Vector Embeddings
It may be difficult to grasp while engaging with for example ChatGPT, how the AI assistant is able to understand and reply to what you are typing. That it somehow knows the meanings of not only the words you are entering (prompting), but its context within the sentence and the entire conversation. How is it doing what it's doing? A key to this is Vector Embedding and the Generative Pre-trained Transformer model.
Since we know we are dealing with computational processes, numerical representations of data - what vector embeddings do is assign a vector (value) to a word, then cluster (group) words with similar meanings together. Following this is the training of the model on millions of conversations that help it to extract context and understanding from the sequence of words you enter via the prompt. But please note, the model doesn't yet fully comprehend it's reply and as you may have already experienced, it's responses may read back in perfect English, but lack in logic. OR worse, be completely false. Although responses like this are growing increasingly rare, it is important to include a further validation step to assess accuracy and potential bias.
Here's a breakdown of the process:
Training on a large dataset: A machine learning model is trained on a massive amount of text data. This data could be news articles, books, social media conversations, or anything else containing a lot of words.
Capturing meaning and relationships: The model learns to identify the meaning and relationships between words in the data. It essentially builds an understanding of how words are used in context.
Words as vectors: During this process, the model creates a unique vector for each word. This vector is a multi-dimensional array of numbers, typically with hundreds or even thousands of dimensions. (see below diagram)
Similar words cluster together: The interesting thing is that words with similar meanings or that are used in similar contexts will have vectors that are close together in this high-dimensional space. For instance, the vectors for "king," "queen," and "prince" would likely be clustered very near each other.
So, vector embeddings don't just create a random vector for each word. They aim to capture the semantic meaning and relationships between words, resulting in a representation where similar words reside close together in the vector space. This allows machines to process and understand the nuances of human language more effectively.
So, what about Embedding for Images?
The concept and approach is similar, but with some key differences. Read more...
