Concepts

Large Language Models – LLMs

Large language models (LLMs) are a category of foundation models trained on immense amounts of data making them capable of understanding and generating natural language and other types of content to perform a wide range of tasks.

^{Reference: https://www.ibm.com/think/topics/large-language-models}

Machine Learning – ML

Machine learning is the subset of artificial intelligence (AI) focused on algorithms that can “learn” the patterns of training data and, subsequently, make accurate inferences about new data. This pattern recognition ability enables machine learning models to make decisions or predictions without explicit, hard-coded instructions.

^{Reference: https://www.ibm.com/think/topics/machine-learning}

Generative AI

Generative AI, sometimes called gen AI, is artificial intelligence (AI) that can create original content such as text, images, video, audio or software code in response to a user’s prompt or request.

Generative AI relies on sophisticated machine learning models called deep learning models algorithms that simulate the learning and decision-making processes of the human brain. These models work by identifying and encoding the patterns and relationships in huge amounts of data, and then using that information to understand users’ natural language requests or questions and respond with relevant new content.

^{Reference: https://www.ibm.com/think/topics/generative-ai}

Deep Learning

Deep learning is a subset of machine learning that uses multilayered neural networks, called deep neural networks, to simulate the complex decision-making power of the human brain. Some form of deep learning powers most of the artificial intelligence (AI) applications in our lives today.

The chief difference between deep learning and machine learning is the structure of the underlying neural network architecture. “Nondeep,” traditional machine learning models use simple neural networks with one or two computational layers. Deep learning models use three or more layers, but typically hundreds or thousands of layers to train the models.

^{Reference: https://www.ibm.com/think/topics/deep-learning}

Supervised Learning

Supervised learning is a machine learning technique that uses human-labeled input and output datasets to train artificial intelligence models. The trained model learns the underlying relationships between inputs and outputs, enabling it to predict correct outputs based on new, unlabeled real-world input data.

^{Reference: https://www.ibm.com/think/topics/supervised-learning}

Unsupervised Learning

Unsupervised learning, also known as unsupervised machine learning, uses machine learning (ML) algorithms to analyze and cluster unlabeled data sets. These algorithms discover hidden patterns or data groupings without the need for human intervention.

^{Reference: https://www.ibm.com/think/topics/unsupervised-learning}