Hyperdimensional Computing Method

A Hyperdimensional Computing Method is an computational approach that represents information as hyperdimensional vectors.

• AKA: Vector Symbolic Architecture.
• Context:
  • It can (typically) be used in the domain of Artificial Intelligence.
  • It can be used for pattern recognition, data mining, and various AI applications.
  • It allows for complex algebraic operations to be performed on high-dimensional data.
  • It can be an Analog Computing Method.
  • …
• Example(s):
  • Utilizing hyperdimensional computing for language processing tasks in Natural Language Processing where words and sentences are represented as high-dimensional vectors.
  • Implementing hyperdimensional computing in pattern recognition for identifying patterns in high-dimensional data such as images or genetic data.
  • Employing hyperdimensional computing in robotics for cognitive mapping and navigation.
  • …
• Counter-Example(s):
  • Using traditional scalar or low-dimensional vector computation methods in artificial intelligence.
  • Implementing relational databases for data representation and storage.
  • …
• See: Vector (Mathematics And Physics), Artificial Intelligence, High-Dimensional Data, Analog Computing, Pattern Recognition, Natural Language Processing, Data Mining.

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References

2023

• (Wikipedia, 2023) ⇒ https://en.wikipedia.org/wiki/Hyperdimensional_computing Retrieved:2023-6-12.
  • Hyperdimensional computing (HDC) is an approach to computation, particularly artificial intelligence, where information is represented as a hyperdimensional (long) vector, an array of numbers. A hyperdimensional vector (hypervector) could include thousands of numbers that represent a point in a space of thousands of dimensions. Vector Symbolic Architectures is an older name for the same broad approach.

2023

• https://www.wired.com/story/hyperdimensional-computing-reimagines-artificial-intelligence/
  • QUOTE: This is the starting point for a radically different approach to computation, known as hyperdimensional computing. The key is that each piece of information, such as the notion of a car or its make, model, or color, or all of it together, is represented as a single entity: a hyperdimensional vector. ... “This is the thing that I’ve been most excited about, practically in my entire career,” Olshausen said. To him and many others, hyperdimensional computing promises a new world in which computing is efficient and robust and machine-made decisions are entirely transparent.
  • ... In 2015, a student of Olshausen’s named Eric Weiss demonstrated one aspect of hyperdimensional computing’s unique abilities. Weiss figured out how to represent a complex image as a single hyperdimensional vector that contains information about all the objects in the image, including their properties, such as colors, positions, and sizes.

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