Machine learning is about making specific decisions, while AI encompasses broader, more interactive applications, especially with generative AI.

Machine learning emphasizes the task of making machines learn from little initial knowledge to improve over time, with deep learning techniques utilizing neural networks being particularly effective in recent years.

A small amount of human data can enable AI systems to generate their own goals, allowing for significant learning from experience.

Artificial intelligence is our longing to create intelligent systems, and at a narrower level, it's a set of computational tools to automate tasks and understand our own minds.

We are exploring automating the solution of machine learning problems through a concept called 'learned to learn', aiming to reduce reliance on human machine learning experts.

Deep learning is a paradigm within machine learning that emphasizes multiple steps of computation rather than a single step, which is essential for achieving true intelligence.

Generative AI is just another step in the journey of using machine learning technologies in finance.

As AI systems become more intelligent, they will take on more complex tasks, relying on pattern recognition and generation, similar to human intelligence.

AI models are not programmed; they are trained, allowing them to develop their own strategies for problem-solving.