Samwise Gamgee: The Unsung Hero of AI – Why Embeddings Matter More Than ChatGPT

Author: Rahul Chaudhary

I recently watched a video where the speaker, Matt Dupree, draws parallels between characters from The Lord of the Rings and the current landscape of AI, specifically focusing on ChatGPT and embeddings. This comparison provides a useful lens to evaluate where real power lies in AI applications.

At the outset, Dupree suggests that while ChatGPT is often viewed as the “hero,” it’s actually the embeddings that play a more critical role—similar to Samwise Gamgee in Tolkien’s narrative. This perspective shifts the focus back to embeddings, a topic that involves significant mathematical and computational backgrounds but is often glossed over.

Understanding ChatGPT Limitations

Before diving into embeddings, let’s clarify why ChatGPT isn’t the silver bullet many believe it to be. Dupree discusses two tweets from industry experts that highlight major flaws:

1. Prompt Drift

   • The first tweet discusses a phenomenon known as “prompt drift,” which refers to the model’s deteriorating performance over time as the original prompts must continually adapt to software updates. This reflects a reliability issue inherent in real-world applications of ChatGPT.

2. Agent Task Failures

   • The second tweet mentions that agents—essentially AI tasks built upon ChatGPT—exhibit a failure rate of 5% to 15% upon executing tasks. This inconsistency is crucial because it challenges the perception of AI reliability.

For instance, if you deploy ChatGPT to make reservations or execute other automated tasks but encounter these failure percentages, you are left with a flawed system that cannot be trusted to perform optimally.

Statistical Insights

Consider the implications: if we deploy an agent to execute a task 100 times, we can anticipate that between 5 and 15 times it will fail. This fundamentally reduces the effectiveness of any task automation.

Furthermore, Dupree shares an anecdote where GPT-4 performed poorly compared to its predecessor, GPT-3.5, when tasked with a simple riddle. This case demonstrates that model size or complexity does not directly equate to improved intelligence—a critical failure in conventional wisdom regarding model training.

Why Embeddings Are the Real Game Changers

Embeddings represent words or sentences as vectors—essentially numeric representations of meaning in a high-dimensional space. OpenAI’s embedding endpoint provides vectors of length 1536 for text inputs, allowing users to measure similarities and relationships mathematically.

For example, if we conduct a cosine similarity check between the vector embeddings of words, we can quantify how closely they are related based on their contextual meanings. Words like “eagle” and “deer” will cluster closely together because they are both animals in a similar semantic space.

The mathematical relationship can be expressed as:

[ cosine\ similarity(A, B) = \frac{A \cdot B}{|A| |B|} ]

Essentially, this allows us to rank items or concepts according to their meanings, sidestepping the unstructured pitfalls found in straightforward text queries.

Practical Applications of Embeddings

Dupree provides concrete examples of how embeddings can revolutionize tasks without facing some of the reliability issues of ChatGPT:

1. Sentiment Analysis

   • Consider embedding tweets about a social media competition. By comparing the embeddings from tweets to a predefined positive or negative sentiment embedding, we can classify the overall sentiment without retraining any model.

2. Product Recommendations

   • By embedding movie descriptions on platforms like Netflix, we allow for dynamic recommendations based solely on semantic connections rather than explicit user input.

3. Code Search

   • In coding environments, if a user is unsure of a function name, system embeddings can identify similar function names based solely on user queries, not the exact wording. This addresses typical search limitations effectively.

How to Implement OpenAI’s Embedding API

Dupree breaks down how to utilize OpenAI’s Embedding API in a few straightforward steps. You can install the OpenAI package and utilize the create_embedding function. With this, you can easily generate embeddings from phrases or entire documents.

# Example R code to create and visualize embeddings
library(openai)

# Create an embedding
embedding_response <- create_embedding(model = "text-embedding-ada-002", input = "Hello World")
embedding_vector <- embedding_response$data$embedding

# Further steps to visualize could involve PCA or t-SNE for plotting.

This minimal setup generates embeddings that you can visualize or utilize for semantic queries or classification tasks.

Semantic Search Strategy

To create a functional semantic search system for code, you would:

1. Generate embeddings for each code term.
2. For a given user input, generate its embedding.
3. Use cosine similarity to identify and rank the terms that are closest in meaning.

This frameworks the power of AI in practical applications beyond what conventional systems offer, reminding us that while ChatGPT serves its purpose, the real unsung hero is indeed the embedding.

In essence, Dupree succeeds in shifting the narrative from ChatGPT’s hype to the more granular yet potent capabilities provided by embeddings—an essential discussion in the evolving landscape of AI.