Master this deck with 21 terms through effective study methods.
Generated from uploaded pdf
The first step in the working of Artificial Intelligence is Data Collection. AI systems rely on large sets of data, which can include images, text, or sensor readings sourced from online platforms, sensors, databases, and user input.
Common types of data used in AI include images, text documents, audio files, sensor data, video streams, and structured databases. These diverse data types help AI systems learn and make informed decisions.
AI processes and learns from data using algorithms to analyze it and identify patterns. This involves feature extraction, pattern recognition, statistical analysis, and model training to improve decision-making capabilities.
Model training in AI focuses on creating new content or making predictions based on learned patterns from large datasets. It involves feeding data to the model, making predictions, and adjusting internal parameters to improve accuracy.
Traditional AI focuses on decision-making and problem-solving, while Generative AI is centered on creating new content such as text, images, audio, and video. Generative AI learns patterns from data to produce original outputs.
Natural Language Processing (NLP) is a branch of AI that enables computers to understand, interpret, and generate human language in both text and speech. It powers applications like voice assistants and language translation.
Expert systems are computer programs that simulate the decision-making ability of human experts in specific domains. They utilize a structured knowledge base and inference engine to solve complex problems.
AI assists in healthcare by analyzing medical images such as X-rays and MRIs, spotting diseases earlier than human doctors, and personalizing treatment plans. It enhances the speed and accuracy of medical diagnostics.
AI in self-driving cars helps understand surroundings, predict the actions of other vehicles, and navigate safely. Features include automatic braking, lane-keeping assistance, and adaptive cruise control.
AI enhances entertainment by analyzing user data to suggest shows or create personalized playlists. Services like Netflix and Spotify use AI to predict content that users will enjoy based on their preferences.
Machine Learning (ML) is a branch of AI that enables computers to learn patterns from data and make predictions or decisions without explicit programming. It relies on data to improve its performance over time.
The Turing Test, proposed by Alan Turing in 1950, suggests that a machine is considered intelligent if it can engage in conversation indistinguishably from a human. It serves as a benchmark for evaluating AI's conversational abilities.
Narrow AI, or Weak AI, is designed to perform specific tasks effectively, such as language translation or playing chess. General AI, or Strong AI, refers to a theoretical AI that can think and learn like a human across various domains, which has not yet been achieved.
The key components of Expert Systems include a knowledge base that contains domain expertise, an inference engine that applies reasoning mechanisms, and rule-based logic to solve complex problems.
Feedback in AI learning is crucial as it allows the system to improve its performance. In reinforcement learning, the agent receives rewards or penalties based on its decisions, learns from mistakes, and adjusts its actions accordingly.
Generative AI is a type of artificial intelligence that creates new content, such as text, images, audio, or video, based on learned patterns from existing data. It generates original outputs in response to prompts.
Since 2010, Deep Learning has leveraged big data, GPUs, and multi-layered neural networks to achieve human-level or superhuman performance in tasks like image recognition and game playing, exemplified by breakthroughs like AlexNet and AlphaGo.
Depth First Search (DFS) is a graph/tree traversal algorithm that explores as deep as possible along one branch before backtracking. It uses a stack data structure to keep track of visited nodes and unvisited neighbors.
Decision-making in AI involves selecting the best action from various options to achieve a specific goal. It relies on the agent's knowledge and sensory input to make informed choices.
AI improves over time by learning from data and experiences. It identifies patterns automatically, adjusts its internal parameters based on feedback, and enhances its performance without human intervention after training.
AI applications in smartphones include face recognition for unlocking devices, voice assistants like Siri and Google Assistant for answering questions, and navigation features that utilize AI for route optimization.