Artificial Intelligence (“AI”):
AI is a technology that enables computer systems to have human-like intelligence. AI machines; It aims to perform processes such as learning, problem solving, language understanding, visual perception and decision-making just like humans. The building blocks of AI are:
- Machine learning: Includes algorithms that enable AI to identify patterns and make predictions or make decisions. For example, on an e-commerce site, customers can be given the automatic determination of recommended products based on past shopping data and browsing habits.
- Natural language processing: Natural language processing is the communication between machines and humans by enabling computers to understand, interpret, and generate human language. An example is when a customer service “chatbot” understands users’ questions and provides answers in natural language.
- Deep learning: Deep learning allows systems to automatically learn and apply data through hierarchical abstraction. An example is when a health app detects signs of illness by analyzing medical images.
- Neural networks: These are connections that are inspired by the human brain based on information processing ability and based on information processing ability, enabling machines to recognize patterns and make decisions. An example is that a facial recognition system can verify people’s identities by scanning their faces.
- Algorithmic bias: Refers to the concept that AI algorithms can exhibit biases based on the data they are trained on/fed on, and that they can lead to potential discrimination or injustice. An example is that an AI algorithm used in recruitment processes is influenced by biases such as gender or race in training data, systematically disadvantaged certain groups.
- Ethical AI: It is the development and use of AI systems with a focus on ethical considerations to ensure fairness, transparency and accountability in decision-making processes. For example, in the decision-making processes of autonomous vehicles, the observance of ethical rules in order to behave fairly towards pedestrians and other drivers can be given.
- Explainable AI: The idea that AI systems’ decisions should be understandable and descriptive represents transparency and trust. An example would be an AI system that clearly states the reasons why a loan application is rejected and explains how this decision was made.
- Supervised learning: A type of ML in which models are trained on labeled datasets makes predictions based on input data and known output labels. An example would be a model that is trained on a labeled dataset to determine whether emails are spam or not.
- Unsupervised learning: An ML approach that analyzes data without defined labels identifies patterns or relationships on its own. An example would be a model that analyzes unlabeled sales data to identify customer segments.
Big Data:
Big data; It is very large, highly diverse, and rapidly changing data. These data, which are difficult to analyze with traditional data processing methods, are often processed with the help of AI. This data has three main characteristics:
- Volume: The volume of data is quite large.
- Velocity: Data is generated quickly and comes in flux (for example: social media data, sensor data).
- Variety : The data can be in different formats (for example: text, video, audio, sensor data, etc.).
