AI is technology that enables computers to simulate human intelligence and problem solving capabilities. Recent breakthroughs in quality have led to the rising popularity of AI. Examples: Speech recognition, self-driving cars, language translation, image generation
Machine learning is a subfield of AI. Machine learning refers to AI algorithms that learn from data to recognize patterns.
AI is the broad concept of simulating human intelligence in machines. Machine learning provides the techniques/algorithms to analyze data and achieve AI capabilities. All machine learning is AI, but not all AI systems rely solely on machine learning.
Current AI:
Reactive Machines have no memory or data storage and react only to present inputs. An example is the Deep Blue chess-playing system.
Limited Memory AI uses past data or experiences for decisions. An example of this is self-driving cars.
Theoretical AI:
Theory of Mind AI would understand beliefs, emotions, and human thought processes.
Self-Awareness AI would have consciousness or a human-like self-awareness.
Generative AI refers to deep-learning models that generate text, images, and other content based on the data they were trained on. Examples: Text generation, image creation, and music composition.
Large Language Models (LLMs) are deep learning models that can understand and generate natural language. LLMs are trained on large text datasets from across the internet (books, websites, etc.)
Prompt engineering is the practice of designing inputs for generative AI tools that produce optimal outputs. Better inputs make for better results.