Blockchain for SMEs: transparency, security and traceability

The possibility of recording the path taken, for example, by products in the food sector is a device that can ensure the protection of Made in Italy.
Not only the food sector, but also the manufacturing, banking and sports sectors, just to name a few, can take advantage of the features of blockchain, ensuring transparency during the production phases, the safety of compliance with regulations and the traceability of the life cycle of the products processed and marketed.

Italy is a world-leading country in the fashion industry. Made in Italy garments have an added value linked to the high quality of Italian tailoring. Guaranteeing, through the blockchain, that the various phases of the realization of the garments have taken place in compliance with the regulations and on Italian territory, without delocalization of production, is certainly for consumers an extra sign of transparency.
The production and distribution chain can exploit blockchain technology to highlight and defend the best quality of products.

Innovative Italian SMEs are focusing on testing DLT solutions to be made available to different sectors for commercial purposes. As technologies develop, the fields of application in different branches of industry are also multiplying.
Even in the financial, insurance and utilities sectors, a decision by governments to invest in new technologies is beginning to be seen.

The OSCE research study

Italy was the first European country to fund a research study conducted by the OSCE (Organization for Security and Cooperation in Europe) that had the task of investigating the implications that blockchain would have on the ecosystem of SMEs and startups.

The main difficulties encountered by the companies, which participated in the study, were related to the complexity, indecipherability and vagueness of the relevant regulations:

• Public funding opportunities at the local and national level are not well defined;
• Lack of comprehensive jurisdiction prevents businesses from fully exploiting innovation potential;
• Lack of clear guidance on funding and how to obtain it increases distrust of technology.

From a supranational and international perspective, cooperation is crucial to accelerate the process of technology diffusion through the sharing of defined standards.

The use of blockchain for agricultural SMEs

In particular, with regard to farms, the use of blockchain can bring enormous benefits.
Already in the digitization phase of paper files for crop registration, inaccuracies can be corrected and removed, quickly and with intuitive mechanisms.
Then, all players in the supply chain are involved in entering information regarding raw materials.

Data is collected either through manual recording or with the use of IoT sensors. Weather conditions, water resources, and chemical treatments used to measure product quality and environmental impact are information that is good to communicate to the consumer.

By leveraging distributed records, both intermediary industries and consumers can be properly informed and learn about all production processes, thus comprehensively communicating product quality and characteristics.

The advantage for producers, on the other hand, lies in having a clearer overview of actual and potential buyers and in the possibility of mapping the various distribution chains in a contextual manner.
Being able to offer consumers a certification of authenticity and quality that specifies and traces the various stages of the production chain from origin to large-scale distribution gives the product an enormous added value.

Alongside these numerous potential advantages, the regulatory development will certainly have to be followed with great attention, which will have to adapt to the numerous innovations that will be brought about by the use of blockchain.

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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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