# Artificial Intelligence Implementations on the Blockchain. Use Cases and Future Applications

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## Abstract

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## 1. Introduction

## 2. On the Construction of an AI on a Blockchain

#### 2.1. The Blockchain as a Transaction Platform

#### 2.2. The Blockchain as a Computing Platform

#### 2.3. The Genetic Algorithm as a Direction for Machine Learning

#### 2.4. The Cellular Automaton as the Neuron of Genetic Algorithms

#### 2.5. Implementing GAs on Blockchains

## 3. Use Cases and Future Applications

#### 3.1. The Integrity and Validity of Information

**Data as fact integrity**: The cryptographic inventions of digital signatures and hashes have led to a general technique for making data reliable within the context and limitations of the technical means, a characteristic called integrity. In practice this means that we can state with (cryptographic) certainty that a piece of data existed no later than a particular time, and that it remains untampered with. These cryptographic techniques need some software to deliver results. Timestamping [28] involves taking the hash of a document and placing it in a timed sequence of hashes that is kept alive essentially without limit on time. Each new document’s hash is placed in a block, which is then hashed, along with a hash of the last block and the current time. As the cryptographic hash is essentially unforgeable without the actual block, this ensures both the inclusion of the new document(s) and the proof that the last block, and by induction all previous blocks and included documents, are securely timestamped. The reliability of the stamp of time is the reliability of the recording of the time in each block, and the space between the blocks.

**Facts by people, securely**: Digital signing takes the evidence of a hash one step further by indicating who it was that made that stamp. Digital signatures are made by a private key, and verified by a public key, which latter also takes the form of an identifier for the private key called a pseudonym. This security model is essential for a blockchain as it ensures that only the proper pseudonymous agent, as holder of the private key, can make new transactions. Money is perhaps the most harshly attacked activity of humanity after wars, and therefore can only survive if protected by strong security. The cryptographic security model of pseudonymous digital signing used in blockchains is battle-hardened and is available for free for all other applications beyond transfers of value. This is no trivial benefit as the Internet has quite poor security models, and big Internet applications such as online banking and autonomous vehicles generally have trouble deploying robust security to users. Injection of information from unknown sources is rampant, and simply adding data stamping and signing as used in blockchain makes the attacker’s job harder.

**Facts as shared knowledge:**A technique known as triple entry accounting [29] adds a further advantage captured by the aphorism “I know that what you see is what I see.” Triple entry takes the above integrity techniques and makes records such as offers and acceptances, payments, receipts and invoices both shared and reliably the same to all relevant parties, which allows software to work with reliable raw data as facts produced by other parties; triple entry accounting does for trading groups what double entry accounting did for the firm. Independence from weak data, whether summarized, prepared, or sanitized, results in the elimination of diverging data sets and unreliable outcomes. For example, clearing and settlement in financial trading is highly simplified if the data is already guaranteed to be the same for all.

**Knowledge as truth**: What remains is the provenance of the data at the time of posting. The blockchain supports two easy controls, and one hard control. Firstly, if the data is a financial transaction on a blockchain, in an asset mediated by that very blockchain, then the transaction record can support its own provenance, gained in part that someone went to the effort of moving money, and in other part that it cost a small fee. Secondly, the use of the pseudonymous digital signatures provides a minimal form of identity system: A document’s utility and provenance can be analyzed within the context of all the documents posted by the same agent. If Alice generally posts good documents, then the next is likely to be good; if Bob posts fake news then people should expect more of the same. Pay on demand is discussed in the next section.

