Computing with high-dimensional vectors: theory and applications in bioinformatics

Hyperdimensional computing is a novel computational paradigm with the potential to redefine the way we usually process information. Unlike traditional computing paradigms anchored in binary logic and linear processing, hyperdimensional computing makes use of high-dimensional structures to process information in a more holistic way. At the base of this relatively new way if computing is the concept of hyperdimensional vectors which have the ability to represent multiple variables simultaneously, resulting in a more efficient and accurate computation, in a way that closely resembles how the human brain processes information. This characteristic leads to the unique capability of effectively managing and processing massive amounts of data making it suitable for a wide range of applications, including artificial intelligence, cognitive science, natural language processing, robotics, bioinformatics, the internet-of-things, among other scientific disciplines. However, hyperdimensional computing is still in its early developmental phases, but major technological players such as IBM and Intel are actively engaged in advancing this field, further highlighting its promising potential to revolutionize the way we process information and develop intelligent systems. Here, we are going to delve into the theoretical foundations of computing with vectors in a high-dimensional space by concluding on exploring the potential applications of hyperdimensional computing in the field of bioinformatics.