The blockchain beast system

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WATCH RELATED VIDEO: Blockchain Expert Explains One Concept in 5 Levels of Difficulty - WIRED

Is Money Bullshit?


Try out PMC Labs and tell us what you think. Learn More. As of today, there are many different implementations of cryptocurrencies working over a blockchain, with different approaches and philosophies. However, many of them share one common feature: they require proof-of-work to support the generation of blocks mining and, eventually, the generation of money.

This proof-of-work scheme often consists in the resolution of a cryptography problem, most commonly breaking a hash value, which can only be achieved through brute-force. The main drawback of proof-of-work is that it requires ridiculously large amounts of energy which do not have any useful outcome beyond supporting the currency. In this paper, we present a theoretical proposal that introduces a proof-of-useful-work scheme to support a cryptocurrency running over a blockchain, which we named Coin.

In this system, the mining scheme requires training deep learning models, and a block is only mined when the performance of such model exceeds a threshold. The distributed system allows for nodes to verify the models delivered by miners in an easy way certainly much more efficiently than the mining process itself , determining when a block is to be generated.

Additionally, this paper presents a proof-of-storage scheme for rewarding users that provide storage for the deep learning models, as well as a theoretical dissertation on how the mechanics of the system could be articulated with the ultimate goal of democratizing access to artificial intelligence.

One year later, this paper would give birth to the Bitcoin network. As of today, Bitcoin is the most used cryptocurrency, although many other alternatives have arisen that are based on similar principles [ 2 , 3 ]. Notwithstanding, the volume of daily operations is of about , [ 4 ], an amount which is still very far away of the number of VISA operations, which would exceed the million daily transactions [ 5 , 6 ]. In spite of the relatively small number of transactions, Bitcoin is a cryptocurrency listed in the currency market, and can be exchanged for dollars, euros or other fiat currency.

Its value in dollars has increased significantly since its appearance in , turning into one of the products with highest financial performance for investors. Additionally, in recent years many other cryptocurrencies have appeared whose workings are very similar to Bitcoin, although with some particularities and different philosophies.

Probably, the most significant example is the Ether, the currency provided by the Ethereum platform, which was presented in by Buterin [ 7 ]. Bitcoin, Ethereum, and many other cryptocurrencies of this kind work atop a technology commonly known as blockchain [ 8 ]. Blockchain technology allows to store transactions in blocks, which are placed sequentially, thus forming a chain. A cryptographic mechanism prevents the chain from being tampered, and the only operation that can alter the blockchain is appending a new block at the end of it.

In other words, this mechanism avoids malicious activities that can try to alter or remove transactions that took place at some time in the past. Blockchains have been widely used with academic purposes, such as developing consensus protocols [ 9 ] or developing data exchange schemas for the internet of things [ 10 ]. Noticeably, they have also been used for not so honest purposes, and particularly some financial frauds, such as Ponzi schemes, have been detected in public networks [ 11 ].

Although a blockchain can be private and centralized, most blockchains standing behind a cryptocurrency such as Bitcoin or Ether are often public and decentralized. In general, a public blockchain allows everyone to query the list of transactions, providing full transparency about the contents of the ledger. Additionally, by being decentralized, a blockchain stores the blocks and validates and confirms transactions using a distributed network of computer systems.

The fact of having a decentralized system is key in these types of cryptocurrencies blockchains. By not relying on a centralized system that runs the platform code, there must be an incentive for users to join the network that is supporting the blockchain, and without whom transactions could not be done, putting an end to the cryptocurrency.

Nowadays, this incentive is the block mining. In summary, a block can only be appended to the blockchain if it has been mined by a user. The mining process often consists in solving a cryptographic problem whose resolution is computationally expensive and requires brute-forcing, but for which given a solution, it is easy to validate.

This cryptographic problem often consists in finding a number, called nonce , which must be appended to the transactions that are about to be included in the block to be mined. Next, the value resulting from applying a hash function over the concatenation of the hash of the previous block, the list of transactions and the nonce will be computed. The purpose will be to find a nonce so that such hash value will be smaller than a given hash.

