AI refers to the simulation of human intelligence in machines, allowing them to perform tasks that typically require human intelligence, such as learning, reasoning, and problem-solving.

ML is a subset of AI that enables machines to learn from data and improve their performance without being explicitly programmed. It focuses on the development of algorithms that allow computers to make predictions or decisions based on patterns in data.

Traditional programming involves explicit instructions, while ML uses data to train algorithms, allowing systems to learn and improve over time.

Types include supervised learning (training with labeled data), unsupervised learning (finding patterns in unlabeled data), and reinforcement learning (learning through trial and error).

Python is widely used for its simplicity and rich libraries. Other languages include R, Java, and Julia.

Deep Learning is a subset of ML that involves neural networks with multiple layers (deep neural networks). It excels in tasks such as image and speech recognition.

NLP enables machines to understand, interpret, and generate human language. AI applications in NLP include chatbots, language translation, and sentiment analysis.

Computer Vision enables machines to interpret and make decisions based on visual data. Applications include image and facial recognition.

AI improves healthcare through disease prediction, diagnostics, personalized medicine, and robotic surgeries, enhancing efficiency and accuracy.

AI is the broader concept, while ML is a subset. AI encompasses any technique that enables machines to mimic human intelligence, and ML is a specific method for achieving AI.

Yes, AI is effective in detecting patterns indicative of fraud in financial transactions, enhancing security and minimizing risks.

AI automates tasks, improves risk management, enhances fraud detection, and enables personalized financial services, transforming the finance sector.

AI processes data from sensors and cameras in autonomous vehicles to make real-time decisions, ensuring safe navigation and efficient driving.

Neural Networks are models inspired by the human brain, consisting of interconnected nodes (neurons). They are used in deep learning for complex tasks like image and speech recognition.

AI enhances customer experiences through personalized recommendations, optimizes supply chain management, and improves inventory management in the retail sector.

Reinforcement Learning involves training models to make decisions by receiving feedback in the form of rewards or penalties based on their actions.

Yes, AI is used for threat detection, anomaly detection, and response automation, strengthening cybersecurity measures.

Transfer Learning allows models trained on one task to be adapted for another task, leveraging knowledge gained in the first task.

AI analyzes user behavior to make personalized recommendations, commonly seen in streaming services, e-commerce platforms, and social media.

Ethical considerations include biases in algorithms, data privacy concerns, transparency in decision-making, and the potential impact on employment. Addressing these issues is crucial for responsible AI development.