At this year’s BlockShow Asia, Yangdong Deng, chief AI scientist of Blockchain startup Matrix, explained how inserting Artificial Intelligence (AI) into the Blockchain ecosystem would make it possible to use Bitcoin mining computational power for scientific innovation.
According to Deng, the current
- computing power being used in Bitcoin mining operations is 8.23×10²² FLOPS floating point operations per second,
- while the total computing power in the world is 1.2×10²³ FLOPS.
According to these calculations, Bitcoin mining is consuming 17 percent of total global computing power, justifying the frequent accusations that Bitcoin mining is wasteful.
Matrix is seeking to reinvent mining algorithms by including AI into the equation through a Bayesian mining system that utilizes a Markov chain Monte Carlo algorithm (MCMC). Because these computations function similarly to traditional mining functions, they work well for Bitcoin mining.
As Deng argues, using AI, the computing power used to verify transactions on the Bitcoin network can be leveraged for other uses outside the world of cryptocurrencies.
One example he gave his scientific research:
- a brain network simulation requires approximately 1018 FLOPS,
- while a complete human metabolic network simulation requires 1025 FLOPS.
According to Deng, other important non-crypto use cases that require massive computing power are chemical reaction simulations, medical diagnoses and complex finance modeling.
Intel recently filed a patent for a Blockchain-based system that also works to harness the energy used in cryptocurrency mining for scientific development – in this case particularly for genetic sequencing.
The BlockShow Asia conference this November included a number of innovative projects in addition to Matrix. 1,500 entrepreneurs and experts gathered at the event in Singapore to share and discover the latest developments in the industry.
Markov chain Monte Carlo
In statistics, Markov chain Monte Carlo (MCMC) methods are a class of algorithms for sampling from a probability distribution based on constructing a Markov chain that has the desired distribution as its equilibrium distribution. The state of the chain after a number of steps is then used as a sample of the desired distribution. The quality of the sample improves as a function of the number of steps.
Random walk Monte Carlo methods make up a large subclass of Markov chain Monte Carlo methods.
Markov chain Monte Carlo methods are primarily used for calculating numerical approximations of multi-dimensional integrals, for example in Bayesian statistics, computational physics, computational biology and computational linguistics.
In Bayesian statistics, the recent development of Markov chain Monte Carlo methods has been a key step in making it possible to compute large hierarchical models that require integrations over hundreds or even thousands of unknown parameters.
In rare event sampling, they are also used for generating samples that gradually populate the rare failure region.
Convergence of the Metropolis-Hastings algorithm.
Markov chain Monte Carlo attempts to approximate the blue distribution with the orange distribution
more on Wikipedia: https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo