You don’t need to know what a neural network is to use ChatGPT, and you don’t need to know what deep learning is to use Copilot. But to use AI to deliver specific solutions, recognising and understanding the language of AI becomes essential and unavoidable. So here are the 10 most common and complex AI terms and what they mean.
In my most recent blogs, I’ve written about how artificial intelligence – now an ever-growing part of personal and working life – is being embraced by the agricultural industry.
Artificial Intelligence
This is a big umbrella term. AI refers to machines and software able to do things that usually require human intelligence, like understanding language, recognising faces, making decisions or playing Go. AI systems range from simple rule-based engines to highly complex learning models.
Machine Learning
Usually, a developer writes rules that tell a computer exactly what to do. Machine learning is a type of AI where computer systems learn patterns from data using algorithms and statistical models. For example, instead of telling a programme every detail about what a cat looks like, you show it thousands of pictures of cats and it figures it out.
Training Data
Those cat images would be training data. By feeding AI this data, it can recognise features of the data, as well as patterns within it and relationships between it. The better and more varied the data, the more accurate and capable the AI model can become.
Neural Network
Inspired by the human brain, a neural network is a system of algorithms that helps machines recognise patterns. It’s made up of small decision makers, called “neurons”, that take information in, process it, and pass the information forward. By connecting layers of these neurons and adjusting their “weights” based on feedback, the network learns to identify patterns and make decisions.
Deep Learning
This is a type of machine learning that uses multiple layers of neural networks. Just as layers of neurons let neural networks undertake complex tasks, layers of neural networks allow deep learning to extract progressively higher and higher-level features from data. For example, in image recognition, early layers might detect edges or colours, while deeper layers recognise objects or scenes.
Large Language Model
A Large Language Model (LLM) is a deep learning system that uses vast quantities of training data, particularly books and websites, to predict what words are likely to come next in a sentence. By understanding these relationships, LLMs can generate, summarise, and translate text by modelling statistical relationships between words. This is the tech behind ChatGPT and similar tools.
Generative AI
These similar tools are collectively known as Generative AI, so-called because they can create new content, such as text, images, audio, and video, based on patterns learned from existing data. It’s just one type of AI under the umbrella, but it has become one of the most widely used and recognised.
Prompt
This is what you type into Generative AI to tell it what you want, be it a question, command or simply a statement. AI uses the prompt to decide how to respond. Asking better questions usually leads to better answers and prompt engineering, the art of crafting a prompt, is a growing field.
Natural Language Processing
Natural Language Processing (NLP) is a field within AI that focuses on enabling machines to understand, interpret, and generate human language. It powers everything from chatbots to translation apps and voice assistants. NLP techniques often rely on both traditional machine learning and deep learning.
Hallucination
This doesn’t mean AI is seeing things; it means it’s making things up. This typically occurs when the model tries to fill in knowledge gaps, extrapolate beyond its training data, or misinterprets ambiguous prompts. Ensuring high-quality data and robust model design helps to avoid hallucinations.
In summary
At its very core, AI is mathematics and data that can be processed en masse using vast amounts of computational power. By appreciating this and these basic terms, it becomes easier to understand what AI can (and can’t) do.
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