Recently, scientists from the Technische Universität Dresden in Germany published groundbreaking research demonstrating a new material design for neuromorphic computing, a technology that could have revolutionary implications for both blockchain and AI.

Using a technique called “reservoir computing,” the team developed a pattern recognition method that uses magnon vortices to perform algorithmic functions almost instantaneously.

It looks complicated because it is. Image source, Nature article, Korber, et. spol., Pattern recognition in reciprocal space with a magnon-scattering tank

Not only did they develop and test the new reservoir material, but they also demonstrated the potential for neuromorphic computers to run on a standard CMOS chip, something that could pervert both blockchain and AI.

Classical computers, such as those that power our smartphones, laptops and most of the world’s supercomputers, use binary transistors that can be either on or off (expressed as “one” or “zero”).

Neuromorphic computers use programmable physical artificial neurons to mimic organic brain activity. Instead of processing binary files, these systems send signals through different patterns of neurons with the added factor of time.

The reason this is important to the blockchain and AI fields in particular is that neuromorphic computers are fundamentally suited to pattern recognition and machine learning algorithms.

Binary systems use Boolean algebra to calculate. Because of this, classic computers remain unchallenged when it comes to number crunching. However, when it comes to pattern recognition, especially when the data is noisy or missing information, these systems struggle.

This is why classical systems take a significant amount of time to solve complex cryptographic puzzles, and why they are completely unsuitable for situations where incomplete data prevents a mathematical solution.

For example, there is an endless flow of real-time data in the finance, artificial intelligence and transportation sectors. Classical computers deal with occluded problems—for example, the problem of driverless cars has so far proven difficult to reduce to a series of “true/false” computational problems.

However, neuromorphic computers are designed to solve problems that involve information scarcity. In the transportation industry, it is impossible for a classical computer to predict traffic flow because there are too many independent variables. A neuromorphic computer can constantly respond to real-time data because it does not process data points one at a time.

Instead, neuromorphic computers process data through pattern configurations that work much like the human brain. Our brains project specific patterns in relation to specific neural functions, and both patterns and functions can change overtime.

Related: How is quantum computing impacting the financial industry?

The main advantage of neuromorphic computing is its level of performance compared to classical and quantum computing consumption is extremely low. This means that neuromorphic computers could significantly reduce the cost in terms of time and energy when it comes to running a blockchain and mining new blocks on existing blockchains.

Neuromorphic computers could also significantly speed up machine learning systems, especially those connected to real-world sensors (cars, robots) or those that process real-time data (crypto market analysis, transport hubs).

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