Dr. María Victoria Carreras Cruz, director of the Bachelor’s Program in Data Intelligence and Cybersecurity Engineering at the Universidad Panamericana Pablo González Bustamante, a student in the Data Intelligence Engineering program, explain how these models work and why it is essential to analyze them critically.
In today’s era of digital transformation, artificial intelligence has established itself as a key tool in various fields of knowledge and decision-making. Understanding how machine learning models work is not only important from a technical standpoint, but also for their proper interpretation and responsible application.
What is machine learning?
Machine learning is a branch of artificial intelligence that enables computers to learn patterns from data and make predictions without being explicitly programmed. It works through mathematical models that identify relationships within large volumes of information.
How did it come about?
Machine learning is the result of an evolution in the way humans have managed information. From ancient records to digital systems, the development of new technologies has made it possible to store ever-increasing amounts of data.
With the advent of computing and, later, the internet, the need arose to create tools capable of analyzing large volumes of data automatically, leading to the development of machine learning models.
What were the early milestones in machine learning?
Several key advancements have shaped the development of machine learning:
- In 1950, Alan Turing proposed the Turing Test to assess intelligence in machines.
- Arthur Samuel developed a program that learned to play checkers. It was he who coined the term “machine learning” after observing that his program was capable of “learning” from previous games.
- Frank Rosenblatt created the Perceptron, one of the first neural network models:
These advances laid the groundwork for modern machine learning.
What has changed with Big Data and the Internet?
The exponential growth of digital data drove the development of machine learning. With the advent of the internet and the concept of big data, it became necessary to create models capable of processing information on a large scale.
This made it possible to develop applications such as speech recognition, pattern analysis, and recommendation systems, establishing their use across multiple industries.
Why is machine learning so important today?
Machine learning is key because it allows us to analyze large volumes of data and turn them into useful information for decision-making. Its development has been driven by the growth of the internet and the constant generation of digital data.
Today, the digital footprint generated by individuals and organizations powers systems capable of identifying patterns, optimizing processes, and solving complex problems, making it an essential tool in fields such as research, industry, and business.
How does a machine learning model work?
A machine learning model is a mathematical function that takes input data and produces an output, usually in the form of a probability. It uses tools such as statistics and algebra to identify patterns in the data.
How does a model learn?
Models learn from examples. During training, they compare their predictions with actual results and adjust their parameters to reduce errors. Over time, they improve their accuracy by identifying more complex patterns.
How do you interpret your results?
A model should not be evaluated solely on the basis of its accuracy. In some cases, it can produce significant errors, such as false positives. Therefore, it is essential to analyze the context, the type of data, and the errors it generates.
What questions should you ask to evaluate it?
- How common is what you're predicting?
- What metric is being used?
- How many errors are there, and what kind?
- What data was it trained on?
Why is critical thinking important?
Artificial intelligence models lack judgment and ethics. They operate on the basis of probabilities. Therefore, they should be used as tools that complement human analysis, not as substitutes.
Artificial Intelligence Training
At the Universidad Panamericana, the artificial intelligence program combines technical knowledge with critical thinking, preparing students to interpret these models responsibly.
In a rapidly evolving field of artificial intelligence, understanding its fundamentals has become an essential skill. It’s not just about using these tools, but about interpreting them correctly, recognizing their limitations, and making informed decisions based on their results.
