How To Use Machine Learning In Favor Of Your Business
Machine Learning in Business: Artificial Intelligence has developed in recent years in companies and has been driven by new technologies in public computing clouds (such as Amazon’s AWS and Google’s GCP) and Big Data.
The beginning of Artificial Intelligence dates back more than 70 years. Alan Turing, an English mathematician, defined in 1950 his Turing Test, the Imitation Game, where a human being tries to distinguish a person from artificial intelligence. Since then, much evolution has taken place. The latest news is from Alpha-Fold, the intelligence that solved a 50-year-old problem and promises to revolutionize science.
Do you know the types of problems around you that could be solved by machine learning? In this article, you will learn about a new development paradigm: machine learning, as well as the technologies that allowed its adoption and the conditions for its use.
Cloud And Big Data
Public clouds gave access to more excellent computing resources without significant investment. It is no longer necessary to buy multiple processing and storage cores or have a dedicated maintenance team. Features are just a few clicks away and only cost what you use.
The low cost of storage brought with it an ease of storing and making information available that, associated with the massive use of computing technologies, gave rise to a large volume of data: Big Data. According to consultancy IDC, the estimate is that stored data doubles every four years.
Faced with this scenario, a branch of artificial intelligence gained strength: Machine Learning, also commonly called by its English name, Machine Learning or the abbreviation ML. In machine learning, the central idea is to use the past to understand the present and the future. Through various algorithms, machine learning extracts patterns from the data. Then, with these patterns, it is possible to understand the present and the future beyond what a human would understand or develop (program a computer).
A computer program developed in classical structure is limited by the people who design it. The classic is the development paradigms used to make a website or a payroll. The program’s heart is a script of rules, steps, and well-defined and pre-determined flows to be followed; all actions are easily understandable.
On the other hand, machine learning is a disruptive technology as it breaks with the classical development method. There is no roadmap of rules. At the core is a mathematical model capable of learning, which receives a mass of data with diverse information about the subject and a cost function. Furthermore, the objective is no longer to hit precisely but to hit approximately. Usually, the cost function is the error to be minimized when there is a solution to the problem in the mass of historical data. The algorithm, also called a model, will seek to combine the other information during the learning process to approximate the solution and minimize the cost (error).
Solution Without Rules Script
If today you had to design a service robot that would sort out the calls and send them to the responsible attendant, how would you do it? Maybe the first way would be to think of a script with menus to limit the user’s choice and allow the rules to be more straightforward. It would work well if the menu had few options. If your menu has 20 votes or multiple menus one after the other, the user experience will be less than satisfactory.
It would be better if the user could write his problem, and his robot would understand and triage it. Now try to imagine step-by-step rules to make this robot work.
Attention To Opportunities
The customer reported a problem that he even knows how to solve and maybe solves daily. He knows what information is needed for the solution, but he doesn’t explain how he does it. Maybe there’s some intuition, some tacit knowledge that he can’t explain, but he wants his help to improve the process. If you face a machine learning solution, a data scientist can help you.
One of the ways that learning happens is with a database of historical examples of the objective and the information needed to explain the purpose. The optimal amount of recorded data and required information depends on the algorithm and application. But if these conditions exist, the chances are high that an algorithm solves the problem.
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