Breaking Down AI Jargon
Rajamanickam Antonimuthu
AI Enthusiast | RAG Developer | Futurist | Entrepreneur
Q: What is an AI model?
A: An AI model is a computer program that learns patterns from data and makes predictions or generates responses. It's like a trained assistant that can answer questions, recognize images, or translate languages.
Q: What does "pre-trained model" mean? A: A pre-trained model is an AI that has already learned from a large dataset before being used for a specific task. Instead of starting from scratch, you can fine-tune it with new data to improve its performance for a particular job.
Q: What is "fine-tuning" in AI? A: Fine-tuning means training an AI model further on a smaller, specialized dataset so it performs better for a specific task. For example, a general chatbot can be fine-tuned on legal documents to answer legal questions.
Q: What is "inference" in AI? A: Inference is when an AI model uses what it has learned to generate an output or make a decision. For example, when you ask ChatGPT a question, it processes the input and generates a response based on its training.
Q: What is a "dataset" in AI? A: A dataset is a collection of text, images, numbers, or other data used to train an AI model. Good-quality datasets help AI learn better and make accurate predictions.
Q: What does "bias in AI" mean? A: Bias in AI happens when a model produces unfair or inaccurate results due to imbalanced or flawed training data. For example, if a hiring AI is trained mostly on resumes from one country, it may unfairly prefer applicants from that region.
Q: What is "zero-shot learning" in AI? A: Zero-shot learning allows AI to handle tasks it hasn’t seen before. For example, if a chatbot correctly answers a question on a new topic without specific training, it is using zero-shot learning.
Q: What is a "Neural Network"? A: A neural network is a type of AI model inspired by how the human brain works. It consists of layers of artificial "neurons" that process information and learn patterns. It is used in deep learning for tasks like image recognition and language processing.
Q: What is "Tokenization" in AI? A: Tokenization is the process of breaking text into smaller parts (tokens) so AI can understand it. For example, "AI is powerful" might be split into ["AI", "is", "powerful"]. AI processes these tokens instead of full sentences.
Q: What is "Generative AI"? A: Generative AI creates new content, such as text, images, music, or code. Models like ChatGPT and DALL·E generate human-like responses or artwork based on input prompts.
Q: What is "Hallucination" in AI? A: AI hallucination happens when an AI generates false or misleading information that sounds convincing but isn't true. This happens because AI sometimes "guesses" when it lacks accurate data.
Q: What does "Overfitting" mean in AI? A: Overfitting occurs when an AI model learns patterns too perfectly from training data but struggles with new, unseen data. It’s like memorizing answers for a test instead of understanding the subject.
Q: What is "Underfitting" in AI? A: Underfitting happens when an AI model doesn’t learn enough from the training data, making poor predictions even on the examples it was trained on.
Q: What is "Transfer Learning"? A: Transfer learning is when an AI model trained on one task is reused for another similar task. For example, a model trained on general images can be fine-tuned to identify medical scans.
Q: What is "Embeddings" in AI? A: Embeddings are a way to convert words, images, or data into numbers that AI models can understand. For example, AI can represent the word "king" as a number vector close to "queen" in meaning.
Q: What is "Vector Database"? A: A vector database stores embeddings (numerical representations of data) so AI can quickly find similar content. It's useful for searching similar images, documents, or text responses.
Q: What is "Natural Language Processing (NLP)"? A: NLP is a branch of AI that helps computers understand and process human language. It powers chatbots, voice assistants, and language translation tools.
Q: What is "Supervised Learning"? A: Supervised learning is when an AI model is trained on labeled data. For example, if you train an AI to recognize cats and dogs by showing labeled images, it's supervised learning.
Q: What is "Unsupervised Learning"? A: Unsupervised learning is when an AI model finds patterns in data without labels. For example, clustering customers into groups based on purchasing behavior without being told who belongs where.
Q: What is "Reinforcement Learning"? A: Reinforcement learning is when AI learns by trial and error, receiving rewards for correct actions. It’s used in robotics, gaming, and optimizing recommendations.
Q: What is "LLM" (Large Language Model)? A: LLM stands for Large Language Model, an AI trained on massive amounts of text data to understand and generate human-like language. ChatGPT and Gemini are examples of LLMs.
Q: What is "Explainability in AI"? A: Explainability refers to how well we can understand and interpret an AI model’s decisions. Some AI models are like "black boxes," making decisions without clear explanations, while others provide insights into their reasoning.
Q: What is Deep Learning? A: Deep learning is a type of machine learning that uses multiple layers of neural networks to process data and learn complex patterns. It powers technologies like facial recognition, self-driving cars, and chatbots.
Q: What is a Large Context Window in AI? A: A large context window means an AI model can remember and process more words in a conversation or document at once. For example, a chatbot with a small window might forget earlier parts of a discussion, while one with a large window can maintain context better.
Q: What is Few-Shot Learning? A: Few-shot learning allows AI to learn a new task with very few examples. For instance, if you show an AI just a couple of labeled images of a new object, it can recognize similar ones without extensive training.
Q: What is Multi-Modal AI? A: Multi-modal AI can process and understand different types of data (like text, images, and audio) together. For example, ChatGPT-4 can analyze both text and images in a conversation.
