10 AI Terms You Should Know

AI is changing the world around us, and it’s essential to understand the key concepts and terms driving this transformation. In this blog post, we’ll break down 10 important AI terms related to large language models and other exciting applications. Don’t worry if you’re not an AI expert – we’ll explain these terms in a way that’s easy to grasp. By the end of this post, you’ll be able to join in on conversations about AI and understand some of the most important terminology.

1. GPT (Generative Pre-trained Transformer)

A pre-trained model is an AI model that has been trained on a large dataset to perform a specific task or set of tasks before being fine-tuned for a particular application. GPT, which stands for Generative Pre-trained Transformer, is a series of large language models developed by OpenAI. These models are based on the Transformer architecture and are pre-trained on vast amounts of text data using unsupervised learning. The goal of GPT models is to generate human-like text by predicting the next word in a sequence based on the context provided by the previous words.

2. Model – (Ex. Generative Model, Language Model, Diffusion Model)

An AI model is a mathematical representation of a real-world problem or system. Generative models, such as language models and diffusion models, are designed to create new content based on learned patterns and representations. Language models, like GPT-4, learn to predict the probability distribution of words or sequences in a given context, enabling them to generate human-like text. Diffusion models, on the other hand, such as Stable Diffusion, are used for image generation and work by gradually denoising a random input to create a coherent image.

3. Weights and Parameters

Weights and parameters are the learnable components of an AI model. During training, the model adjusts these values to minimize the difference between its predictions and the actual outcomes. The weights and parameters encode the knowledge and patterns learned from the training data, allowing the model to make accurate predictions or generate relevant outputs.

The size of an AI model refers to the number of weights and parameters it contains. Larger models, with billions or even trillions of parameters, can learn more complex patterns and relationships from vast amounts of data. For example, GPT-4, one of the largest language models, allegedly has over 1 trillion parameters, enabling it to generate highly coherent and contextually relevant text. However, larger models also require more computational resources and energy for training and inference, which has implications for cost and environmental impact.

4. Training and Inference

Training refers to the process of teaching an AI model using data, allowing it to learn patterns and relationships. During training, the model updates its weights and parameters to minimize prediction errors. Inference, on the other hand, is the process of using a trained model to make predictions or generate outputs based on new, unseen data.

5. Tokens

Tokens are the basic units of input and output in AI models, particularly in natural language processing (NLP). They can represent individual words, sub-words, or even characters. Tokenization is the process of breaking down text into these smaller units, enabling the model to process and understand the input more effectively. For instance, consider the word “unbelievable.” A word-level tokenizer would treat this as a single token, whereas a sub-word tokenizer might break it down into smaller units like “un”, “believ”, and “able”. This allows the model to handle out-of-vocabulary words more effectively by recognizing familiar sub-word components. For most LLMs, on average 75 words will be converted to approximately 100 tokens.

6. Fine-Tuning

Fine-tuning is the process of adapting a pre-trained AI model to a specific task or domain. Instead of training a model from scratch, fine-tuning allows you to leverage the knowledge gained from pre-training on a large dataset and tailor it to your specific needs. By fine-tuning a model on a smaller, task-specific data set, you can achieve better performance on specific tasks, but sometimes at the cost of general model performance.’

7. Zero-shot and Few-shot Learning

Zero-shot learning refers to an AI model’s ability to perform a task without any additional training examples for that specific task. Few-shot learning involves learning from a small number of examples. Large language models, like GPT-4, have demonstrated impressive zero-shot and few-shot learning capabilities, owing to the vast knowledge and patterns learned during pre-training.

8. Prompts and Prompt Engineering

A prompt is an input given to an AI model to guide its output generation. Prompt engineering is the art of designing effective prompts to steer the model’s generation process towards a desired output. By carefully crafting the input prompt, you can influence the model’s response in terms of content, style, or format. Effective prompt engineering requires understanding the model’s strengths and limitations and experimenting with different prompt structures.

9. API

API stands for Application Programming Interface, which is a set of protocols and tools that allows different software applications to communicate and interact with each other. In the context of AI, APIs enable developers to integrate AI capabilities, such as language models or computer vision models, into their applications without building the models from scratch.

10. Embeddings

In the context of AI, embeddings are dense vector representations of data points, such as words, phrases, documents, images, audio snippets, or video frames. Each data point is mapped to a high-dimensional vector space, where the position of the vector captures the semantic and structural relationships between the data points.

The key idea is that words with similar meanings or properties should be closer together in the vector space. This allows AI models to understand and reason about the relationships between words and concepts. Similarly, in computer vision, image embeddings are used to represent images or image regions in a high-dimensional vector space. Images which are similar looking would also have similar embedding vectors. Convolutional neural networks (CNNs) are commonly used to extract features from images and generate embeddings that capture the visual semantics of the image.

Familiarizing yourself with these AI terms is essential for navigating the rapidly evolving landscape of artificial intelligence. By understanding concepts like generative models, fine-tuning, zero-shot learning, prompt engineering, and agents, you’ll be better positioned to comprehend the latest advancements in AI and their potential applications across various domains. As AI continues to transform industries and shape our future, staying informed about these key terms will help you make the most of the opportunities and challenges that lie ahead.

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