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Using AI Language Models

A brief overview of how to use AI Language Models

AI Vocabulary

Activation Function

  • Definition: A function applied to the output of a neuron in a neural network, introducing non-linearity.
  • Example: Common activation functions include ReLU (Rectified Linear Unit) and Sigmoid, which are essential in neural networks

 AI-Augmented

  • Definition: AI that assists and enhances human tasks but allows users to maintain control over the final product.
  • Example: Grammarly offers grammar and style suggestions, but the user decides which suggestions to accept. 

                              Related terms- Transformational, Human-in-the-Loop (HITL).

 Attention Mechanism

  • Definition: A component in neural networks, particularly in transformers, that allows models to focus on important parts of the input.
  • Example: Transformers like BERT use attention to understand word context in sentences.

 AI-Autonomous

  • Definition: Describes AI that operates independently, producing complete outputs based on an initial prompt without further human intervention.
  • Example: Auto-GPT, which can autonomously complete multi-step tasks after an initial instruction.

                            Related Terms -  Transactional. 

 Algorithm

  • Definition: A set of rules or instructions for solving a problem, forming the foundation of AI systems.
  • Example: The k-nearest neighbors (k-NN) algorithm is used to classify data points based on their neighbors.

  Artificial Intelligence (AI)

  • Definition: The field of computer science focused on creating systems that can perform tasks requiring human-like intelligence.
  • Example: Siri and Alexa, which use AI to interpret and respond to voice commands.

  Artificial Neural Network (ANN)

  • Definition: A computing system inspired by the human brain's structure, consisting of interconnected "neurons."
  • Example: Image recognition systems often use ANNs to detect objects in photos.

 Autonomous Agent

  • Definition: An AI-based system that can make decisions and perform actions without human intervention.
  • Example: Self-driving cars use autonomous agents to make driving decisions in real-time.

 Batch Size

  • Definition: The number of training examples processed in one iteration by the model.
  • Example: A neural network might be trained in batches of 32 or 64 images to optimize processing time.

Backpropagation

  • Definition: An algorithm used in neural networks to calculate the error of each neuron and adjust weights, improving accuracy.
  • Example: Used in training deep learning models to minimize prediction errors in tasks like image classification.

Bias-Variance Tradeoff

  • Definition: A key concept in machine learning that explains the balance between underfitting (high bias) and overfitting (high variance).
  • Example: Adjusting model complexity to achieve an optimal balance, such as tuning a decision tree’s depth.

Black Box

  • Definition: A term describing AI models, particularly neural networks, whose decision-making processes are difficult to interpret or explain.
  • Example: Many deep learning models are considered black boxes due to their complex internal structure.

 Chatbot

  • Definition: An AI program that interacts with users through text or voice, commonly used for customer service.
  • Example: ChatGPT, which can answer questions, provide explanations, and generate text-based responses.

ChatGPT

  • Definition: A popular AI chatbot developed by OpenAI, used for answering questions, brainstorming, and generating content.
  • Example: Used by students and professionals to draft emails, brainstorm ideas, and write reports.

Classifier

  • Definition: An algorithm or model used to categorize or label data into predefined groups.
  • Example: Spam filters classify emails as "spam" or "not spam."

Clustering

  • Definition: An unsupervised learning technique that groups similar data points, identifying patterns without pre-labeled data.
  • Example: Market segmentation, where customers are grouped into clusters based on purchasing behavior.

Cognitive Computing

  • Definition: A branch of AI aimed at simulating human thought processes in a computerized model.
  • Example: IBM’s Watson, which assists in decision-making by processing natural language queries.

Computer Vision

  • Definition: An AI field focused on enabling computers to interpret visual information.
  • Example: Face recognition in smartphones to unlock the device.

Convolutional Neural Network (CNN)

  • Definition: A deep learning algorithm used for image recognition.
  • Example: Google Photos uses CNNs to categorize images by content, like "beaches" or "food."

Data Bias

  • Definition: A skew or imbalance in data that can lead to biased AI outcomes.
  • Example: Facial recognition systems that perform better on certain demographics due to biased training data.

Decision Tree

  • Definition: A flowchart-like model used for decision-making based on data conditions.
  • Example: Used in loan approval processes, where each branch represents a decision rule.

Deep Learning

  • Definition: A branch of machine learning that uses neural networks with multiple layers.
  • Example: DeepMind's AlphaGo, which uses deep learning to play and win at the game Go.

Dropout

  • Definition: A regularization technique where random neurons are ignored during training to prevent overfitting.
  • Example: Used in neural networks to make them more robust by randomly disabling certain neurons.

Edge Computing

  • Definition: The practice of processing data near the source, rather than in a central cloud.
  • Example: Smart home devices like thermostats use edge computing to process data locally.

Epoch

  • Definition: In machine learning, one complete pass of the training dataset through the model.
  • Example: A deep learning model may be trained over 100 epochs to improve accuracy on an image classification task.

Embedding

  • Definition: A dense, low-dimensional representation of data, often used in NLP to represent words in a continuous vector space.
  • Example: Word embeddings like Word2Vec or GloVe provide contextual word representations.

Explainability

  • Definition: The degree to which humans can understand how an AI model makes decisions.
  • Example: Linear regression models are highly explainable, while deep neural networks are less so.

