Artificial Intelligence (AI) is not just a trend; it is a revolution transforming the way we live and work. In Brazil, AI-powered solutions are increasingly accessible and have become a reality across various industries.

Recent studies by McKinsey reveal that 65% of companies worldwide began using AI regularly in their operations in 2024, nearly doubling the percentage from the previous year.

But how can you implement AI in your business, and what real benefits can Artificial Intelligence deliver? From agribusiness to healthcare, the possibilities unlocked by advancements in machine learning, task automation, and the optimization of complex processes are countless. Explore the key highlights on this topic below.

Simply put, Artificial Intelligence can be defined as the ability of computer systems to perform complex tasks by recognizing patterns and analyzing a wide range of data. Computer systems can then perform tasks that previously required human intervention, such as customer service, data analysis, pattern identification and process optimization. To do this, it uses concepts such as machine learning, natural language processing and neural networks. This idea of machines being able to "think" like humans ended up revolutionizing the corporate routine and the economy as a whole, with companies using AI to improve their business and driving new investments in technologies for this area.

Today, artificial intelligence can be applied to improve processes and is dictating digital transformation on various business fronts.

The first steps

Despite its large-scale popularization in recent years, Artificial Intelligence dates back to the 1940s. The first studies into the possibility of computer systems performing activities very close to human capabilities began with neurophysiologist Warren McCulloch and mathematician Walter Pitts. During this period, the first complex computer calculation machines were developed - most notably Alan Turing's, which deciphered German messages, helping the Allies win the Second World War.

Gradually evolving

In the 1950s, simple programs that could solve complex problems by simulating human thought began to be developed. They were still very primitive compared to subsequent developments, but the creations of scientists John McCarthy and Marvin Minsky drove the industry forward. It was also in this decade that Alan Turing proposed a test that bears his surname and checks whether a machine can exhibit intelligent behavior similar to that of a human being. The Turing Test has become a benchmark for the field.

Starting a new era

However, it was in the 1990s that studies related to the field of Artificial Intelligence developed at a faster pace, until we reached today's resources. In this phase, after many ups and downs related to investments in the sector, computer games, voice recognition applications and machine learning have evolved considerably. Based on the history of AI evolution, the US Department of Defense - DARPA (Defense Advanced Research Projects Agency) - has created a concept of 3 major waves of innovation in these technologies:

1st Wave: Systems based on simple rules

In the early days, AI was limited to systems that followed predefined rules. These systems, known as expert systems, were designed to solve specific problems based on large sets of if-then statements.

Wave 2: Introduction of statistical learning

With the growth of computing power and the availability of data, AI began to use statistical learning algorithms to find patterns in large volumes of information. It was at this stage that machine learning evolved and data classification and image recognition were consolidated.

3rd Wave: Generative models and adaptive AI (current)

The third wave brought significant advances, such as the creation of deep neural networks (deep learning) and the implementation of generative models, which not only learn patterns, but also create new content, such as images and texts. Examples include tools such as GPT and DALL-E. In addition, advanced automation and adaptive systems allow AI to make decisions in real time and adjust to new data on an ongoing basis

Narrow AI

This is the most common form of AI today, which performs specific tasks with high performance. A practical example is a virtual assistant, such as Alexa or Google Assistant, which can answer questions, set alarms and control connected home devices. Although extremely effective at limited tasks, narrow AI does not have the ability to generalize or transfer its learning to different contexts.

General AI

Still in the development and research phase, General AI represents a system that can perform any cognitive task that a human being would be capable of and includes the concept of machine learning. Solutions such as ChatGPT, Copilot and Bard can be classified at this stage and, as well as expanding their use to various fronts, are opening up discussions about the ethical use of Artificial Intelligence in business.

Main technologies and approaches: AI in everyday life

There is a wide range of technologies linked to the use of AI, which make it possible to expand operational capacity in a variety of everyday contexts. Thanks to studies over the last few decades, Artificial Intelligence is currently developing a series of actions that are closely linked to human capacity and interaction and that go beyond logical sequences and commands. These technologies include

Machine Learning

Machine learning focuses on building systems that improve their performance over time because they increase their capacity to learn patterns and situations. Machine learning algorithms are among the most widely used in AI. They are trained from a set of data and evolve as this database is fed. Recently, the use of deep neural networks (Deep Learning) to increase the accuracy of models and the application of pre-trained models, such as GPT, to reduce implementation time and costs, have become popular.

Natural Language Processing (NLP)

This is a technology that is advancing rapidly, thanks to the availability of big data, more advanced computing and better algorithms. It involves the ability of machines and systems to understand, interpret and respond to human interaction in a more natural way. NLP is able, for example, to identifypositive or negative comments, analyze opinions and produce complex content or analyses.

Computer Vision

This technology stands out for its ability to interpret and recognize images and describe them accurately and efficiently. It is used, for example, in the identification of license plates by autonomous vehicles and in more agile diagnosis in imaging tests in the health sector, as well as being the basis of facial recognition systems. Among the advances in the field of computer vision are the use of convolutional neural networks (CNNs), which enable accurate detection and classification of images, and semantic segmentation algorithms, which identify each pixel in an image with detailed context.

