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УДК 62
Гуванчгелдиева С.,
студентка.
Научный руководитель: Ходжабалканова С.,
преподавательница.
Инженерно-технологический университет Туркменистана им. Огузхана.
Ашхабад, Туркменистан.
КАК АЛГОРИТМЫ ИИ СОБИРАЮТ И ИСПОЛЬЗУЮТ ПЕРСОНАЛЬНЫЕ ДАННЫЕ
Аннотация
Быстрое развитие искусственного интеллекта (ИИ) преобразовало различные секторы, от здравоохранения до финансов и маркетинга. В основе этой трансформации лежит сбор и использование персональных данных. Понимание того, как алгоритмы ИИ собирают, обрабатывают и используют эти данные, необходимо для понимания как преимуществ, так и этических соображений, связанных с технологией ИИ.
Ключевые слова:
быстрое развитие, искусственного интеллекта, секторы, финансов и маркетинга, трансформации.
Guvanchgeldiyeva S.,
student.
Scientific supervisor: Hojabalkanova S.,
teacher.
Ashgabat, Turkmenistan.
HOW AI ALGORITHMS COLLECT AND USE PERSONAL DATA Annotation
The rapid advancement of artificial intelligence (AI) has transformed various sectors, from healthcare to finance and marketing. At the heart of this transformation lies the collection and utilization of personal data. Understanding how AI algorithms gather, process, and leverage this data is essential for grasping both the benefits and the ethical considerations surrounding AI technology.
Key words:
Rapid development, artificial intelligence, sectors, finance and marketing, transformation.
The rapid advancement of artificial intelligence (AI) has transformed various sectors, from healthcare to finance and marketing. At the heart of this transformation lies the collection and utilization of personal data. Understanding how AI algorithms gather, process, and leverage this data is essential for grasping both the benefits and the ethical considerations surrounding AI technology.
1. Data Collection Methods
AI algorithms rely on diverse methods to collect personal data. The primary sources include:
1.1. Direct User Input
Many applications and services require users to provide personal information directly. This can occur during account creation, surveys, or feedback forms. Users typically input details such as names, email addresses, and preferences, which AI algorithms can later analyze to enhance user experience.
1.2. Web Scraping
AI can collect data from publicly available online sources through web scraping techniques. This involves automated bots that scan websites to gather information. For instance, social media platforms, review sites, and e-commerce websites can provide valuable user-generated content that AI algorithms can analyze for trends and insights.
1.3. Sensor Data
With the rise of IoT (Internet of Things) devices, AI can collect data from various sensors embedded in devices like smartphones, smartwatches, and home assistants. These devices track user behavior, location, and health metrics, which are invaluable for tailoring AI-driven services.
1.4. Third-Party Data Brokers
AI systems often utilize data from third-party vendors, known as data brokers. These entities aggregate personal information from various sources, including public records, online activities, and purchase histories. AI algorithms can leverage this comprehensive data for targeted advertising, recommendations, and market analysis.
2. Data Processing Techniques
Once collected, personal data undergoes various processing techniques to make it usable for AI algorithms:
2.1. Data Cleaning
Before analysis, raw data is cleaned to remove inconsistencies, duplicates, and irrelevant information. This step is crucial for ensuring the accuracy and reliability of the insights generated by AI.
2.2. Data Aggregation
Data from multiple sources is often combined to create a more comprehensive user profile. This aggregation allows AI algorithms to identify patterns and correlations that would not be evident from isolated datasets.
2.3. Machine Learning
AI algorithms employ machine learning techniques to analyze processed data. By training on historical data, these algorithms can learn to make predictions or recommendations based on new, incoming data. This capability is what enables personalized experiences, such as content recommendations on streaming services or targeted ads on social media.
3. Use of Personal Data
The utilization of personal data by AI algorithms spans various applications:
3.1. Personalization
One of the most significant advantages of AI is its ability to personalize user experiences. By analyzing user data, AI can tailor content, recommendations, and advertisements to individual preferences. This personalization enhances user engagement and satisfaction, leading to improved customer loyalty.
3.2. Predictive Analytics
AI algorithms can analyze trends and patterns in personal data to make predictions about future behaviors. For instance, in healthcare, AI can predict potential health issues based on individual medical histories, enabling proactive care and intervention.
3.3. Decision-Making Support
In business settings, AI can provide insights derived from personal data analysis, supporting decisionmaking processes. This can include market analysis, customer segmentation, and risk assessment, allowing companies to make data-driven strategies.
4. Ethical Considerations and Challenges
While the collection and use of personal data by AI algorithms offer numerous benefits, they also raise significant ethical concerns:
4.1. Privacy Issues
The extensive collection of personal data often occurs without explicit user consent or understanding. This
can lead to privacy violations and a lack of trust between users and organizations.
4.2. Data Security
With the accumulation of vast amounts of personal data, the risk of data breaches increases. Organizations must implement robust security measures to protect sensitive information from unauthorized access.
4.3. Bias and Discrimination
AI algorithms can inadvertently perpetuate biases present in the data they are trained on. If the training data is not representative of the diverse population, AI systems may produce biased outcomes, leading to discrimination in areas like hiring, lending, and law enforcement. Список использованной литературы:
1. Семакин И. Г., Шиена Т. Ю. Информатика. Углубленный уровень: учебник для 10 класса. М.Бином, 2014.
2. Тарасевич Ю.Ю. Математическое и компьютерное моделирование. М.:2004.
3. Угринович Н.Д. Информатика и ИКТ. Учебник для 11 класса. Москва. Бином. Лаборатория знаний, 2009.
4. Панкратова Л. П., Челак Н. Е. Контроль знаний по информатике: тесты, контрольные задание, экзаменационные вопросы, компьютерные проекты. СПб: БХВ-Петербург, 2004.
© Гуванчгелдиева С., 2024
УДК 62
Гурбанмырадова Л.,
Преподаватель,
Международный университет нефти и газа имени Ягшыгелди Какаева,
Ашхабад, Туркменистан Гайыпова Б., Преподаватель,
Туркменский государственный университет имени Махтумкули,
Ашхабад, Туркменистан Сердарова С., Студент,
Международный университет нефти и газа имени Ягшыгелди Какаева
Какаев А., Студент,
Международный университет нефти и газа имени Ягшыгелди Какаева,
Ашхабад, Туркменистан
РОЛЬ СИСТЕМ ВЫСОКОГО НАПРЯЖЕНИЯ В ЭЛЕКТРОТЕХНИКЕ
Аннотация
Системы высокого напряжения играют ключевую роль в современной энергетике, обеспечивая эффективную передачу электрической энергии на большие расстояния. В этой статье мы рассмотрим преимущества и возможности их использования.
Ключевые слова:
системы высокого напряжения, электроэнергетика, передача электроэнергии, линии электропередачи, изоляция, трансформаторы, подстанции, электрическая прочность.
