So now that we’ve learned a few things ourselves, we’re going to ease the confusion and demystify some of the most essential terms that lay the foundation for comprehending the game-changing field of AI. Whether you are a newcomer or seeking to refresh your knowledge of the core concepts of AI, our goal is to provide you with crystal-clear explanations of the key AI terms.
We’ve highlighted the terms below we think have the biggest impact in the creative industries with our own explanation of how it is changing the way we work (or will be in the near future).
Algorithm:
A set of rules or instructions given to a computer to perform a specific task or solve a problem.
Artificial Intelligence (AI):
The simulation of human intelligence processes by machines, especially computer systems. It includes tasks such as learning, reasoning, problem-solving, perception, and language understanding.
Artificial General Intelligence (AGI):
An AI system that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks, similar to human intelligence.
AI Governance:
The establishment of policies, regulations, and frameworks to govern the development and use of AI technologies responsibly.
Autonomous Systems:
AI-driven systems capable of performing tasks or making decisions without direct human intervention.
Bias:
In AI, bias refers to systematic errors in decision-making due to skewed or incomplete training data or flawed algorithms.
Computer Vision:
The field of AI that focuses on enabling computers to interpret and understand visual information from images or videos.
Data Preprocessing:
The process of cleaning, transforming, and preparing data before feeding it into an AI model for training.
Deep Learning:
A specialized form of machine learning that uses neural networks with multiple layers to process and learn from complex data, often achieving state-of-the-art results in tasks such as image and speech recognition.
Discriminator:
The discriminator's task is to distinguish between real data from the training set and data generated by the generator.
Ethical AI:
The consideration of moral principles and values in the development and deployment of AI systems to ensure fairness, transparency, and accountability.
Generative AI:
This is a subset of artificial intelligence that focuses on creating new content rather than just making decisions or predictions. It involves using algorithms to generate new data that resembles a given set of training data. Generative AI techniques have gained significant attention in recent years due to their ability to produce realistic and creative outputs.
Why is it important? Generative AI has become extremely popular recently, especially in the e-commerce industry. It can be broken down into two areas text and image. For instance, it could be used to create product descriptions automatically or fully create AI models and clothing. While generative AI for text doesn’t seem to cause much uproar it is not quite the same for generative AI images. How generative AI is used in e-commerce whether that is models or backgrounds will be integrated or used is just entering it’s experimental phase and it will be important to watch how this develops.
Generative Adversarial Network (GAN):
It is a type of neural network architecture consisting of two main components: a generator and a discriminator that are a framework for Generative AI.
Why is important to understand? GANs have been remarkably successful in image generation and style transfer.
Generative Pre-Trained Transformer (GPT):
It is a type of language model developed by OpenAI. GPT uses a transformer architecture, a deep learning model that excels at processing sequential data, such as language. The "pre-trained" part means that GPT is trained on a massive amount of text data before fine-tuning it for specific tasks.
Why is it important? GPT is known for its ability to generate human-like text, answer questions, and perform various language-related tasks effectively. This can effectively change how we produce product descriptions and meta data.
Generator:
The generator's role is to create synthetic data, such as images, videos, or audio, that resembles real data from a given training set.
Machine Learning (ML):
A subset of AI that focuses on developing algorithms and statistical models that allow computers to improve their performance on a specific task through learning from data, without being explicitly programmed.
Natural Language Processing (NLP):
The field of AI that focuses on enabling computers to understand, interpret, and generate human language.
Why it’s important? We’ve all been using NLP in the form of tools like Grammarly or spell check. Like Generative AI and GPT it holds the power to change how we create the written content that ends up on product detail pages. It’s one thing to be able to use these tools what is most important is our ability to learn to work with them and integrate them into the way we work.
Neural Network:
A computational model inspired by the human brain's interconnected neurons. It is the foundation of deep learning algorithms.
Training Data:
The data used to train an AI model. It serves as examples for the algorithm to learn from.
Singularity:
A hypothetical point in the future where AI becomes capable of self-improvement, leading to rapid and uncontrollable advances in technology.
Supervised Learning:
A type of machine learning where the model is trained on labeled data, with input-output pairs, to make predictions or classifications.
Unsupervised Learning:
A type of machine learning where the model is trained on unlabeled data and must find patterns and relationships within the data.
The field of AI is rapidly evolving, and new terms (and acronyms) are likely to emerge. Hopefully, this glossary gives all of us a head start on understanding the new world of AI we seem destined to live in.