Recommendation Systems

They use algorithms to analyze user behavior and suggest personalized content or products, optimizing the customer experience. It is the basis of products such as streaming networks and social networks. It allows for the personalization of content, either in a collaborative way, when there is a preference base of similar users, or in content, when it takes into account the consumer's access history. There are also hybrid recommendation systems, which combine these two forms of personalization.

We can see that, although there is a wide range of technologies and applications, the use of AI to contribute to the economy and business growth in different areas is growing. Investment in AI technologies continues to grow and its applications range from start-ups to complex operations in large businesses. The benefits of AI for companies are numerous and the applications are increasingly geared to the reality of each sector. Here are some examples.

Healthcare

Computer vision applications perform scans and speed up imaging diagnoses, optimizing hospital flow and treatments. More precise genetic analyses provide personalized treatments, while virtual assistants reduce waiting times, facilitate exam scheduling and triage appointments. The agile processing of a high volume of data supports professionals in finding better treatments and comparing cases.

Agribusiness

Precise monitoring, analysis and management of climatic risks, better resource management, early identification of failures and automated systems for efficient use of inputs are among the actions present in this sector. In livestock farming, we can also mention the monitoring of herd health and behavior and the use of autonomous operations to supply and control feed.

Manufacturing

Predictive maintenance and analysis reduce failures and unscheduled downtime. More precise inspections increase production quality and predictive modeling reduces process bottlenecks, as well as the possibility of greater agility in internal logistics through AI-guided equipment.

Financial Services

In addition to service chatbots, which are already well known to the public, AI brings to the sector solutions for fraud prevention, more accurate credit analysis, reduction of payment failures and efficient document processing.

Energy and Utilities

The predictability provided by AI solutions allows for more efficient management of resources, detection of anomalies or failures in distribution equipment. It also speeds up customer service and the intelligent management of supply networks.

Operational efficiency: allows for the automation of repetitive tasks, such as data processing and initial customer service, reducing operating costs and freeing up teams to focus on more strategic activities. In addition, AI-based systems can identify bottlenecks and optimize processes, increasing overall productivity.

Improved Decision Making: AI transforms raw data into valuable insights, empowering managers to make strategic decisions based on predictive analysis and market trends. Machine learning models help identify patterns, predict future scenarios and mitigate risks with greater assertiveness.

Personalization: by analysing consumer behaviour and preferences, it provides personalized deliveries, as well as more efficient marketing and communication strategies. It also creates humanized customer service with Natural Language Processing.

Scalability: by reducing operational actions and streamlining various processes, it allows for accelerated business growth, without the need to increase costs or structure. It helps businesses adapt to peaks in demand, without compromising the quality of delivery.

One of AI's most critical challenges is related to the bias of the data used to train the algorithms. This can reflect social inequalities or prejudices, resulting in unfair or discriminatory decisions. To minimize this risk, it is essential to ensure diversity in the data sets and to implement auditing techniques and continuous monitoring of AI systems. In addition, it is essential that these development projects have multidisciplinary teams to avoid this type of problem.

Another factor that has raised discussion is the relocation of professionals and the new skills needed to reduce the impact on the job market. Computer skills coupled with soft skills will be essential to maintain AI projects efficiently and apply continuous improvement to operations.

Data privacy and security and businesses with complex internal processes that require ethical and legal care will also be challenged when implementing AI solutions. Ensuring compliance with legal requirements and the protection and responsible use of data will be a discussion in many operations.

The so-called third stage of AI, which refers to Artificial Superintelligence (ASI), already features a series of discussions and studies that point to a phase in which synthetic intelligence surpasses human intelligence.

Beyond fiction movies or disconcerting predictions, we need to understand that Artificial Intelligence should bring a series of gains to businesses all over the planet. And this involves the development of even more powerful tools.

Quantum computing is being touted as the next frontier, which should boost AI's potential even further, with the possibility of performing complex calculations at astonishing speed, for example. The phenomenon of quantum entanglement, in which computers can operate in multiple states simultaneously, is also seen as one of the points to be popularized in the near future.As we have seen, human-machine interaction is a path of no return, increasingly consolidated for the future of business. We need to take a close look at regulations and security for the responsible development of Artificial Intelligence, breaking down the barriers of technology for a much more prosperous and efficient future.

It's not just today that AI permeates the human imagination. Several science fiction films have dealt with the subject, such as:

2001: A Space Odyssey (1968)
The Matrix Trilogy (1999-2003)
A.I.: Artificial Intelligence (2001)
Ex_Machina (2014)

Books on the subject, meanwhile, also range from an introduction to the topic to discussions about the future of AI. We recommend:

The Next Wave: Artificial Intelligence, Power and the Greatest Dilemma of the 21st Century (Mustafa Suleyman, Michael Bhaskar and Alessandra Bonrruquer)

Introduction to Artificial Intelligence (Tom Taulli)

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