#### 3.2. Programs Stored on Chain and Composed Within Transactions—Pay Per Use

#### 3.3. Trained AI Frameworks that can be Parsed Via Pay on Demand

#### 3.4. Artificial Intelligence Agents Trained Via Submitted Blockchain Data and Operated on Chain.

#### 3.5. Proof of Work Via dSHA256 as a Source of Randomness and Monte Carlo Method Via ASICs

^{18}random numbers per second, in effect, making the miners pseudo-random number generators (PRNGs). The result of this is the generation of an impressively large number of random numbers, for every block. A well-known computational method that is capable of providing solutions in Non-deterministic Polynomial-time – Hard (NP-hard) and Non-deterministic Polynomial-time – Complete (NP-complete) problems via utilizing the random numbers that a SHA256 miner can produce is the Monte Carlo method. Monte Carlo is a category of computational algorithms, which is based on continuous and repetitive random sampling in order to solve complex problems. The underlying concept is to use random solutions to solve problems that can be deterministic in nature. The method is often used in physical and mathematical cases and is very useful when it is difficult or impossible to use other approaches. The Monte Carlo method is used in three categories of problems: Optimization, numerical integration and guessing results from a probability distribution [49].

#### 3.6. Solving Physical Problems via CNNs and Simulation of Quantum Computing

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- The Archimedes Palimpsest; University of Pennsylvania Libraries: Philadelphia, PA, USA; Available online: http://archimedespalimpsest.net/ (accessed on 1 June 2016).
- Cultural Tragedy: Massive Inferno Engulfs 200-Year-Old Museum in Brazil. Available online: https://www.cbsnews.com/news/fire-national-museum-brazil-rio-de-janeiro-today-2018-09-02/ (accessed on 6 March 2019).
- Wright, C. (Pseudonym: Nakamoto, S). Bitcoin: A Peer-to-Peer Electronic Cash System; Whitepaper: Sydney, Australia, October 2008. [Google Scholar]
- Sgantzos, K. Implementing a Church-Turing-Deutsch Principle Machine on a Blockchain. In Proceedings of the 12th Hellenic Society for Computational Biology and Bioinformatics Conference, Athens, Greece, 11–13 October 2017; Available online: https://sites.google.com/site/hscbb17/program/HSCBB17-Booklet.pdf (accessed on 26 May 2019).
- Meiklejohn, S.; Pomarole, M.; Jordan, G.; Levchenko, K.; McCoy, D.; Voelker, G.M.; Savage, S. A fistful of bitcoins: Characterizing payments among men with no names. In Proceedings of the 2013 Conference on Internet Measurement Conference, Barcelona, Spain, 23–25 October 2013. [Google Scholar]
- Merkle, R.C. A Digital Signature based on a Conventional Encryption Function. In Advances in Cryptology—CRYPTO ’87, Santa Barbara, California, August 21-25, 1988, Lecture Notes in Computer Science; Springer: Berlin, Germany, 1988; Volume 293, pp. 369–378. [Google Scholar]
- Khalilov, M.C.K.; Levi, A. A Survey on Anonymity and Privacy in Bitcoin-like Digital Cash Systems. IEEE Commun. Surv. Tutor.
**2017**, 20, 2543–2585. [Google Scholar] [CrossRef] - Opcode List in BitcoinSV (v. 0.2).
^{©}2009 S. Nakamoto,^{©}2009–2016 The Bitcoin Core developers,^{©}2018-19 Bitcoin Association. Distributed under the Open BSV Software License. Available online: https://github.com/bitcoin-sv/bitcoin-sv/blob/master/src/script/script.h (accessed on 12 June 2019). - Aisopos, K.; Kakarountas, A.P.; Michail, H.; Goutis, C.E. High throughput implementation of the new Secure Hash Algorithm through partial unrolling. In Proceedings of the IEEE 2005 International Workshop on Signal Processing Systems (SiPS’05), Athens, Greece, 2–4 November 2005; pp. 99–103. [Google Scholar]
- Wang, H. A Variant to Turing’s Theory of Computing Machines. JACM