Because hash functions are computed only in one direction i. Once a user has successfully mined a block, he obtains a reward, which is provided by the network. As an example, mining a block in the Bitcoin network is currently rewarded with a total of In addition, the user mining the block is rewarded with the fees involved in the transactions that are included in the block. In most current cryptocurrencies schemes, these fees are not compulsory but highly recommended for a transaction to be included faster in the blockchain: this is because miners are responsible for choosing the transactions included in a block, and it is likely that they will choose those with larger fees.

This block mining scheme receives the name of proof-of-work , since it requires to have performed a computational work in this case, brute-force for breaking the cryptographic problem in order to be able to generate a new block to be appended to the blockchain and thus receive a reward.

The dynamics of mining in a blockchain can be studied from the perspective of game theory [ 12 ]. The main problem of this mining scheme has to do with the huge amount of energy required to support the blockchain network. This issue is raising severe concerns and being a subject of careful study [ 13 ], as can be seen for example in several research works focused on studying the energetic impact of the Bitcoin network [ 14 ].

In the specific case of this cryptocurrency, special hardware known as ASIC has been designed and manufactured to accelerate the brute-force process needed for mining. Some countries where electric supply is cheap e. Today, the estimation of the amount of electrical power consumed by the Bitcoin network is about 70 TWh per year [ 16 ]. This amount is similar to the overall consumption of a country like Austria, and, what is worse, all this power is spent in the brute-force process, whose only purpose is to compute the nonces for mining a new block, without any other useful application beyond that scope whatsoever.

For this reason, some authors are proposing a new paradigm in which the mathematical problem required to be solved for mining a new block could have some interest by itself, beyond the usefulness of supporting the blockchain network. In some cases, this approach is called proof-of-useful-work [ 17 ], thus recognizing that the computational work done for mining a block must have some interest or useful application.

In this paper, we propose a novel proof-of-useful-work scheme for supporting a blockchain network, whose interest resides on the fact that the mining process is equivalent to the training of artificial intelligence models with many potential applications.

The remainder of this paper is structured as follows: in Section 2 the concept of proof-of-useful-work and some attempts to introduce such a concept into a blockchain are described, including some with a scope similar to the one presented in this paper. In Section 3 some theoretical notions which are of interest for those readers not familiar with the concept of deep learning are provided.

In the following four sections we introduce our proposal as follows: in Section 4 the requirements that the proof-of-work scheme must follow in order to be implemented within a Blockchain network are described. Section 5 details the proof-of-useful-work approach proposed in the paper, by which deep learning models are trained and evaluated in order to achieve block mining; Section 6 delves into the process of generating a valid deep learning architecture given a hash; Section 7 describes an additional proof-of-storage mechanism for being able to support the storage of models and data in a distributed system, and finally in Section 8 we provide some hints of how artificial intelligence could be democratized in this scheme, by allowing currency owners to propose and vote on problems of interest to the community.

Finally, in Section 9 we propose some possible alternatives regarding the implementation of this conceptual framework, and Section 10 provides some conclusive remarks about the notions and system proposed in this paper. Some developers and researchers have already started to work on Blockchain projects which already adhere to the proof-of-useful work scheme, especially in academic settings. For example, Zhan et al. Similarly, Ball et al.

An example of a proof-of-useful-work scheme that has led to the development of a cryptocurrency is PrimeCoin [ 21 ], whose mining algorithm consists in searching for Cunningham chains and bi-twins chains of prime numbers. When it comes to the combination of artificial intelligence and blockchain, there are many works including projects beyond academia claiming to combine both fields of study in very different ways. A common case consists in trying to apply machine learning for crypto-trading, or extensively trying to predict the price of a cryptocurrency using machine learning [ 22 , 23 ].

