ebook - Unlocking AI | AI Course | Bundle Offer (including RAG ebook)
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. Some AI models are trained from scratch using raw data, such as custom-built neural networks for stock price prediction, medical diagnosis models, and self-trained chatbots for specific industries. Others use pre-trained models to speed up development.
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. Examples include BERT for NLP tasks, VGG16 for image classification, Whisper for speech recognition, and DALL·E for image generation.
Q: What is "fine-tuning" in AI?
A: Fine-tuning is the process of taking a pre-trained AI model and training it further on a smaller, specialized dataset to improve its performance for a specific task. This allows the model to retain its general knowledge while adapting to domain-specific requirements. For example, a general chatbot like GPT can be fine-tuned on legal documents to provide more accurate answers to legal questions, or an image recognition model can be fine-tuned on medical scans to detect diseases more effectively.
Q: What is "inference" in AI?
A: Inference is the process where an AI model applies what it has learned during training to generate an output or make a decision. It happens when the model takes new input data and produces a prediction, classification, or response. For example, when you ask ChatGPT a question, it analyzes the input and generates a relevant response based on its training. Similarly, a self-driving car uses inference to recognize traffic signs and make driving decisions in real time.
Q: What is a "dataset" in AI?
A: A dataset is a structured collection of text, images, numbers, or other types of data used to train and evaluate AI models. It serves as the learning material for AI, helping it recognize patterns and make accurate predictions. High-quality datasets with diverse and well-labeled data improve model performance, while poor-quality datasets can lead to biased or inaccurate results. For example, an AI model for speech recognition may be trained on a dataset of audio recordings in multiple languages to enhance its accuracy.
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 designed to mimic the way the human brain processes information. It consists of layers of interconnected artificial "neurons" that learn patterns from data. Each neuron receives inputs, applies weights, and passes the result through an activation function to make decisions. Neural networks are widely used in deep learning for tasks like image recognition, speech processing, and language translation. Examples include CNNs for image classification and RNNs for speech recognition.
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 is a type of artificial intelligence that creates new content, such as text, images, music, or code, rather than just analyzing existing data. It learns patterns from large datasets and generates outputs based on input prompts. For example, ChatGPT creates human-like text responses, DALL·E generates images from descriptions, and MuseNet composes music. Generative AI is used in creative writing, design, and automation to enhance productivity and innovation.
Q: What is "Hallucination" in AI?
A: AI hallucination occurs when an AI model generates false, misleading, or nonsensical information that sounds convincing but isn't true. This happens because AI models predict responses based on patterns in their training data and may "guess" when they lack accurate information. For example, a chatbot might invent a fake citation or provide incorrect historical facts. Reducing hallucinations requires high-quality training data, better fact-checking, and techniques like grounding AI responses in verified sources.
Q: What does "Overfitting" mean in AI?
A: Overfitting happens when an AI model learns patterns from training data too precisely, including noise or irrelevant details, making it perform poorly on new, unseen data. It’s like a student memorizing answers instead of understanding the subject—excelling on practice tests but failing real exams. Overfitting can be reduced using techniques like regularization, dropout, data augmentation, and cross-validation.
Q: What is "Underfitting" in AI?
A: Underfitting occurs when an AI model fails to learn meaningful patterns from the training data, leading to poor performance on both the training set and new data. This happens when the model is too simple or lacks enough training time to capture the underlying relationships. It's like a student studying too little and struggling with both practice and real tests. Underfitting can be addressed by using a more complex model, increasing training time, or providing more relevant features in the data.
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 numerical representations of words, images, or other data that help AI models understand relationships and similarities between them. They convert complex information into dense vectors in a way that preserves meaning. For example, in a word embedding model, the word "king" is mapped to a vector close to "queen", capturing their semantic relationship. Embeddings are widely used in natural language processing (NLP), recommendation systems, and image recognition to improve AI performance.
Q: What is "Vector Database"?