**1957**, 4, 63–92. [Google Scholar] [CrossRef] - Alberts, B.; Johnson, A.; Lewis, J.; Raff, M.; Roberts, K.; Walter, P. Molecular Biology of the Cell, 5th ed.; Garland Science: New York, NY, USA, 2007; pp. 264–328. [Google Scholar]
- What is DNA Replication? Available online: https://www.yourgenome.org/facts/what-is-dna-replication (accessed on 24 February 2017).
- Genetic Algorithms, Mathworks. 2017. Available online: https://www.mathworks.com/matlabcentral/fileexchange/11565-genetic-algorithms-application (accessed on 25 May 2019).
- von Neumann, J. The General and Logical Theory of Automata. In Cerebral Mechanisms in Behavior—The Hixon Symposium; Jeffress, L.A., Ed.; John Wiley & Sons: New York, NY, USA, 1951; pp. 1–31. [Google Scholar]
- Gardner, M. Mathematical Games: The fantastic combinations of John Conway’s new solitaire game of life. Sci. Am.
**1970**, 223, 120–123. [Google Scholar] [CrossRef] - Wolfram, S. Statistical Mechanics of Cellular Automata. Rev. Mod. Phys.
**1983**, 55, 601–644. [Google Scholar] [CrossRef] - Wolfram, S. A New Kind of Science; Wolfram Media Inc.: Champaign, IL, USA, 2002. [Google Scholar]
- William, G. Cellular automata as convolutional neural networks. Department of Applied Physics, Stanford University. arXiv
**2018**, arXiv:1809.02942. [Google Scholar] - Michael, O.R. Turing, Church, Gödel, Computability, Complexity and Randomization: A Personal View. Available online: http://www.bu.edu/cphs/files/2013/08/Rabin.pdf (accessed on 26 May 2019).
- Nielsen, M. Interesting Problems: The Church–Turing–Deutsch Principle. Available online: http://michaelnielsen.org/blog/interesting-problems-the-church-turing-deutsch-principle/ (accessed on 31 May 2019).
- Deutsch, D. Quantum theory, the Church—Turing principle and the universal quantum computer. In Proceedings of the Royal Society, London, UK, July 1984. [Google Scholar]
- LeCun, Y.; Bengio, Y.; Hinton, G. Quantum Deep Learning Triuniverse. Nature
**2015**, 521, 436. Available online: https://www.scirp.org/reference/ReferencesPapers.aspx?ReferenceID=1899138 (accessed on 3 June 2019). [CrossRef] [PubMed] - Chepurnoy, A.; Kharin, V.; Meshkov, D. Self-Reproducing Coins as Universal Turing Machine. 2018. Available online: https://arxiv.org/pdf/1806.10116.pdf (accessed on 6 June 2019).
- Justin, D.; Harris, B.W. Decentralized & Collaborative AI on Blockchain. In Proceedings of the 2019 IEEE International Conference on Blockchain (Blockchain), Atlanta, GA, USA, 14–17 July 2019. [Google Scholar]
- Donath, J. Why Fake News Stories Thrive Online. 2016. Available online: https://edition.cnn.com/2016/11/20/opinions/fake-news-stories-thrive-donath/ (accessed on 10 June 2019).
- Gerck, E. The Einstein Phenomenon and Fake News. 2019. Available online: https://www.researchgate.net/publication/331561385_The_Einstein_Phenomenon_and_fake_news (accessed on 10 June 2019).
- Tencent Keen Security Lab. Experimental Security Research of Tesla Autopilot. 2019. Available online: https://keenlab.tencent.com/en/whitepapers/Experimental_Security_Research_of_Tesla_Autopilot.pdf (accessed on 09 June 2019).
- Haber, S.; Stornetta, W.S. How to time-stamp a digital document. J. Cryptol.
**1991**, 3. [Google Scholar] [CrossRef] - Grigg, I. Triple Entry Accounting. 2004. Available online: https://iang.org/papers/triple_entry.html (accessed on 26 June 2019).
- Grigg, I. PKI Considered Harmful. 2008. Available online: https://iang.org/ssl/pki_considered_harmful.html (accessed on 26 June 2019).
- Grigg, I. An Open Audit of an Open Certification Authority. Large Installation Systems Administration (LISA). 2008. Available online: https://iang.org/papers/open_audit_lisa.html (accessed on 26 June 2019).
- Media, B. Add any File to the BSV Blockchain. 2019. Available online: https://add.bico.media/ (accessed on 10 June 2019).
- Github.inc. The World’s Leading Software Development Platform. 2019. Available online: https://github.com/ (accessed on 10 June 2019).
