What is Actually Happening inside AI, DL, or ML

In today’s era of AI hype, many people perceive Artificial Intelligence, Machine Learning, and Deep Learning systems as some kind of alien technology, mysterious, magical, and beyond human understanding. There’s a common misconception that these systems deliver perfectly intelligent and accurate results without any flaws. As a result, people have some fear towards it and often assume there’s no need to understand how they work, just use them and trust the outcome. However, this belief is misleading.

If I told you that AI, Deep Learning (DL), and Machine Learning (ML) systems are fundamentally built on mathematical operations used to recognize, learn, and model patterns from real-world phenomena, and use that mathematical model to predict future outcomes, would you believe it? It’s true. At their core, these systems rely on complex mathematical calculations to generate new content, predict outcomes, and analyze data probabilistically based on previously learned patterns. While the processes may seem mysterious, they are grounded in mathematics, just at a level of complexity that can be challenging for humans to follow without the aid of computational tools.

Major inventions and revolutions do not emerge in the world spontaneously — they manifest for a reason, aligned with the needs and conditions of their time. When we seek to deeply understand such transformative technologies, especially those that resonate with us on an intrinsic level.

Then why do we need computers?

This is a billion-dollar question. If artificial intelligence (AI), machine learning (ML), and deep learning (DL) are fundamentally based on mathematical concepts, then why are they categorized as part of computer science?

The answer lies in the very reason computers were invented. Humans created computers to perform calculations faster and more accurately than the human brain. While the human brain is capable of a wide range of functions — such as reasoning, emotions, creativity, and memory — it is not optimized for performing large-scale mathematical operations quickly and consistently. To handle the complexity and scale of mathematical computations required in AI and related fields, we needed a specialized tool. That tool is the modern computer.

As time progressed, computer technology matured significantly. Humans developed more efficient mechanisms for performing specific tasks by enhancing computer architecture by adding new hardware components like memory, CPU with multiple cores, parallel processing units like GPUs, introducing abstraction layers through software, enabling parallel processing, and improving context switching speed. Unlike the human brain, which is not optimized for rapid task switching or repetitive computations, computers can perform such operations with remarkable efficiency and consistency. This technological evolution opened up a new dimension of possibilities, allowing humans to greatly enhance their productivity by leveraging the strengths of machines.

Evolution of computers

In the early days, humans performed all the mathematical computations related to machine learning manually. Even the foundational concepts behind Deep Learning and Artificial Intelligence were initially built upon machine learning algorithms. However, due to the growing complexity of these calculations, it eventually became impractical for humans to handle them without assistance.

As time progressed, organizations and the open-source community began introducing abstraction layers to manage hardware more efficiently, leading to the development of operating systems. These systems made it easier to interact with the underlying hardware without needing to write machine-level code.

Following this, individuals and researchers introduced even higher layers of abstraction in the form of programming languages. Over time, robust ecosystems formed around these languages with the creation of supporting libraries and modules. These included:

• Data structures to efficiently manage and organize collections of data
• Algorithms for operations like sorting, searching, and insertion
• Mathematical modules for performing complex computations such as mean, max, min, square root, etc.
• Concurrency and parallel processing modules to execute multiple tasks simultaneously
• GPU-based processing for accelerating deep learning and heavy computational workloads

Today, the computer has become a well-equipped powerhouse capable of solving the complex and computationally intensive demands of machine learning and deep learning algorithms. Tasks such as regression analysis, convolutions, and matrix operations, which were once tedious or impossible to perform manually, can now be executed with incredible speed and precision.

Recognizing this computational power, AI researchers and scientists began to ask: Why not fully leverage this to solve AI, ML, and DL challenges?

These algorithms require the ability to:

• Compute large volumes of operations at high speed
• Store and manipulate vast numbers of variables in parallel
• Efficiently structure, cache, and retrieve intermediate results
• Permanently save final outputs in memory without relying on physical storage like paper

All of these capabilities are inherent in modern computer systems, delivered in a way that surpasses the limits of the human brain in both speed and accuracy.

Starting with simple machine learning algorithms, researchers gradually moved toward more complex models, such as deep learning architectures, neural networks, and multi-component AI systems. And this evolution is far from over — the field continues to grow, pushing boundaries further with each advancement.

What is next?

To truly understand what’s happening beneath the surface of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), it’s essential to explore their origins and foundational structures. Gaining this understanding requires us to deconstruct the complexity step by step, examining each conceptual and technical layer with care and clarity.

In this upcoming article series, I will guide you through that journey, breaking down these intricate topics into digestible parts by addressing some of the most fundamental and thought-provoking questions as follows.

• What is the role of mathematics and concepts used in AI, ML and DL?
• Why are patterns important and their usage in AI, ML and DL?
• How do statistics and probability relate to this field?
• How have the mathematical concepts of learning evolved?
• What role does this technology play in today’s society?