In this “New Normal” series, to advance in the use of LLM models, we need to address the behind-the-scenes creation of models from Deep Machine Learning and its way of thinking through Neural Networks. This article will explore the question of AI learning, which was based in part on how we learn.
The idea that machines can learn is one of the most fascinating and revolutionary concepts within Artificial Intelligence (AI). The concept of Machine Learning (ML) dates back to the mid-20th century, but it was in recent decades that it truly flourished, transforming into a powerful tool that lies at the heart of many of the technological innovations we use today.
In this article, we will explore the Machine Learning (ML) process, from its conception, through its evolution to Deep Learning (DL), and take a look at what the future may hold. We will also make a comparison between human learning processes and machine learning.
The Conception of Machine Learning
The concept of Machine Learning emerged from the idea that, instead of programming a computer to perform a specific task, we could teach it to learn on its own from data. This concept was introduced by Arthur Samuel in the 1950s, when he developed a chess program that could improve its performance by playing repeatedly against itself. This was one of the first examples of a system that “learned” from its experience.
At the time, Machine Learning was based on statistical and optimization methods, such as linear regression and decision trees. While these methods were effective for simple problems, they had significant limitations when applied to more complex problems with large volumes of data.
Evolution of Machine Learning
As data volumes increased and computational power grew, machine learning methods began to evolve. In the 1980s and 1990s, more advanced techniques emerged, such as artificial neural networks and support vector machines (SVMs), which allowed machine learning systems to handle more complex and diverse problems.
Neural networks, inspired by the functioning of the human brain, were particularly revolutionary. They consist of layers of artificial neurons that process information hierarchically, allowing the system to learn more abstract representations of data. However, neural networks of that era had limitations, such as the difficulty in training deep networks, which limited their performance on more challenging tasks.
The Leap to Deep Learning
The real breakthrough in machine learning occurred in the early 2010s, with the advent of Deep Learning. Deep Learning is a subset of Machine Learning (ML) that uses deep neural networks, composed of many layers, to model more complex patterns in data.
The key to the success of Deep Learning was the combination of large volumes of data (big data), increased processing power (such as the use of GPUs), and advances in training algorithms (such as backpropagation). Deep neural networks proved to be incredibly effective in tasks such as image recognition, natural language processing, and, more recently, in generative AI.
For example, deep neural network models are the foundation of systems that can recognize faces in photos, translate languages in real time, or even generate images and texts that appear to have been created by humans. The ability to learn complex and abstract representations of data made Deep Learning the central technology behind many modern AI applications.
Comparing Human and Machine Learning
Human learning and machine learning share similarities, but also have fundamental differences. Both processes involve absorbing information, forming patterns from that information, and applying those patterns to make decisions or perform tasks.
Human Learning:
- Human learning is a continuous process that begins from childhood and extends throughout life. We learn through experiences, observations, social interactions, and formal studies. Our brain is capable of generalizing from a limited number of examples, and we use intuition, emotion, and context to make decisions.
- Humans also learn adaptively, adjusting to new environments and challenges, often without the need for large amounts of data. Additionally, creativity and the ability to think “outside the box” are distinctive characteristics of human learning.
Machine Learning:
- Machine Learning, on the other hand, is highly dependent on data. The more data an AI system has, the better it tends to learn. The learning process involves training models on large datasets and adjusting them until they can make predictions or decisions based on new data.
- Machines do not have intuition or emotions; they make decisions based purely on patterns observed in the data. While machine learning models can adapt to new data, this adaptation typically requires new rounds of training and hyperparameter adjustments.
Next Steps: The Future of Machine Learning
The future of Machine Learning is full of promise. Continuous advances in areas such as reinforcement learning, explainable AI (XAI), and the integration of AI with other emerging technologies, such as quantum computing, are on the horizon.
- Reinforcement Learning: This approach, which allows machines to learn through trial and error in simulated environments, is advancing rapidly and could lead to significant improvements in areas such as robotics and gaming.
- Explainable AI (XAI): Making AI model decisions more transparent and understandable to humans is a growing focus, especially in areas where trust and ethics are crucial.
- Quantum Computing: While still in early stages, quantum computing has the potential to dramatically accelerate machine learning, allowing models to solve extremely complex problems that are beyond the reach of classical computers.
Conclusion
Machine Learning has evolved from basic and experimental concepts to become one of the most transformative technologies of our time, with Deep Learning opening new possibilities and frontiers. While machines are still far from completely replicating the complexity and adaptability of human learning, they are rapidly becoming indispensable tools in virtually every industry.
As we continue to explore and expand the limits of what machines can learn, it is important to remember that the Machine Learning process, despite being powerful, is still a human creation. And, just as we humans continue to learn and evolve, machines will also follow this path, with advances that promise to shape the future of technology and society.
Note: This newsletter was written with ChatGPT 4.0 as my assistant for research, development and interaction of the subjects and examples presented. I can estimate that about 40% of the creative work and development of this newsletter was developed by the GPT LLM from ChatGPT, and the remaining 60% from my “personal model,” developed with my learning and experience throughout my professional life.
