AI (Artificial Intelligence):
AI is a broad field of computer science that aims to create machines or systems capable of performing tasks that typically require human intelligence. This includes tasks such as problem-solving, learning, reasoning, perception, natural language understanding, and decision-making. AI encompasses various approaches, including machine learning and other techniques.
AI is what folks are working with today.
GAI (General Artificial Intelligence):
GAI refers to a hypothetical AI system that possesses general intelligence, similar to human intelligence. Unlike specialized AI systems that excel in specific tasks, a GAI would be capable of understanding, learning, and performing a wide range of tasks at a human level or beyond. As of now, true GAI does not yet exist, and most AI systems are specialized in narrow tasks.
GAI is what many in the AI field hope to one day achieve.
ML (Machine Learning):
Machine Learning is a subset of AI that focuses on the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions based on data. Instead of being explicitly programmed, ML models learn patterns and structures from large datasets, allowing them to generalize and make predictions on new, unseen data.
ML is AI that can make predictions based on data.
LLM (Large Language Model):
LLM refers to Large Language Models, which are advanced AI models that can process and generate human-like text. These models are built using deep learning and NLP techniques and are trained on massive datasets to understand grammar, context, and semantic relationships in text. Examples include GPT-3, GPT-4, etc.
LLM is AI that learns from ingesting large amounts of data from multiple sources.
GANs (Generative Adversarial Networks):
GANs are a specific type of machine learning model used for generative tasks. They consist of two neural networks, the generator and the discriminator, which are trained in a competitive manner. The generator creates new data samples, while the discriminator tries to distinguish between real and generated data. Through this adversarial process, GANs can produce realistic data, such as images, music, or text.
Think of GAN as when AI is learned using both true and false, or positive and negative, data.
GPTs (Generative Pre-trained Transformers):
GPTs are a family of large language models based on the Transformer architecture. They are capable of generating human-like text and have been pre-trained on vast amounts of text data before fine-tuning for specific tasks. GPT-3 (Generative Pre-trained Transformer 3) is one of the most well-known models in this family, developed by OpenAI.
GPT is similar to LLM in that it starts with large data sets to learn, but then is fine-tuned and transformed to allow for better sequence-to-sequence required tasks such as translations.
In summary, AI is a broad field focused on creating intelligent machines, while ML is a subset of AI that deals with algorithms learning from data. LLMs and GPTs are large language models capable of generating human-like text. GAI is a theoretical concept of an AI system with general intelligence beyond human capabilities. GANs are a type of ML model used for generative tasks, creating realistic data samples.