Introduction
Artificial intelligence (AI) is a technology that, since the mid-20th century, has been the dream of scientists and engineers, but has only gained significant momentum in the past decade. Its role is growing day by day in our daily lives, business environments, and industrial production. This article comprehensively examines the historical roots of AI, how individuals have incorporated AI technology into both their personal and professional lives, its positive and negative impacts on society and lifestyles, and how it is integrated into high-tech sectors such as electronics, communications, satellites, space, and aviation—analyzing what these applications mean for both employees and company owners.
- The History of AI Use and Its Place in Daily Life
Historical Development and Foundations
The origins of artificial intelligence can be traced back to the question posed by Alan Turing, “Can machines think?”, in the 1950s. The 1956 Dartmouth Conference, with contributions from pioneers such as John McCarthy, Marvin Minsky, and Claude Shannon, marked the official recognition of AI as a discipline. Although early on, limited computing power and a lack of data restricted the scope of AI applications, the development of expert systems in the 1980s led to the first practical examples in fields like finance, medicine, and engineering.
The 1990s witnessed the evolution of machine learning algorithms, and with the advent of big data, deep learning, and neural network-based methods in the late 2000s and 2010s, AI became central to both individuals’ lives and institutions.
AI Applications in Daily Life
- Mobile Assistants and Personal Devices: Applications such as Siri, Google Assistant, and Alexa use natural language processing (NLP) and deep learning techniques to understand voice commands and meet users’ needs in managing schedules, accessing information, and automating home functions. These systems are trained using transformer architectures (e.g., BERT, GPT) and continuously improve through feedback.
- Finance and Banking: AI is used in fraud detection, credit risk analysis, and algorithmic investment strategies by analyzing millions of transactions in real time, identifying unusual patterns, and enabling preventive measures. For instance, deep learning algorithms forecast future risks based on historical transaction data.
- Healthcare: In areas such as medical image analysis, disease diagnosis, drug discovery, and robotic surgery, AI employs convolutional neural networks (CNNs) and other deep learning methods to detect abnormalities in images, facilitating early diagnosis and personalized treatment processes.
- Education: Digital learning platforms tailored to an individual’s pace and style of learning offer personalized curricula through AI algorithms. These systems analyze a student’s past performance to provide targeted support in areas where improvement is needed.
- E-commerce and Digital Marketing: AI systems that analyze user behavior offer personalized product recommendations, optimize customer service through chatbots, and dynamically adjust pricing strategies to enhance competitive strength.
- Transportation: Autonomous vehicles, smart traffic management systems, and logistics optimization employ AI to process sensor data continuously, make real-time decisions, and improve traffic flow.
- The Impacts of AI on Society and Our Lifestyles
Positive Impacts
- Efficiency and Speed: AI enables the automation of routine tasks, allowing people to focus on more creative and strategic roles. For example, businesses can make faster and more accurate decisions by analyzing big data.
- Personalized Experiences: AI offers solutions tailored to individual needs in fields ranging from education to healthcare, thereby enhancing the quality of life.
- Risk Management and Security: In financial transactions and cybersecurity, AI detects anomalies to minimize risks.
- Innovation: With its capacity for continuous learning and adaptation, AI opens the door to new business models and technological advancements.
Negative Impacts
- Workforce Transformation: Increased automation poses the risk of job loss for workers engaged in repetitive tasks, necessitating a transformation in professional skills.
- Privacy and Ethical Issues: The extensive use of big data and the lack of transparency in algorithms can lead to data security and ethical challenges.
- Dependency and Social Interaction: Growing reliance on digital systems may reduce face-to-face communication and social interaction.
- Technological Inequality: Differences in access to AI technologies can lead to new forms of inequality within society.
