Artificial Intelligence, Deep Learning, and Machine Learning Series
The entropy of the world is increasing at an unprecedented rate. If I repeat that in an alternative way, the world is advancing at an accelerating pace like never before. This rapid progression is largely driven by technological advancements. Throughout history, several critical events have significantly contributed to the rise of technology. One of the most groundbreaking milestones was the discovery of nuclear energy, which revolutionized the world and set humanity on a new trajectory. From my perspective, the next technological breakthrough with an equally transformative impact is the rise of Artificial Intelligence (AI), following in the footsteps of nuclear energy as a game-changing force in human history.
As humans, we are naturally resistant to change and often fear it. Historically, we tend to embrace change and adapt only when faced with existential threats. For example, during the time of nuclear energy discovery, the world was engaged in World War. Humanity had no choice but to adapt to nuclear advancements. Believe it or not, this rapid technological evolution may soon become our next existential threat. Technology is deeply interconnected with the economy, which, in turn, influences politics. Politics shapes society, beliefs, and even our very existence. This creates a continuous cycle where each factor impacts the next, driving an ever-evolving and interdependent world.
So why do we wait until the last moment? As wise individuals, we should have the ability to anticipate these shifts and prepare in advance. Instead of being late resistors to the change, we should become early adopters. This is precisely why I began learning about Artificial Intelligence (AI). Through this series, I aim to document my personal journey — sharing my understanding of AI, Deep Learning (DL), and Machine Learning (ML), how I perceive their mechanisms, and how they can be applied to solve real-world problems.
AI, DL, ML? Why three terms?
You may be wondering what the meanings and true differences of Artificial Intelligence (AI), Deep Learning (DL), and Machine Learning (ML)
The events in this world often follow recognizable patterns. Algorithms aim to predict outcomes by analyzing these patterns in events or objects. To do this effectively, we must first train the algorithms on relevant patterns so they can learn and make accurate predictions.
In that sense
Machine Learning is a technique used to train mathematical algorithms on data that represents patterns in objects or past events. Once trained, these algorithms can predict the probability of future outcomes. ML is often used to identify patterns with linear relationships between input features and outputs. This basic form of pattern recognition is applied in tasks like spam detection (learning from past spam), housing price prediction (based on historical data), and object classification (by analyzing similarities).
Deep Learning is a technique used to recognize more advanced patterns that traditional Machine Learning often cannot capture. It uses a layered approach, allowing the model to identify complex, non-linear relationships between input features and outputs. To achieve this, deep learning models incorporate activation functions and are structured as neural networks, designed to mimic the way neurons work in the human brain. Instead of relying on a single algorithm, deep learning builds a network of interconnected layers and optimizes it by applying learning algorithms across multiple pathways.
In AI, we build neural networks along with other essential components to mimic human learning from past mistakes and emulate human intelligence. Instead of relying solely on neural networks, AI systems incorporate rule-based logic, reasoning, search algorithms, and more to make intelligent decisions. A good example is self-driving cars. Beyond recognizing patterns in video feeds, they must make real-time, rational decisions — like reacting to sudden obstacles or unexpected behavior on the road — which goes beyond just learning from past data.
Conclusion
As we’ve discussed above, we now have a foundational understanding of the importance of staying aware of the rapid advancements in AI, Machine Learning (ML), and Deep Learning (DL). This is just the beginning of a series where we will dive deeper into the fascinating world of emerging technologies.
This article wraps up our introductory exploration, but in the upcoming posts, I’ll be sharing more advanced insights and practical knowledge from the field. So stay tuned as we continue to navigate and swim through this exciting tech paradigm.
Remember, understanding the need for AI, its core principles, and the mechanisms behind it is far more valuable than simply applying frameworks to build models. By learning the basics, you can uncover hidden patterns and make groundbreaking discoveries.
Stay curious, stay tuned.
