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In the current world of technology data drives decision-making. Due to this, predictive modeling has emerged as one of the most powerful tools for anticipating future outcomes. From forecasting customer behavior to predicting equipment failure, predictive modeling helps businesses, governments, and researchers make informed decisions based on historical data. 

We touched on the differences between Artificial Intelligence, Machine learning, and deep learning. Machine learning has changed industries by enabling computers to make data-driven decisions. Among the most widely used algorithms for classification and regression tasks (A problem that requires predicting a continuous numerical value, ex. Predicting the price of a house based on various factors) are Decision Trees and Random Forests. These two methods offer efficient and powerful solutions for various problems, from customer segmentation to fraud detection. 

Artificial intelligence (AI) has become deeply embedded in industries ranging from healthcare to finance, helping automate tasks, improve decision-making, and enhance user experiences. However, as AI systems grow more complex, so do the challenges of understanding how they arrive at their conclusions. The “black box” nature of AI models—especially deep learning—raises concerns about transparency, fairness, and accountability.

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are often used interchangeably, but they are distinct concepts with different levels of complexity and functionality. AI is the overarching field that enables machines to simulate human intelligence. ML is a subset of AI that allows machines to learn from data. DL is a specialized branch of ML that mimics the way the human brain processes information. 

Artificial Intelligence (AI) has evolved significantly over the past few decades, with deep learning standing out as one of its most transformative breakthroughs. Deep learning is a subset of machine learning. It enables computers to process vast amounts of data, recognize patterns, and make intelligent decisions—often surpassing human performance in tasks like image recognition, speech processing, and natural language understanding. 

In the last edition we investigated natural language processing (NLP) and the role it plays in modern technology. While NLP is a powerful tool for extracting text, it takes too long for humans to analyze the vast amounts of data that is gathered. This is where knowledge graphs play a key role. These specialized graphs provide structured, interconnected data that artificial intelligence systems (AI) can reason with after NLP has processed raw text.

One of the most transformative fields in the emerging powerhouse of artificial intelligence (AI) is Natural Language Processing (NLP). It focuses on the interaction between humans and computers using natural language—allowing machines to understand, interpret, and even generate human language in a meaningful way. From voice assistants like Siri and Alexa to real-time language translation and chatbots, NLP is revolutionizing how we communicate with technology.