If you are uncertain about issues related to Artificial Intelligence, like Machine Learning, here we simply explain what it is and how this incredible tool that is modernizing our lives and the world around us works.
We currently live in a world surrounded by computers, smart phones, robots and their great ability to understand and respond to human behavior, greatly facilitating various fields of work.
Machine Learning is a branch of Artificial Intelligence in which machines have the ability to adapt and imitate human behavior.
According to pioneer in the field of artificial intelligence and computer games, Arthur Samuel, he invented the term Machine Learning and defined it as: “field of study in which Machine Learning algorithms enable computers to learn data, and even improve themselves without being explicitly programmed.”
The basic premise of Machine Learning is to create algorithms that can receive input data and use statistical analysis to predict a solution, while the results are updated as new data is available.
Machine Learning focuses on perfecting software that can access data, use them to learn and improve automatically through algorithms, without being assisted by a human.
An algorithm is a sequence of instructions logically ordered that solve a particular problem. In Machine Learning algorithms discover natural patterns within the data, obtain information and predict the unknown to make better decisions.
The learning process starts by feeding with good quality data. Followed by training the machines, through building Machine Learning models, using the data and different algorithms.
MACHINE LEARNING METHODS
Machine Learning can be classified into 3 types of algorithms:
Supervised learning algorithms
Algorithms that rely on previously labeled data. That is, have the ability to apply what they learned in the past to new data, using labeled examples to achieve predict future cases.
For example, the computer can distinguish images of aircraft of the car. These labels are placed by humans to ensure the effectiveness and quality of data.
The idea is that machines can learn many examples, so that from there can make calculations and other human has no need to enter the information repeated times.
After sufficient training, the system can provide targets for any input that is new. The learning algorithm can also compare their output with the correct and intentional output to find out errors and modify the model sequency.
Some examples of supervised learning algorithms are probably many know, like spam detection and speech recognition.
Unsupervised learning algorithms
The algorithm studies the data to identify patterns. These algorithms are applied when the information used to train the machine does not have a previous indication or is not labeled or classified.
However, a large amount of data is provided with the characteristics of an object (parts of a car) so it can deduce what it is from the data collected. An example of unsupervised learning algorithms is classifying information.
Machine learning reinforcement algorithms
The basis of this learning is reinforcement. The machine is able to learn to interact with their environment by producing actions based on trial and error in a number of different situations.
In this learning method a simple reward feedback it is required for the agent to learn what action is best; this is known as the booster signal.
Although the algorithm knows the results from the beginning, it does not know what are the best decisions to obtain them.
The automatic reinforcement learning algorithm progressively relates the success patterns to repeat them again and again, until they are perfected and fully efficient.
This method allows the machines and software agents automatically determine the ideal behavior within a specific context to maximize performance. For example, a car automatic navigation system.
Current applications and perspectives of Machine Learning
Knowing a little more about what Machine Learning is and what it is for, it can be deduced that it is an excellent method capable of working in any area, from the automating everyday tasks, to offering intelligent information in various fields.
Problems that were thought to be unsolvable, now believed to Machine Learning could be solve almost any problem and achieve amazing results in many cases, provided sufficient data. It is so functional that industries in all sectors try to benefit from these techniques.
Since Machine Learning is based on processing and data analysis are translated into findings, it can be adapted to any industry that has database system large enough.
Currently, some of the uses of Machine Learning to highlight among many others are:
• Voice recognition systems
• Internet browsers
• Optimization and implementation of digital campaigns advertising
• Economic predictions and fluctuations in the stock market
• Fraud detection
• Medical Diagnostics
• Classification of DNA sequences
• Mapping and 3D modeling
We live in a time in which innovative technologies constantly and every day the progress is impressive emerge. It is believed that the improvement of Machine Learning and other derivatives of Artificial Intelligence, will continue to impact and practically transforming our lives and the world we know.