Artificial Intelligence, AI for short, is one of the technologies that has the most possibilities of reflection in technology and business. Derived from the moment we currently live in an increasingly connected and intelligent world.
An AI system can analyze data in large quantities, identify patterns and trends, and make predictions automatically, quickly, and accurately. Likewise, AI makes everyday experiences smarter through predictive analysis and the combination of different AI techniques in applications that we use daily. For example, we can manufacture technological equipment, compose musical pieces, even carry out banking operations, all through different algorithms. The technology behind all these advances is related to AI.
To understand what artificial intelligence is, we will allow us to define it as the scientific field of computing that focuses on creating programs and mechanisms that can show behaviors considered intelligent. In other words, we could say that it is the process in which machines think like human beings.
Although there is a concept about artificial intelligence fueled by science fiction such as exterminators, robots, and pills, the reality is that AI had existed since 1956, when a group of scientists initiated the “Artificial Intelligence” research project at Dartmouth College in the United States. At the beginning of the project, the goal was that human intelligence could be described so precisely that a machine could simulate it. However, research led to different fields. With the passage of time and the evolution of science, AI began to generate significant results in modern life.
Within the main techniques of Artificial Intelligence, specifically in cases where it is used for business, there are four significant fields;
Machine learning. In a field where the most significant advances have been made, we can define that Machine Learning is the science that is responsible for making computers perform actions without the need for explicit programming. Examples of Machine Learning algorithms include decision diagrams, grouping algorithms, genetic algorithms, Bayesian networks, and Deep Learning.
Deep learning. A machine learning technique that uses neural networks to perform classification tasks. Examples of practical Deep Learning applications include vehicle identification, pedestrians and autonomous vehicle license plates, image recognition, translation, and natural language processing.
Intelligent data Discovery. This technique enables the total automation of the IE cycle: the incorporation and preparation of data, predictive analysis and patterns, and the identification of hypotheses. An exciting example of intelligent data recovery in action. The information that no IE tool had discovered.
Predictive análisis. A model informs about the likelihood of a situation occurring based on several previous factors, such as; age, zip code, gender, etc. Therefore, the central concept of predictive analysis (or modeling) means that several variables combined with results can generate a model that provides a score representing the probability of an event.
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We can also measure campaigns and campaign efficiency by tracking the effectiveness of current and previous campaigns. We constantly monitor results as you run in the campaign, continually measuring KPIs in real-time. Within our services, we have:
ALY A-I: Digital strategies and AD spending through artificial intelligence. The best Allied to optimize and automate the digital system and ad spend through artificial intelligence.
OPI brand: You can ask the world whatever you want, from brand tracking to media measurements. You can ask and then analyze what you need to understand about consumers.
OPI CX: This is a solution to collect, transform, and analyze customer feedback powered by artificial intelligence and gamification.
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