- Agora Startup Page for Bitcoin SV Applications & Services. Available online: https://www.agora.icu/ (accessed on 12 June 2019).
- Grigg, I.; Petro, C.C. Using Electronic Markets to Achieve Efficient Task Distribution. In Proceedings of the Financial Cryptography First International Conference FC’97, Anguilla, British West Indies, 24–28 February 1997; Springer-Verlag: Germany 1997 LNCS1318. Available online: https://iang.org/papers/task_market.html (accessed on 10 June 2019).
- Money Button Is an API and a UI/UX Layer for the Bitcoin SV Blockchain. Available online: https://docs.moneybutton.com/ (accessed on 7 June 2019).
- Brownlee, J. A Gentle Introduction to Scikit-Learn: A Python Machine Learning Library. 2014. Available online: https://machinelearningmastery.com/a-gentle-introduction-to-scikit-learn-a-python-machine-learning-library/ (accessed on 26 June 2019).
- Hamel, L. Deep Learning Finds Fake News with 97% Accuracy. 2019. Available online: https://opendatascience.com/deep-learning-finds-fake-news-with-97-accuracy/ (accessed on 10 June 2019).
- Weiss, G. Multiagent Systems, 2nd ed.; The MIT Press: Cambridge, MA, USA, 2013. [Google Scholar]
- Stuart, J.R.; Norvig, P. Artificial Intelligence: A Modern Approach, 2nd ed.; Prentice Hall: Upper Saddle River, NJ, USA, 2003; Chapter 2; ISBN 0-13-790395-2. [Google Scholar]
- WeatherSV, Index and Retrieve Climate Data Immutably Stored on a Distributed Ledger. 2019. Available online: https://weathersv.app/find (accessed on 11 June 2019).
- Christodoulou, P.; Christodoulou, K.; Andreou, A. A decentralized application for logistics: Using blockchain in real-world applications. Cyprus Rev.
**2018**, 30, 171–183. [Google Scholar] - McLaren, D.; Agyeman, J. Sharing Cities: A Case for Truly Smart and Sustainable Cities; MIT Press: Cambridge, MA, USA, 2015. [Google Scholar]
- Zweispace Starts to Record Tokyo Earthquake Detector Data into the Bitcoin SV (BSV) Blockchain. Available online: https://coingeek.com/zweispace-starts-to-record-tokyo-earthquake-detector-data-into-the-bitcoin-sv-bsv-blockchain/ (accessed on 14 June 2019).
- Eastlake, D., III; Hansen, T. US Secure Hash Algorithms (SHA and SHA-Based HMAC and HKDF), RFC 6234 (Informational), Internet Engineering Task Force. 2011. Available online: http://www.ietf.org/rfc/rfc6234.txt (accessed on 12 June 2019).
- Tschorsch, F.; Scheuermann, B. Bitcoin and Beyond: A Technical Survey on Decentralized Digital Currencies. IEEE Commun. Surv. Tutor.
**2016**, 18, 2084–2123. [Google Scholar] [CrossRef] - George, P. Bitness: Bitcoin Usage in an Enterprise Environment, Opportunities and Challenges. Master’s Thesis, University of Greenwich, London, UK, 2013. [Google Scholar]
- Hashrate Comparing data. Available online: https://blockchair.com/compare (accessed on 14 June 2019).
- Kroese, D.P.; Brereton, T.; Taimre, T.; Botev, Z.I. Why the Monte Carlo method is so important today. WIREs Comput. Stat.
**2014**, 6, 386–392. [Google Scholar] [CrossRef] - Mathieu, M.; Couprie, C.; LeCun, Y. Deep multi-scale video prediction beyond mean square error. arXiv
**2016**, preprint. arXiv:1511.05440. [Google Scholar] - Iten, R.; Metger, T.; Wilming, H.; del Rio, L.; Renner, R. Discovering Physical Concepts with Neural Networks; Institute for Theoretical Physics: ETH Zurich, Zurich, Switzerland, 2018. [Google Scholar]
- Kurzweil, R. Don’t Fear Artificial Intelligence. 2014. Available online: http://time.com/3641921/dont-fear-artificial-intelligence/ (accessed on 14 June 2019).
- Watson, AI for Business, 2016–2019, ©IBM. Available online: https://www.ibm.com/watson/ (accessed on 14 June 2019).
- Gödel, K. Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme I. Monatshefte Math. Phys.
**1931**, 38, 173–198. [Google Scholar] [CrossRef] - Asimov Isaac. The Last Question”, Science Fiction Quarterly. 1956. Available online: https://www.multivax.com/last_question.html (accessed on 14 June 2019).

