199
фильмы - позволяет, при необходимости, демонстрировать изучаемые процессы в
динамике.
Достигнуть
упомянутых
целей
образования
помогает
использование
мультимедийных визуальных средств обучения и ресурсов Интернета, которые обладают
некоторыми возможностями повышения эффективности процесса обучения [3]:
- в процессе обучения активизировать не один канал восприятия, а несколько, что
делает возможной интеграцию информации, которая доставляется разными органами
чувств;
визуально представлять динамические процессы и абстрактную информацию;
формировать у обучающегося системность построения изучаемого материала.
Значимое преимущество мультимедийных визуальных средств в образовательном
процессе – это возможность его индивидуализации. При использовании мультимедийных
визуальных средств обучаемые могут принимать решения, работать самостоятельно над
учебными материалами с использованием интерактивных возможностей мультимедийных
программ и решать, в какой последовательности их изучать. Другими словами, студенты
принимают активное участие в образовательном процессе. То есть на процесс обучения
студенты имеют возможность выбирать материал, который они хотят изучать, и могут
повторять его несколько раз, чтобы лучше усвоить информацию. Эффективность
применения мультимедийных визуальных технологии в учебном процессе зависит от
многих факторов, в том числе и от уровня самой техники, и от качества используемых
обучающих программ, и от методики обучения, применяемой преподавателем.
Применение традиционных форм, средств, методов обучения с использованием
мультимедийных визуальных технологий могут существенно повысить эффективность и
интенсификацию образовательного процесса, решить стоящие перед образовательным
учреждением задачи обучения и воспитания активно и творчески мыслящего
обучающегося. Студент должен быть ориентирован на постоянное освоение
мультимедийных визуальных технологий, готов к условиям быстро меняющейся среды и
постоянного увеличения информационного потока, т.е. мультимедийные визуальные
технология позволяют добиться решения основной задачи: развития познавательных
навыков студентов, умений самостоятельно конструировать свои знания, ориентироваться
в информационном пространстве, развития критического и творческого мышления.
Литература:
1.
Роберт И.В. Современные информационные технологии в образовании: дидактические
проблемы; перспективы использования. М.: «Школа-Пресс», 1994. 205 с.
2.
Тыщенко О.Б., Уткес М.В. Границы возможностей компьютера в обучении. //
Образование. — 2002. — № 4. — С. 85–91.
3.
Мультимедиа в образовании: специализированный учебный курс / Бент Б. Андресен,
Катя ван ден Бринк; пер. с англ. 2-е изд., испр. и доп. М.: Дрофа, 2007. С. 31.
4.
I.B.Aminov, A.Nizamov. Modern cognitive and communicative means and technologies in
the learning process .Proceedings of International Scientific-Practical Conference on ―Cognitive
researchin education‖. organized by samarkand regional center for retraining andadvanced
training of public education staff,Uzbekistan. 15th April 2021. In Association with Novateur
Publication India's. Journal impact factor: 7.223. Page No.: 11-13.
ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN INCREASING DATA
RELIABILITY
Nazarov Fayzullo Maxmadiyarovich.
PhD, Associate Professor, Samarkand State University, Samarkand city
Yarmatov Sherzodjon Shokir o‘g‘li.
Assistant, Samarkand State University, Samarkand city
200
Abstract.
Mind perception is the intellectual ability of humanity, intellectual abilities
include such parameters as human perception, memory, scope of thinking. Artificial intelligence
refers to the mind that uses machines to perform on the basis of the functioning of the human
mind. Mankind‘s intellect may not be limited, but machines can run much faster than humans
when it comes to solving a problem over time. Tasks like doing a lot of calculations in a short
period of time are the strengths of digital computers. In this respect, they are superior to humans
in many ways. But in many other areas, humans are far superior to machine systems. For
example, in an unfamiliar environment, a person can make a decision much faster than a car. In
recent times, scientists have used games to prove the superiority of machines over humans, and
have managed to do so with a vengeance, but now the creation of such machines and astonishing
people with it has lost its relevance.
Keywords:
Control systems, artificial intelligence, mathematical models, data flow, deep
learning.
Recent research by researchers has shown that intelligent systems cannot be built without
a deep understanding of human intelligence and intelligent movement in general, so neural
networks are important in artificial intelligence. The inherent strength of the human mind is its
ability to be flexible. We are able to adapt to different environmental conditions and change our
behavior through learning. Because our learning ability is so much higher than that of computers,
machine learning is a central sub-field of artificial intelligence.
The word "intellect" is derived from the Latin word "intellectus", which means to know
(determine), understand or comprehend (mind) [1]. If we look at the history of artificial
intelligence, it is only in the second half of the twentieth century that humans created the first
prototypes of artificial intelligence, but long before that they began to talk about "thinking
machines". For example, in the nineteenth century, Mary Shelley 's Frankenstein and Samuel
Butler 's Darwin developed the idea of artificial humans and machines of thought in fiction.
