Parameters and Tokens: A Deep Dive into AI’s Core Buzzwords

The world of Artificial Intelligence is booming, and with it comes a flood of new terminology. While phrases like “Artificial Intelligence” and “Machine Learning” have become part of our everyday vocabulary, the more technical buzzwords often remain a mystery. Two of the most fundamental yet misunderstood terms are “parameters” and “tokens.” Understanding these concepts is key to grasping how modern AI platforms like Gemini, DeepMind, ChatGPT, and Claude function.

Parameters: The Brain’s Connections

In a simplified sense, a large language model (LLM) is a vast and complex neural network, inspired by the human brain. Parameters are the fundamental components of this network. Think of them as the neural connections—the “synapses” and “weights”—that the AI uses to process information.

During the training process, the AI is fed an enormous amount of data—trillions of words, images, and other forms of information. As it processes this data, the model’s parameters are adjusted and fine-tuned. These adjustments are what allow the model to “learn.” A larger number of parameters generally means a more complex and capable model, as it has more connections to store and process information. For example, a model with 175 billion parameters (like some versions of GPT-3) has an incredible number of internal connections, allowing it to understand and generate text with a high degree of nuance and coherence.

Tokens: The Language’s Atoms

If parameters are the connections, tokens are the fundamental units of language that these connections work with. A token is a piece of a word, a whole word, or even a punctuation mark. It’s how the AI breaks down and understands human language.

For instance, the sentence “I love my AI” might be broken down into the following tokens: [“I”, “love”, “my”, “AI”]. The word “intelligence” might be broken into two tokens: [“intel”, “ligence”]. This process of converting text into tokens is called “tokenization.” When you submit a prompt to an AI, the system first tokenizes your input. It then processes these tokens and generates a response, which it then converts back into human-readable text. Tokens are also often used to measure the cost and length of an AI interaction, as the model’s processing is directly tied to the number of tokens it has to work with.

50 Other AI Buzzwords

Beyond parameters and tokens, the world of AI is filled with specialized jargon. Here are 50 other key buzzwords you’ll encounter in platforms and discussions about AI:

1. Artificial General Intelligence (AGI): A hypothetical AI that can understand, learn, and apply knowledge across a wide range of tasks at a human level.
2. Machine Learning (ML): A subset of AI where algorithms are trained to learn from data and improve their performance without being explicitly programmed.
3. Deep Learning: A subfield of ML that uses neural networks with many layers (hence “deep”).
4. Neural Network: A series of algorithms that mimic the human brain’s structure and function.
5. Generative AI: AI that can generate new content, such as text, images, or audio.
6. Large Language Model (LLM): A type of AI trained on a massive amount of text data to generate human-like language.
7. Transformer: A neural network architecture that uses an “attention mechanism” to process sequences of data, revolutionizing modern LLMs.
8. Prompt Engineering: The art and science of crafting effective prompts to get the desired output from an AI model.
9. Fine-Tuning: The process of taking a pre-trained model and training it on a smaller, more specific dataset to adapt it for a particular task.
10. Hallucination: When an AI generates false or nonsensical information, presenting it as fact.
11. Attention Mechanism: A key component in transformers that allows the model to weigh the importance of different parts of the input sequence.
12. Temperature: A hyperparameter that controls the randomness of an AI’s output. Higher temperature leads to more creative, less predictable results.
13. Supervised Learning: Training an algorithm on a labeled dataset, where the correct answers are provided.
14. Unsupervised Learning: Training an algorithm on unlabeled data to discover hidden patterns and structures.
15. Reinforcement Learning: An AI learning by trial and error, receiving rewards for good actions and penalties for bad ones.
16. Multimodal AI: An AI that can process and understand multiple types of data, such as text, images, and audio, simultaneously.
17. Embeddings: A numerical representation of words, phrases, or other data, allowing the AI to understand their contextual relationships.
18. Vector Database: A specialized database designed to store and search for vector embeddings.
19. Algorithm: A set of instructions or rules that an AI uses to solve a problem or perform a task.
20. Hyperparameters: Settings that are configured before the training process begins, such as learning rate or temperature.
21. API (Application Programming Interface): A set of rules that allows one software application to communicate with another.
22. Overfitting: When a model learns the training data too well, including its noise and fluctuations, leading to poor performance on new data.
23. Underfitting: When a model is too simple to capture the underlying patterns in the training data.
24. Inference: The process of using a trained AI model to make a prediction or generate an output.
25. Data Mining: The process of discovering patterns and insights from large datasets.
26. Natural Language Processing (NLP): A field of AI focused on enabling computers to understand, interpret, and generate human language.
27. Computer Vision: A field of AI that enables computers to “see” and interpret visual data.
28. Sentiment Analysis: The process of using NLP to determine the emotional tone or opinion expressed in a piece of text.
29. Zero-Shot Learning: When an AI model can perform a task it was not explicitly trained on, without any examples.
30. Few-Shot Learning: When an AI can perform a task with only a few examples.
31. Prompt Tuning: A method of fine-tuning that adjusts only a small set of parameters related to the prompt, rather than the entire model.
32. Federated Learning: A machine learning approach where a model is trained across multiple decentralized devices, such as smartphones, without sharing the data.
33. Edge AI: AI algorithms that are processed locally on a device, rather than in the cloud.
34. AI Alignment: The research field concerned with ensuring AI systems’ goals and values align with human values.
35. Model Distillation: A technique where a smaller, more efficient “student” model is trained to replicate the behavior of a larger, more complex “teacher” model.
36. Agentic AI: An AI system capable of independently carrying out a sequence of actions to achieve a goal.
37. Chatbot: A software application designed to simulate conversation with human users.
38. Reinforcement Learning from Human Feedback (RLHF): A training method where human evaluators provide feedback to a model to guide its learning and improve its output.
39. Explainable AI (XAI): AI systems that are transparent and interpretable, allowing human users to understand how they arrive at their decisions.
40. Algorithmic Bias: When an AI model produces systematically skewed outputs due to biases in its training data.
41. Dataset: A collection of data used to train, validate, and test an AI model.
42. Training: The process of teaching an AI model by feeding it data and allowing it to adjust its internal parameters.
43. Validation Set: A subset of the dataset used to evaluate the model’s performance during training and adjust hyperparameters.
44. Test Set: A final, independent subset of the dataset used to evaluate the model’s performance after training is complete.
45. Autoregressive Model: A model that generates a sequence of data one step at a time, with each new step dependent on the previous ones.
46. API Key: A unique code used to authenticate and authorize a user or application to use an API.
47. Hallucination: When an AI confidently generates false, inaccurate, or nonsensical information.
48. Tokenization: The process of converting text into numerical tokens for an AI to process.
49. Prompt: The input text or instruction given to an AI model to generate a response.
50. Embedding: A numerical representation of a word, phrase, or other data that captures its meaning and context.

How Do Prompt AIs Work?

Layman’s Terms

Imagine you’re trying to guess the next word in a sentence. You’ve read countless books, articles, and websites, so you have a pretty good idea of what words often follow one another. When someone says, “The quick brown fox…”, you’re likely to think of “jumped” because you’ve seen that phrase so many times before.

A prompt-based AI works similarly, but on a massive scale. It’s not “thinking” in the human sense. Instead, it has been trained on a colossal amount of text from the internet, books, and other sources. This training has allowed it to learn the statistical relationships between trillions of words and phrases.

When you give it a prompt, like “Write a poem about a lost star,” the AI breaks your request down into tokens. It then uses its internal “knowledge” (its parameters) to predict the most probable sequence of tokens that should follow. It essentially keeps guessing the next most likely token, one after another, until it has generated a complete response. The “creativity” or “randomness” is controlled by a setting called “temperature,” which can make the AI’s guesses more or less adventurous.

Technological Explanation

At a deeper level, large language models (LLMs) are built on a specific type of neural network architecture called a Transformer. The Transformer model is designed to efficiently process sequential data, such as text. It utilizes an attention mechanism that allows it to weigh the importance of different words in the input sequence, no matter how far apart they are.