Feature Engineering

  • Definition: The process of selecting and transforming variables in raw data to improve model performance.
  • Example: Converting categorical data like "city" into numerical values for a prediction model.

Feature Extraction

  • Definition: Selecting important attributes from data to improve model efficiency.
  • Example: Using the key features of images (edges, shapes) in a model to identify objects.

Fine-tuning

  • Definition: The process of adapting a pre-trained model to a new, similar task.
  • Example: Fine-tuning a language model for a specific industry, such as legal or medical terminology.

Fuzzy Logic

  • Definition: A form of logic that allows for degrees of truth, rather than binary values.
  • Example: Washing machines that adjust the wash cycle based on the perceived dirtiness of the clothes.

Generative Adversarial Network (GAN)

  • Definition: A neural network used to generate new data by pitting two networks against each other.
  • Example: Creating realistic synthetic images for video game characters.

Generative AI

  • Definition: AI systems designed to create new content.
  • Example: DALL-E generates images from text prompts.

Generative Pre-trained Transformer (GPT)

  • Definition: A model architecture for AI language models.
  • Example: GPT-4, which powers advanced language models like ChatGPT.

Gradient Descent

  • Definition: An optimization algorithm that minimizes a model's error.
  • Example: Used in training neural networks to adjust weights and reduce prediction error.

Gradient Clipping

  • Definition: A technique to prevent exploding gradients by limiting the gradient's size during training.
  • Example: Used in training RNNs to avoid excessively large updates to model weights.

Hallucination

  • Definition: In AI, hallucinations are made-up or incorrect information generated by the model, even though it sounds confident or realistic.
  • Example: An AI might say that "Abraham Lincoln invented the telephone," even though this is not true—it hallucinated the information.

Human-in-the-Loop (HITL)

  • Definition: A design approach in which AI systems and human operators work interactively. In this model, humans are integrated at critical stages of the AI workflow to make decisions, provide feedback, or offer oversight, especially where human judgment, intuition, or ethical concerns are important.
  • Examples: AI scans for potential issues, like tumors, and a doctor reviews the results to confirm accuracy. This combination improves diagnosis by blending AI speed with expert judgment.

Hyperparameters

  • Definition: Pre-set configurations that control the training process.
  • Example: Setting the learning rate of a model before training.

Inference

  • Definition: The phase where an AI model applies its knowledge to make predictions.
  • Example: A language model generating text based on user prompts.

Knowledge Graph

  • Definition: A data structure representing relationships between entities.
  • Example: Google’s knowledge graph that connects related search terms.

Latent Space

  • Definition: A lower-dimensional space where data reveals hidden patterns.
  • Example: Word2Vec embeds words in a latent space where similar words are closer.

Large Language Model (LLM)

  • Definition: AI models trained on massive datasets for generating language.
  • Example: GPT-4, used for generating human-like text responses.

Loss Function

  • Definition: A method for evaluating how well a model’s predictions align with actual data.
  • Example: Mean Squared Error (MSE) used in regression models.

Machine Learning (ML)

  • Definition: A subset of AI that enables machines to learn from data.
  • Example: Netflix’s recommendation system uses machine learning to suggest shows.

Natural Language Processing (NLP)

  • Definition: A field of AI that helps computers understand human language.
  • Example: Google Translate uses NLP to convert languages.

Neural Network

  • Definition: A series of algorithms that mimic the human brain to recognize patterns.
  • Example: Used in image recognition systems to identify objects.

Overfitting

  • Definition: When a model learns the training data too closely, resulting in poor performance on new data.
  • Example: A model that performs well on training data but fails to generalize on test data.

Recurrent Neural Network (RNN)

  • Definition: A neural network designed for sequential data.
  • Example: Used in speech recognition and language modeling tasks.

Regularization

  • Definition: Techniques used to reduce overfitting by adding a penalty to the model’s complexity.
  • Example: L2 regularization (Ridge regression) reduces overfitting by penalizing large coefficients in regression.

Reinforcement Learning

  • Definition: A type of machine learning where an agent learns by receiving rewards or penalties.
  • Example: AlphaGo uses reinforcement learning to improve its game strategy.

Supervised Learning

  • Definition: A type of machine learning where a model is trained on labeled data.
  • Example: A model trained on labeled images to classify animals.

Tokenization

  • Definition: Breaking text into individual units for text analysis.
  • Example: Splitting a sentence into words or phrases in NLP tasks.

Transfer Learning

  • Definition: Using a pre-trained model on a new, similar task.
  • Example: Fine-tuning a language model for specific domain knowledge.

Training Data

  • Definition: The dataset used to teach an AI model.
  • Example: Labeled photos of cats and dogs used to train an image classifier.

Underfitting

  • Definition: When an AI model is too simple to capture data patterns.
  • Example: A model that performs poorly on both training and test data.

Unsupervised Learning

  • Definition: Machine learning without labeled data, where the model identifies patterns on its own.
  • Example: Clustering customers based on buying habits without pre-labeled groups.

Validation Set

  • Definition: A subset of data used to tune and evaluate a model’s performance during training.
  • Example: Holding out a part of the dataset to prevent overfitting.

Zero-shot Learning

  • Definition: When a model makes predictions on unseen categories.
  • Example: An NLP model that can answer questions about a topic it wasn’t specifically trained on.