New research shows how brain-like computers could revolutionize blockchain and artificial intelligence

New research shows how brain-like computers could revolutionize blockchain and artificial intelligence

Researchers at the Technische Universitt Dresden in Germany recently published groundbreaking research showing a new material design for neuromorphic computing, a technology that could have game-changing implications for both blockchain and artificial intelligence.

Using a technique called ‘reservoir computing’, the team developed a method for pattern recognition that uses a magnon vortex to perform algorithmic functions almost instantaneously.

Operating principle of a diffusion magnon reservoir. Source: “Pattern recognition in reciprocal space with a magnon dispersion tank”,Nature

Not only did the researchers develop and test the new tank material, but they also demonstrated the potential for neuromorphic computing to run on a standard CMOS chip, something that could upend both blockchain and artificial intelligence (AI).

Classical computers, like those that power smartphones, laptops, and most of the world’s supercomputers, use binary transistors that can be turned on or off (expressed as ones or zeros).

Neuromorphic computers use programmable physical artificial neurons to mimic organic brain activity. Instead of processing binaries, these systems send signals through various models of neurons with the added factor of time.

The reason this is important for the fields of blockchain and artificial intelligence in particular is because neuromorphic computers are fundamentally suited for pattern recognition and machine learning algorithms.

Binary systems use Boolean algebra to calculate. For this reason, classical computers go unchallenged when it comes to crunching numbers. 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 wholly unsuitable for situations where incomplete data prevents a mathematically based solution.

In the fields of finance, artificial intelligence, and transportation, for example, there is an endless influx of real-time data. Classical computers wrestle with hidden problems The challenge of driverless cars, for example, has so far proven difficult to reduce to a series of true/false computational problems.

However, neuromorphic computers are built to deal with problems involving a lack of information. In the transportation sector, it is impossible for a classical computer to predict traffic flow because there are too many independent variables. A neuromorphic computer can constantly react to real-time data because it doesn’t process data one at a time.

Instead, neuromorphic computers run data through pattern configurations that function somewhat like the human brain. Human brains flash specific patterns in relation to specific neural functions, and both patterns and functions can change over time.

Related: How does quantum computing affect the financial sector?

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

Neuromorphic computers could also provide significant acceleration for machine learning systems, especially those that interface with real-world sensors (self-driving cars, robots) or those that process data in real time (cryptocurrency market analytics, transportation hubs).

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