Essential AI Terms and Definitions

1. Accuracy –

How well a model predicts correct data and outcomes.

2. Algorithm –

It is a finite sequence of specific instructions that is used to solve complex problems.

3. Blockchain –

It is a decentralized technology that records all transactions in various computers. It is secure and completely transparent.

4. Autonomous –

Those machines that can do their tasks completely on their own without any human interference are said to be Autonomous.

5. Bias –

One of the most basic AI concepts, that is used to refer to a certain type of preference. In computer science, it refers to the unjust preference in data, algorithms, or input which might give the wrong output.

6. Chatbot –

It is a computer program used to strike up conversations with human beings. It is used to convert manually handled queries to automated responses on specific inputs.

7. Training Data –

Datasets used to help AI learn patterns and features so that it can generate correct outcomes later on.

8. Corpus –

A large set of data which are structured in a way that it can instruct or train algorithms, systems, technology, etc.

9. Deep Learning –

When a machine analyses the unstructured data, it can be a text, image, or file that does not have a label and it tries to form a pattern of the data provided.

10. Hallucination –

When an AI does not have a proper set of data on a user-generated query, it still tries to answer it by providing the next best solution which might not be the answer the user would be looking for. It decreases the accuracy of a technology.

11. Turing test –

This test is used to analyze human intelligence in a machine. A human being is asked to have a conversation between a person and a machine. If the judge cannot figure out which response is by a human, then it means that the machine has passed the Turing test.

12. Structured Data –

Organized data in a tabular format.

13. Unstructured Data –

Data that has no format, such as images, files, etc.

14. Dataset –

A collection of structured data in a table format.

15. Data mining –

It is a process of finding out logical patterns and insights from a large pool of datasets.

16. Entity Annotation –

This is one of the most important AI terms used to refer to the labeling of unstructured data so that it becomes easy for machines to understand the data.

17. Forward chaining –

It is a technique to move from known facts and uses rules to move forward until a desired solution is found.

18. Intent –

The purpose or reason behind a user’s interaction with an AI.

19. Label –

An identity or name assigned to any data or an element within a system.

20. Machine Translation –

Translation of text by AI and computer algorithms, completely independent of human interference.

21. Machine Learning –

One of the basic AI concepts that everyone should know. It is a field in AI where its general focus is to create algorithms and models that make it easy for machines to make predictions based on the data.

22. Natural Language Generation (NLG) –

This refers to the ability of a machine to transform structured data into text that can be understood by humans.

23. Natural language processing (NLP) –

The process of teaching computers to understand and interpret human language.

24. Overfitting –

A problem where a machine memorizes the data of a training file, but does not understand it.

25. Predictive Analytics –

It is the use of data, algorithms, and machine learning techniques to anticipate outcomes arising from historical data.

26. Weak AI –

It has limited features and is only specialized at a specific task.

27. Mapping System –

It acts as a GPS for machines that helps them understand their surroundings.

28. Composite AI –

It is used to create a much more advanced AI version and broaden its features.

29. Controlled Vocabulary –

Organized list of keywords in a specific system to standardize its workings.

30. Data drift –

This refers to a problem where there is a shift in the input data of a machine that impacts its accuracy as well as its performance.

31. Data scarcity –

Limited data available for a task or a problem which creates a problem for a model to generate accurate results.

32. Disambiguation –

It refers to the clarity given to a machine when there is more than 1 meaning of a word or a phrase.

33. Emotion AI –

It is a technology that is designed to address the emotional needs of human beings and how to behave with them.

34. AI ethics –

This AI term is used to refer to the ethical principles that guide and oversee the development of an AI.

35. Automatic Speech Recognition (ASR) –

This technology converts human-entered voices into written text.

36. Brute force search –

Finding all the possible solutions to a problem until and unless a correct output is found. It is an extremely time-consuming process too.

37. Data Architect –

A professional who designs the data layout or framework of an organization’s data architecture.

38. Data lake –

Storage of a wide pool of unstructured data in its true form.

39. Pattern recognition –

The identification of patterns within data, signals, images, etc.

40. Text-to-Speech (TTS) –

It converts written text into verbal and spoken language language.

41. Image Recognition –

This feature helps machines understand and search for patterns in an image.

42. AI Safety –

It refers to the practices involved in seeing the ethical functioning of a machine and decreasing any chances of a hostile movement.

43. Emergent Behavior –

When a machine performs an unexpected output.

44. Generative AI –

An AI term used to refer to Artificial Intelligence that can generate content on its own, it can be an image, video, or voice.

45. Guardrails –

Restriction on AI models so that they stay within an ethical limit and do not create any disturbing and problematic content.

46. Multimodal AI –

An AI that can monitor and process data from multiple sources.

47. Prompt Chaining –

When an AI model can link previous conversations to produce future outputs for a user-generated input. ChatGPT has this feature.

48. Zero-shot learning –

It is an AI term used to make a model generate outputs from unrecognized data without actually giving it proper training.

49. Hybrid AI –

Combination of multiple artificial intelligence technologies, to create a more robust and fast AI system.

50. Artificial general intelligence, or AGI –

A basic AI terminology for beginners, AGI is devoted to much more advanced learning with a feature to do tasks better than human beings and also to teach itself.

Artificial Intelligence (AI) is a rapidly evolving field with numerous terms and concepts that may seem difficult to beginners. Whether you’re just starting your journey into AI or looking to expand your knowledge, There are many AI terms beginners should know.

In this blog post, we’ll explore 50 AI terms that every beginner should know, demonstrating the terminology and providing a solid foundation for further exploration in the field.