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# Is “Embeddings” killing Embeddings?

## 🌈 Abstract

The article discusses the pushback against the use of embeddings in AI and the challenges in explaining this concept to non-technical or non-mathematical people.

## 🙋 Q&A

### [01] Explaining Embeddings

**1. What are embeddings according to OpenAI and McKinsey?**

- According to OpenAI, embeddings are vectors (lists) of floating-point numbers where the distance between two vectors measures their relatedness. Small distances suggest high relatedness and large distances suggest low relatedness.
- According to McKinsey, embeddings are created by assigning each item from incoming data to a dense vector in a high-dimensional space. Since close vectors are similar by construction, embeddings can be used to find similar items or to understand the context or intent of the data.

**2. Why do people have a pushback against using the term "embeddings"?**

- The term "embeddings" makes it sound like something is being put "inside" the prompt or input, which is not intuitive for people. The mathematical meaning of embeddings as a structure contained within another structure does not align with how people understand the term "embed" in everyday language.
- People understand concepts like "Prompt Engineering" and "Fine-Tuning" better because the terms are more intuitive and describe the actual actions being performed. "Embeddings" sounds more abstract and complex.

**3. How does the author suggest explaining embeddings instead?**

- The author suggests describing embeddings as a "source translation" process, where the input is translated into a format that the model can better understand, and then comparisons can be made between these translated representations.
- This framing focuses on the practical purpose of embeddings (translating input to a format the model can use) rather than the technical details of the mathematical structure.
- The author also suggests using visual aids like the TensorFlow Embeddings Projector to help concretize the concept of embeddings for non-technical audiences.

### [02] Simplifying Explanations

**1. What is the author's key point about simplifying explanations?**

- The author emphasizes the importance of explaining complex technical concepts in a way that makes sense to the listener's perspective, rather than using jargon or assuming the listener has the same level of expertise.
- The author uses the example of a surgeon explaining a procedure to a patient - the surgeon simplifies the explanation to the patient's level of understanding, rather than using highly technical medical terminology.
- Similarly, the author suggests that when explaining AI concepts like embeddings, data scientists should translate the technical details into more intuitive, relatable terms that the audience can understand.

**2. What is the author's advice for avoiding "sounding clever" when explaining technical concepts?**

- The author advises against assuming the listener has the same level of context and expertise as the speaker.
- Instead, the author recommends thinking about the listener's perspective and translating the concepts into language and examples that make sense in the listener's world.
- The goal should be to build trust and understanding, rather than impressing the listener with technical jargon.

**3. What is the author's final recommendation for explaining embeddings?**

- The author suggests using a term like "source translation" to describe the process of converting the input data into a format the model can better understand.
- This framing focuses on the practical purpose of embeddings, rather than the technical mathematical details.
- The author also encourages the use of visual aids, like the TensorFlow Embeddings Projector, to help concretize the concept of embeddings for non-technical audiences.

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