Have you ever found yourself lost in a sea of technical jargon when it comes to artificial intelligence (AI)? Don't worry, you're not alone! AI is growing fast, bringing lots of new words that might be hard to understand, even for tech-savvy guys. I
That's why we've created the ultimate guide on AI Glossary. If you're a curious beginner or a seasoned professional, this glossary guide will help you understand AI terms better.
So, without further ado, let’s move on to our main segment.
Alphabetical Listing of 60+ AI Glossary Terms
Now, you’ll discover the list of AI terms, arranged alphabetically for easy reference. Each term comes with a simple explanation. So, keep reading
Artificial general intelligence, or AGI
A concept that suggests a highly advanced AI that surpasses human abilities in various tasks and keeps getting better on its own.
AI alignment
AI alignment is a process of making sure an AI system functions the way we want it to. This includes small tasks like writing a sentence correctly and big ideas like following values and moral standards.
AI ethics
They are the principles designed to stop AI from harming people by guiding how AI should collect data and handle bias.
AI safety
A field that studies the long-term effects of AI. It includes the potential sudden rise of a super-intelligent AI that might be hostile to humans.
Algorithm
The algorithm is a set of instructions that enables a computer program to learn and analyze data and recognize patterns. It then uses this information to learn and perform tasks independently.
Alignment
Alignment is adjusting an AI to improve its performance in achieving desired outcomes. This can involve tasks like moderating content or ensuring positive interactions with humans.
Anthropomorphism
Anthropomorphism is the attribution of human characteristics, emotions, or behaviors to non-human entities or objects. In AI, this can mean thinking a chatbot is more humanlike and aware than it really is. It can be believing it feels emotions like happiness or sadness, or even considering it sentient.
Artificial intelligence, or AI
Artificial intelligence (AI) refers to technology that enables machines to perform tasks that typically require human intelligence, like learning and decision-making.
AI website builder
An AI website builder is a tool or platform that uses artificial intelligence (AI) to allow users to build a website without manually working. It can generate an entire website just from your prompt.
Take Dorik AI website builder, for example. It can create any type of website following your prompts. It can also generate SEO-optimized website copies and pixel-perfect visuals with the help of AI.
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Bias
In the context of bias, errors in large language models happen because of the data they learn from. This can mean wrongly linking certain traits to specific races or groups due to stereotypes.
Black box AI
Black box AI systems operate invisibly. Users input data and receive output, but the machine's internal steps remain hidden.
Chatbot
A chatbot refers to a computer program that is designed to simulate a conversation with human users. They are often used for customer service or information retrieval.
Related Read: Best AI Chatbot
ChatGPT
ChatGPT is an advanced conversational AI developed by OpenAI. The AI tool is capable of engaging in a natural and informative dialogue with users.
Related Read: How to use ChatGPT
Cognitive computing
Cognitive computing is the same thing as artificial intelligence.
Constitutional AI
Anthropic, an AI startup, developed Constitutional AI. The tool trains AI systems to adhere to specific values or principles defined in a constitution.
Data augmentation
Data augmentation refines AI training by mixing up existing data or including a wider variety of data sources for better learning.
Deep learning
Deep learning is a kind of AI that mimics the structure of the human brain. It is used for complex pattern recognition.
Dall-E
DALL-E is an AI model developed by OpenAI. The tool is capable of generating images from textual descriptions, showcasing creativity and imagination. It’s one of the best AI image generators.
Related Read: How to Use DALL-E
Diffusion
Diffusion models in machine learning add random noise to existing data, such as photos, and train their networks to reconstruct the original data.
Related Read: What is Stable Diffusion
Data mining
Data mining is the process of searching through large datasets to find patterns and extract valuable information. Extracting names of entities from the dataset is one example of a data mining process.
Data validation
This is the process of checking the data quality before utilizing it for the development and training of AI models.
Emergent behavior
It is the situation when an AI model exhibits unintended abilities.
EU AI Act
The EU AI Act outlines regulations to ensure responsible AI deployment without infringing on data privacy rights.
End-to-end learning, or E2E
E2E is a deep learning process where a model is trained to complete a task in one go. It’s done without step-by-step training, learning from inputs to solve it.
Foom
Foom is also known as fast takeoff or hard takeoff. This concept suggests that if someone creates AGI, it might be too late to save humanity.
Generative adversarial network (GAN)
A generative AI model consists of two neural networks: a generator and a discriminator. The generator produces new content, while the discriminator verifies its authenticity.
Generative AI
Generative AI creates new data, images, or text, often using techniques like neural networks to mimic and generate content.
Related Read: Generative AI vs Predictive AI
Generative pre-trained transformer (GPT)
GPTs are AI algorithms, developed by OpenAI that power many popular natural language processing and generative models. Examples include GPT-3, GPT-3.5, and GPT-4 from the GPT algorithm family.
