Machine learning: what it is and what are the advantages for business

The terms Machine Learning (ML) and Artificial Intelligence (AI) are increasingly well-known, mainly due to the popularization of GPT Chat. Although they are currently topics both in the corporate environment and in schools, and are no longer restricted to conversations between technology professionals, ML and AI go far beyond the relationship with chatbots. Therefore, we have prepared content so that you know exactly what they are and how these technologies impact the daily lives of companies and consumers.

What does Machine Learning mean?

Machine Learning focuses on the development of algorithms and mathematical models that allow a system to learn from data to perform tasks partially or fully autonomously, without constant human intervention, that is, without being continually programmed to do so.

The machine learns on its own from historical data provided to it by the user and generates a trained model that can receive new information to make more accurate predictions. In addition, it improves its performance based on its own experience.

The term Machine Learning was first used in the 1950s by computer scientist Arthur Lee Samuel, who based his work on the studies of British scientist Alan Turing, known as the father of computing and who, during the Second World War (1939 to 1945), created a machine that was able to reveal war secrets after deciphering the Enigma code, which used encryption in communication.

What is Machine Learning

Machine Learning is the ability of computer systems to understand and analyze large volumes of data with a certain degree of autonomy. It is a concept that computer systems can make decisions without human guidance after complex Machine Learning algorithms process a large mass of data and interpret it, identifying its patterns.

There are two learning methods: unsupervised and supervised.

Unsupervised learning is when the machine tries to learn on its own from the data presented and without predefined results as parameters. In this model, there is no defined objective to achieve. The goal is to find structure, patterns and relationships in the data without any prior guidance.

Supervised learning is the algorithm trained from already classified examples, in which it understands the pattern of that exposed variable.

To use the full potential of this technology, it is necessary to have qualified data to present to the machine and to have the analytical capacity of those who will direct it to obtain more assertive results and better performance.

Difference Between AI and Machine Learning

Machine Learning (ML) is an area of AI (Artificial Intelligence), a field of science related to the ability of a machine to simulate and replicate human intelligence. AI learns, analyzes, categorizes, and identifies patterns for data-based decision-making. Machine Learning is the system that can teach a machine to learn from the analysis of a data set.

AI (Artificial Intelligence) and ML (Machine Learning) tools help make sense of data, automate processes, and scale the digital experience to support human work.

Machine learning in companies

Machine Learning has helped companies of different sizes and segments, as it automates decision-making, predicting risks and identifying opportunities. It is not new in computing, but technological developments in recent years have made it possible to overcome obstacles for companies to implement Machine Learning, as there is now greater access to technology and processing capacity.

The current lending landscape, for example, requires advanced data and analytical approaches. According to Experian’s Global Decision-Making Report , to navigate complex times with greater confidence, companies can:

Employ alternative datasets to complement traditional datasets and improve overall analytical capabilities;
Leverage synthetic data through Machine Learning solutions to test and train models in various scenarios, highlight potential risks and enable more proactive decisions;
Recognize data limitations and evaluate analytical models to identify potential bias and ensure access to credit for all eligible customers.

Therefore, innovative companies will need to use all relevant data sources, such as traditional sources, search for new data, and evaluate the possibilities of alternative and synthetic data. Machine learning solutions enable companies to quickly use these traditional and non-traditional data sets and build more efficient processes, such as credit risk prediction models. The result is a more accurate view and the identification of new indicators of potential risk.

To adopt layered security and create end-to-end experiences, companies need more advanced technologies. The use of Machine Learning also improves, for example, the detection of new fraud patterns and reduces false positives (rejection of a financial transaction from a real user and good customer due to an error in an anti-fraud system that indicates that the transaction may be fraudulent). This results in improved performance in each part of the authentication and fraud prevention process. With Machine Learning, the combination of solutions can boost results, reduce scams, approve more customers and reduce costs with manual reviews.

Global data on the use of Machine Learning and Artificial Intelligence

Companies have recognized the importance of the digital journey, especially with the Covid-19 pandemic, which has led to a greater presence of people in the online environment. Most institutions have driven their digital transformation during this period to expand their businesses and offer a positive experience to the consumer, prioritizing technological resources.

Experian’s Global Decision Making Report found that implementing new Machine Learning models for decision making was at the top of the top 5 business priorities in 2021. Companies were already preparing to operate in a new world, which is why they were prioritizing investments in digital.

Companies around the world were improving the digital customer journey in the following ways:

44% Improving the performance of existing AI (Artificial Intelligence) models with the aim of improving customer decisions;
42% Implementing new methods and building new AI models to improve customer decisions;
40% Strengthening the security of mobile/digital channels.

Machine Learning and Deep Learning

Deep Learning, also known as deep neural networks, is a sub-area of Artificial Intelligence.

Machine Learning algorithms reach a certain level, but with Deep Learning they can go further, as there are more parameters and deeper layers to descend the variable levels. This resource must be analyzed and used when necessary, because to delve deeper requires a very large amount of data. Deep Learning algorithms are generally capable of solving more complex problems than traditional Machine Learning algorithms. On the other hand, they require more computational power and a large data set to train models, especially in image, voice, and text recognition problems.

Machine Learning and GPT Chat

Chat GPT (acronym for “Generative Pre-Trained Transformer”) is an Artificial Intelligence tool created through Machine Learning .

Launched in November 2022 by the company OpenIA, it is an evolution of the virtual assistant and establishes real conversations with users based on data that feeds the algorithm. The tool is a trained language model that can answer questions based on patterns and the combination of information available on the web. The big difference is that the answers are creative and contextualized.

Interestingly, the International Conference on Machine Learning (ICML), the most important event on Machine Learning, which took place in January 2023, prohibited its academic articles from being entirely generated by AI tools, such as Chat GPT, due to ethical issues related to intellectual property. AI, however, could be used to improve and edit articles. The decision was controversial and was even questioned by some authors.