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The documentation mentions that embeddings measure the relatedness of text strings, and that they’re commonly used for search, clustering, anomaly detection, re

The documentation mentions that embeddings measure the relatedness of text strings, and that they’re commonly used for search, clustering, anomaly detection, recommendations, diversity management, and classification. I’m not well versed in AI at all. Could anyone give some more fleshed out examples of some of the kinds of data that are fed into a tool like this, what the tool does with it, and what kinds of applications people might make with it?

minimaxir 4y ago
The documentation contains a good amount of hypothetical real-world use cases: https://beta.openai.com/docs/guides/embeddings/use-cases https://beta.openai.com/docs/guides/embeddings/use-cases
charcircuit 4y ago
For example say you own an online store with thousands of products and you want to implement search. What you can do is for each product you can feed the name and description into the model to get an embedding. When people type in a search query you convert their query into an embedding and then find the 10 closest embeddings.
jncraton 4y ago
You can use any sort of natural language text as input. The system transforms the input text into a multidimensional vector that roughly represents the semantic meaning of the input. If you do this for many different inputs, you can get representations for each of them and store them in a database alongside the inputs. From there, you can use traditional methods to search for nearby vectors to efficiently search by semantic meaning. One earlier related example is word2vec from 2013. This tool transforms individual words into embeddings, but is conceptually similar. Wikipedia has a decent overview that might be helpful: https://en.wikipedia.org/wiki/Word2vec https://en.wikipedia.org/wiki/Word2vec That work demonstrated the utility of transforming meaning into vector space for search but also for basic semantic reasoning. For example, you could perform an operation like "brother - man + woman" on the embedding and the result would be an embedding very close to "sister".
whakim 4y ago
These sentence embeddings transform chunks of text into n-dimensional vectors. The key feature of these kinds of embeddings is that semantically similar pieces of text produces similar vectors (even when the words themselves aren't the same!) Once you have that, you can do all kinds of things. To take a few of the use cases you listed, imagine that you had a corpus of documents (perhaps responses to a survey, or data you scraped/pulled from a service like Twitter or Reddit, etc.). You can now cluster those responses (find groups of vectors that are close to each other in n-dimensional space); you can find outliers (find the vectors that are far from others); build recommendation engines (find vectors mathematically similar to vectors representing a user's previous activity), etc.
boywitharupee 4y ago
I wonder if you can feed email threads and get insights by asking questions, "What time is my upcoming flight?" or "Where is Lucy's wedding location?"

minimaxir 4y ago

The documentation contains a good amount of hypothetical real-world use cases: https://beta.openai.com/docs/guides/embeddings/use-cases https://beta.openai.com/docs/guides/embeddings/use-cases

charcircuit 4y ago

For example say you own an online store with thousands of products and you want to implement search. What you can do is for each product you can feed the name and description into the model to get an embedding. When people type in a search query you convert their query into an embedding and then find the 10 closest embeddings.

jncraton 4y ago

You can use any sort of natural language text as input. The system transforms the input text into a multidimensional vector that roughly represents the semantic meaning of the input. If you do this for many different inputs, you can get representations for each of them and store them in a database alongside the inputs. From there, you can use traditional methods to search for nearby vectors to efficiently search by semantic meaning. One earlier related example is word2vec from 2013. This tool transforms individual words into embeddings, but is conceptually similar. Wikipedia has a decent overview that might be helpful: https://en.wikipedia.org/wiki/Word2vec https://en.wikipedia.org/wiki/Word2vec That work demonstrated the utility of transforming meaning into vector space for search but also for basic semantic reasoning. For example, you could perform an operation like "brother - man + woman" on the embedding and the result would be an embedding very close to "sister".

whakim 4y ago

These sentence embeddings transform chunks of text into n-dimensional vectors. The key feature of these kinds of embeddings is that semantically similar pieces of text produces similar vectors (even when the words themselves aren't the same!) Once you have that, you can do all kinds of things. To take a few of the use cases you listed, imagine that you had a corpus of documents (perhaps responses to a survey, or data you scraped/pulled from a service like Twitter or Reddit, etc.). You can now cluster those responses (find groups of vectors that are close to each other in n-dimensional space); you can find outliers (find the vectors that are far from others); build recommendation engines (find vectors mathematically similar to vectors representing a user's previous activity), etc.

boywitharupee 4y ago

I wonder if you can feed email threads and get insights by asking questions, "What time is my upcoming flight?" or "Where is Lucy's wedding location?"