Artificial intelligence encompasses a variety of innovative technologies, from Machine Learning to the Internet of Things. Do you want to learn how to apply all of them in your software? So, don’t stop reading this post!
Artificial intelligence is not a new concept. It all started in 1956 when university professor John McCarthy envisioned a future where machines would be able to perform tasks autonomously.
At that time, the idea was taken as a work of science fiction, but it was not forgotten, and today it is a reality. So how do you implement artificial intelligence in software?
Over the years, we hear a lot about Machine Learning, Deep Learning, Predictive Analytics, Big Data Analytics, and the Internet of Things. And not in vain, since artificial intelligence encompasses all these technologies.
Thanks to them, the machines gained autonomy, managing to think on their own. Many scientists are already working on projects to make machines capable of also recognizing people’s emotions and showing all kinds of feelings.
Although this may be controversial, as it involves debates about the replication of human consciousness, such events are already possible.
The topic is very interesting and, in this post, we will focus on how you can take advantage of technology for your business. Discover how to implement artificial intelligence in your business software.
Artificial intelligence (AI) represents
The development of computer systems capable of executing tasks that are normally performed by people such as visual perception, speech recognition, decision making, and language translation.
It is a branch of systems engineering, designed to create machines that behave like human beings.
This result is obtained using algorithms that discover patterns and generate insights from the analyzed data, resulting in more accurate decision making in a process that avoids the need for programming for specific actions. The goal is to make machines mimic the human’s unique powers of reason.
After the industrial revolution, the machines and tools used to support manual work did not stop evolving and with the advancement of information technologies (IT), they will now be autonomous, eliminating supervision or any other human activity from the process.
Artificial intelligence still has a long field to explore and represents the latest craze in innovation. In short order, it should bring about major changes in the way things work and in the way companies and individuals relate to technology.
What are the technologies behind AI?
It must also be, taught at the beginning, as a child.
Afterward, he repeats actions, builds habits, learns new things, incorporates learning into existing behavioral patterns, and creates a continuous learning cycle, allowing him to quickly adjust to changes in scenery.
The main technologies that make this work well are:
Machine Learning involves software that uses data to understand situations and environments with the least possible programming.
Instead of programming patterns of operation, Machine Learning consists of letting the system learn those patterns by itself, from the data it receives and analyzes, interpreting the information about current conditions.
In other words, Machine Learning is a technology that belongs to the field of artificial intelligence dedicated to measuring the ability of software to learn on its own and to make this possible. Today machine learning is the main technology that powers artificial intelligence.
You can see Machine Learning in action when you search for a certain product on the Internet, visit a virtual store, when a featured product appears as an ideal recommendation for you.
As we delve into more advanced levels of machine learning, deep learning kicks in and builds a complex architecture just like the neural networks of a human brain.
This means not having to program a prospecting system with an indescribable quality of intelligence. Instead, the full potential for future intelligence and powers of reason remain latent in the program itself, which will develop over time. It is very much like the curious mind of a child, but infinitely flexible.
This allows the software to make sense of the patterns, whether with problems, missing details, or other factors that can cause confusion in the interpretation of the analyzes.
Although the possibilities of Deep Learning are vast, its requirements are also great, since it demands Big Data technologies, a well-organized database, as well as an information processing superpower.
Deep Learning is a subset of Machine Learning and therefore belongs to the most advanced field of AI. In the end, it is this that makes it possible for machines to behave as closely as possible to humans.
Natural Language Processing
Natural Language Processing (NLP) benefits from Machine Learning techniques and helps software find recognizable patterns in large volumes of data.
One of the ways it works is by analyzing voice data, texts, videos, images, and facial expressions to recognize the emotions of users.
The algorithm does not analyze just one situation. This explores various types of databases available, such as social networks, and associates all user interaction, such as posts and messages received.
By gathering as much information as possible, the algorithm manages to find patterns and reach a conclusion about how the user feels about brands and products.
How is AI implemented in SaaS?
Many companies already use artificial intelligence in their software. Some famous examples are:
Google performs the automatic filling of searches and predicts with high precision what the user wants to investigate. Google also already uses AI to test 100% autonomous cars, avoiding collisions, and traffic jams.
Amazon: one of the world’s leading e-commerce companies makes personalized product and service recommendations to its users using Machine Learning algorithms.
Waze: the navigation application gathers user data, on the Internet, of the region in which it is located, via satellite, to indicate the best routes with just one click.
Facebook: the most famous social network in the world has facial recognition of published images to recommend tags in photos.
Siri – The personal assistant app uses data processing to communicate with the user.
Today, we can say that artificial intelligence is divided into levels, but all of them depend on the following formula:
Database + Big Data Analytics + Cloud Computing = smarter machines and systems.
In this case, if you want to develop intelligent software or apply AI to existing software, the most important thing is to find a good Cloud Computing service provider, which includes database management services, Business Intelligence, in its portfolio. Development of software and structures in the cloud, such as SaaS (Software as a Service) and PaaS (Platform as a Service).
What is the relationship between AI, predictive analytics, and the Internet of Things?
This data bank must be constantly fed through the automated collection of data.
From that, BI (Business Intelligence) enters with the function of analyzing, identifying patterns, and extracting valuable information.
Generally, this information reports current conditions and scenarios, but, with the help of predictive analysis, BI encompasses the entire history of records and points to trends, facilitating the projection of scenarios and results with good levels of precision.
In other words, predictive analysis is what allows the forecast of future events and the probability of results.
For its part, IoT (Internet of Things), ensures that technological devices are transformed into “smarts”. As with cell phones, they started to have Internet resources, they communicate with each other, make decisions on their own, and are monitored remotely by users.
Imagine a “smart” fridge. Thanks to the IoT, she could send data to the owner about energy consumption, ideal temperature setting for the moment, the correct time for defrosting and cleaning, etc.
In addition, the user could program the functions remotely, via the Internet, and the refrigerator runs everything on its own, without the need for someone nearby.
In the case of IoT, success depends on Internet resources allied to advances in areas pertaining to artificial intelligence, such as Machine Learning.
How does AI contribute to generating user experience?
Like almost all artificial intelligence used in Marketing and Sales SaaS, it has a cognitive format. Thus, the system begins to identify behaviors and act in advance of the wishes and needs of customers.
When approached with the solution they need before or at the exact moment, the customer is surprised with the dedicated attention and begins to feel important to the company. As a form of gratitude, they end up buying the product or service, and in many cases, they are more easily loyal.
And not only that. AI must connect all the environments of human life, such as personal life, home, studies, profession, travel, and others in a unified experience.
She will help us to follow events, monitor the state of physical and mental health, anticipate needs, and remind us of important tasks. She is so discreet and sensitive, that nowadays many people already use her and do not perceive it.
How does AI help with decision making?
Through data analysis, AI discovers customer behavior patterns, projects their needs and actions into the future, such as searching for product and service solutions and informs managers through reports which marketing strategies will be most likely to succeed. Success.
Considering the advancement of predictive analytics techniques combined with multiple data sources, structured statistical models, and Machine Learning technology, artificial intelligence gives managers incredible power to anticipate demands, risks, and opportunities, as well as simulate results of strategies based on the projected scenario.
With this tool, decision-making happens early and with a higher level of success, which generates a great competitive advantage in business.
Now that you know how to implement artificial intelligence in the software, have the support of a specialist in the function and increase the prospects of your business in the medium and long term.