Artificial intelligence (AI)
Artificial intelligence has become a part of daily life. Basically, AI is the system that can mimic human intelligence. It can do things such as learning, reasoning, problem solving and natural language understanding. Moreover, AI can do works that humans do but in an efficient and more organized manner. It is best for saving time.
Machine learning (ML)
Machine learning is a part of artificial intelligence, that helps systems to learn from data and improve their performance without being explicitly programmed. AI is a broad concept of creating machine intelligence, ML is a practical method through which AL is realized.
Artificial Intelligence
Basically, AI consists of different branches such as machine learning, robotics, natural language processing, deep learning, computer vision, etc. Furthermore, the basic function of AI is to create machines that perform tasks such as speech recognizing, image analyzing or playing complex strategy games. AI is divided into two types which are described below
Narrow AI (Weak AI): This type of AI is designed for tasks such as voice assistant (Siri, Alexa) or system that recommends platform like Netflix or YouTube. Moreover, this system only perform task in its programmed domain.
General AI (Strong AI): Basically, it is a theoretical concept where machine can perform any task that humans can do. It shows creativity and emotional intelligence.
Superintelligent AI: It is a concept about AI that in future it will surpass human intelligence in every field of life. Furthermore, this can be taken as a treat by human. Today, humans are controlling AI and in future AI will be able to control humans.
Machine Learning (ML): Machine learning is an engine that generate many AI application. ML uses algorithms and statical technique to learn from data patterns. Moreover, it does not follow fixed rules. ML learns relationship from data and predictions are improved with experience. Different type of machine learning includes
Supervised learning: In this type of ML models are trained by using labeled data. For Example: to predict house price based on location and size.
Unsupervised learning: Models are created to work with unlabeled data to find hidden patterns or grouping. For Example: segmentation of customer in marketing.
Reinforcement learning: Basically, trail and error method is used for systems to learn. Systems receive rewards and penalties for their actions. For Example: self-driving cars to navigate traffic.
Application of AI and ML
Healthcare: Diagnose disease, suggest medicine and personalized treatment plan.
Finance: Manage accounts, fraud detection and risk analysis.
Education: Smart teaching, timetable plans, automated learning.
Transportation: Self-driving vehicle and traffic prediction. Security: Cybercrime detection, facial recognition.
Security: Cybercrime detection, facial recognition.