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Artificial Intelligence and Generative AI

Find resources on artificial intelligence, ChatGPT, AI academic productivity tools, plagiarism, prompt engineering, GPT misinformation and hallucinations, AI image tools, AI literacy, and discussions related to ethics.

What is Artificial Intelligence?

Artificial intelligence refers to the development of computer systems that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and natural language understanding.

Artificial intelligence is "the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience."

Source: Encyclopaedia Britannica

What is Generative Artificial Intelligence?

Generative artificial intelligence refers to algorithms and models that can generate new content or data, such as images, videos, music, or text, based on patterns learned from existing information.

Other Types of Artificial Intelligence

  • Chatbot: A chatbot is a software application that uses natural language processing (NLP) and machine learning to simulate conversation with humans, either via text or voice interfaces.
  • Machine Learning (ML): Machine learning is a subset of artificial intelligence that involves training computer systems to learn from data and improve their performance over time through experience.
  • Natural Language Processing (NLP): NLP is a subfield of artificial intelligence that deals with the interaction between computers and human language, including text and speech processing, sentiment analysis, machine translation, and dialogue systems.
  • Large Language Model (LLM): A large language model is a type of machine learning model that is trained on vast amounts of text data to generate language outputs that are coherent and contextually appropriate.

Other Terms

Using Large Language Models (LLMs)

  • Hallucination: In the context of AI, hallucination refers to the phenomenon where a model generates inaccurate or imaginary output that cannot be explained by its training data, often due to overfitting or underfitting.
  • Prompt: A prompt is a specific task or question that is given to an AI system to elicit a response or output.
  • Prompt Engineering: Prompt engineering is the process of designing and refining prompts to elicit desired responses or behaviors from AI systems, in order to improve their performance and versatility.

Understanding Large Language Models (LLMs)

  • Parameters: Parameters are settings or values that are adjusted during the training process to optimize the performance of an AI model, such as the learning rate, regularization strength, or number of hidden layers.
  •  Temperature: In the context of generative AI, temperature refers to a parameter that controls the "randomness" or "diversity" of generated samples, with higher temperatures resulting in more diverse and less predictable outputs.
  • Tokens: In Natural Language Processing and machine learning, tokens refer to individual words or phrases in a text dataset, which are used as input features for models to analyze and understand the meaning of the text.
  • Training Data: Training data is the set of examples or inputs used to train an AI system, which helps the model learn patterns and relationships in the data and make predictions or decisions.