Vectorizing allows us to give meaning to vectors so that machines can reason more abstractly and efficiently than ever before. This means that machines can process and recognize languages, objects, and emotions better than ever.
By imbuing vectors with semantic meaning, machines can recognize the relationships between words and how they relate to one another in a piece of text. This allows them to summarize the text in a more programmatic way. For example, they can pick up on synonyms, antonyms, and homophones. This also allows them to identify the tone of a piece of language and how it relates to the meaning of the sentence.
Previously, machines could only recognize sequences of characters or numbers, leading to inefficient reasoning and longer processing times. When machines are able to recognize the meaning behind sequences, they are able to process information faster and more accurately.
In summary, vectorizing provides a way for machines to recognize the relationships between words and the meaning behind them. This leads to more efficient reasoning and faster processing times for machines. Vectorizing continues to be an important tool in improving the capabilities of machine learning and language processing.Original Article: https://www.wired.com/story/hyperdimensional-computing-reimagines-artificial-intelligence/