The BrXnd Marketing X AI Conference is coming to NYC on 5/16. Only a few tickets left. Get yours now! →
X
Sign Up for Updates
Sign up for our mailing list to keep up with what we're up to and be the first to know about new Marketing X AI events.
Sign up for our mailing list to keep up with what we're up to and be the first to know about new Marketing X AI events.
Embeddings are a way of representing data that can be used in AI applications. They are essentially a mapping of words, phrases, or other pieces of data into a vector space. This vector space allows for the data to be represented in a way that is more meaningful and easier to analyze for AI algorithms.
Embeddings are widely used in natural language processing (NLP) tasks such as language translation, text summarization, sentiment analysis, and text classification. They can also be used in other areas of AI such as computer vision, audio processing, and recommendation systems.
To understand a bit about how embeddings work, you can check out the BrXndscape marketing AI landscape. Every company and category in the landscape has a set of use cases (based on the website scrapes and generated with the help of AI) that has an associated set of vectors that you can use to do similarity search.
To understand what this means, let’s imagine it works in two-dimensional space (it doesn’t—it is working with many, many times that number of dimensions). If I tell it that one use case of WellSaid, a text-to-speech product, is to create a podcast from blog posts, it will give me a set of dimensions that locate that “idea” in vector space. Again, in our two-dimensional toy version, let’s pretend it looks something like this:
Each dot indicates a different company/use case. They are situated based on the vector values assigned. When you search for something, it gets its own location in space, and that can be compared to other clusters to show relative similarity and closest neighbors. This is a toy because the real version happens in 1500+ dimensional space, not two, but hopefully, it makes it a lot more comprehensible. It’s an incredibly powerful tool, and it works because of all the hard work that already went into training the model off the huge corpus Open AI works with.
There’s more to it, but that’s a good place to start. Go have a click around, and let me know if there are companies to add or if you have any feedback.