Artificial intelligence has become a constant subject of science fiction to this day. In the 1940s
and 1950s, researchers in various fields (mathematics, psychology, engineering, economics, and
political science) began to discuss the possibility of creating artificial intelligence [2]. Artificial
intelligence as a practical science of mechanizing thinking could, of course, only begin when
programmable computers were available. In the early 1950s, Herbert Simon, Allen Newell, and
Cliff Shaw experimented with writing programs to mimic human thought processes. As a result
of the experiments, a program called Logic Theorist emerged, consisting of rules of axioms that
had already been proven. When he is given a new logical expression, he seeks out all possible
operations to find proof of the new expression using heuristics. This process was an important
step in the development of artificial intelligence . Although the work of Simon et al. And Shenon
demonstrated the concept of intelligent computer software, 1956 is the beginning of the subject
of artificial intelligence. This is because the first artificial intelligence conference, organized by
John McCarthy, Marvin Minsky, Nathaniel Rochester and Claude Shannon at Dartmouth
College in New Hampshire , was held in 1956. It was at that conference that John McCarthy, the
creator of the LISP programming language, proposed the term artificial intelligence. The
Dartmouth Conference paved the way for the use of computers to process symbols, the need for
new languages, and the role of computers to prove the theorem instead of focusing on hardware
that simulates the mind [2]. To this day, the benefits and harms of artificial intelligence to
humanity are being discussed among people working in the field of science. On May 23, 2017,
Google‘s Alpha Go artificial intelligence program defeated then-world number one Ke Jin in a
three-game game. In strategic strategy games, computers have been beating people, but in Alpha
Go, they have not been able to do so because the game was innumerable for mathematical
algorithms, the number of combinations in the game board cells was estimated to be greater than
the number of atoms in the universe. [1,2]. This victory has put an end to many controversies,
showing that the possibilities of artificial intelligence are not limited. The types of artificial
intelligence technologies can be illustrated by Figure 1 below.
201
Figure 1. Types of artificial intelligence technology.
The basis of artificial intelligence is neural networks, on the basis of neural networks can
be organized machine learning and in-depth learning technologies.
2. Mathematical models of artificial intelligence technologies
Artificial intelligence and machine learning offer businesses ample opportunities to
improve their operations and increase their profits. Artificial intelligence models include
methods and algorithms used to teach computers to process and analyze data, just like humans.
All models of machine learning are aimed at teaching a specific function
(f)
that provides the
most accurate correlation between input values
(x)
and output values
(y)
[2,3].
)
(
X
f
Y
(1)
Basically the most common case is when we have data on the
X
and
Y
parameters and we
can build an artificial intelligence model to ensure the best correlation between these values
[6,7]. In these processes, the result may not be 100% accurate, otherwise it will be a simple
mathematical calculation without requiring machine learning. Instead, the function
f we teach
can be used to predict a new (y )
using a new
(x)
, which allows for a predictive analysis. The
composition of artificial intelligence can be illustrated by Figure 2 [1,3].
Figure 2. The structure of artificial intelligence technology
Different artificial intelligence models achieve this result by applying different
approaches. There are many types of artificial intelligence models and we will look at the most
202
popular of them, but before that we need to look at different types of machine learning. There are
three main types of machine learning:
Controlled
Uncontrolled
Semi-controlled
Study of controlled machines. In the controlled learning model, the person teaches the
algorithm or what to look for [1,2] .
Artificial intelligence models created with controlled learning are often used to perform
predictive analysis. These models use past decisions made by experts on the subject to predict
future decisions that an expert can make [3].
In the uncontrolled learning model, the software teaches algorithms. In some cases, the
teaching method used by the curriculum mimics that of humanity, but they do not always require
the same teaching method.
Artificial intelligence models based on uncontrolled learning are often used to perform
descriptive analysis. These tasks include summarizing content, classifying content, and sorting
content.
Semi-controlled learning models combine some of the previous two models we
discussed. In a semi-controlled learning environment, a person performs part of the training and
the software is designed to manage the rest based on the initial training performed by the person.
Because artificial intelligence models created with semi-controlled learning are somewhat
generalized to both teaching methods, they are able to perform both predictive and descriptive
analysis tasks depending on the intended purpose [1].
There are currently a variety of artificial intelligence models available, and they all differ
somewhat from each other, these models are listed below.
Linear regression
Logistic regression
Decision trees
Random forest
Neural networks
Deep learning
References
1.Miller, Tim . Explanation in artificial intelligence: Insights from the social sciences. DOI:
10.1016 / j.artint.2018.07.007. October, 2018.
2.Ghosh, Ashish , Chakraborty, Debasrita , Law, Anwesha . Artificial Intelligence in the Internet
of Things. DOI: 10.1049 / trit.2018.1008. CAAI Transactions on Intelligence Technology.
October, 2018.
3.Toorajipour, Reza , Sohrabpour, Vahid , Nazarpour, Ali , Oghazi, Pejvak , Fischl, Maria .
Artificial intelligence in supply chain management: A systematic literature review. DOI: 10.1016
/ j.jbusres.2020.09.009. Journal of Business Research. January, 2021.
TALABALARGA ZAMONAVIY TA‘LIMNI RAQAMLI TEXNOLOGIYALAR
YORDAMIDA BERISHNING PEDAGOGIK ZARURATI
Parmonov Abdutolib Abduvahob o‗g‗li
O‗zbekiston Milliy universitetining Jizzax filiali ―Biotexnologiya‖ kafedrasi katta o‗qituvchisi
Annotatsiya:
Ushbu maqolani yozishda ta‘lim-tarbiya jarayonining rivojlanish
bosqichlarini o‗rganish orqali zamonaviy ta‘limning farqini va ahamiyatini tadqiq etish va bu
tadqiqot natijalariga asosan xulosa chiqarishni maqsad qilganmiz.
Kalit so‗zlar:
raqamli texnologiyalar, axborot-kommunikatsiya texnologiyalari, oliy
ta‘lim tizimi, zamonaviy ta‘lim.
