A lot of times we hear reports about how digital currency mining is wasteful, with megawatts of electricity being used for completing tough cryptographic problems for the sole purpose of receiving the block reward.

The tremendous growth of cryptocurrencies has created an exponential demand for computing power. When the popularity of a cryptocurrency grows, and more miners go live on the network, the difficult of the mining algorithms increases as well, this is meant to control the currency supply. So as to adjust to these difficult levels miners are constantly adding newer and faster hardware.

As an example in the case of bitcoin the total energy use is estimated to be about 31 tera-watt hours a year, this is more energy use than 150 individual countries in the world. Even though this mining process is necessary and attracts value within the world of crypto currencies it does not make sense outside of this context.

Mining result in a lot of wasted computational power as well as electricity, both of which can be repurposed to help solve real world problems. Matrix attempts to address this problem by ensuring that their mining mechanism can be used to solve compute-intense problems that are applicable within the physical world.

Combining Bayesian Computing and AI for Value Creation

Matrix makes use of the Markov Chain Monte Carlo (MCMC) computation, which comprises a class of algorithms for sampling from a probability distribution. MCMC based Bayesian computing plays a fundamental role in numerous big data applications such as gene regulatory network, clinical diagnosis, video analytics, and structural modelling. As a result, a distributed network of MCMC computing nodes can be used to solve computing power intensive problems. This forms a parallel between the value crypto currencies have within the digital world as well as the values they can provide within the physical world.

Matrix adopts a Hybrid PoS + PoW consensus mechanism, instead of the traditional Hash computations, the mechanism makes use of value adding computation through the use of the Markov Chain Monte Carlo(MCMC) computations. This computation is to be used as the Proof-of-Work protocol. This allows the mining process to not only generate MAN tokens but to also be used to power real-world applications.

The Markov Chain Monte Carlo(MCMC) has significant scientific, engineering and financial applications, essentially MCMC can be used in almost anything that requires predictive modelling. MCMC allows us to sample probability distributions even if the knowledge or information was incomplete.

Applications of Markov Chain Monte Carlo and Artificial Intelligence in Medicine

There are various applications of MCMC within the field of medicine, predictive modelling can be used to determine an illness or disease based on the symptoms and physical conditions of a patient. Doctors can also model the future course of an illness or the risk of developing an illness helping guide screening and or treatment decisions. For example, predictive models have been developed in gastroenterology to predict the risk of disease flares for inflammatory bowel disease and risk of hepatocellular carcinoma among patients with cirrhosis. A recent study also found that Bayesian MCMC showed considerable promise as an alternative in the analysis and modelling of in-ICU mortality outcomes.

Over the year AI research has made considerable strides in pathogen and disease identification through the use of deep learning and image recognition technology. Artificial intelligence has been used to recognise patterns associated with viral diseases as well as diseases that have bacterial, fungal or genetic origin. Matrix leverages the use of blockchain technology and artificial intelligence to create a truly intelligent blockchain.

The Matrix bayesian proof of work algorithm provide pathological image recognition technology combined with machine learning methods and deep learning facilities that can help physicians within their field. Helping in decision making as well as increasing the accuracy and efficiency of diagnosis.

Matrix is currently working with the Beijing Cancer Hospital, the 302 Hospital in Beijing as well as several other top Chinese hospitals in the development of a diagnosis and treatment solution for thyroid and liver cancer. Liver cancer is currently the second most common cancer in China accounting for 350,000 deaths a year, while thyroid cancer continues to increase at double digit rates.

The use of Artificial intelligence and machine learning in disease diagnosis comes in especially useful in countries like china where there is a shortage of doctors. There are only 1.5 doctors for every 1000 people, with doctors routinely unable to read patient scans or imaging as carefully as they’d like. Matrix’s technology would considerably ease their burden.

Even though this technology will not be replacing Doctors anytime soon, the use of artificial intelligence within the field of medicine could lead to accessible, affordable health care for everyone.