Embedding

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AI Encoding & AI Embedding

The terms "AI encodings" and "AI embeddings" are sometimes used interchangeably, but there is a subtle difference between the two.

  • Encodings are a general term for any representation of data that is used by a Machine Learning (ML) model. This could be a one-hot encoding, a bag-of-words representation, or a more complex representation such as a word embedding.
  • Embeddings are a specific type of AI encoding that is learned from data. Embeddings are typically represented as vectors of real numbers, and they capture the meaning and context of the data they represent.


In other words, all embeddings are encodings, but not all encodings are embeddings. Here are some examples of AI encodings that are not embeddings:

  • One-hot Encoding is a simple way to represent categorical data as a vector. For example, the word "dog" would be represented as a vector of 100 zeros, with a single 1 at the index corresponding to the word "dog" in a vocabulary of 100 words.
  • Bag-of-words is a more sophisticated way to represent text data as a vector. This involves counting the number of times each word appears in a document, and then representing the document as a vector of these counts.


AI Embeddings are a type of representation of text that captures the meaning of the text. This can be used for tasks such as search, classification, and recommendation. allow the model to search in a “database” and return the best result. Here are some examples of AI Embeddings:

  • Word embeddings are a type of embedding that represents words as vectors of real numbers. These vectors are typically learned from a large corpus of text, and they capture the meaning and context of the words they represent.
  • Image embeddings are a type of embedding that represents images as vectors of real numbers. These vectors are typically learned from a large dataset of images, and they capture the visual features of the images they represent.

Embedding...

  • projecting an input into another more convenient representation space. For example we can project (embed) faces into a space in which face matching can be more reliable. | Chomba Bupe
  • a mapping of a discrete — categorical — variable to a vector of continuous numbers. In the context of neural networks, embeddings are low-dimensional, learned continuous vector representations of discrete variables. Neural Network embeddings are useful because they can reduce the dimensionality of categorical variables and meaningfully represent categories in the transformed space. Neural Network Embeddings Explained | Will Koehrsen - Towards Data Science
  • a relatively low-dimensional space into which you can translate high-dimensional vectors. Embeddings make it easier to do Machine Learning (ML) on large inputs like sparse vectors representing words. Ideally, an embedding captures some of the semantics of the input by placing semantically similar inputs close together in the embedding space. An embedding can be learned and reused across models. Embeddings | Machine Learning Crash Course
  • Search: Embeddings can be used to rank search results by relevance to a query string.
  • Clustering: Embeddings can be used to group text strings by similarity.
  • Recommendations: Embeddings can be used to recommend items that are related to a user's interests.
  • Anomaly detection: Embeddings can be used to identify outliers with little relatedness.
  • Diversity measurement: Embeddings can be used to analyze similarity distributions.
  • Classification: Embeddings can be used to classify text strings by their most similar label.


By employing techniques like Word Embeddings, Sentence Embeddings, or Contextual embedding, vector embeddings provide a compact and meaningful representation of textual data. Word embeddings, for instance, map words to fixed-length vectors, where words with similar meanings are positioned closer to one another in the vector space. This allows for efficient semantic search, information retrieval, and language understanding tasks.


Embeddings have 3 primary purposes:

  1. Finding nearest neighbors in the embedding space. These can be used to make recommendations based on user interests or cluster categories.
  2. As input to a Machine Learning (ML) model for a supervised task.
  3. For visualization of concepts and relations between categories.

Fine-tuning

A process of retraining a language model on a new dataset of data. This can be used to improve the model's performance on a specific task, such as generating text, translating languages, or answering questions. Fine-tuning is a way to add new knowledge to an existing AI model. It’s a simple upgrade that allows the model to learn new information. On the other hand, embeddings represent text as numbers so that it can be easily utilized by machine learning models and algorithms. Fine-tuning is a way to add new knowledge to an existing AI model. For example, OpenAI’s base models such as Davinc, Curie, Babbage, and Ada are suitable for fine-tuning. Another example is fine-tuning a binary classifier to rate each completion for truthfulness based on expert-labeled examples. When building software that uses AI to generate content and conduct chat sessions, incorporating proprietary content is essential for providing relevant answers.

Prompting vs AI Model Fine-Tuning vs AI Embeddings

Beyond simple prompt engineering, there are two design approaches to consider: building an embedding database of all proprietary content and dynamically searching for relevant information at runtime, or sending the content to the AI provider to fine-tune the model.

Feature AI Model Fine-tuning AI Embeddings
Purpose Improve the performance of a language model on a specific task Capture the meaning of text
Process Retrain the language model on a new dataset of data Calculate a numerical representation of the text
Applications Text generation, translation, question answering Search, classification, recommendation
Advantages Can improve the performance of a language model significantly Efficient and easy to use
Disadvantages Can be time-consuming and expensive May not be as accurate as fine-tuning
  • Prompting: is the simplest technique. It involves providing the LLM with a text prompt that describes the task you want it to perform. The LLM then generates text that is consistent with the prompt. This is a very efficient technique, as it does not require any retraining of the LLM. However, it can be less accurate than fine-tuning or embeddings, as the LLM may not be able to understand the prompt perfectly.
  • Fine-tuning: is a more powerful technique than prompting. It involves retraining the LLM on a dataset of examples for the specific task you want it to perform. This can improve the accuracy of the LLM, but it also requires more training data and compute resources.
  • Embeddings: are a middle ground between prompting and fine-tuning. They involve using a small model to learn a representation of the input text. This representation is then used to initialize the LLM, which can then be fine-tuned on a dataset of examples for the specific task you want it to perform. This can improve the accuracy of the LLM over prompting, without requiring as much training data or compute resources as fine-tuning.


OpenAI Note

Embeddings are a numerical representation of text that can be used to measure the relateness between two pieces of text. Our second generation embedding model, text-embedding-ada-002 is a designed to replace the previous 16 first-generation embedding models at a fraction of the cost. An embedding is a vector (list) of floating point numbers. The distance between two vectors measures their relatedness. Small distances suggest high relatedness and large distances suggest low relatedness.

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