Artificial Intelligence (AI) refers to computer systems that perform tasks that imitate human thinking, such as learning, reasoning, problem-solving, or communication. Developers aim to create algorithms that can perceive their environment, make decisions, and act or improve independently based on those decisions.<\/p>
1) Predictive AI<\/strong>\u2014Forecasting.\u00a0<\/strong>This type of AI uses<\/span>\u00a0past data to predict what is likely to happen in the future.<\/p> Typical applications:<\/p> Keyword: Probability. Predictive AI does not make specific predictions but provides probabilities based on past data.<\/p> 2) Generative AI<\/strong>\u2014Content Creation.\u00a0<\/strong>It produces<\/span> new text, images, music, or video based on learned patterns.<\/p> Examples:<\/p> Keyword: Creativity. Generative AI functions like a diligent artist: it learns extensively and then creates new content in its own style.<\/p> 3) Classification and recognition AI<\/strong>\u2014Pattern identification. <\/strong>It recognizes<\/span> and categorizes input data, such as images, sounds, and movements.<\/p> Examples:<\/p> Keyword: Pattern. This type does not interpret meaning but excels at rapid and accurate recognition.<\/p> Machine Learning (ML) is one of the most important fields of AI. Its essence is that computer systems learn patterns from data and examples, enabling them to generalize. Instead of following pre-programmed rules, ML algorithms learn from experience, processing datasets to solve tasks. This allows an app to recognize faces, recommend movies, or translate texts. In other words, AI is the broader goal (human-like intelligence), while ML is the \u201cengine\u201d that enables systems to adapt and improve.<\/p> Three main approaches to machine learning:<\/p> Large Language Models are AI systems trained on vast text corpora to recognize linguistic patterns and generate new text. Examples include GPT (OpenAI), BERT (Google), and Claude (Anthropic).<\/p> Applications:<\/p> LLMs are based on machine learning, particularly deep learning. They are trained on massive text datasets (books, articles, web pages) and learn statistical relationships between words and expressions, such as which word is likely to follow another in context. This does not mean accurate \u201cunderstanding\u201d but pattern recognition and probability estimation.<\/p> Training often occurs in two phases: pre-training, which involves learning general linguistic patterns, and fine\u2013tuning, which involves adapting to specific tasks or domains (e.g., medical texts, customer service dialogues).<\/p> Opportunities:<\/p> Limitations:<\/p> One common problem of LLMs is \u201challucination,\u201d which is producing plausible but factually incorrect or entirely fabricated information. This happens because the model does not \u201cknow\u201d reality – it simply continues linguistic patterns. It may \u201cinvent\u201d quotes, sources, or facts that do not exist.<\/p> LLM outputs are often well-formulated, making them appear convincing. They may present conclusions that sound logical but are unsupported by facts. Critical source-checking is essential, especially in research, education, or professional work. AI-generated text is not a source; it must always be compared against reliable literature and verified data.<\/p> <\/p>","protected":false},"excerpt":{"rendered":" Artificial Intelligence (AI) refers to computer systems that perform tasks that imitate human thinking, such as learning, reasoning, problem-solving, or communication. Developers aim to create algorithms that can perceive their environment, make decisions, and act or improve independently based on those decisions. Main categories of AI by capability (intelligence level) Artificial Narrow Intelligence (ANI). This<\/p>\n","protected":false},"author":58,"featured_media":0,"parent":818,"menu_order":1,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"_eb_attr":"","footnotes":""},"categories":[25],"tags":[],"class_list":["post-828","page","type-page","status-publish","hentry","category-oktatas-en"],"acf":[],"yoast_head":"\n
Machine Learning<\/h3>
Large Language Models (LLMs)<\/h3>
How do LLMs learn?<\/h4>
Opportunities and limitations of LLMs<\/h4>
The phenomenon of \u201challucination\u201d<\/h4>
Why should AI-generated content always be verified?<\/h4>