Q: What is Computer Vision? A: Computer Vision is a field of AI that helps machines interpret and analyze images and videos. It’s used in applications like facial recognition, self-driving cars, and medical imaging.
Q: What is Pretraining vs. Fine-Tuning? A: Pretraining is when an AI learns general knowledge from a massive dataset before being adapted for a specific task. Fine-tuning is the process of further training the AI on a smaller, specialized dataset to improve performance on a particular task.
Q: What is Data Augmentation in AI? A: Data augmentation is a technique used to expand a dataset by modifying existing data. For example, in image recognition, flipping, rotating, or changing brightness in images helps AI models learn better without needing new data.
Q: What is a Loss Function in AI? A: A loss function measures how far the AI’s predictions are from the correct answers. AI models adjust their learning based on the loss function to improve accuracy.
Q: What is a Gradient Descent? A: Gradient Descent is an optimization algorithm that helps AI models adjust their internal settings (weights) to minimize errors and improve learning over time.
Q: What is Model Drift? A: Model drift happens when an AI model's accuracy decreases over time because the real-world data it encounters has changed from the data it was trained on. This often happens in fraud detection and recommendation systems.
Q: What is Catastrophic Forgetting in AI? A: Catastrophic forgetting happens when an AI model trained on new data forgets what it learned from previous data. This is common in deep learning models that don’t store past knowledge effectively.
Q: What is Explainable AI (XAI)? A: Explainable AI (XAI) refers to AI systems designed to be transparent about their decisions. It helps users understand why an AI model made a particular prediction, improving trust and accountability.
Q: What is Reinforcement Learning with Human Feedback (RLHF)? A: RLHF is a technique where AI is trained using human feedback to improve its responses. This is how models like ChatGPT are fine-tuned to be more helpful and accurate.
Q: What is Federated Learning? A: Federated learning is a privacy-focused AI training method where data remains on users' devices instead of being sent to a central server. AI models learn from distributed data without compromising user privacy.
Q: What is a Transformer Model in AI? A: A transformer is a deep learning model architecture designed to handle large-scale language processing tasks. Models like GPT, BERT, and T5 use transformers to understand and generate text.
Q: What is a Knowledge Graph? A: A knowledge graph is a structured representation of information that connects concepts and relationships. Google Search and chatbots use knowledge graphs to provide relevant answers by linking related topics.
Q: What is Retrieval-Augmented Generation (RAG)? A: RAG is an AI approach that improves responses by retrieving relevant information from an external database before generating an answer. It helps chatbots provide more accurate and up-to-date responses.
Q: What is an AI Accelerator? A: AI accelerators, like GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units), are specialized hardware designed to speed up AI computations, making training and inference faster.
Q: What is Edge AI? A: Edge AI runs AI models directly on local devices (like smartphones and cameras) instead of relying on cloud servers. This allows for faster processing and improved privacy.
Q: What is Quantization in AI? A: Quantization reduces the size of AI models by using lower-precision numbers, making them run faster and more efficiently, especially on edge devices.
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Q: What are parameters in AI models?
Parameters are the internal variables of an AI model that are learned during training. They define how the model processes input data to make predictions. For example:
When you hear about models with billions of parameters (e.g., GPT-3 with 175 billion parameters), it means the model has a vast number of internal variables, allowing it to capture intricate patterns in data.
Q:? What is precision in Machine Learning?
Precision is a metric used to evaluate the performance of a classification model. It measures the accuracy of the positive predictions made by the model. For example:
Formula:
Precision=True Positives/(True Positives+False Positives)
High precision means the model is good at avoiding false positives.
Q: What is recall in Machine Learning?
Recall (also called sensitivity) measures how well a model identifies all relevant positive cases. For example:
Formula:
Recall=True Positives/(True Positives+False Negatives)
High recall means the model is good at finding most of the positive cases.
Q: What is the difference between precision and recall?
For example, in a medical test:
Often, there’s a trade-off between precision and recall, and the right balance depends on the use case.
Q: What is the F1 score?
The F1 score is a single metric that combines precision and recall. It is the harmonic mean of the two and is useful when you want to balance both metrics.
Formula:
F1 Score=2×Precision×Recall/(recision+Recall)
The F1 score ranges from 0 to 1, where 1 is the best possible score. It is commonly used in classification tasks, especially when the dataset is imbalanced.
Q: What does "billion parameters" mean in AI models?
When an AI model has billions of parameters, it means the model has a very large number of internal variables that it uses to learn from data. For example:
More parameters generally allow the model to capture more complex patterns, but they also require more computational resources and data to train.
Q: What is a confusion matrix?
A confusion matrix is a table used to evaluate the performance of a classification model. It shows the following:
The confusion matrix helps calculate metrics like precision, recall, and accuracy.
Q:? What is accuracy in Machine Learning?
Accuracy measures the percentage of correct predictions made by a model.
Formula:
Accuracy=(True Positives+True Negatives)/Total Predictions
While accuracy is a useful metric, it can be misleading in imbalanced datasets (e.g., when 95% of the data belongs to one class). In such cases, precision, recall, and F1 score are more informative.
Q: What is a loss function?
A loss function measures how well an AI model performs by comparing its predictions to the actual values. The goal of training is to minimize the loss function. Common loss functions include:
A lower loss indicates a better-performing model.