Other works have focused on the application of artificial intelligence to blockchain security [ 24 ]. Some examples of projects arising from the fusion of artificial intelligence and blockchain are DML standing for decentralized machine learning [ 26 ] or SingularityNET [ 27 ]. These approaches allow distributed machine learning, with the possibility to deploy smart contracts over a blockchain, and there is not a close integration of both ideas. Another related project is AICoin [ 28 ], although their approach is not very clear.

Finally, in the last quarter of Fetch. AI was presented [ 29 ], which mentions both useful proof-of-work and artificial intelligence, although again, the technical details are not clear. In any case, to the best of our knowledge, there are no works using deep learning as a proof-of-work scheme. This concept is hardly new, and the first approaches go as far back as [ 30 ], and derivative works have been presented under different names, including proof-of-space [ 31 , 32 ] and proof-of-storage [ 33 ].

Proof-of-storage models running over blockchain have been recently presented [ 17 ], and have led to actual cryptocurrencies, such as Permacoin [ 34 ], Sia [ 35 ] or Filecoin [ 36 ]. Deep learning is a rather generic name used to encompass different artificial intelligence techniques that present some common properties: 1 they require large amounts of data to learn useful models, and 2 they are able to automatically extract relevant features from raw or almost raw data.

One of the most widely used deep learning techniques are deep neural networks, which are used for solving a variety of problems both in academy and industry. Some of the common implementations of deep neural networks are convolutional neural networks [ 37 ], which are often used for computer vision problems, or long-short term memory LSTM networks [ 38 ], rather used for audio and natural language processing.

In a nutshell, these networks work as follows: a batch of data of a given size is introduced to the network.

Then, the first convolutional layer applies a convolution or most often a cross-correlation operator over the input, generating an output called feature maps , which will be used as the input to the following convolutional layer. Optionally, there could be pooling layers in between the convolutional layers, which serve for the purpose of reducing dimensionality by performing subsampling. After the input is consumed by all the convolutional layers, the output feature maps will be flattened into a vector and introduced into a classifier, which can be a classical feed-forward network such as a multi-layer perceptron or a recurrent network.

Once a classification is obtained, it is compared to the actual value and then the network weights are upgraded using backpropagation. It is worth mentioning that the previous process describes the most typical type of convolutional network, although there are different implementations with some particular features.

For example, fully convolutional networks [ 39 ] use additional convolutional and pooling layers for classification, instead of a fully-connected subnetwork; or residual networks [ 40 ] incorporate additional passes of feature maps between non-sequential layers. Regardless of the specific architecture, a common feature of all deep neural networks is that there exist a remarkable amount of hyperparameters that must be decided upon, to mention a few: the batch size, the number of convolutional layers and of fully-connected layers, the number of convolutional kernels and their size, the pooling setup, the number of neurons in fully-connected or recurrent layers, or the learning setup optimizer, learning rate, etc.

The particular setup of these hyperparameters could have an important effect on the neural network performance. Unfortunately, there are no systematic ways to determine the best allocation of hyperparameters, so a common approach is to test different alternatives by trial and error. In-depth surveys exploring neural architecture search have been published by Baldominos et al.

In this paper, we will not focus on any of these techniques in particular, but rather propose a theoretical framework to carry out neural architecture search in a distributed system, as part of a proof-of-work scheme in a blockchain network supporting a cryptocurrency. In our proposal, the problem to be solved in order to be able to append a new block to the blockchain consists in the resolution of an artificial intelligence problem.

In particular, we propose that these problems require a machine learning model to be trained and evaluated. The following aspects must be observed for the mining scheme to be able to be introduced into a blockchain network:. The problem must be complex, requiring some computational effort, in order to guarantee that some actual work was performed by miners in order to be able to obtain the reward associated with block mining.

In order to guarantee the integrity of the blockchain, the hash of the previous block must be introduced as a variable of the problem. The mining scheme must have a competitive component, so that it is the first miner to solve the problem or conversely, the miner who provides the best solution the one that mines the block and obtains the reward.

Given a problem solution, it must be easy to verify that the solution is valid and to assess its quality. Once a miner has found a block and this has been appended to the blockchain, all other potential blocks being under mining by other miners must be discarded.

