I understand that AI/ML jargon can be confusing. Here's a brief explanation of some common terms and concepts to help you better understand them:
Weights: In machine learning, weights are parameters used in models to determine the strength of the relationship between input features and the output prediction. They are learned during the training process and are adjusted to minimize the error in the model's predictions.
LoRA: LoRA stands for "Learning from Rules and Arguments." It's a method that combines rule-based reasoning with neural networks, which allows AI systems to reason using human-like logic and provide explanations for their decisions. LoRA can enhance the interpretability and transparency of AI systems.
LLM: LLM stands for "Large Language Model." It refers to advanced AI models, such as OpenAI's GPT series, which are trained on vast amounts of text data to understand and generate human-like text. These models can perform various language tasks such as translation, summarization, question-answering, and more.
Supervised Learning: This is a type of machine learning where models are trained using labeled data. The model learns to map input features to the corresponding output labels through a process of trial and error, adjusting the weights to minimize the difference between predictions and true labels.
Unsupervised Learning: In contrast to supervised learning, unsupervised learning does not use labeled data. Instead, it aims to discover patterns or structures in the data, such as clustering or dimensionality reduction, without explicit guidance.
Reinforcement Learning: This is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties and aims to maximize its cumulative reward over time.
Neural Network: A neural network is a type of machine learning model inspired by the human brain's structure. It consists of interconnected layers of nodes (neurons) that process and transmit information. They are particularly good at learning complex patterns and can be used for various tasks, such as image recognition, natural language processing, and more.
Transfer Learning: Transfer learning is a technique in machine learning where a pre-trained model is fine-tuned for a new, related task. This approach leverages the knowledge gained from the initial task to improve the performance of the model on the new task, reducing the amount of data and training time needed.
These are just a few of the many concepts and tools used in AI/ML. Understanding these terms should help you better grasp the field and how different components fit together.
Picked the wrong one. LoRA, Low-rank Adaptation of LLMs (https://arxiv.org/pdf/2106.09685.pdf), consists in adapting the weights of a big neural network to a target task (here, answering to instructions). It doesn't touch the weights of the original model, but rather adds the product of two low-rank matrices to select layers. The weights from those matrices are learnable. The method allows to adapt big models on (relatively) low-memory GPUs.
Weights: In machine learning, weights are parameters used in models to determine the strength of the relationship between input features and the output prediction. They are learned during the training process and are adjusted to minimize the error in the model's predictions.
LoRA: LoRA stands for "Learning from Rules and Arguments." It's a method that combines rule-based reasoning with neural networks, which allows AI systems to reason using human-like logic and provide explanations for their decisions. LoRA can enhance the interpretability and transparency of AI systems.
LLM: LLM stands for "Large Language Model." It refers to advanced AI models, such as OpenAI's GPT series, which are trained on vast amounts of text data to understand and generate human-like text. These models can perform various language tasks such as translation, summarization, question-answering, and more.
Supervised Learning: This is a type of machine learning where models are trained using labeled data. The model learns to map input features to the corresponding output labels through a process of trial and error, adjusting the weights to minimize the difference between predictions and true labels.
Unsupervised Learning: In contrast to supervised learning, unsupervised learning does not use labeled data. Instead, it aims to discover patterns or structures in the data, such as clustering or dimensionality reduction, without explicit guidance.
Reinforcement Learning: This is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties and aims to maximize its cumulative reward over time.
Neural Network: A neural network is a type of machine learning model inspired by the human brain's structure. It consists of interconnected layers of nodes (neurons) that process and transmit information. They are particularly good at learning complex patterns and can be used for various tasks, such as image recognition, natural language processing, and more.
Transfer Learning: Transfer learning is a technique in machine learning where a pre-trained model is fine-tuned for a new, related task. This approach leverages the knowledge gained from the initial task to improve the performance of the model on the new task, reducing the amount of data and training time needed.
These are just a few of the many concepts and tools used in AI/ML. Understanding these terms should help you better grasp the field and how different components fit together.
-- ChatGPT 4