A conspicuous question is, what would be our motivation for implementing genetic algorithms on a medium like blockchain, while more centralized approaches produce equally meaningful results without the cost of maintaining such a network? There are numerous reasons to store a neural network on the blockchain, many of which were explored in our previous work [ 4 ], but here we will narrow down the significant ones leading to our conclusion.

By investigating six use cases and future applications, we demonstrate how AI entities can utilize the capabilities of blockchain for important purposes including, but not limited to, deep learning, Internet of things (IoT) and Monte Carlo analysis. We also explore the possibility of storing externally trained AI agents on such a medium and utilize them via pay per use. Finally, we describe an already trained neural network employed to recover the relevant physical variables, both in quantum and classical systems.

3.1. The Integrity and Validity of Information

Blockchain as a data and framework store presents a number of advantages over the Internet or over internal stores. By way of two exemplary challenges to the AI world, we present how blockchain can address these in novel ways.

One of the biggest challenges in data science today is the collection of a proper dataset, which can be utilized for training a neural network. The pluralism of data over the Internet is enormous, but the quality is minimal due to the habit of people to post inaccurate things, mainly, because there is no control. A characteristic paradigm is the “fake news” explosion in recent years, which tends to propagate faster than well documented and verified news [ 25 ]. Internet giants like Facebook and Google have tried to tackle the problem via several computational methods, but even though there seems to be a sufficient theoretical basis for separating “signal” from “noise” [ 26 ], the problem still thrives as of today.

A second challenge is adversarial interference with the processing. Tesla’s autopilot was shown to be vulnerable to remote root privilege attacks that could control the steering system and disturb the “autowipers” function [ 27 ]. By introducing false information in the physical world such as minor changes in the road, it was possible to mislead the car into the opposite lane. The consequential risks of such vulnerability include, but are not limited to, human injuries and death. Many other examples abound. Blockchains can address these issues in a comprehensive way through integrity, security, triple entry and provenance.

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 [ : 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 [ 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.

Blockchains go further and incorporate a public database that ensures everyone has access to the same data, and some parties are financially motivated to keep that database alive. This ability to always find the data comes at the cost of privacy—whatever is posted to the blockchain as a document is readable by all. There is some promise of more exotic cryptography and software techniques to allow posting and recovery of private data into a public store, but these techniques remain experimental, and the bar of confidentiality or privacy is typically a high one.

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.

Consider two trivial statements, “this statement is true” and the equally light “this statement is false”. Both can as easily be posted, but only one is reliable. Software can guarantee both statements were made at a time, but cannot guarantee the content is reliable or even meaningful.

Then, to encourage statements that may be relied upon by others requires more: Posters need to be incentivized to post useful and reliable statements, and to not post useless and unreliable ones. Due to the pseudonymous nature of blockchain, posting stake or gamification is suggested as a control however these methods limit participation through the cost of capital and time, and leave aside the question of how to punish [ 24 ]. A more serious feedback control on bad participation would be a due process to also incentivize agents to not post unreliable data. The process itself would also need to pass the same test of reliability as the statements delivered.

Such a due process is typically called Public Key Infrastructure (PKI). The more common Internet secure browsing form organizes a certification authority to make signed statements, called certificates. Its due process is described in documents such as a certificate practice statement, which are reviewed and approved by browsers and other relying parties. Reliance based on commercial authorities and their statements is typically only strong enough for relatively weak statements because it lacks an incentive model to properly handle the liability for bad data [ 30 ]. CAcert has extended the concept to cover a wide range of stronger statements through a cooperative form that includes arbitration to allocate liability in the case of bad data [ 31 ].

Blockchains are therefore not only ideal storage for the data of deep learning, they include much data worth analyzing, and they are ideal storage for the trained frameworks themselves. In time we expect the discrimination between good and bad data to become easier based on pseudonyms and incentive models.