Of course, the core of the proposal is the proof-of-useful-work mechanism, which is described in this section so that it complies with the requirements outlined in the previous section. Regarding Requirement 1, we propose that the problem to solve by miners is the training of an artificial intelligence model, and more particularly a deep learning architecture probably a convolutional neural network or a variation of it, although the specifics can remain open and are not described in this paper.

Training a deep learning model out of data involves learning its parameters weights using some kind of gradient descent algorithm.

This is an iterative process which is generally regarded as a computationally expensive task, therefore fulfilling such requirement. In order to append a new block to the blockchain, first the hash value from the last block will be taken.

Next, the miner will choose a maximum of N transactions from among the set of pending transactions, which are those which have not been yet included into the blockchain and thus remain unconfirmed.



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Amid pressing demands to achieve critical sustainable development goals, governments in developing countries face the additional complex task of embracing new digital technologies such as blockchains. This paper develops a framework interlinking development, technology, and government institutions that policymakers and development practitioners could use to address such a conundrum. State capacity and democratic governance are introduced as drivers in the overall analysis. With this in hand, blockchain technology is revisited from the perspective of governments in the Global South, identifying in the process key traits and proposing a new typology. An overview of the status of blockchain deployments in the Global South follows, complemented by a closer look at country examples to distill trends, patterns and risks.

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Sunra Thompson for BuzzFeed News. A few feet away sits the syringe that will, soon enough, plunge into the fat between my thumb and forefinger and deposit a glass-encased microchip roughly the size of an engorged grain of rice. My choice to get microchipped was not ceremonial. It was neither a transhumanist statement nor the fulfillment of a childhood dream born of afternoons reading science fiction. Some of most powerful corporations in the world — Apple, Facebook, and Google; the Goliaths, the big guys, the companies that make the safest bets and rarely lose — are pouring resources and muscle into the payments industry, historically a complicated, low-margin business. Meanwhile, companies like Uber and Airbnb have been forced to become payments giants themselves, helping to facilitate and process millions of transactions and millions of dollars each day. To hear Silicon Valley tell it, the broken-in leather wallet is on life support. I wanted to pull the plug. Which is how, ultimately, I found myself in this sterile Swedish backroom staring down a syringe the size of a pipe cleaner.


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the blockchain beast system

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When it comes to new technologies, government has earned a reputation as a slow paced and lumbering beast. But former British minister for digital and the creative industries Margot James, speaking at the recent Blockchain Live conference held in London, attempted to disabuse this notion - laying out the UK government's plans for technology of the moment, blockchain. James also held up the example of the UK's financial regulator, the FCA, and its sandbox approach which "lets firms test products and services in a live market setting" but with "appropriate safeguards". It's a pioneering approach that is reportedly now being imitated across the world. However, James stressed the importance of looking at the applications of blockchain beyond the financial world. She highlighted a number of blockchain projects for particular praise, including the partnership between IBM, Nestle and Unilever to improve the traceability of contaminated food.


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Quantum computers and the Bitcoin blockchain has been saved. Quantum computers and the Bitcoin blockchain has been removed. One of the most well-known applications of quantum computers is breaking the mathematical difficulty underlying most of currently used cryptography. Since Google announced that it achieved quantum supremacy there has been an increasing number of articles on the web predicting the demise of currently used cryptography in general, and Bitcoin in particular. The goal of this article is to present a balanced view regarding the risks that quantum computers pose to Bitcoin. A great amount of digital ink has been spilled on the topic of how quantum computers pose an existential threat to currently used asymmetric cryptography. We will therefore not discuss this in detail, but only explain the aspects that are relevant for the analysis in this article.

But first, we need to start from square one and do some background work. First, we must remember that Revelation is a first-century letter to seven churches in Asia Minor Rev. Letters in antiquity are much like modern letters—situational, personal, and contextual. To understand a letter between two people or groups of people , you really need to know a thing or two about what necessitated the sending of the letter in the first place.


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  1. Masar

    What an entertaining question