate program in AI/ML could provide structured learning and credibility.

8. Apply for Internships or Junior Roles
Internships: Many companies offer internships or junior roles specifically tailored for career switchers. Apply for these to gain hands-on experience.
Open Source Contributions: Contributing to open-source AI projects can also provide practical experience and demonstrate your skills to potential employers.

9. Prepare for Job Applications
Resume and LinkedIn: Update your resume and LinkedIn profile to reflect your new skills and projects.
Mock Interviews: Practice AI/ML interview questions, focusing on coding problems, ML theory, and system design.
Summary

Re-entering the AI development field requires a strategic approach: refreshing your existing knowledge, learning new AI/ML concepts, gaining practical experience, and continuously networking. With dedication and structured learning, you can narrow the gap and position yourself for exciting opportunities in AI development within a year.