Top AI Acronyms You Should Know in 2023

Do not forget to improve your AI knowledge with these Top AI Acronyms in 2023

With the expansion of Artificial Intelligence, it has become a word that even a non-tech-savvy would know. No wonder people are easily aware of AI Acronyms but there is so much more in the AI dictionary than what meets the eye. Machine Learning, Deep Learning are a few easy terms but there is more to AI Acronyms.

The article enlists the top AI Acronyms you should know in 2023

Augmented intelligence:

Tools and technologies are created to improve human workers and help them perform more efficiently. Instead of replacing humans, this is considered a complement to them.

Machine learning: an area of artificial intelligence where a machine applies an algorithm to carry out a task or solve a problem. Machine learning technologies acquire knowledge by identifying patterns in datasets that they can utilize to provide long-term, more comprehensive results.

Another name for this is data mining.

Deep learning:

A branch of machine learning that, in essence, layers neural networks on top of one another to outperform all previous machine learning algorithms in accuracy.

Natural Language Processing (NLP): utilizing computer programs or other types of technology to decipher, interpret, and alter spoken language. Another name for this is text mining.

Structured data: Data that has been formatted or divided into fields, such as in a spreadsheet or database.

Unstructured data: Information that is not formatted in any specific way. Unstructured data examples could include pictures, movies, emails, books, posts on social media, or medical records.

Semi-structured data: Despite not existing in a database or spreadsheet, some data may have characteristics that make it simpler to arrange. XML files and NoSQL databases are a couple of examples.

Neural network: This artificial intelligence network effectively mimics the functioning of the human brain. Data is interpreted by a network of firing "neurons," which then make judgments and gradually learn from the input.

Predictive analytics: when a computer is capable of forecasting the future utilizing data from the past and the present.

Decision intelligence: It includes real-world applications in the area of complex adaptive systems. Decision intelligence is a practical discipline that frames a variety of decision-making approaches. To design, model, align, execute, monitor, and fine-tune decision models, decision intelligence offers a framework that combines some conventional and cutting-edge methodologies.