Introduction to Artificial Intelligence Questions and Answers for Viva

Answer-1: Artificial Intelligence (AI) is a branch of computer science focused on creating systems capable of performing tasks that would normally require human intelligence, such as learning, reasoning, problem-solving, and decision-making.

Answer-2: AI can be categorized into three types: Narrow AI (Weak AI), General AI (Strong AI), and Superintelligent AI. Narrow AI is designed for specific tasks, General AI can perform any intellectual task a human can, and Superintelligent AI surpasses human intelligence in all aspects.

Answer-3: The main areas of AI include machine learning, natural language processing, robotics, expert systems, and computer vision.

Answer-4: Machine Learning is a subset of AI that enables systems to learn and improve from experience without being explicitly programmed. It involves algorithms that detect patterns in data and make decisions based on that data.

Answer-5: Deep Learning is a subset of machine learning that uses neural networks with many layers (deep neural networks) to analyze large amounts of data. It is particularly useful in tasks like image recognition and natural language processing.

Answer-6: The Turing Test, proposed by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behavior indistinguishable from that of a human.

Answer-7: NLP is a field of AI that focuses on the interaction between computers and human language, allowing machines to understand, interpret, and respond to text or speech in a way that is meaningful.

Answer-8: An Expert System is an AI application that mimics the decision-making abilities of a human expert. It uses knowledge bases and inference rules to solve specific problems within a particular domain.

Answer-9: A Neural Network is a computational model inspired by the way biological neural networks in the human brain process information. It consists of interconnected nodes (neurons) organized in layers to process data.

Answer-10: Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize cumulative rewards.

Answer-11: Supervised learning uses labeled data to train models, whereas unsupervised learning uses unlabeled data and the system tries to find hidden patterns or intrinsic structures.

Answer-12: AI is the overarching concept of machines performing tasks that require human-like intelligence. Machine Learning is a subset of AI that enables machines to learn from data, while Deep Learning is a further subset of ML that uses deep neural networks.

Answer-13: Big Data is crucial in AI as it provides the large amounts of data needed to train AI models, especially in fields like machine learning and deep learning, which require vast datasets to identify patterns and make predictions.

Answer-14: A Decision Tree is a flowchart-like structure used in machine learning for classification and regression tasks. It splits data into subsets based on feature values to predict outcomes.

Answer-15: Overfitting occurs when a model learns the details and noise in the training data to the extent that it negatively impacts the performance of the model on new data.

Answer-16: Underfitting happens when a machine learning model is too simple and cannot capture the underlying trend in the data, leading to poor performance.

Answer-17: Data preprocessing is crucial as it involves cleaning and organizing raw data to improve the quality of input for machine learning models, ensuring more accurate and reliable outcomes.

Answer-18: The main types of neural networks include feedforward neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs).

Answer-19: A CNN is a type of deep learning model primarily used for image processing tasks. It uses convolutional layers to extract features from input images.

Answer-20: Backpropagation is a training algorithm used for neural networks, where the model adjusts weights by propagating errors backward from the output layer to the input layer.

Answer-21: AI Ethics deals with the moral implications and societal impact of AI technologies, including issues related to fairness, transparency, accountability, and bias in AI systems.

Answer-22: An AI algorithm is a set of rules or instructions followed by a computer to perform tasks that simulate human intelligence, such as learning, decision-making, or problem-solving.

Answer-23: AI plays a significant role in automation by enabling systems to perform tasks autonomously, without human intervention, in fields such as manufacturing, logistics, and customer service.

Answer-24: AI refers to machines mimicking human cognitive functions, while human intelligence involves complex biological processes, emotional intelligence, and abstract reasoning, which AI currently cannot replicate fully.

Answer-25: AI is transforming industries like healthcare, finance, education, retail, and automotive by improving efficiency, enhancing decision-making, automating tasks, and enabling predictive analytics.

Answer-26: A chatbot is an AI-driven software application designed to simulate conversation with users, often used in customer service to answer questions and provide assistance.

Answer-27: The quality and quantity of training data are essential in AI as they directly impact the performance of machine learning models. More accurate and diverse data lead to better generalization and results.

Answer-28: Genetic algorithms are optimization algorithms inspired by natural selection, where potential solutions evolve over generations to find the best solution to a problem.

Answer-29: A Support Vector Machine is a supervised learning model used for classification and regression tasks. It finds the hyperplane that best separates different classes in the data.

Answer-30: AI is revolutionizing healthcare by enabling more accurate diagnostics, personalized treatment plans, drug discovery, and improving operational efficiency in healthcare systems.

Answer-31: AI has limitations such as lack of common sense, dependence on large datasets, difficulty in understanding context, and susceptibility to bias in algorithms.

Answer-32: Transfer Learning is a machine learning technique where a pre-trained model on one task is used to solve a different but related task, reducing the need for large amounts of data.

Answer-33: Machine Intelligence is a broader term that includes AI but also encompasses systems that exhibit human-like cognitive functions, without necessarily being based on AI principles.

Answer-34: AI is used in cybersecurity for threat detection, anomaly detection, malware analysis, and automating responses to security breaches.

Answer-35: A Markov Decision Process is a mathematical model used in reinforcement learning, where an agent makes decisions in an environment to maximize a cumulative reward based on its actions.

Answer-36: Deep learning uses multi-layered neural networks and large amounts of data to learn high-level representations, whereas traditional machine learning relies on simpler models and features.

Answer-37: The main goal of AI is to create intelligent agents that can solve complex problems, improve efficiency, and assist humans in decision-making processes.

Answer-38: Unsupervised learning is used for identifying patterns in data without prior labels, often used in clustering and anomaly detection.

Answer-39: A Recurrent Neural Network is a type of neural network designed for processing sequential data, such as time-series data or text

Answer-40: AI in business includes applications such as customer service chatbots, recommendation systems, predictive analytics, process automation, and personalized marketing.

Answer-41: A neuron in a neural network is a computational unit that receives inputs, applies a function, and passes the output to the next layer.

Answer-42: Some of the main challenges in AI include creating systems that can generalize well, ensuring data privacy and security, dealing with ethical concerns, and avoiding bias in models.

Answer-43: Supervised learning is a machine learning approach where the model is trained on labeled data to make predictions or classifications based on input-output pairs.

Answer-44: The purpose of an AI learning algorithm is to allow the system to learn from data and improve its performance over time, without being explicitly programmed for every specific task.

Answer-45: AI is used in autonomous vehicles to make real-time decisions, process sensor data, navigate, detect obstacles, and ensure safe driving through computer vision, reinforcement learning, and robotics.

Answer-46: AI is the broader field of creating intelligent systems capable of learning and adapting, while automation refers to the use of technology to perform repetitive tasks without human intervention.

Answer-47: AI impacts employment by automating certain jobs, which may lead to job displacement, but also creates new opportunities in AI development, data science, and related fields.

Answer-48: Bias in AI occurs when algorithms produce unfair or skewed results due to biased data or incorrect assumptions in the model's design.

Answer-49: A Generative Adversarial Network is a type of neural network where two networks (a generator and a discriminator) compete to generate realistic data, such as images or text.

Answer-50: AI improves decision-making by providing data-driven insights, automating complex calculations, identifying patterns, and making predictions that support better, more informed decisions.