Artificial Intelligence

AGI (Artificial General Intelligence)

AGI is a type of AI that can perform any task that a human can, using general problem-solving abilities. It’s different from the more common AI, which is designed for specific tasks like recognizing faces or playing chess.

Alignment

Alignment ensures that AI systems behave in ways that are ethical and in line with human values, preventing them from causing harm or acting in ways that are unintended.

Chatbot

Chatbots are AI systems that communicate with people through text or voice. They can help answer questions, provide support, or carry out tasks.

CNN (Convolutional Neural Network)

A deep learning architecture designed to process grid-like data, such as images. It is commonly used in image recognition tasks, like detecting faces or objects in photos.

Computer Vision

A field of AI that enables machines to interpret and make decisions based on visual data. For example, AI-powered cameras can identify objects in images and assist in tasks like facial recognition.

DALL·E

An AI model developed by OpenAI that generates images from textual descriptions. For example, you can ask DALL·E to create a picture of “a cat wearing a suit,” and it will generate a unique image based on that description.

Explainability

Explainability refers to how easily humans can understand how an AI system makes decisions. It is important for trust and accountability. For example, if a loan decision is made by an AI, explainability would allow people to understand why that decision was made.

Few-shot Learning

Few-shot learning is a type of AI that can learn from only a few examples instead of needing thousands or millions. This is similar to how humans can learn from a small number of experiences.

GPT (Generative Pre-trained Transformer)

A type of language model trained on vast text corpora to generate human-like text. For example, GPT can write essays, articles, or poems based on prompts given by users.

Hallucination

Hallucination happens when an AI generates information that seems real but is actually incorrect or made up, like a chatbot giving false facts.

LLM (Large Language Model)

A neural network trained on massive amounts of text to understand and generate language. These models can perform various language-related tasks, such as translation, summarization, and question answering.

Midjourney

An image generation model that transforms textual prompts into stylized digital artwork. For instance, you could ask Midjourney to create “a futuristic city at sunset,” and it would generate a unique, artistic interpretation of that prompt.

NLP (Natural Language Processing)

A subfield of AI focused on enabling machines to understand, interpret, and generate human language. NLP powers many applications, like voice assistants (Siri, Alexa) and text translation (Google Translate).

Perplexity

Perplexity measures how well a language model predicts a sample, with lower values indicating better performance. It’s a way to assess how well the AI understands and processes language.

RLHF (Reinforcement Learning from Human Feedback)

RLHF involves training AI systems to align with human preferences by using feedback from humans as reinforcement signals. For example, a robot learning to pick up trash might receive positive feedback when it does it correctly.

Temperature

Temperature is a parameter in generative models that controls the randomness of output. Higher values produce more creative and varied responses, while lower values lead to more focused and predictable outputs.

Token

A token is a unit of input (word, subword, or character) that language models process. For example, the sentence “Hello, world!” would be broken into tokens that the AI understands.

Transfer Learning

Transfer learning is a method where a model trained on one task is adapted for a related task, improving learning efficiency. For example, a model trained to recognize cats might be adapted to recognize dogs with less additional training.

Transformer

A deep learning model architecture designed for sequential data, particularly effective in natural language processing tasks. Transformers are used in many state-of-the-art language models, like GPT and BERT.