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“DEVELOPMENT ISSUES OF INNOVATIVE ECONOMY IN
THE AGRICULTURAL SECTOR”
International scientific-practical conference on March 25-26, 2021.
Web:
http://conference.sbtsue.uz/uz
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RESULTS
From the point of view of the depth of penetration of intelligent IT in decision-making processes,
there are three options for replacing the human mind with a computer:
Replace a person in routine operations. This area is occupied by all kinds of digital assistants and
business process robotization (RPA) tools that relieve a person from routine worries, as well as support
service automation systems: software robots cope with most of the issues, and only in non-trivial situations
there is a switch to a "live" employee.
Replace a person where in real life he will not be able to work, for example, in dangerous industries,
in a harmful environment or inaccessible territories.
Replace a person in those tasks with which a computer program copes better than a person. First
of all, we are talking about collecting and analyzing large amounts of data.
AI in the fight against fraud
On July 11, 2019, it became known that in just two years, artificial intelligence and machine learning
will be used to counter fraud three times more often than in July 2019. This data was obtained in a joint
study by SAS and the Association of Certified Fraud Examiners (ACFE). As of July 2019, 13% of the
organizations that took part in the survey already use such anti-fraud tools, and another 25% said that they
plan to implement them within the next year or two. Read more here.
AI in the electric power industry
At the design level: improved forecasting of generation and demand for energy resources,
assessment of the reliability of power generating equipment, automation of generation increase in case of
a jump in demand.
At the production level: optimization of preventive maintenance of equipment, increase of
generation efficiency, reduction of losses, prevention of theft of energy resources.
At the promotion level: optimization of pricing depending on the time of day and dynamic pricing.
At the service delivery level: automatic selection of the most profitable supplier, detailed
consumption statistics, automated customer service, optimization of energy consumption based on
customer habits and behavior.
AI in the manufacturing sector
At the design level: improving the efficiency of new product development, automated supplier
evaluation, and analysis of spare parts and parts requirements.
At the production level: improving the process of task execution, automating assembly lines,
reducing the number of errors, reducing the delivery time of raw materials.
At the promotion level: forecasting the volume of support and maintenance services, pricing
management.
At the level of service provision: improving the planning of routes of the fleet of vehicles, the
demand for fleet resources, improving the quality of training of service engineers.
AI in industry and in manufacturing
More than half of the industry leaders believe that over the next five years, the world will transfer
the management of assets of great value
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in particular, factories, equipment and machine tools-to artificial
intelligence solutions. This global trend was revealed in a joint study by Siemens and Longitude Research.
More than 500 top managers from the energy, manufacturing, infrastructure, transport, and heavy industry
sectors took part in the survey on the development and implementation of AI, Siemens reported on October
26, 2020.
The survey asked respondents the following questions: what if you could automate a number of day-
to-day operational decisions in your organization so that employees could focus on strategic projects such
as developing new product lines or expanding the business? How good does an AI model need to be before
you're ready to hand over control to it? Should its performance be at the level of engineers, or should it
exceed it? What if a mistake can lead to serious financial losses or even injuries? These and other scenarios
were proposed to 515 top managers of the industrial sector (including in the fields of energy,
manufacturing, heavy industry, infrastructure and transport).
The study showed that the level of trust in AI is already very high for 2020: 56% of respondents
prefer to implement an ideal AI model instead of finding an experienced employee (44%). This means that
the remaining 44% probably have more confidence in the decisions made by humans, even if the evidence
is in favor of AI.
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