What is artificial intelligence, and why do we need it?

Artificial intelligence has various definitions. John McCarthy, the father of artificial intelligence and creator of the programming language LISP, was the head of the computer science department at Stanford University. In his 2007 article “What Is Artificial Intelligence,” McCarthy addressed some pertinent questions. When asked what artificial intelligence is, McCarthy responded that it is the science and engineering of making intelligent machines, and brilliant computer programs. It’s related to the similar task of using computers to understand human intelligence, but artificial intelligence does not need to be limited to biologically observable methods. Former MIT professor of artificial intelligence and computer science, Patrick Winston, defined artificial intelligence as “algorithms enabled by constraints, exposed by representations that support models directed at loops that link thinking, perception, and action.”

According to McCarthy (2007), there are various forms of intelligence. We can affirm that: “Intelligence is the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals, and some machines.”

When McCarthy (2007) was questioned about what intelligence is and whether we can consider a computer as an intelligent machine, he argued that intelligence involves mechanisms. AI research has discovered how to make computers carry out some of them and not others. If doing a task requires only mechanisms that are well understood today, computer programs can give awe-inspiring performances on these tasks. Such programs should be considered “somewhat intelligent” We are acknowledging that intelligent machines can perform tasks or make decisions autonomously based on pre-defined algorithms or rules. This intelligence may be limited and specific to certain tasks, but it still signifies information processing capability and adaptation. Similarly, when a child begins to babble or calls out for “mama” or “papa,” it is a sign of early cognitive and linguistic development. In this way, we can recognize that both examples highlight the gradual and continuous progression toward more complex forms of intelligence and cognitive abilities.

Artificial intelligence can have various applications ranging from machine learning, deep learning, neural networks, computer vision, and natural language processing, among others. Although artificial intelligence is an old technology, its application has gained relevance recently in the technology market and organizations. Research conducted by the company Gartner in 2019 shows that 37% of organizations have implemented AI in some form in their products and services. The percentage of companies employing AI has grown by 270% in the last four years.

There are several types of artificial intelligence currently, as you can see in the image below. According to current literature, machine learning is an area of ​​artificial intelligence that has gained much relevance in recent years. Machine learning is a subcategory of artificial intelligence that uses algorithms to automatically learn insights and recognize data patterns, applying this learning to make increasingly better decisions.

Machine learning algorithms are being applied globally in virtually every major sector, including business, government, finance, agriculture, transportation, cybersecurity, and marketing. The rapid adoption of these technologies across various fields is evidence of the value added by machine learning (and consequently, by data science). With insights derived from vast datasets, often in real-time, organizations can operate more efficiently and gain a competitive advantage.

Dustin Harris (2020) argues that there are various types of artificial intelligence, but among them, the most promising is limited memory artificial intelligence. As shown in the image above, this category is included in type 2. This type of artificial intelligence can store previous data and predictions while collecting information and weighing possible decisions. According to Harris (2020), several machine learning models utilize limited memory intelligence, including:

• Reinforcement Learning: This model learns to make better predictions through repeated trial and error. For example, recommendation systems, autonomous vehicles, and online games adopt this approach.
• Recurrent Neural Networks (RNNs): Utilize sequential data to gather information from previous inputs to influence the current input and output. They are commonly used for ordinal or temporal problems such as language translation, natural language processing, speech recognition, and image captioning.
• Evolutionary Generative Adversarial Networks (E-GANs): Evolve, growing to explore slightly modified paths based on previous experiences with each new decision. For example, DALEE-E and Midjourney adopt this approach.
• Transformers: These are node networks that learn how to perform a specific task by training on existing data. Instead of having to group elements, transformers can execute processes so that each element in the input data pays attention to all other elements. For example, ChatGPT, Siri, and Cortana adopt this approach.

According to Feldmann (2018), “We have reached electronization” (p. 56). Even with the advent of information technology about 70 years ago, the fear of unemployment caused by computers or automation quickly dissipated, and a vast number of new professions have emerged since then, from computer programmers to web designers, just to name a few. Why would it be different now? The answer lies in the fact that with artificial intelligence, humans lose the monopoly on cognitive ability. In other words, until recently, machines and computers could only perform repetitive tasks and replace manual labor. From now on, activities that require logical reasoning, decision-making ability, and intelligence could be performed by robots or computers.

Furthermore, artificial intelligence is undoubtedly a powerful tool. You may even deny it, but its impact will still be felt throughout society and will affect you in some way, or you can simply accept it and use it as a tool to boost your work. Just as typists were impacted by the advent of information technology about 70 years ago, those who understand and use AI correctly can get ahead of those who simply ignore this technology. We must continue to study its negative impacts and seek ways to mitigate them.

To understand the importance of artificial intelligence, it is crucial to recognize the rapid development of our society over the past seven decades, especially in the field of technology. Since the industrial revolution 150 years ago, AI represents the third technological revolution, revolutionizing our world and dramatically transforming businesses, while also generating concerns for governments worldwide due to its potential. More than ever, we need artificial intelligence, as humans are not proficient in all areas. In critical contexts, relying on human “gut feeling” can be detrimental, as humans are poor decision-makers and struggle with analyzing complex data. Artificial intelligence can be a valuable tool for reducing human errors in high-risk jobs, such as in the oil industry, medicine, and manufacturing.

With the world population constantly growing, currently around 8 billion people, it becomes increasingly necessary to increase the production of equipment and consumer goods. If we rely solely on humans to meet future demands, we may not be able to do so. Additionally, many studies indicate that humans are productive for only about 3 to 4 hours per day. Therefore, it is crucial to seek ways to increase efficiency and productivity while ensuring a better quality of life through adequate breaks and time off. Artificial intelligence, when used ethically and responsibly, can be a powerful ally in addressing present and future societal challenges, driving innovation, and promoting human well-being.