A: A vector database is a specialized database designed to store and search embeddings—numerical representations of data—efficiently. It enables AI models to quickly find similar images, documents, or text by comparing vectors based on their mathematical proximity. This makes it ideal for tasks like image recognition, recommendation systems, and semantic search. Popular vector databases include FAISS, Pinecone, and ChromaDB.
Q: What is "Natural Language Processing (NLP)"?
A: Natural Language Processing (NLP) is a branch of AI that enables computers to understand, interpret, and generate human language. It powers applications like chatbots, voice assistants (e.g., Siri, Alexa), language translation (e.g., Google Translate), and sentiment analysis. NLP combines linguistics and machine learning to help AI process text and speech in a way that feels natural to humans.
Q: What is "Supervised Learning"?
A: Supervised learning is a type of machine learning where an AI model is trained using labeled data, meaning each input has a known correct output. The model learns by mapping inputs to the correct labels and improving its predictions over time. For example, training an AI to recognize cats and dogs involves showing it images labeled as "cat" or "dog" so it can learn to classify new images accurately. Supervised learning is widely used in image recognition, speech recognition, and spam detection.
Q: What is "Unsupervised Learning"?
A: Unsupervised learning is a type of machine learning where an AI model discovers patterns and structures in data without labeled answers. Instead of learning from predefined categories, the model identifies hidden relationships, such as grouping similar items together. For example, in customer segmentation, AI can cluster customers based on purchasing behavior without being told which group they belong to. Unsupervised learning is commonly used in anomaly detection, recommendation systems, and data compression.
Q: What is "Reinforcement Learning"?
A: Reinforcement Learning (RL) is a type of machine learning where an AI learns by trial and error, receiving rewards for good actions and penalties for mistakes. Over time, it optimizes its decisions to maximize long-term rewards. RL is widely used in robotics, game-playing AI (e.g., AlphaGo), self-driving cars, and personalized 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 subset of machine learning that uses artificial neural networks with multiple layers (deep neural networks) to process data and learn complex patterns. It excels at tasks like image recognition, speech processing, and natural language understanding. Deep learning powers advanced AI applications such as facial recognition, self-driving cars, chatbots (e.g., ChatGPT), and medical image analysis.
ebook - Unlocking AI: A Simple Guide for Beginners | AI Course
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.
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:
In a neural network, parameters include weights and biases that connect neurons.
The more parameters a model has, the more complex it can be.
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:
If a model predicts 100 emails as spam, and 90 of them are actually spam, the precision is 90%.
Formula:
Precision=True Positives/(True Positives+False Positives)
True Positives: Correctly predicted positive cases.
False Positives: Incorrectly predicted positive cases.
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:
If there are 100 spam emails in a dataset, and the model correctly identifies 80 of them, the recall is 80%.
Formula:
Recall=True Positives/(True Positives+False Negatives)
True Positives: Correctly predicted positive cases.
False Negatives: Positive cases that the model missed.
High recall means the model is good at finding most of the positive cases.
Q: What is the difference between precision and recall?
Precision focuses on the accuracy of positive predictions (avoiding false positives).
Recall focuses on capturing as many positive cases as possible (avoiding false negatives).
For example, in a medical test:
High precision means most positive test results are correct (few false alarms).
High recall means most actual cases of the disease are detected (few missed cases).
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:
GPT-3, a language model, has 175 billion parameters.
PaLM, Google’s language model, has 540 billion parameters.
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:
True Positives (TP): Correctly predicted positive cases.
True Negatives (TN): Correctly predicted negative cases.
False Positives (FP): Incorrectly predicted positive cases.
False Negatives (FN): Incorrectly predicted negative cases.
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:
Mean Squared Error (MSE): Used in regression tasks.
Cross-Entropy Loss: Used in classification tasks.
A lower loss indicates a better-performing model.
ebook - Unlocking AI | AI Course | Bundle Offer (including RAG ebook)
No comments:
Post a Comment