Google Gemini
Google Gemini is a chatbot by Google, like ChatGPT. Gemini fetches information from the latest web, while ChatGPT is limited to pre-2021 data and offline.
Related Read: How to Use Gemini AI
Hallucination
It’s an inaccurate AI response, which might occur when generative AI confidently produces incorrect answers, often without clear reasons.
Large language model, or LLM
A large language model (LLM) is an AI system that is able to understand and generate human-like text, often with impressive accuracy.
Large Language Model Meta AI (LLaMA)
LLaMA, an open-source Large Language Model, was released by Meta.
Machine learning, or ML
Machine learning is a type of artificial intelligence that enables computers to learn from data, discover patterns, and make judgments with minimal human interaction. It results in improved performance over time.
Microsoft Bing
Bing is a web search engine developed and operated by Microsoft. It provides internet search services, including web, image, video, and map searches.
Related Read: How to Use Bing AI
Multimodal AI
Multimodal artificial intelligence (AI) is a machine learning model that can process and combine various data types including numerical data, texts, audio, video, or images to produce output or make projections.
Natural language processing
Natural language processing (NLP) is an area of artificial intelligence which enables computers to iengage in interactions with humans by understanding, interpreting, and manipulating human language.
Natural language generation (NLG)
Natural language generation is a subfield of AI focused on enabling computers to produce human-like spoken or written output text based on data inputs.
Neural network
A neural network is a computational framework modeled after the human brain. It comprises interconnected nodes that process input to recognize patterns and make decisions as human brains do.
Overfitting
Overfitting is the situation that happens when a machine learning model learns and performs on the training data too well, but performs poorly on unseen or new data.
OpenAI
OpenAI is a nonprofit organization dedicated to artificial intelligence research to advance AI safely and beneficially for humankind.
Paperclips
Paperclips theory shows how AI, fixated on a single goal, may ignore broader consequences and can cause harm in its constant effort.
Parameters
Parameters are variables used to specify how input data is converted into output. These variables are learned from training data.
Prompt
Prompts are the instructions or cues given to AI models to guide their responses. The clarity in a prompt directly impacts the quality of generated content.
Prompt Engineering
Prompt engineering refers to the practice of crafting proficient prompts to guide AI models in generating desired outputs. Its objective is to optimize the performance and relevance of generated content.
Prompt chaining
Prompt chaining guides AI models through sequential steps of a large and complex task by using a simple set of instructions.
Pathways Language Model (PaLM)
PaLM is a super-powerful language model by Google, trained on 540 billion parameters to tackle various tasks and understand language in a very efficient way.
Q-learning
Q-learning is a type of model-free reinforcement learning. In this method an agent learns to make decisions by estimating the value of taking different actions in various situations.
Reinforcement learning from human feedback (RLHF)
RLHF is a technique where an AI agent learns through interactions with humans. It receives feedback from humans to improve its decision-making and behavior.
Stochastic parrot
It is a metaphorical argument against large language models that are designed to mimic human-like text well, but without actually understanding the meaning.
Synthetic data
These are artificially generated data used to train machine learning models. It offers advantages like privacy preservation and augmentation of limited real-world datasets.
Style transfer
A creative transformation AI technique that modifies the style of an image or video into another image or video while preserving the real content.
Temperature
An important parameter in language generation models that controls the randomness of generated output by balancing between its creativity and predictability.
Text-to-image generation
An AI technology that converts textual descriptions into corresponding images. It utilizes deep learning architectures for realistic image synthesis.
Tokens
These are the smallest units of text used in natural language processing derived from a text. It can be a word or a character.
Training data
Training data refers to the data which is used to train machine learning models. From these training data, ML learns patterns to make predictions or decisions on new, unseen data.
Transformer model
Transformer models are a powerful type of neural network architecture that excels at understanding sequential data, like text.
Turing test
A benchmark for AI's human-like intelligence. This test is a classic approach to determining a machine's ability to exhibit human-like intelligence.
Unsupervised learning
This training is a Machine learning technique where models learn from unlabeled data and discover patterns and structures without explicit supervision.
Variational autoencoder
Variational autoencoders (VAEs) are versatile models in AI that employ deep learning to generate new content, detect anomalies, and filter noise.
Weak AI, aka narrow AI
These are AI systems designed for performing specific tasks. These AI systems may be highly effective in their specific area but they lack general intelligence.
Zero-shot learning
Zero-shot learning is a technique that enables machine learning models to perform tasks and identify entirely new things without prior training.
Conclusion
We've covered 60+ most popular AI glossary terms to help you better understand the world of artificial intelligence.
Remember they are the initial terms to understand and there are a lot more. While working with AI you’ll face more terms and understand the rest with time.
