What is Weak AI and how is it used today
Weak AI, also known as narrow AI, refers to artificial intelligence systems that are designed to perform specific tasks and do not have the ability to learn or adapt to new situations beyond their specific programming. These systems are trained to perform specific tasks such as image recognition, natural language processing, and decision making, but…
Differences between General AI and Strong AI
General AI, also known as artificial general intelligence (AGI), is a type of artificial intelligence that is capable of performing any intellectual task that a human can. AGI would be able to understand and learn any subject, rather than being specifically designed for a particular task or set of tasks. It would be able to…
What does the term “model” means in AI
In artificial intelligence (AI), a model is a representation of a system, process, or concept. It can be a mathematical equation, a set of rules, a diagram, or a physical system that represents a concept or process. Models are used to help understand, predict, and replicate the behavior of a system or concept. Models are…
Linguistic annotation uses and importance in AI
Linguistic annotation is the process of adding linguistic information to text data. This can include adding information about the structure of the text, such as the parts of speech of individual words or the syntax of sentences, as well as adding more contextual information, such as named entities, relationships between words, and the overall meaning…
Why Natural Language Generation (NLG) is important in AI and Machine Learning
Natural language generation (NLG) is the process of automatically producing human-like text from data or other inputs. It is an important aspect of artificial intelligence (AI) and machine learning because it allows systems to communicate with humans in a way that is more natural and intuitive, rather than just presenting data in a raw or…
Forward chaining in AI
Forward chaining is a method of reasoning that is used in artificial intelligence (AI) and computer science to solve problems by starting with the available data and working forwards to deduce new conclusions. It is often used in rule-based systems, where the goal is to find a set of rules that can be applied to…
What is Entity extraction and how it is used in AI and neural networks?
Entity extraction, also known as named entity recognition, is the process of identifying and extracting specific entities or objects from a text, image, or video. In the context of artificial intelligence (AI) and neural networks, entity extraction is often used to automatically identify and classify specific entities within a given data set. There are several…
How to use Entity annotation in artificial intelligence systems
Entity annotation, also known as entity tagging or entity labeling, is the process of identifying and labeling specific entities or objects within a text, image, or video. In the context of artificial intelligence (AI) systems, entity annotation is often used to train and evaluate machine learning algorithms and models that are designed to recognize and…
Computational learning theory and fundamentals
Computational learning theory is a subfield of artificial intelligence (AI) that focuses on the study of algorithms and theoretical models that can be used to learn from data. It aims to understand how computers can learn from data and make predictions or decisions based on that learning. In the context of AI, computational learning theory…
What is Cognitive computing and how it differs from Artificial Intelligence
Cognitive computing is a subfield of artificial intelligence (AI) that aims to develop systems that can perform tasks that are typically associated with human cognition, such as understanding natural language, learning, and problem-solving. Cognitive computing systems are designed to be able to learn and adapt over time, allowing them to improve their performance on a…
