With the transformations that are happening in the world, many companies need to adjust as quickly and assertively as possible to stay in the sales game. But, how to do it? With Artificial intelligence, all of it and much more is possible, according to Frederico Gonçalves, our head of Analytics: “the most relevant thing is to be ahead with decision-making, both in operations and business”.
In practice, Artificial intelligence is adopted in many aspects of business. It can be used to analyze data and turn it into knowledge, not just for specific processes, but for the business as a whole.
An example of how Artificial intelligence can be applied: imagine a company selling a product. With history data collected from the company, it can better understand its consumer, know when there is a sales peak, which places sell the most, among other things
In product manufacturing, the use of AI makes it possible to improve production by adding quality analysis, automation in some processes, and production increase analysis in peak sales periods.
Another common example in the market is the use of Artificial Intelligence for image analysis, which is used in airports, security cameras, companies and even in the virtual assistants that we have at home. With this technology, cameras can analyze the image and identify whatever is necessary, such as a face, a badge and even a risky situation.
In this article, we will explain the different areas that are within Artificial intelligence and the different ways they can help companies, from data collection to decision making.
What Is Artificial Intelligence?
If you search for the definition of Artificial intelligence on the internet, you will find numerous explanations. Here, we will continue with the definition that Artificial intelligence is a field that seeks to replicate human behavior, such as the ability to see, speak or make decisions.
Artificial Intelligence is a field in constant evolution, with the development of new techniques at all times. Since the 2000s, the field has undergone fast growth, with new techniques or discoveries leaving the research and testing phases and entering the market in an accelerated pace.
AI can be divided into two types: Narrow AI and Artifical General Intelligence. The concept behind Artificial General Intelligence is that of technologies capable of applying knowledge and skills in different contexts — much like the R2-D2 and C3PO robots do in the Star Wars franchise films.
Despite the great evolution of the field and the work of many companies and researchers to make this possible, the most common is to use Artificial intelligence is performing a more restricted task, in which technology surpasses human beings — this type of AI is classified as Narrow AI.
Some examples of Narrow AI: face recognition; systems that recommend movies you might like; NLP projects (Natural Language Processing), such as speech recognition and machine translation; and computer vision systems for autonomous cars, capable of identifying people, traffic signs and taking actions according to different objects found in its path.
Now that we understand the two types of Artificial Intelligence, let’s get into their research fields, Machine Learning (ML) and Deep Learning (DL).
AI research fields
See the figure below. In it, we see a much larger context, but we’ll focus on the AI sphere. Artificial Intelligence has a subfield of Machine Learning, which, in turn, encompasses the subfield of Deep Learning.
As we have seen before, in Artificial Intelligence, a machine is able to mimic human behaviors. In order for this to be possible, it first needs to learn.
This learning takes place through experience — in this case, from existing data. “The key word for AI is learning. This is what allows a computer to have the ability to reproduce human behaviors,” comments Frederico, head of Analytics.
The difference between ML and DL — Machine Learning and Deep Learning, respectively — is exactly how their training is done. In Machine Learning, developers need to program what the computer needs to identify in the data that will be analyzed.
In Deep Learning, on the other hand, developers don’t have to do this, because the computer itself analyzes the data and finds patterns that will be used as a basis for analyzing other data in the future.
Let’s go to an example. Suppose you need your system to identify faces within an image data base.
If you use Machine Learning to analyze this image data base, you’ll need to program the patterns the computer needs to find in the image to identify what a face is — for example, a mouth, two eyes, a nose, and the contour of a face.
If you use Deep Learning, it is not necessary to program the patterns, because DL, after analyzing the data base images, will identify what is needed to classify a face in the image itself.
The use of Machine learning or Deep Learning depends mainly on the problem being addressed in each project. However, other factors also go into account, such as: project cost, project development time, and what kind of deliverables this project will have.
The use of AI in different business fields can bring many positive results, such as: process efficiency, increased productivity, predictions and decision making. All this is possible when data is extracted and analyzed correctly, using existing technologies discussed in this article.
However, it is necessary to produce a pre-evaluation and/or proof of concept before the project stars, to verify its feasibility. The data that the team extracts will be the raw material to transform it into information, knowledge and wisdom.
Here at Venturus, we already have some AI cases using both Machine Learning and Deep Learning. We have already assisted industries in visual inspection of products with image analysis, created a water leak detection system, built systems to predict the failure of equipment and produced predictive maintenance planning, among several other cases spread across our fields of action such as energy, manufacturing and payment methods.