Integrating blockchain with artificial intelligence, means leveraging algorithms and machine learning to make data interpretation more secure. AI works by exploiting a model similar to neural networks, and the blockchain, precisely because of its peer-to-peer structure, can represent not only a practical reproduction of the network of connections of intelligent systems, but also the ideal environment for their development and growth. All transactions that take place on the blockchain follow a direct exchange pattern between nodes that, with equal power, give rise to a process of mutual validation.

Incorruptible, immutable, blockchain

Thus, the blockchain is the practical representation of the theoretical operation of artificial intelligence and can, in this sense, give a decisive impetus to its development.
Maxim Orlovsky (on medium.com, in the article “How does blockchain relate to artificial intelligence?“), an expert on artificial intelligence, explained, extremely effectively, that blockchain can be to AI what writing has been to human history. With writing, concepts have been fundamentally changed in the sense of persistence and consensus. By persistence we mean their static nature over time, that is, the invariability of what is written despite generational succession. Consensus, on the other hand, indicates the fact that what is written can be traced back to a single meaning for all those who interpret it. These two characteristics of writing refer to the concepts of immutability and trust that characterize the blockchain. The incorruptibility of the data entered is a guarantee to prevent the modification of the information afterwards, while the trust mechanism subjects the blocks to active observation by all the components of the network.

In addition to what has been said, that is, the advantages that are structural of the functioning of distributed ledgers, another extremely convenient aspect of the integration process of the two technologies is the possibility of exploiting the data recorded on the blockchain to make predictions.
Distributed Ledgers are distributed platforms that aim to store the data entered by all participating nodes. This structure becomes a treasure trove of information that can be mined and interpreted by AI systems in the various fields of commerce, sponsorship, entertainment, and computing.

Energy, online sales, sponsorship: some application fields of blockchain

The use of this information can bring enormous benefits in various sectors, thanks to its predictive capabilities. The ability to correctly interpret the repertoire of information in the database allows you to provide consumers with personalized offers, perfectly adhering to their preferences.
Just think, for example, of the energy sector. Today there are smart devices inside every home that record the energy consumption of each household. If this data were placed on the blockchain and processed by artificial intelligence systems, reliable predictions could be made about future household consumption, bringing incredible benefits to both individuals and businesses. For consumers, a dedicated commercial formula could be hypothesized, based on the amount of energy consumed. For companies, however, it would finally be possible to produce energy in flexible quantities proportionate to market demand. It will be possible, therefore, to support the peaks and troughs of the request, on the basis of what is detected by the data analyzed in the reference periods. Not only companies and consumers would benefit from the synergic activity of the two technologies, but the reduction of overproduction and therefore of waste represent undoubtedly a new face of ecology.

Another field of application lies in online sales. Today’s e-commerce giants, such as Amazon, have an almost unlimited database of contacts, allowing them to adapt both their offerings and their advertisements to the characteristics of their customers. In the context of a free market, it is conceivable that physical stores, online stores, and consumers would cooperate to enter personal data into a distributed register. In this way, a common database containing consumers’ activities and data is created, which can compete even with global giants, facilitating competition and the growth of small brands. Thanks to the mechanisms of artificial intelligence, consumers will be able to make their data available to obtain advantageous offers, also from an economic point of view. While companies will be able to identify the consumers that are closest to their target and work on loyalty with targeted programs.

This mechanism can be replicated in the field of sport, with particular attention to sponsorship. Collaboration contracts between sponsors and clubs (large and small) can be facilitated by the use of a database that provides both clubs and businesses with information about the partner that best suits their needs. This process requires the integrated use of both blockchain and AI.

Decentralizing the machine learning and AI industry could make the market open, free and competitive. At the same time, it would significantly accelerate the development and research of new forms of AI due to the amount of data available within distributed ledgers.

Thus, the possibility of a new frontier of technology with blockchain and artificial intelligence as its protagonists is on the horizon.

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AI is a branch of computer science that studies the design of hardware and software systems capable of making machines perform actions coordinated with the audio-visual, space-time and decision-making perceptions typical of humans. An intelligent system, therefore, is not only equipped with the ability to perform computational operations or store data, but also possesses varied social or spatial interaction capabilities. Machines can reproduce some of the usual behaviors of human beings; this denotes a particular, innovative form of intelligence endowed to the system.

The birth of this technology is conventionally traced back to 1956, the year of the advent of the computer, because, for the first time, in a conference that brought together experts in the computer field, the term “intelligent system” was used. To date, the use of algorithms, capable of reproducing the complex reasoning of man, has allowed to improve more and more these systems. These realities are characterized by a continuous growth in number and a constant updating and development of the mathematical equations on which they are based. In this way, a variety of possible behaviors is guaranteed, always growing and more and more adaptable to different external stimuli.

Algorithms are able to make decisions and choices related to the contexts in which they are inserted. Think, for example, of cars: a driverless vehicle, equipped with artificial intelligence systems, in case of risk can decide whether to steer or brake according to the circumstance. The possibility of choice on the part of the car denotes an awareness of the consequences of the action on the basis of risk predictions. The predictive ability depends on the recording of data obtained by the sensors, which collect information extrapolated from reality, to communicate them to the system that interprets them according to the decision-making process.

Basic and extended artificial intelligence

Artificial intelligence has two different modes: basic or extended. The term “basic intelligence” refers to all knowledge acquired through the collection and processing of data. With “extended intelligence“, instead, we refer to the possibility of learning from the experiences of the system. In this last case, in the future of informatics it is possible to make the machine able to acquire information, a peculiar characteristic of the human being. Algorithms, so, become more and more precise and allow the interpretation of more and more accurate information, thanks to the already known data, which are part of the system’s background. Artificial intelligence can have two main modes of execution: formal languages and decisions. In the first case, to perform an action or solve a problem, it is chosen to use a predefined approach in the strings: the path to follow is decided according to the result that is intended to be achieved. In the second case, which instead implies the completion of a choice by the machine; a tree model is used: the algorithm is asked to evaluate every possible action or decision considering the consequences. The second approach is based on predictive models developed from initial information and pre-entered data, to make predictions about all possible consequences accurately.

Machine learning allows machines to perform an action even if it has not yet been programmed. The machine’s archive of experience becomes, as with humans, a source of continuous updating from which new algorithms can be learned and developed. It is a learning process aimed at the progressive refinement of the string code. Machines can learn from their mistakes and this is an enormous advantage for technological development.

A.I. in everyday life

In everyday life, artificial intelligence is extremely prevalent. Voice recognition programs, present in smartphones, as well as security systems that exploit facial recognition algorithms, are the result of the achievements of computer science in the field of AI. Interaction with smartphones is now ingrained in consumer habits and is continuously being integrated, through geolocation systems and motion sensors. Interaction between devices is also a product of artificial intelligence.

Vehicles that move without a driver have become a reality, although for the moment they are relegated to a few specific sectors. Other fields where extensive use of AI is being made are the stock market, medicine and robotics. In the medical field, for example, it has become a commonly used tool for certain types of analysis and in certain diagnoses, as well as for the creation of robots used to accompany patients or specific surgical operations.

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