The Ultimate AI Glossary
Essential AI Terms You Should Know
Understanding the basic terminology used in the AI world allows you to confidently join in with the discussion, thus making it easier for you to learn more about artificial intelligence. However, with so many terms floating around, knowing what’s worth learning can be difficult. Fortunately, we’ve compiled a list of AI-related terms that are essential in understanding the technology.
Artificial Intelligence
Artificial intelligence is the simulation of human intelligence through computer algorithms capable of making basic or complex decisions.
AI is being utilized throughout technology and has become a heavily ingrained part of modern society, even if you don’t realize it. For instance, AI is used in everything from autocorrect on your phone to Google Maps to your smart fridge.
How Does AI Work?
Artificial intelligence combines large datasets, analytical processing technology, and algorithms to create – and ultimately select – choices on decision trees.
Computer Vision
Computer vision is a field of AI that allows computers to view, process, and understand digital images, videos, and other visual inputs.
While you’re less likely to encounter computer vision in your day-to-day life compared to general AI models, there are some common uses of the technology, especially in relation to self-driving vehicles like Teslas,which utilize computer vision to identify and interpret objects around it, such as traffic lights and other cars.
How Does Computer Vision Work?
In simple terms, computer vision tries to mimic the human brain's interpretation of visual imagery. To achieve this, AI algorithms must be fed with vast amounts of visual data so they can be analyzed and key elements can be extracted by the AI model. Computer vision models then use this data to accurately interpret visual imagery.
Deep Learning
Deep learning refers to a machine learning model that attempts to emulate the behavior of a human brain, enabling machine learning models to learn and change their outcomes through training data.
Deep learning technology is used in many smart devices and applications, including Siri and Google Voice Search, to translate voice audio into something computers can understand.
How Does Deep Learning Work?
Explaining how deep learning works in simple terms is challenging, as it requires a knowledge of nearly all the terms outlined in this glossary. However, the main takeaway is that deep learning works by mimicking human brain behavior through complex algorithmic models and AI neural networks.
Generative AI
Generative AI refers to artificial intelligence models that can output contextual responses based on user inputs. They can produce text, audio, images, and videos.
Generative AI is an increasingly popular model of artificial intelligence. If you’ve experimented with ChatGPT, or a similar alternative, such as Google’s Bard, then you’ve encountered generative AI.
How Does Generative AI Work?
Generative AI uses neural networks that process and analyze large datasets to identify patterns. It then uses this information to generate contextual and human-like responses based on user prompts.
Hallucination
An AI hallucination is where a generative AI model produces factually incorrect information but presents it confidently as truth.
Generative AI models like ChatGPT and Character.ai face the prospect of hallucinations. However, many AI companies are working toward remedying the generation of false information by including sources in generated answers.
Why Do AI Hallucinations Happen?
AI hallucinations typically occur due to incomplete datasets or biased training data. This leads to the AI model attempting to fill in its knowledge gaps when encountering a query it doesn’t know how to answer accurately.
Large Language Model (LLM)
Large Language Models refer to AI algorithms that use deep learning techniques and vast datasets to understand, analyze, and produce new content based on probabilities and predictions.
Large Language Models are the basis for most popular AI platforms available today, like ChatGPT and Bing.ai. They act as the algorithm that allows these platforms to produce human-like, contextual, and relevant responses.
How Do Large Language Models Work?
Large language models like GPT-4 work by training on vast amounts of text data to learn patterns and relationships between words, allowing them to generate coherent and contextually relevant responses by predicting the next word or phrase given the input context.
Machine Learning (ML)
Machine Learning (ML) is a branch of artificial intelligence that’s focused on using data and algorithms to mimic how humans learn in order to continually improve the accuracy of the ML model.
Machine learning models are something you likely encounter every day. They are used to power face recognition on smart devices, such as iPhones, and are widely used by social media platforms to optimize the content you see.
How Does Machine Learning Work?
Machine learning employs two main techniques: supervised learning, where a model is trained on labeled input and output data to predict future outputs, and unsupervised learning, which uncovers hidden patterns or inherent structures within input data without explicit labels.
Natural Language Processing (NLP)
Natural Language Processing (NLP) is a branch of artificial intelligence that’s concerned with translating human language into something a machine can understand. This understanding is then used to allow machines to produce human-like text.
If you have an email account, you will have encountered natural language processing before. NLP is used to help spam filters identify terms and patterns in potentially harmful emails, making it easier to label them as such.
How Does Natural Language Processing Work?
Natural Language Processing (NLP) involves using computational algorithms and models to understand and manipulate human language. It analyzes and processes text or speech data, employing techniques like tokenization, syntactic parsing, semantic analysis, and machine learning to extract meaning, enable language understanding, and facilitate automated language tasks.
Neural Network
Neural networks are a type of artificial intelligence that trains computers to process data similarly to a human brain.
Neural networks are an essential background technology that powers the moving parts of artificial intelligence, such as pattern recognition and system identification. These technologies are used in radar systems, vehicle control, and even quantum chemistry.
How Do AI Neural Networks Work?
Inspired by the human brain, AI neural networks consist of interconnected layers of artificial neurons that process input data through weighted connections, applying activation functions to produce output. Through repeated training iterations using labeled data, the network adjusts the weights to optimize its ability to make predictions or perform tasks based on patterns and correlations learned from the training data.
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