Algorithm: A logical sequence of explicit, step-by-step instructions to solve any problem. These are the building blocks that make up machine learning and artificial intelligence. The goal of an algorithm is to teach AI, neural network, or other machines how to figure things out on their own.
Artificial Intelligence: Computers capable of doing human-like tasks: decision-making, object classification and detection, speech recognition and translation.
Artificial neural network: An algorithm that attempts to mimic the biological neural networks that make up the human brain, with layers of connected “neurons” sending information to each other.
Big data: Large amounts of structured and unstructured data that is too complex to be handled by standard data-processing software.
Chatbots: Simulates a real conversation through text or voice messages. Chatbots use machine learning to identify communication patterns and thus learn to imitate real conversations. After each dialogue, they become smarter.
Cognitive computing: A computerized model that mimics human thought processes to assist humans in finding solutions to complex problems, rather than automating things. It overlaps with AI and uses many of the same underlying technologies.
Computer vision: A field of AI that uses AI technologies to solve complex problems using information from images and video, such as object detection.
Data mining: The process of sorting through large sets of data in order to identify recurring patterns while establishing problem-solving relationships.
Machine learning: A set of algorithms that allows you to feed machines (computers) data and let them learn a few tricks on their own, without being explicitly programmed to do so. Machine learning is a subset of AI. It can be fed only with structured data, while AI can handle both structured and unstructured pieces of information.