ELIZA was an early natural language processing program developed at the MIT Artificial Intelligence Laboratory in the 1960s. It utilized simple pattern matching techniques to simulate conversation with users. ELIZA is known for its ability to engage in text-based interactions that mimicked a Rogerian psychotherapist, asking open-ended questions and reflecting users' statements back to them. While ELIZA's conversations were quite limited in scope and sophistication, it played a significant role in the history of AI and natural language processing, serving as a foundation for later developments in the field.
Fine-tuning refers to the process of adapting a pre-trained language model (LLM) for a specific task or domain by further training it on a smaller and more specific dataset. This allows the model to specialize its knowledge and adapt to the specific patterns and nuances of the target task or domain. Fine-tuning often involves adjusting the weights of the model's layers while keeping the knowledge learned from the initial pre-training intact. This process is common in transfer learning, where knowledge gained from one task is leveraged to improve performance on a related but different task.
The description you provided matches the "Transformers" architecture. Transformers are a type of neural network architecture that gained popularity in various natural language processing (NLP) tasks, especially after the introduction of models like BERT, GPT (Generative Pre-trained Transformer), and others. Transformers utilize self-attention mechanisms to process input data in parallel and capture relationships between different words or tokens in a sequence, which makes them particularly effective for tasks involving sequential data, such as language understanding and generation.
Neural networks are composed of layers of interconnected nodes, often referred to as neurons or units. These networks are designed to analyze and transform data, and they are inspired by the structure and functioning of the human brain. The connections between the nodes have associated weights that are adjusted during the training process, allowing the network to learn and adapt to the patterns in the data. Neural networks have been used in various machine learning tasks to perform tasks like classification, regression, and more complex tasks like image recognition and natural language processing.
The technique you're describing is called "Reinforcement Learning." It's a machine learning paradigm where an agent interacts with an environment, learns to take actions to maximize a cumulative reward signal over time. Reinforcement learning is indeed used in various applications, including robotics for tasks like autonomous navigation, and in game playing where agents learn to make strategic decisions to win games.
Deep learning is a branch of machine learning that employs neural networks with multiple layers to simulate and resolve complex problems. It involves training neural networks on large amounts of data to learn hierarchical representations of the underlying patterns in the data. Deep learning has shown remarkable success in various domains, including image and speech recognition, natural language processing, and many other tasks where the data has intricate structures.
Azure Blob Storage is a highly scalable and cost-effective cloud storage solution that can store millions of pictures. It is designed for storing unstructured data such as images, videos, documents, and logs. Blob Storage offers tiered pricing options based on the frequency of access to the data, allowing you to optimize costs by storing less frequently accessed data in lower-cost tiers. It also offers automatic backup and geo-replication for data durability and availability.
Field-programmable gate arrays (FPGAs) are a processing platform in Azure that would provide the ability to update the logic over time for image classification, while also providing low latency for inferencing without having to batch.
"Supervised machine learning" is a technique where a neural network (or any other machine learning model) learns to predict outcomes or categorize data using labeled datasets. In supervised learning, the model is trained on input-output pairs, where the inputs are the features of the data and the outputs are the corresponding labels or target values. The goal is for the model to learn the underlying patterns in the data so that it can make accurate predictions on new, unseen data.
