Machine learning (ML), and artificial intelligence (AI) are two distinct areas within the larger field of computer science. AI refers to the development of computer systems capable of performing tasks that would normally require human intelligence. This includes understanding natural language and recognizing patterns as well as making decisions. ML is a subfield within AI that focuses more on algorithms that enable computers to learn from data, without having to be programmed.
AI and ML are two different things. AI can be used to refer to a wide range of technologies and approaches while ML can be used to describe a specific method for building AI systems. AI systems can be built using many methods, including decision trees, rule-based systems and evolutionary algorithms. ML relies on algorithms that are able to learn from data and improve their performance over the course of time.
A key difference between AI/ML is the fact that AI systems can perform many tasks while ML algorithms are usually designed for a single task. An AI system may be programmed to recognize faces in images, understand natural language and make decisions based upon complex data. A ML algorithm might, on the contrary, be used to identify spam email addresses, predict stock prices and classify images.
There are many types of ML algorithms available, including unsupervised learning, supervised learning and reinforcement learning. Supervised learning algorithms can be trained using labeled data. This means that both the input and output data are included in the data sets used to train them. Unsupervised learning algorithms are trained using unlabeled data. This means that the data used to train the algorithm only includes input data. There is no corresponding output data. Reinforcement learning algorithms can be trained by interfacing with the environment and receiving rewards, or penalties, based on their actions.
These are five uses cases for AI/ML
- Natural language processing (NLP: AI and ML are two of the most powerful tools for building systems that can comprehend and generate human speech, such as chatbots or virtual assistants.
- Computer vision: AI/ML can be used for creating systems that recognize and classify objects in photos and videos. This includes self-driving cars, facial recognition systems, and self-driving cars.
- Predictive analytics: AI/ML can be used for systems that predict future outcomes based upon past data such as stock price prediction or demand forecasting.
- Robotics: AI/ML can be used for robots that learn from their environment. This includes warehouse robots as well as service robots.
- Fraud detection: AI/ML can be used for systems that can detect patterns in data that could indicate fraudulent activity. This includes identity verification and credit card fraud detection.