**Figure 1.**The basic structure of the blockchain [7].

**Figure 2.**Conway’s Game of Life as a convolutional neural network. Two convolutional filters identify the value of the center pixel and count the number of neighbors. These features are then scored and summed to generate a prediction for the system at the next time point.

**Figure 3.**Evolution of a cellular automaton rule 110. Every non-boundary transaction spends three outputs, and generates three new ones with identical bit values. Hatching indicates “mid” flag being un-set. Numbers in the cells on the right pane correspond to the transaction numbers on the left [23].

**Figure 4.**Agora, the homepage for Metanet [33].

**Figure 5.**Representation of a 2D embedding space with five embedding vectors each representing a different word [38]: Red—queen, blue—king, green—man, black—woman and yellow—oil.

**Figure 6.**WeatherSV demonstrates the ability to index and retrieve climate data immutably stored on a distributed ledger [41].

**Figure 7.**Learning physical representations. (

**a**) Human problem analysis. Experimental observations are compressed into a simple representation (encoding). If any question is asked about the physical setting, the human should be able to produce a correct answer using only the representation and not the original data. The process of producing the answer (by applying a physical model to the representation) is called decoding; (

**b**) Neural network structure for SciNet. Observations are encoded as real parameters fed to an encoder (a feed-forward neural network), which compresses the data into a representation (latent representation). The question is also encoded in a number of real parameters, which, together with the representation, are fed to the decoder network to produce an answer [51].

**Figure 8.**Quantum tomography. SciNet is given tomographic data for one or two qubits and an operational description of a measurement as a question input and has to predict the probability of outcomes for this measurement. It was trained with both tomographically complete and incomplete sets of measurements, and found that, given tomographically complete data, SciNet could be used to find the minimal number of parameters needed to describe a quantum state (two parameters for one qubit and six parameters for two qubits) [51].

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**MDPI and ACS Style**

Sgantzos, K.; Grigg, I.
Artificial Intelligence Implementations on the Blockchain. Use Cases and Future Applications. *Future Internet* **2019**, *11*, 170.
https://doi.org/10.3390/fi11080170

**AMA Style**

Sgantzos K, Grigg I.
Artificial Intelligence Implementations on the Blockchain. Use Cases and Future Applications. *Future Internet*. 2019; 11(8):170.
https://doi.org/10.3390/fi11080170

**Chicago/Turabian Style**

Sgantzos, Konstantinos, and Ian Grigg.
2019. "Artificial Intelligence Implementations on the Blockchain. Use Cases and Future Applications" *Future Internet* 11, no. 8: 170.
https://doi.org/10.3390/fi11080170