To obtain good results from the use of Machine

[soniya55531]

Piffer explains that with the use of computers, the spread of the internet and the advancement of communication and data transfer infrastructures, we have reached a level of information abundance. “In Machine Learning we can think of data as experience , so that we are able to improve, under various pre-defined measures, the execution of tasks as new data is inserted into algorithms. With the use of Machine Learning, from the available data, we can solve problems without explicitly programming the solution”, he highlights.

Learning, it is important to be aware of the various challenges that arise during its execution phase, such as the quality of the data obtained, a solid understanding of the appropriate methodology for creating AI projects, as well as clarity on how to create a project that covers all the necessary steps. In addition, it is essential that companies have the technical maturity to deal with adversities , and once mastered, the Machine Learning technique can bring positive impacts to companies, with benefits such as automating routine tasks, reducing costs, calculating risks more efficiently, improving personalization and service, preventing fraud and solving problems that humans cannot, for example.


Inteligov Labs
As an example of the application of Machine Learning and AI, Inteligov china mobile database Labs presents solid experiments with great potential to impact the market in which it operates by optimizing data intelligence processes in government relations, with political transformation work through technology. By assisting decision-making, Inteligov is able to anticipate relevant information that is only perceptible when analyzed together with hundreds of variables.


“We believe there is a proper procedure for creating products that use AI. This procedure starts with the search for a deep understanding of the different fields of study of Artificial Intelligence, whose application is relevant to the decision-making process. It has as an intermediate point the prototyping and validation internally and with clients, goes through the process of including functionalities within the platform and is renewed as new feedback arises”, says Piffer.


He also reports the launch of the first project within Inteligov Labs, the Thermometer. “With it, we intend to provide customers with information on the probability of approval of a legislative proposal. We created a Machine Learning project using supervised classification algorithms, using data from thousands of projects in the federal legislature. With this, we are able to show the approval trend of each project over time,” he states.