15 AI Lingos Simplified.

1. Context:

   Context is nothing but the information available to the AI, based on which it generate or predict the new information.

2. Decoder:

   AI calculation done only on standard tokens for efficiency and consistency, but those token are not human readable, A decoder transforms numerical tokens to actual data (string, RGB etc) so that human can read or use them.

3. Embedding:

   Embeddings are the vector representation of tokens which are positioned by there semantic relationship. A sentence can be encoded into numeric tokens, word by word, but there is no way to know how each word is related to each other, for that we use positional encoding and the output data that we get are inter-linked by their position in vector space, aka embeddings.

4. Encoder:

   AI calculation done only on standard tokens for efficiency and consistency, but those token are not human readable, A decoder transforms numerical tokens to actual data (string, RGB etc) so that human can read or use them.

5. Knowledge Cut off:

   Knowledge Cut off refers to the point in time after which the AI model's training data no longer includes information from the real world. This means that the model may not be aware of events, discoveries, or updates that have occurred after the cut off date. It is a crucial consideration when using AI models, as they may provide outdated or incomplete information beyond this point.

6. Multi Head Attention:

   A mechanism where the AI splits input data into multiple "perspectives" to analyze relationships in parallel. Imagine a team of specialists examining the same sentence – one focusing on grammar, another on emotion, and a third on context. By combining these perspectives, the model gains a nuanced understanding of how words interact dynamically rather than relying on rigid rules.

7. Positional Encoding:

   A mathematical "GPS" added to embeddings to convey word order. Unlike humans who intuitively grasp sequence, transformers process sentences all at once. Positional encodings use sine/cosine waves or learned patterns to give tokens coordinates like "third word in a question" or "final item in a list," preserving structural context.

8. Self Attention:

   The AI’s ability to let words weigh their relevance to one another. For the sentence "The chef seasoned the soup while tasting it," self-attention links "it" to "soup" by calculating relationship scores. This mimics how humans subconsciously connect pronouns to their antecedents without explicit rules.

9. Semantic Meaning:

   Semantic Meaning refers to the meaning conveyed by words, phrases, or sentences in a language. It is concerned with understanding the underlying concepts and ideas expressed in the text. In AI, models like transformers are designed to capture and understand semantic meaning, enabling them to generate human-like responses and understand the context in which words are used.

10. Soft Max:

    Soft Max is a mathematical function used in neural networks to convert a vector of real numbers into a probability distribution. It is often used in the output layer of classification models, where each class is assigned a probability score. The class with the highest probability is typically selected as the model's prediction. Soft Max ensures that the probabilities sum to one, making them interpretable as probabilities. If an AI assigns values [5, 3, 1] to potential next tokens ("cat," "dog," "zebrafish"), softmax converts these to [0.84, 0.13, 0.03], clearly signaling "cat" as the most likely choice while retaining alternatives.

11. Temperature:

    Temperature is a parameter used in AI models, particularly in the context of sampling from a probability distribution. It influences the randomness of the model's predictions. A higher temperature leads to more randomness, while a lower temperature makes the predictions more deterministic. Adjusting the temperature can help control the creativity and variability of the model's outputs.

12. Tokenization:

    Breaking language into Lego blocks. For efficiency, "unbelievable" might become ["un", "##believe", "##able"], balancing vocabulary size with reconstruction capability. Languages like Finnish or Mandarin require specialized tokenizers to handle agglutination or character-based writing systems.

13. Transformer:

    The neural architecture revolutionizing AI. By replacing sequential processing with parallel self-attention layers, transformers decode nuanced patterns in data – whether text, images, or proteins. Their ability to model long-range dependencies makes them adept at tasks like summarizing novels or predicting molecular interactions.

14. Vectors:

    In the context of AI, vectors are mathematical representations of data points in a multi-dimensional space. They are used to encode information in a way that can be processed by machine learning models. Vectors can represent a variety of data types, including text, images, and numerical data. Understanding the relationships between vectors is crucial for tasks like similarity search and clustering.

15. Vocab Size:

    Vocab Size refers to the number of unique tokens in a vocabulary used by an AI model. The vocabulary is a set of all possible words, phrases, or other meaningful units that the model can recognize and generate. The size of the vocabulary impacts the model's ability to understand and generate text, as well as its computational complexity. A larger vocabulary can lead to more accurate and diverse outputs but may also require more computational resources.

Closing Thoughts: Demystifying the AI Lexicon:

Understanding AI doesn’t require a PhD—just the right metaphors. From transformers acting as linguistic detectives to temperature controlling creative risk-taking, these 15 terms reveal the hidden logic powering tools like chatbots and image generators. By framing vectors as "word coordinates" and tokenization as "language Legos," we bridge the gap between technical complexity and human intuition.

What makes these concepts revolutionary isn’t their mathematical elegance, but how they mirror human cognition. Self-attention replicates our ability to focus on context, while embeddings map meaning much like our brains associate ideas. Even the dreaded "knowledge cutoff" reflects a very human trait: the limits of what we’ve learned so far.

Next time you use AI, remember—it’s not magic. It’s layers of carefully engineered decisions, from vocab size trade-offs to multi-head attention debates. With this foundation, you’re no longer just a user; you’re a informed participant in the AI conversation.