The process begins with tokenization, where the input prompt is converted into a sequence of numerical tokens. Each token is then transformed into a high-dimensional vector called an embedding. This embedding represents the token’s meaning and contextual relationship to other tokens.

The sequence of embeddings is then fed into the Transformer’s network, which consists of multiple layers of “self-attention” and “feed-forward” networks. The self-attention layers allow the model to build a rich representation of the input by considering how each token relates to every other token in the sequence. For example, in the sentence “The animal didn’t cross the street because it was too tired,” the attention mechanism helps the model correctly associate “it” with “the animal.”

Finally, the model uses a statistical process to predict the next token based on the input and its internal state. This is a probabilistic process, where the model calculates a probability distribution over its entire vocabulary for what the next token should be. It then samples from this distribution to select the next token, a process that is influenced by the “temperature” hyperparameter. The newly generated token is then added to the sequence, and the process repeats until a complete response is formed.

How Hard Is It to Run or Create Your Own AI?

The difficulty of running or creating your own AI depends entirely on your goals.

• Running an existing AI: This is surprisingly easy. Platforms like Hugging Face and various cloud providers offer access to pre-trained models. You can use their APIs to run models for a variety of tasks with relatively little technical expertise. You can also run smaller, open-source models on your local computer, provided you have a powerful enough GPU.
• Fine-tuning a pre-trained model: This is the middle ground and is a very common practice. It requires some technical skill, including knowledge of programming languages like Python and machine learning frameworks like PyTorch or TensorFlow. However, you don’t need the massive computational resources or data required for training from scratch. You can take an existing model and train it on a few hundred or a few thousand specific examples to specialize it for a particular task.
• Creating an AI from scratch: This is extraordinarily difficult and resource-intensive. It requires a deep understanding of machine learning algorithms, a massive, clean dataset, and a vast amount of computational power (hundreds or thousands of GPUs running for months). The cost of creating a foundation model on the scale of ChatGPT or Gemini can run into the tens or even hundreds of millions of dollars. This is why only a handful of large tech companies and well-funded research labs can undertake such a task.

How to Create Your Own AI

If you’re interested in building your own AI, the best and most accessible approach is to focus on a specific problem and use existing tools and frameworks. Here’s a step-by-step guide to get you started:

1. Define the Problem: Start with a clear and specific goal. Do you want to classify emails as spam or not spam? Do you want to predict housing prices? Or perhaps you want to build a simple chatbot? The problem will dictate the type of AI you need to build.

2. Gather and Prepare Your Data: Data is the lifeblood of AI. For a supervised learning task (like spam classification), you’ll need a large, labeled dataset of emails marked as “spam” or “not spam.” For an unsupervised task, you’ll need a large dataset without labels. The quality of your data is paramount; it must be clean, relevant, and free of errors. This is often the most time-consuming part of the process.

3. Choose Your Tools: You don’t need to write everything from scratch. Python is the dominant programming language for AI, and it has an incredible ecosystem of libraries: * TensorFlow: A powerful open-source library for deep learning. * PyTorch: Another popular deep learning framework, often favored in research. * Scikit-learn: A library for traditional machine learning tasks, great for beginners.

4. Select a Model: Based on your problem, choose the right type of model. For image recognition, you might use a convolutional neural network (CNN). For text generation, you might use a pre-trained transformer model.

5. Train the Model: This is where you use your data to “teach” the model. You’ll feed your prepared data into your chosen framework and let the model’s algorithms adjust its parameters to minimize prediction errors. This process can take minutes or hours depending on the size of your dataset and the complexity of your model.

6. Evaluate and Refine: Once training is complete, test your model on a separate dataset (the test set) to see how well it performs on new, unseen data. If its performance is poor, you might need to go back and adjust your data, your model’s hyperparameters, or even your overall approach.

By following these steps, you can start building and understanding AI on a practical level, without the immense barriers of creating a large-scale foundation model from the ground up.