Algoverse Research AI Lecture 1

General AI/ML Terms

  1. Artificial Intelligence (AI): The field concerned with creating machines capable of intelligent behavior, including learning from experience.

  2. Machine Learning (ML): A subset of AI where algorithms learn from and make predictions based on data.

  3. Language Model (LM): A type of machine learning model designed to understand and generate human language.

  4. Generative Models: Models that learn the underlying distribution of data and can generate new data points.

  5. Autoregressive Models: A specific type of generative model that predicts the next item in a sequence based on previous items.

Technical Concepts

  1. Weights: Numerical parameters in a model that are adjusted during training to improve predictions.

  2. Training: The process of optimizing a model's weights using labeled data.

  3. Inference: Using a trained model to make predictions on new data.

  4. Overfitting: When a model learns the training data too well, including noise, leading to poor generalization.

  5. Underfitting: When a model fails to capture the underlying patterns in the data, resulting in poor performance.

Model Types and Tasks

  1. Neural Networks (NNs): A class of models inspired by the human brain, consisting of layers of interconnected nodes.

  2. Transformers: A type of neural network architecture particularly effective for language tasks.

  3. Classification: A task where the output variable is categorical (e.g., spam vs. not spam).

  4. Regression: A task where the output variable is continuous (e.g., predicting house prices).

Research and Analysis

  1. Literature Review: The process of reviewing existing research to identify gaps and formulate new research questions.

  2. Benchmark: A standard dataset and metric used to evaluate model performance.

  3. Baseline: An existing method used as a comparison point for new methods.

  4. Ablations: Experiments that systematically remove parts of a model or method to evaluate their contribution.

  5. Prompting: Providing a specific input (or prefix) to guide a language model's output.

Research Components

  1. Abstract: A concise summary of a research paper's content and findings.

  2. Introduction: The section of a paper that motivates the research problem and outlines the approach.

  3. Methods: Describes the techniques or algorithms used in the research.

  4. Experimental Setup: Explains the datasets, benchmarks, and evaluation metrics used.

  5. Results: Summarizes the findings and outcomes of experiments.

  6. Discussion: A section that reflects on the findings, limitations, and potential future work.