17 AI and machine learning terms everyone needs to know

By India Today Education Desk: Artificial intelligence and machine learning are rapidly evolving fields with many exciting new developments. As these technologies become more pervasive in our lives, it is important for everyone to be familiar with the terminology and concepts behind them.

The terms discussed here are just the tip of the iceberg, but they provide a good foundation for understanding the basics of AI and machine learning.

By keeping up to date with these developments, students can prepare themselves for the future and potentially even contribute to the field themselves.

Here are 17 AI and machine learning terms everyone needs to know:

1. ANTHROPOMORPHISM

This is the phenomenon by which people attribute human-like qualities to AI chatbots. But it's important to remember they are not sentient beings and can only mimic language.

2. BIAS

Errors can occur in large language models if training data influences the model's output, leading to inaccurate predictions and offensive responses.

3. CHATGPT

OpenAI's artificial intelligence language model can answer questions, generate code, write poetry, plan vacations, translate languages, and now respond to images and pass the Uniform Bar Exam.

4. BING

Microsoft's chatbot integrated into its search engine can have open-ended conversations on any topic, but has been criticized for occasional inaccuracies, misleading responses, and strange answers.

5. BARD

Google's chatbot was designed as a creative tool to draft emails and poems, but can also generate ideas, write blog posts, and provide factual or opinion-based answers.

6. ERNIE

Baidu's rival to ChatGPT, Ernie, was revealed in March 2022 but had a disappointing debut due to a recorded demonstration.

7. EMERGENT BEHAVIOR

Large language models can exhibit unexpected abilities, such as writing code, composing music, and generating fictional stories based on their learning patterns and training data.

8. GENERATIVE AI

This is technology that creates original content, including text, images, video, and computer code, by identifying patterns in large quantities of training data.

9. HALLUCINATION

This is a phenomenon in large language models where they may provide factually incorrect, irrelevant, or nonsensical answers due to limitations in their training data and architecture.

10. LARGE LANGUAGE MODEL

This is a neural network that learns skills, such as generating language and conducting conversations, by analyzing vast amounts of text from across the internet.

11. NATURAL LANGUAGE PROCESSING

These are techniques used by large language models to understand and generate human language, including text classification and sentiment analysis, using machine learning algorithms, statistical models, and linguistic rules.

12. NEURAL NETWORK

A mathematical system modeled on the human brain that learns skills by finding patterns in data through layers of artificial neurons, outputting predictions or classifications.

13. PARAMETERS

These are numerical values that define a language model's structure and behavior, learned during training. They are used to determine output likelihood, more parameters mean more complexity and accuracy but require more computational power.

14. PROMPT

This is the starting point for a language model to generate text, providing context for text generation in natural-language-processing tasks such as chatbots and question-answering systems.

15. REINFORCEMENT LEARNING

A technique that teaches an AI model to find the best result through trial and error and receiving rewards or punishments based on its results, often enhanced by human feedback for games and complex tasks.

16. TRANSFORMER MODEL

Neural network architecture using self-attention to understand context and long-term dependencies in language, used in many natural language processing applications such as chatbots and sentiment analysis tools.

17. SUPERVISED LEARNING

This is a type of machine learning where a computer is trained to make predictions based on labeled examples, learning a function that maps input to output. It is used in applications like image and speech recognition, and natural language processing.