The video explains that while learning to use AI tools like Chart GPT can make someone more productive at their job, learning to build AI tools like Chat CPT can ensure having a job in the future. To learn to build AI tools, one needs to master mathematics, statistics, and programming in Python. The video recommends taking courses in these areas, and learning from Professor Andrew Ng's Machine Learning and Deep Learning specializations on Coursera. To understand Chat CPT, the video suggests taking the Natural Language Processing course in the Deep Learning specialization, which covers the Transformer architecture used by Chat CPT.
Sure, here are the key facts extracted from the text:
1. Learning to use AI tools like ChatGPT can increase productivity at work.
2. Learning to build AI tools like ChatGPT can help ensure job security.
3. The field of AI is less likely to be replaced by automation.
4. OpenAI pays its AI engineers almost 1 million dollars.
5. AI is based on interconnected nodes called artificial neurons.
6. Deep learning is a subset of machine learning.
7. To master deep learning and neural networks, one needs to start with mathematics, statistics, and programming.
8. Core math subjects for machine learning include linear algebra, calculus, and probability theory.
9. Learning Python is essential for machine learning.
10. Knowledge of basic programming concepts like if-else statements and functions is sufficient to start with machine learning.
11. Machine learning specialization courses are available on Coursera, with a focus on supervised and unsupervised learning algorithms, neural networks, and more.
12. Convolutional neural networks are used in computer vision, which has applications in autonomous driving and face recognition.
13. Natural language processing, including Transformer architecture, is essential for understanding systems like ChatGPT.
14. The path to AI and machine learning may seem long but is crucial for the Next Generation technology.
15. Data science is closely related to machine learning and offers career opportunities with less emphasis on machine learning expertise.